Nomogram for Predicting Pathological Discordance between Colposcopy- directed Biopsies and Cold Knife Conization Findings

Nomogram for Predicting Pathological Discordance between Colposcopy- directed Biopsies and Cold Knife Conization Findings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nomogram for Predicting Pathological Discordance between Colposcopy- directed Biopsies and Cold Knife Conization Findings Jingfang Wang, Yueyue Ma, Jiarong Li, SongQuan Wen, Shuling Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7599091/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective We aimed to develop a nomogram for predicting the probability of discordance between colposcopy-directed biopsy and cold knife conization pathological findings. Methods This was a quantitative research involving a case-control study. We retrospectively reviewed the records of patients diagnosed with high-grade squamous intraepithelial lesions through colposcopy-directed biopsy, who underwent cold knife conization at the Second Hospital of Shanxi Medical University between September 2018 and September 2021. The nomogram was developed using multivariate logistic regression analysis to predict the risk of pathological discrepancies between colposcopy-directed biopsy and cold knife conization findings. Results The colposcopy-directed biopsy accuracy rate for identifying high-grade squamous intraepithelial lesions was 72.8%. Multivariate analysis showed that cervical intraepithelial neoplasia Grade 3 (odds ratio [OR] = 9.455, P-value < 0.001), positive endocervical curettage (OR = 5.407, P-value < 0.001), findings of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude high-grade squamous intraepithelial lesions/atypical glandular cells (OR = 1.791, P-value = 0.044), and peripheral blood lymphocyte count (OR = 0.523, P-value = 0.018) were associated with colposcopy-directed biopsy underestimation. Cervical intraepithelial neoplasia 2 (OR = 2.369, P-value < 0.001), negative endocervical curettage (OR = 3.271, P-value < 0.001), negative for intraepithelial lesions or malignancy/atypical squamous cells of undetermined significance/low-grade squamous intraepithelial lesion (OR = 2.362, P-value = 0.004), and peripheral blood monocyte count (OR = 7.989, P-value = 0.016) were associated with colposcopy-directed biopsy overestimation per the multivariate analysis. The above factors were used to construct nomograms for predicting colposcopy-directed biopsy underestimation or overestimation, which had area under the curve values of 0.815 (95% confidence interval [CI]: 0.767−0.863) and 0.742 (95% CI: 0.690−0.793) for underestimation and overestimation, respectively. Conclusions Our results suggest a significant discordance between colposcopy-directed biopsy and cold knife conization pathological results, which could prompt nonessential conization or delayed treatment, particularly for fertile women. Our nomogram models may help estimate the probability of colposcopy-directed biopsy underestimation and overestimation, enhancing individualized treatment plans. High-grade squamous intraepithelial lesions colposcopy-directed biopsy pathological discordance nomograms Figures Figure 1 Figure 2 Figure 3 Introduction Cervical cancer is the fourth most common carcinoma and the fourth leading cause of cancer-related death in women, with an estimated 604,000 new cases and 342,000 deaths worldwide in 2020. 1 The burden of cervical cancer is particularly severe in developing countries such as China, where, according to national cancer statistics, the 2015 incidence and mortality rates were 9.89 and 3.05 per 10,000, respectively. 2 Cervical cancer is usually prevented by screening for cervical lesions and the timely treatment of precancerous lesions. Cervical intraepithelial neoplasia is a crucial process in the development of cervical cancer. Most cervical intraepithelial neoplasia Grade 1 lesions regress spontaneously, while Grades 2 and 3 are considered high-grade squamous intraepithelial lesions, which progress to invasive cancer in 30% of cases, if not actively treated. 3 The screening and assessment of cervical lesions have become more precise and standardized recently. A three-step screening method is used internationally: cervical thin-layer liquid-based cytology or cytology combined with human papillomavirus (HPV) testing as the primary screening 4 ; reference colposcopy in suspicious or positive cases; and histopathological analysis of lesion specimens obtained via colposcopy-directed biopsy (CDB) or endocervical curettage. 5 Therefore, colposcopy is crucial for the early detection of cervical lesions, and its accurate use reduces the rate of nonessential biopsies, conization procedures, and cautery treatments for cervical erosions. 6 However, CDB results could be inconsistent with the subsequent histopathological diagnosis following cervical cold knife conization (CKC). Colposcopic accuracy is influenced by factors such as the patient’s age, menopause status, HPV status, cytology results, and type of transformation zone. Skilled and experienced colposcopists are lacking in low- and middle-income countries, 7 and the overall agreement between the CDB and surgically excised specimens is 42%. 8 CDB underestimation of the cervical intraepithelial neoplasia grade may delay treatment, while overestimation could expose patients to nonessential surgical treatments or an elevated risk of adverse obstetric events. A meta-analysis has shown that cervical treatment is associated with an increased risk of overall, severe, and extreme prematurity, spontaneous preterm birth, threatened preterm labor, premature rupture of the membranes, chorioamnionitis, low birth weight, neonatal admission, and perinatal death. 9 Therefore, identifying the factors that influence the discordance between CDB and CKC findings is crucial to improving the preoperative accuracy of cervical lesion evaluation. Cervical cancer remains a common gynecologic malignancy worldwide, with persistent high-risk HPV infection being its primary cause. For patients diagnosed with high-grade squamous intraepithelial lesions or early-stage cervical cancer, cervical conization represents a crucial diagnostic and therapeutic procedure. Among various techniques, CKC represents a classical surgical method that provides clear resection margins and adequate specimen size—essential features for accurate pathological assessment. Numerous clinical studies have demonstrated that CKC offers notable advantages in diagnostic precision, treatment effectiveness, and reduction of recurrence, particularly in cases involving extensive lesions, endocervical involvement, or suspected microinvasive disease. Accurate histopathological evaluation is critical for determining appropriate treatment strategies. However, discrepancies may occur between preliminary diagnostic biopsies (such as those obtained through CDB) and final conization specimens, potentially leading to overtreatment or undertreatment. Nomogram is a clinical tool that integrates key factors such as age, menopausal status, cytology, and histology to visually estimate individual risk of under- or overdiagnosis. It assists in decision-making for further intervention or conservative management and is suitable for routine use. Therefore, to address the clinical challenges in the management of cervical cancer, we aimed to develop a nomogram to estimate the probability of pathological discrepancy between CDB and CKC results. Methods Study Design and Population Data were retrospectively collected from the digital records of women diagnosed with high-grade squamous intraepithelial lesions through CDB who underwent cervical CKC between September 2018 and September 2021 at the Department of Gynecology, Second Hospital of Shanxi Medical University. This study was approved by the Ethics Committees of the Second Hospital of Shanxi Medical University (IRB/112/2022). The requirement for informed consent was waived, as the study was observational, and personal information was anonymous. The exemption of the informed consent procedure was approved by the acknowledged ethics committee. Colposcopic examinations and image acquisition were performed using the Philips Kangkewei SLC-3000 digital colposcopy system. The exclusion criteria were as follows: incomplete colposcopy impressions; missing or incomplete HPV, cytology, or histopathology reports; pregnancy; and history of treatments, such as total hysterectomy or pelvic radiation. We analyzed the following demographic and clinical characteristics: age, menopause status, abnormal vaginal bleeding, pregnancy and childbirth, high-risk HPV status, high-risk HPV types, cytology results, colposcopic impressions, abnormal vessels, transformation zone type, endocervical curettage results, CDB results, involvement of glands, preoperative peripheral blood lymphocyte count, monocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and CKC histopathological results. Study Variables and Detection Methods The high-risk human papillomavirus (hr-HPV) status was determined using the HybriMax HPV genotyping method (HybriBio Ltd, Chaozhou, China). This assay identifies 15 high-risk HPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, and 68). A positive result was defined as the presence of at least one high-risk HPV type, while the absence of detectable high-risk types was considered negative. Genotyping specifically identified HPV 16 and 18, along with other high-risk genotypes including 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68. Cytological evaluation was conducted according to The Bethesda System. Results were classified as negative for intraepithelial lesion or malignancy, atypical squamous cells of undetermined significance, atypical squamous cells — cannot exclude high-grade squamous intraepithelial lesion, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, squamous cell carcinoma, or adenocarcinoma. Colposcopic examination and classification were performed by experienced colposcopists. Colposcopic impressions were documented as normal, low-grade lesion, high-grade lesion, or suspected invasive cancer. Aberrant vascular patterns observed during colposcopy included punctation, mosaic, and atypical vessels. The transformation zone was categorized into three types based on the visibility of the squamocolumnar junction: Type 1, entirely ectocervical and fully visible; Type 2, partially extending into the endocervical canal but fully visible with instrumental assistance; Type 3, predominantly or entirely within the endocervical canal and not fully visible. Histopathological assessment included endocervical curettage, which was performed independently before conization. Endocervical curettage results were reported as negative (no pathological changes) or positive (presence of cervical intraepithelial neoplasia or more severe lesions). Cone biopsy specimens, which served as the primary pathological outcome of this study, were independently evaluated by two senior pathologists. The pathological evaluation comprised final diagnoses (chronic inflammation, cervical intraepithelial neoplasia 1, 2, 3, adenocarcinoma in situ , microinvasive carcinoma [Stage ≤ IA1], or invasive carcinoma [Stage ≥ IA2]), margin status (endocervical, ectocervical, or basal margins reported as positive [involved by lesion] or negative [free of lesion]), and gland involvement (specifically indicating whether high-grade squamous intraepithelial lesion [cervical intraepithelial neoplasia 2/3] involved endocervical glands, recorded as “yes” or “no”). Nomogram Development The nomogram was developed using multivariate logistic regression analysis to predict the risk of pathological discrepancies between CDB and CKC findings. Univariate analysis was first used to identify statistically significant variables (P-value < 0.05), which were subsequently incorporated into a multivariate model through backward stepwise selection, with the Akaike Information Criterion being the stopping rule. The nomogram was constructed using the rms package in R software (version 4.1.2), with points assigned to each predictor according to their regression coefficients. Internal validation was performed via bootstrap resampling with 1,000 iterations. The discriminative ability of the model was evaluated using the area under the receiver operating characteristic curve (AUC), while calibration was assessed through calibration plots and the Hosmer-Lemeshow goodness - of-fit test. Statistical Analysis Data analyses were performed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, U.S.A.). Statistical significance was set at P-value < 0.05. The mean imputation method was used to estimate missing values. Logistic regression modelling with a forward stepwise approach was used for multivariate analyses. The RStudio Desktop (2022.02.2 + 485) was used to establish a nomogram prediction model. The AUC was used to assess the predictive performance of the model in terms of the calibration curve to determine the predictive consistency. Patient and Public Involvement The patients or the public were not involved in the design, conduct, reporting, or dissemination plans of our research. Availability of Data and Materials : In accordance with the journal’s guidelines, we will provide our data for independent analysis through the team selected by the Editorial Team for additional data analysis or for the reproducibility of this study in other centers if such is requested. Results This study included 607 patients. Table 1 summarizes the baseline clinical characteristics of the study population. The median age was 44 (range: 21–77) years, with 456 and 151 non-menopausal and menopausal patients, respectively. The median number of pregnancies and vaginal deliveries was 3.0 and 2.0, respectively. The cytological finding was negative for intraepithelial lesions or malignancy in 143 (23.6%), atypical squamous cells of undetermined significance in 154 (25.4%), low-grade squamous intraepithelial lesion in 116 (19.1%), high-grade squamous intraepithelial lesions in 166 (27.3%), atypical squamous cells cannot exclude an high-grade squamous intraepithelial lesions in 26 (4.3%), and atypical glandular cells in 2 (0.3%) patients. Of the total 607 patients, 546 underwent endocervical curettage, among whom 254 had inaccurate results. Table 1 Characteristics of the study group (n=607) Variables No.(%) Median (range) Age (yr) ＜35 126 (20.8%) 44(21-77) 35-45 210 (34.6%) ＞45 271 (44.6%) Menopausal status No menopause 456 (75.1%) Menopause 151 (24.9%) Menopause age ＜50 59 (39.1%) ≥50 92 (60.9%) Abnormal vaginal bleeding no 445 (73.3%) yes 162 (26.7%) No. of pregnancies 0 11 (1.8%) 3(0，11) 1 68 (11.2%) 2 131 (21.6%) 3 169 (27.8%) ≥4 228 (37.6%) No. of vaginal delivery 0 96 (15.8%) 2(0，8) 1 173 (28.5%) 2 218 (35.9%) ≥3 120 (19.8%) HR-HPV type 0 24 (3.9%) 1 444 (73.1%) ≥2 139 (22.9%) Cytological results NILM 143 (23.6%) ASC-US 154 (25.4%) LSIL 116 (19.1%) HSIL 166 (27.3%) ASC-H 26 (4.3%) AGC 2 (0.3%) Impression of colposcopy ≤LSIL 143 (23.6%) ＞HSIL 464 (76.4%) Acetowhite changes none 22 (2.6%) thin 139 (22.9%) dense 446 (73.5%) Abnormal vessels no 378 (62.3%) yes 229 (37.7%) Transformation zone type TZI 79 (13.0%) TZII 478 (78.7%) TZIII 50 (8.2%) Diagnosis of colposcopy CINII 205 (33.8%) CINIII 402 (66.2%) Involvement of glands no 307 (50.6%) yes 300 (49.4%) ECC no 61 (10.0%) yes 546 (90.0%) negative 292 (48.1%) positive 254 (41.8%) Diagnosis of conization NILM 39 (6.4%) CINI 64 (10.5%) CINII 187 (30.8%) CINIII 255 (42.0%) AIS 2 (0.3%) SCC 60 (9.9%) Interval between colposcopy and conization (day) ＜30 456 (75.1%) 16(1,183) 30-60 116 (19.1%) ＞60 35 (5.8%) discrepancy of pathologic colposcopy underestimation 103(17.0%) colposcopy overestimation 62(10.2%) LYM(×10 9 L) 1.87(0.57,4.57) MON(×10 9 L) 0.4(0.15,1.3) NLR 1.643(0.324,10.638) PLR 128.571(41.538,350.485) LMR 4.714(1.426,14.783) Table 2 presents the differences in the histopathological results obtained via CDB and CKC. Among those with CDB-diagnosed high-grade squamous intraepithelial lesions, the overall incidence of diagnostic concordance/correct diagnosis was 72.8% (442 of 607). The incidence of colposcopic underestimation was 10.2% (62 of 607), and that of colposcopy overestimation was 17.0% (103 of 607). Table 2 Comparison of colposcopy-directed biopsy with cold knife conization pathological diagnosis (cases) CDB Conization (%) χ 2 (P-value) NILM CINI CINII CINIII AIS/SCC Total CINII 22(10.7) 34(16.6) 90(43.9) 56(27.3) 3(1.5) 205(100.0) 76.016 (＜0.001) CINIII 17(4.2) 30(7.5) 97(24.1) 199(49.5) 59(14.7) 402(100.0) Tables 3 and 4 present the univariate analyses. The CDB underdiagnosis and correct or overdiagnosis groups exhibited significant differences concerning age and peripheral blood lymphocyte count, with patients in the underdiagnosis group being older and having a lower peripheral blood lymphocyte count (P-value <0.05). Furthermore, menopause, ≥3 vaginal deliveries, cytological diagnosis of high-grade squamous intraepithelial lesions, abnormal vessels, CDB diagnosis of cervical intraepithelial neoplasia 3, and abnormal endocervical curettage were significantly higher in the underdiagnosis group (P-value <0.05). The monocyte count was significantly higher in the CDB overdiagnosis group than in the correct or underdiagnosis groups. Moreover, the cytological diagnosis of high-grade squamous intraepithelial lesions, abnormal vessels, CDB diagnosis of cervical intraepithelial neoplasia 3, involvement of glands, and positive endocervical curettage were significantly lower in the CDB overdiagnosis group than in the other two groups (P-value <0.05). Table 3 Univariate analysis of colposcopy-directed biopsy underestimation Variables correctly or over-diagnosed group (n=545) under-diagnosed group (n=62) Z/χ 2 P -value Age (yr) 43(36，50) 48(41.75，56) -3.179 0.001 LYM (×10 9 L) 1.890(1.545，2.255) 1.700(1.430，2.045) -2.330 0.020 MON (×10 9 L) 0.400(0.330,0.490) 0.400(0.320.0.450) -0.644 0.520 NLR 1.630(1.260,2.069) 1.775(1.470,2.078) -1.854 0.064 PLR 127.481(102.564,158.304) 139.617(111.701,170.332) -1.559 0.119 LMR 4.733(3.823,5.856) 4.472(3.877，5.311) -1.411 0.158 Menopausal status n(%) NO 418(76.7) 38(61.3) 7.071 0.008 YES 127(23.3) 24(38.7) No. of pregnancies n(%) ≤3 343(62.9) 36(58.1) 0.563 0.453 ＞3 202(37.1) 26(41.9) No. of vaginal delivery n(%) ＜3 444(81.5) 43(69.4) 5.150 0.023 ≥3 101(18.5) 19(30.6) Abnormal vaginal bleeding n(%) NO 401(73.6) 44(71.0) 0.194 0.660 YES 144(26.4) 18(29.0) HR-HPV a n(%) negative 19(3.5) 5(8.1) 1.985 0.159 positive 526(96.5) 57(91.9) HR-HPV type n(%) ≤1 415(76.1) 53(85.5) 2.749 0.097 ＞1 130(23.9) 9(14.5) cytological results n(%) NILM/ASC-US/LSIL 383(70.3) 30(48.4) 12.264 ＜0.001 HSIL/ASC-H/AGC 162(29.7) 32(51.6) Impression of colposcopy n(%) ≤LSIL 133(24.4) 10(16.1) 2.117 0.146 ＞HSIL 412(75.6) 52(83.9) Abnormal vessels n(%) NO 347(63.7) 31(50.0) 4.428 0.035 YES 198(36.3) 31(50.0) TZ type a n(%) I/II 504(92.5) 56(90.3) 0.123 0.726 III 41(7.5) 6(9.7) Diagnosis of CDB n(%) CINII 202(37.1) 3(4.8) 25.846 ＜0.001 CINIII 343(62.9) 59(95.2) Involvement of glands n(%) NO 277(50.8） 30(48.4) 0.132 0.716 YES 268(49.2） 32(51.6) ECC results n(%) negative 340(62.4) 13(21.0) 39.241 ＜0.001 positive 205(37.6) 49(79.0) Table 4 Univariate analysis of colposcopy-directed biopsy overestimation Variables Correctly or under-diagnosed group (n=504) over-diagnosed group (n=103) Z/χ 2 P -value Age (yr) 44(36，50) 45(34，51) -0.163 0.871 LYM(×10 9 L) 1.845(1.513，2.208) 1.980(1.550，2.370) -1.718 0.086 MON (×10 9 L) 0.390(0.320，0.478) 0.420(0.350，0.500) -2.294 0.022 NLR 1.675(1.284，2.080) 1.604(1.210，2.030) -0.943 0.346 PLR 129.470(103.309,162.018) 122.786(101.333，155.811) -1.175 0.240 LMR 4.727(3.848,5.792) 4.526(3.677，5.735) -1.013 0.311 Menopausal status n(%) NO 384(76.2) 72(69.9) 1.809 0.179 YES 120(23.8) 31(30.1) No. of pregnancies n(%) ≤3 317(62.9) 62(60.2) 0.266 0.606 ＞3 187(37.1) 41(39.8) No. of vaginal delivery n(%) ＜3 399(79.2) 88(85.4) 2.120 0.145 ≥3 105(20.8) 15(14.6) Abnormal vaginal bleeding n(%) NO 364(72.2） 81(78.6) 1.801 0.180 YES 140(27.8) 22(21.4) HR-HPV a n(%) negative 16(3.2) 8(7.8) 3.617 0.057 positive 488(96.8) 95(92.2) HR-HPV type n(%) ≤1 389(77.2) 79(76.7) 0.011 0.915 ＞1 115(22.8) 24(23.3) cytological results n(%) NILM/ASC-US/LSIL 326(64.7) 87(84.5) 15.393 ＜0.001 HSIL/ASC-H/AGC 178(35.3) 16(15.5) Impression of colposcopy n(%) ≤LSIL 112(22.2) 31(30.1) 2.945 0.086 ＞HSIL 392(77.8) 72(69.9) Abnormal vessels n(%) NO 303(60.1) 75(72.8) 5.868 0.015 YES 201(39.9) 28(27.2) TZ type a n(%) I/II 467(92.7) 93(90.3) 0.671 0.413 III 37(7.3) 10(9.7) Diagnosis of CDB n(%) CINII 149(29.6) 56(54.4) 23.527 ＜0.001 CINIII 355(70.4) 47(45.6) Involvement of glands n(%) NO 243(48.2) 64(62.1) 6.631 0.010 YES 261(51.8) 39(37.9) ECC results n(%) negative 269(53.4) 84(81.6) 27.909 ＜0.001 positive 235(46.6) 19(18.4) The multivariate logistic regression analysis indicated cervical intraepithelial neoplasia 3 (odds ratio [OR]=9.455, P-value <0.001), abnormal endocervical curettage (OR=5.407, P - value <0.001), high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude an high-grade squamous intraepithelial lesions/atypical glandular cells (OR=1.791, P - value =0.044), and peripheral blood lymphocyte count (OR=0.523, P-value =0.018) as factors associated with CDB underestimation (Table 5). In contrast, cervical intraepithelial neoplasia 2 (OR=2.369, P-value <0.001), normal endocervical curettage (OR=3.271, P-value <0.001), negative for intraepithelial lesions or malignancy/atypical squamous cells of undetermined significance/low-grade squamous intraepithelial lesion (OR=2.362, P-value=0.004), and monocyte count (OR=7.989, P-value =0.016) were associated with CDB overestimation (Table 6). Table 5 Multifactorial logistic regression analysis of colposcopy-directed biopsy underestimation Variables B SE Wald P OR(95% Cl) CINIII 2.247 0.606 13.733 ＜0.001 9.455(2.882～31.022) ECC positive 1.688 0.333 25.623 ＜0.001 5.407(2.813～10.394) HSIL/ASC-H/AGC 0.583 0.290 4.038 0.044 1.791(1.014～3.161) LYM (×10 9 L) -0.648 0.274 5.571 0.018 0.523(0.306～0.896) Table 6 Multifactorial logistic regression analysis of colposcopy-directed biopsy overestimation Variables B SE Wald P OR(95% Cl) CINII -0.862 0.230 14.114 ＜0.001 2.369(1.511～3.715) ECC negative -1.185 0.276 18.454 ＜0.001 3.271(1.905～5.618) NILM/LSIL/ASC-US -0.859 0.297 8.353 0.004 2.362(1.319～4.230) MON (×10 9 L) 2.078 0.860 5.843 0.016 7.989(1.482～43.078) The variables from the final multivariate model were used to construct predictive nomograms (Fig. 1a and b). Each nomogram included four risk factors; the nomogram for estimating underestimation risk included the results of CDB, endocervical curettage, cytology, and peripheral blood lymphocyte count, while that for estimating overestimation risk included the results of CDB, endocervical curettage, cytology, and monocyte count. The regression weight of each predictor was the length of each predictor’s line segment, and the total score was equivalent to the linear predictor. The receiver operator characteristic curves of the nomograms are shown in Figure 2a and b. The AUC of the nomogram for estimating underestimation risk was 0.815 (95% confidence interval [CI]: 0.767−0.863), and its sensitivity and specificity were 77.4% and 75.6%, respectively. The AUC of the nomogram for estimating overestimation risk was 0.742 (95% CI: 0.690−0.793), and its sensitivity and specificity were 60.2% and 79.2%, respectively. Internal validation was performed through 10-fold cross-validation. The C-indices for the underestimation and overestimation models were 0.813 and 0.727, respectively. The calibration plots exhibited excellent consistency between the actual and predicted discrepancy probability with an additional 1,000 bootstraps ( Fig. 3a, mean absolute error = 0.006; Fig. 3b, mean absolute error = 0.009). Discussion Summary of Main Results: We evaluated the concordance of the pathological findings between CDB and CKC and found that CDB may overestimate and underestimate cervical lesions. In addition, we identified the factors associated with pathological discordance and used them to develop nomogram models to predict the possibility of CDB underestimation and overestimation. Results in the Context of Published Literature: Colposcopic impression has been used to identify areas with the highest degree of visual abnormality, and CDB from those areas has become the standard diagnostic procedure for precancerous cervical lesions. However, the concordance between CDB and conization diagnoses varies substantially within countries and hospitals. Consequently, women are usually misdiagnosed or submitted to nonessential biopsies. Therefore, we aimed to address this gap by identifying the factors influencing CDB accuracy and improving the preoperative accuracy of cervical lesion diagnosis. The reported rates of biopsy underestimation are 75.0% for normal findings, 24.7% for cervical intraepithelial neoplasia 1, 23.4% for cervical intraepithelial neoplasia 2, and 24.2% for cervical intraepithelial neoplasia 3, with an overall rate of 23.1%. 10 Conversely, those of biopsy overestimation are 30.1% for cervical intraepithelial neoplasia 1, 42.6% for cervical intraepithelial neoplasia 2, 26.6% for cervical intraepithelial neoplasia 3, and 46.0% for MIC/squamous cell carcinoma, with an overall rate of 33.6%. 10 In the present study, colposcopic and conization diagnoses were concordant in 72.8% of cases, comparable to previously reported findings in China. 11 While some investigators have identified the inaccuracy inherent to CDB as the primary cause of the difference, others have examined factors that may influence the consistency between CDB and conization biopsy results. Fan et al. suggested that old age (≥50), postmenopausal status, and transformation zone type 3 are positively associated with CDB underdiagnosis, while three or more biopsies and cone width ≥21 mm might improve CDB accuracy. 12 Using the database of the Gardasil clinical trials, Stoler et al. 8 included 594 cases and identified patients' age, number of biopsies, lesion sizes, presence of HPV 16/18 genotypes, and geographic region as factors affecting the agreement between CDB and excisional biopsy results. The present study indicated that cervical intraepithelial neoplasia 3, abnormal endocervical curettage, high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude high-grade squamous intraepithelial lesions/atypical glandular cells, and peripheral blood lymphocyte count were the independent factors associated with biopsy underestimation. However, cervical intraepithelial neoplasia 2, negative endocervical curettage, negative for intraepithelial lesions or malignancy/atypical squamous cells of undetermined significance/low-grade squamous intraepithelial lesion, and monocyte count were independently associated with biopsy overestimation. Furthermore, the nomogram models developed using these factors showed good discriminative capability. The models predict whether colposcopy biopsy is overestimated or underestimated. If the model shows that colposcopy biopsy is highly overestimated, a follow-up or loop electrosurgical excision procedure is recommended, particularly for women with fertility requirements. If the model shows that colposcopy biopsy is likely to be underestimated, thorough examinations before operation, such as magnetic resonance imaging and squamous cell carcinoma antigen, are essential. Clinicians may appropriately enlarge the conization scope according to the predicted results to reduce the chance of a positive margin and residual lesion and avoid second operations. Patients with cytological findings of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells were previously found to have a 33-fold higher risk for cervical cancer than did those with low-grade squamous intraepithelial lesions/atypical squamous cells of undetermined significance/negative for intraepithelial lesions or malignancy. 13 In the current study, we found a higher missed diagnosis rate in women with high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells. Hence, in patients with atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion and high-grade squamous intraepithelial lesions, CDB may not represent a good diagnostic or reliable management method, and immediate conization without punch biopsy may prevent overlooking high-grade lesions. 10 Furthermore, our multivariate logistic analysis showed that cytology results were an independent factor influencing biopsy overestimation and that patients with cytological findings suggestive of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells were less likely to have an overestimated diagnosis. This implies that patients with cytological findings suggestive of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells are more likely to have high-grade squamous intraepithelial lesion findings, while those with cytological findings suggestive of low-grade squamous intraepithelial lesion/atypical squamous cells of undetermined significance/negative for intraepithelial lesions or malignancy are more likely to be negative for intraepithelial lesions or malignancy/low-grade squamous intraepithelial lesion findings. We suggest that, in clinical settings, it is essential to individually interpret the pathological report of cervical biopsy according to the results of cervical cytology for patients who are diagnosed with high-grade squamous intraepithelial lesions via colposcopic biopsy. This would provide an early warning to high-risk patients whose conditions are progressing to cervical cancer, enhancing doctor-patient communication. The present study indicated CDB diagnosis and endocervical curettage results as independent factors influencing pathological discordance between CDB and conization. Patients with a CDB diagnosis of cervical intraepithelial neoplasia 2 and normal endocervical curettage were at a higher risk of biopsy overestimation. However, patients with a CDB diagnosis of cervical intraepithelial neoplasia 3 and abnormal endocervical curettage were more likely to have an underestimated biopsy. The risk of biopsy underestimation was 8.253 times higher in patients with a preoperative histological diagnosis of cervical intraepithelial neoplasia 3 than in those with cervical intraepithelial neoplasia 2. Patients with cervical intraepithelial neoplasia 3 are at a higher risk of progression to cervical cancer; the lesions tend to extend into the cervical canal, and some of them are hopper or multicentric, making them susceptible to missed diagnosis via colposcopy. A higher rate of biopsy-missed diagnoses was reported in patients with abnormal endocervical curettage. An abnormal endocervical curettage indicates the presence of lesions in the cervical canal; however, the specimens obtained through endocervical curettage are usually unduly small to assess the cervical canal adequately, and some lesions may be missed. Although endocervical curettage results independently influence pathological differences between CDB and conization, the endocervical curettage test is not feasible in some individuals, and the specific indications for this test remain debated. 14 There is no clear and refined expert consensus or guideline regarding the selection of endocervical curettage patients globally. The presence of inflammatory cells in tumor tissue was first suggested by the German pathologist Rudolf Virchow in 1863, who speculated that cancer susceptibility and severity are associated with functional polymorphisms of inflammatory cytokine genes. 15 Subsequently, the role of inflammatory cytokines in the tumor microenvironment has been focused on in the field of tumor immune response, and many studies have suggested inflammatory immune indicators as predictors of early diagnosis, staging, and prognosis of cervical cancer. 16-19 Tas et al. reported that the platelet-to-lymphocyte ratio was significantly higher in patients with cervical cancer than in those with low-grade squamous intraepithelial lesion and high-grade squamous intraepithelial lesion diagnoses or healthy individuals (P-value <0.001), and their logistic regression analysis revealed that the neutrophil-to-lymphocyte ratio (OR: 1.643, 95% CI: 1.009–3.142, P-value =0.047) and platelet-to-lymphocyte ratio (OR: 1.032, 95% CI: 1.003–1.062, P-value =0.029) were predictors of squamous cell carcinoma. 25 In addition, the measurement of systemic inflammatory indicators is simple, cost-effective, and readily available. Therefore, five indicators, namely peripheral blood lymphocyte count, monocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio, were investigated in our study for their predictive value for pathological discordance between CDB and CKC. The multivariate logistic regression analysis revealed peripheral blood lymphocyte count as an independent factor for predicting biopsy underestimation and monocyte count as an independent factor for predicting biopsy overestimation, while neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio were not significantly associated with pathological discordance. These findings may be owing to the small sample size; therefore, further research is necessary. Strengths and Weaknesses: To the best of our knowledge, this is the first study to associate inflammatory indicators with pathological discordance between biopsy and conization in China. Furthermore, we developed nomogram models to visualize the risk of pathological discordance. Nonetheless, this study had some limitations. First, the retrospective study design has inherent drawbacks, such as selection bias. Second, this was a single-institution study, and the sample size might have been insufficient to define the diagnostic performance of colposcopy with high accuracy. Finally, the nomogram models were constructed using a small sample size and lack external validation. Therefore, multi-center studies with large sample sizes are warranted to confirm our findings. Implications for Practice and Future Research: This study is the first to associate inflammatory indicators with the pathological discrepancies between biopsy and conization and to advance predictive nomogram models. We believe these models hold significant potential for clinical application. Integrating routinely available parameters—including cervical biopsy histology, endocervical curettage findings, cytological classification, and peripheral blood lymphocyte or monocyte counts—facilitates the quantitative assessment of the individualized risk of CDB underestimation or overestimation. For patients predicted to have a high underestimation risk, more comprehensive preoperative evaluations (such as magnetic resonance imaging or squamous cell carcinoma antigen testing) may be recommended, along with appropriately extending the cone margins to reduce the risk of positive margins and residual lesions. Conversely, for those with a high probability of overestimation, particularly young patients with fertility desires, more conservative management strategies (e.g., active surveillance or loop electrosurgical excision procedure) could be considered to avoid overtreatment. This tool facilitates precise and individualized clinical decision-making, addressing limitations in current diagnostic workflows. However, future multi-center studies with larger sample sizes are warranted to validate and optimize the clinical utility of the models. Our nomogram models may help estimate the probability of CDB underestimation and overestimation, enhancing individualized treatment plans. Conclusions In this study, we found discordance in the pathological findings between CDB and CKC. After identifying the factors influencing this discordance, we developed nomogram models for predicting the possibility of biopsy underestimation and overestimation. Declarations Funding sources : This project is supported by the National Natural Science Foundation of China (Grant No.81702583). Research Project Supported by Shanxi Scholarship Council of China (grant no.2022-195), Shanxi Graduate Education Teaching Reform Project in 2022 (107) (grant no.2022YJJG105), the Outstanding Youth Fund Project of Shanxi Province (grant no.201901D211506) and China Postdoctoral Science Foundation (grant no.2019M651072) to Dr. Weihong Zhao, and is supported by the Nature Science Foundation of Shanxi Province (grant no.201901D111364) and the Shanxi Province Key National Science and Technology Cooperation Projects (grant no.202104041101006) to Dr. Jingfang Wang. Declaration of competing interests : I have nothing to declare. Patient consent for publication: Not applicable Availability of data and materials : In accordance with the journal’s guidelines, we will provide our data for independent analysis through the team selected by the Editorial Team for additional data analysis or the reproducibility of this study in other centers if such is requested. Ethics approval: This study was approved by the Ethics Committees of the Second Hospital of Shanxi Medical University (IRB/112/2022). The requirement for informed consent was waived, as the study was observational, and personal information was anonymous. The exemption of the informed consent procedure was approved by the acknowledged ethics committee. Author contributions : JF W,YY M, JR L, SL W, SQ W, QL, and WH Z contributed conception and design of the study. JF W and YY M drafted the manuscript. JF W and JR L participated in data analysis. SL W, YB and WH Z participated in data acquisition. WH Z critically reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgments : Not applicable Declaration of generative AI and AI-assisted technologies in the writing process References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32. Santesso N, Mustafa RA, Schünemann HJ, et al. World Health Organization Guidelines for treatment of cervical intraepithelial neoplasia 2–3 and screen-and-treat strategies to prevent cervical cancer. Int J Gynaecol Obstet. 2016;132(3):252–58. Castellsagué X. Natural history and epidemiology of HPV infection and cervical cancer. Gynecol Oncol. 2008;110(3 Suppl 2):S4–07. Wang WJ, Wang D, Zhao M, et al. Serum lncRNAs (CCAT2, LINC01133, LINC00511) with Squamous Cell Carcinoma Antigen Panel as Novel Non-Invasive Biomarkers for Detection of Cervical Squamous Carcinoma. Cancer Manag Res. 2020;12:9495–502. Fan A, Wang C, Zhang L, et al. Diagnostic value of the 2011 International Federation for Cervical Pathology and Colposcopy Terminology in predicting cervical lesions. Oncotarget. 2018;9(10):9166–76. Xue P, Ng MTA, Qiao YL. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence. Bmc Med. 2020;18(1):169. Stoler MH, Vichnin MD, Ferenczy A, et al. The accuracy of colposcopic biopsy: analyses from the placebo arm of the Gardasil clinical trials. Int J Cancer. 2011;128(6):1354–62. Kyrgiou M, Athanasiou A, Paraskevaidi M, et al. Adverse obstetric outcomes after local treatment for cervical preinvasive and early invasive disease according to cone depth: systematic review and meta-analysis. BMJ. 2016;354:i3633. Jung Y, Lee AR, Lee SJ, et al. Clinical factors that affect diagnostic discrepancy between colposcopically directed biopsies and loop electrosurgical excision procedure conization of the uterine cervix. Obstet Gynecol Sci. 2018;61(4):477–88. Kim SI, Kim SJ, Suh DH, et al. Pathologic discrepancies between colposcopy-directed biopsy and loop electrosurgical excision procedure of the uterine cervix in women with cytologic high-grade squamous intraepithelial lesions. J Gynecol Oncol. 2020;31(2):e13. Fan A, Zhang L, Wang C, et al. Analysis of clinical factors correlated with the accuracy of colposcopically directed biopsy. Arch Gynecol Obstet. 2017;296(5):965–72. Pretorius RG, Belinson JL, Peterson P, Burchette RJ. Factors That Virtually Exclude Cervical Cancer at Colposcopy. J Low Genit Tract Dis. 2015;19(4):319–22. Li Y, Luo H, Zhang X, et al. Development and validation of a clinical prediction model for endocervical curettage decision-making in cervical lesions. BMC Cancer. 2021;21(1):804. Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357(9255):539 – 45. Lima P, Mantoani P, Murta E, Nomelini RS. Laboratory parameters as predictors of prognosis in uterine cervical neoplasia. Eur J Obstet Gynecol Reprod Biol. 2021;256:391–96. Trinh H, Dzul SP, Hyder J, et al. Prognostic value of changes in neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and lymphocyte-to-monocyte ratio (LMR) for patients with cervical cancer undergoing definitive chemoradiotherapy (dCRT). Clin Chim Acta. 2020;510:711–16. Prabawa I, Bhargah A, Liwang F, et al. Pretreatment Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) as a Predictive Value of Hematological Markers in Cervical Cancer. Asian Pac J Cancer Prev. 2019;20(3):863–68. Tas M, Yavuz A, Ak M, Ozcelik B. Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Discriminating Precancerous Pathologies from Cervical Cancer. J Oncol. 2019;2019:2476082. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 16 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 12 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7599091","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532423166,"identity":"8445afb3-29f8-4006-bc02-930144e86ecc","order_by":0,"name":"Jingfang Wang","email":"","orcid":"","institution":"Shanxi Medical University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingfang","middleName":"","lastName":"Wang","suffix":""},{"id":532423167,"identity":"b0d8decd-22c2-41c0-af63-98607cbfffd2","order_by":1,"name":"Yueyue Ma","email":"","orcid":"","institution":"Shanxi Medical University，Taiyuan","correspondingAuthor":false,"prefix":"","firstName":"Yueyue","middleName":"","lastName":"Ma","suffix":""},{"id":532423168,"identity":"cb9f9e94-b3e7-4b50-9a5a-50b124d09ecf","order_by":2,"name":"Jiarong Li","email":"","orcid":"","institution":"Shanxi Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Jiarong","middleName":"","lastName":"Li","suffix":""},{"id":532423169,"identity":"d502ec0d-3d1e-4ef7-be0a-6287ae88b606","order_by":3,"name":"SongQuan Wen","email":"","orcid":"","institution":"Shanxi Medical University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"SongQuan","middleName":"","lastName":"Wen","suffix":""},{"id":532423170,"identity":"35d9f126-b253-4c4b-a659-f573f08612ef","order_by":4,"name":"Shuling Wang","email":"","orcid":"","institution":"Shanxi Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Shuling","middleName":"","lastName":"Wang","suffix":""},{"id":532423171,"identity":"f9709375-aad3-4c28-8eec-e50f1a7b9639","order_by":5,"name":"Ying Bai","email":"","orcid":"","institution":"Shanxi Medical University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Bai","suffix":""},{"id":532423172,"identity":"e0efcab0-2fb0-4b52-af29-ec40fafac615","order_by":6,"name":"Qi Li","email":"","orcid":"","institution":"Shanxi Medical University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Li","suffix":""},{"id":532423173,"identity":"43aee258-bade-4f7f-a67f-96f81ac47dce","order_by":7,"name":"Weihong Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHACAxCRwA/lMTYQrUWygWQtBgeI1SI/I3njY54/h/OMz58x3czDYCO74QDzswd4rbiRVmzMw3O42OxGWtptHoY04w0H2MwN8GqRyDGT5pG4nbjtBvMxoJbDiRsO8LBJ4HdYjvlvHoPbiZv7D7YBtfwnrIXhRo4ZM0/C7cQNDMkgWw4Q1mJw5lmx5JwD/xNnAP1yc45BsvHMw2xm+B3Wnrzxw5s/aYn9/WfMbrypsJPtO978DL/DBBIYmHgQlgIxM171QMB/gIHxByFFo2AUjIJRMLIBAG/fTohtmSo0AAAAAElFTkSuQmCC","orcid":"","institution":"Shanxi Medical University Second Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-09-12 10:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7599091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7599091/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94138417,"identity":"2aa74297-8355-432b-8444-9ac7089f1c0f","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209519,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/c6cb468e37f2eb62d13ebf0a.png"},{"id":94138416,"identity":"fae8548d-ec00-4a5d-a71d-8ce3cd729936","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126432,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/6d5fe99023f7356c04c23311.png"},{"id":94138425,"identity":"b8987a6d-b021-4e7b-862f-d90de012237d","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89344,"visible":true,"origin":"","legend":"","description":"","filename":"NomogramforPredictingPathologicalDiscordancebetweenColposcopydirectedBiopsiesandColdKnifeConizationFindings.docx","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/e3bd26623695685ed0aea33c.docx"},{"id":94138420,"identity":"5b3a88b6-bdfb-4530-b685-b8179a61199b","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150258,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/a049be0f7738fc1c6e6bd5b3.png"},{"id":94139299,"identity":"84784ef3-7d16-47d1-bce6-041b3a60ccd6","added_by":"auto","created_at":"2025-10-22 19:32:47","extension":"json","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9938,"visible":true,"origin":"","legend":"","description":"","filename":"a3f01580c4544563be88815aa4320e4e.json","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/6c11e58717f8cc83fbec6bb4.json"},{"id":94140460,"identity":"0b2c0d25-5f4c-4785-9634-16496a2567c2","added_by":"auto","created_at":"2025-10-22 19:40:47","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130539,"visible":true,"origin":"","legend":"","description":"","filename":"a3f01580c4544563be88815aa4320e4e1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/54b9f561ecade0e55b07084f.xml"},{"id":94138421,"identity":"4d8234ab-05c0-46e8-885a-380c88dbb185","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209519,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/d2f72a5934c80d14c6e35c78.png"},{"id":94138422,"identity":"618a2e67-8db5-4e4e-a554-1668f83058ec","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126432,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/6c0103192f9ecec20b78b389.png"},{"id":94138428,"identity":"6a30ff06-9ec5-49f4-8ec7-d2a00d040192","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150258,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/17dfd964009b78e21e436fa5.png"},{"id":94140459,"identity":"68f4ad01-3a31-462e-a2d3-20e8a38a8abc","added_by":"auto","created_at":"2025-10-22 19:40:47","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45465,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/c699878339f5773980f468d3.png"},{"id":94139301,"identity":"5806ba13-e390-4bbf-9d54-b1d514c0b5be","added_by":"auto","created_at":"2025-10-22 19:32:47","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40622,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/1b538e3588c605a946dd807b.png"},{"id":94138426,"identity":"77951a7e-c74f-45a5-9c86-1ec199328ec3","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44612,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/68a54bd3f97663fdc6732e37.png"},{"id":94138429,"identity":"cdda8775-a2cf-4347-a24b-0251a4833ed3","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128814,"visible":true,"origin":"","legend":"","description":"","filename":"a3f01580c4544563be88815aa4320e4e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/fe99e28ce79867f9d210b2ba.xml"},{"id":94138431,"identity":"9b48fbb3-4575-4f77-9a0e-d0f74c3b7367","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135778,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/aeff7884c1244749d1feda4f.html"},{"id":94138414,"identity":"143bb0fe-34e6-4a1c-a4e2-1a48c01f7636","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209519,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/f36413e4a092ef6f7c51b398.png"},{"id":94138415,"identity":"98251edd-f5b3-4205-9235-b7a8c91031b4","added_by":"auto","created_at":"2025-10-22 19:24:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126432,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/8359068991252f6f8821775b.png"},{"id":94139300,"identity":"609bdad8-2ac9-4b83-ab8e-c36a5e66cfcb","added_by":"auto","created_at":"2025-10-22 19:32:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150258,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/1fa96370f8c5564e04f32e4a.png"},{"id":94140830,"identity":"ec84a300-7c39-49b2-88fb-1f3fa2c0fd4f","added_by":"auto","created_at":"2025-10-22 19:48:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1487677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7599091/v1/f4641b32-c466-4d27-838d-70b6c0f7079b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram for Predicting Pathological Discordance between Colposcopy- directed Biopsies and Cold Knife Conization Findings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer is the fourth most common carcinoma and the fourth leading cause of cancer-related death in women, with an estimated 604,000 new cases and 342,000 deaths worldwide in 2020.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The burden of cervical cancer is particularly severe in developing countries such as China, where, according to national cancer statistics, the 2015 incidence and mortality rates were 9.89 and 3.05 per 10,000, respectively.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Cervical cancer is usually prevented by screening for cervical lesions and the timely treatment of precancerous lesions. Cervical intraepithelial neoplasia is a crucial process in the development of cervical cancer. Most cervical intraepithelial neoplasia Grade 1 lesions regress spontaneously, while Grades 2 and 3 are considered high-grade squamous intraepithelial lesions, which progress to invasive cancer in 30% of cases, if not actively treated.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe screening and assessment of cervical lesions have become more precise and standardized recently. A three-step screening method is used internationally: cervical thin-layer liquid-based cytology or cytology combined with human papillomavirus (HPV) testing as the primary screening\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e; reference colposcopy in suspicious or positive cases; and histopathological analysis of lesion specimens obtained via colposcopy-directed biopsy (CDB) or endocervical curettage.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Therefore, colposcopy is crucial for the early detection of cervical lesions, and its accurate use reduces the rate of nonessential biopsies, conization procedures, and cautery treatments for cervical erosions.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e However, CDB results could be inconsistent with the subsequent histopathological diagnosis following cervical cold knife conization (CKC). Colposcopic accuracy is influenced by factors such as the patient\u0026rsquo;s age, menopause status, HPV status, cytology results, and type of transformation zone.\u003c/p\u003e\u003cp\u003eSkilled and experienced colposcopists are lacking in low- and middle-income countries,\u003csup\u003e7\u003c/sup\u003e and the overall agreement between the CDB and surgically excised specimens is 42%.\u003csup\u003e8\u003c/sup\u003e CDB underestimation of the cervical intraepithelial neoplasia grade may delay treatment, while overestimation could expose patients to nonessential surgical treatments or an elevated risk of adverse obstetric events. A meta-analysis has shown that cervical treatment is associated with an increased risk of overall, severe, and extreme prematurity, spontaneous preterm birth, threatened preterm labor, premature rupture of the membranes, chorioamnionitis, low birth weight, neonatal admission, and perinatal death.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Therefore, identifying the factors that influence the discordance between CDB and CKC findings is crucial to improving the preoperative accuracy of cervical lesion evaluation.\u003c/p\u003e\u003cp\u003eCervical cancer remains a common gynecologic malignancy worldwide, with persistent high-risk HPV infection being its primary cause. For patients diagnosed with high-grade squamous intraepithelial lesions or early-stage cervical cancer, cervical conization represents a crucial diagnostic and therapeutic procedure. Among various techniques, CKC represents a classical surgical method that provides clear resection margins and adequate specimen size\u0026mdash;essential features for accurate pathological assessment. Numerous clinical studies have demonstrated that CKC offers notable advantages in diagnostic precision, treatment effectiveness, and reduction of recurrence, particularly in cases involving extensive lesions, endocervical involvement, or suspected microinvasive disease.\u003c/p\u003e\u003cp\u003eAccurate histopathological evaluation is critical for determining appropriate treatment strategies. However, discrepancies may occur between preliminary diagnostic biopsies (such as those obtained through CDB) and final conization specimens, potentially leading to overtreatment or undertreatment. Nomogram is a clinical tool that integrates key factors such as age, menopausal status, cytology, and histology to visually estimate individual risk of under- or overdiagnosis. It assists in decision-making for further intervention or conservative management and is suitable for routine use.\u003c/p\u003e\u003cp\u003eTherefore, to address the clinical challenges in the management of cervical cancer, we aimed to develop a nomogram to estimate the probability of pathological discrepancy between CDB and CKC results.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eData were retrospectively collected from the digital records of women diagnosed with high-grade squamous intraepithelial lesions through CDB who underwent cervical CKC between September 2018 and September 2021 at the Department of Gynecology, Second Hospital of Shanxi Medical University. This study was approved by the Ethics Committees of the Second Hospital of Shanxi Medical University (IRB/112/2022). The requirement for informed consent was waived, as the study was observational, and personal information was anonymous. The exemption of the informed consent procedure was approved by the acknowledged ethics committee. Colposcopic examinations and image acquisition were performed using the Philips Kangkewei SLC-3000 digital colposcopy system. The exclusion criteria were as follows: incomplete colposcopy impressions; missing or incomplete HPV, cytology, or histopathology reports; pregnancy; and history of treatments, such as total hysterectomy or pelvic radiation.\u003c/p\u003e\u003cp\u003eWe analyzed the following demographic and clinical characteristics: age, menopause status, abnormal vaginal bleeding, pregnancy and childbirth, high-risk HPV status, high-risk HPV types, cytology results, colposcopic impressions, abnormal vessels, transformation zone type, endocervical curettage results, CDB results, involvement of glands, preoperative peripheral blood lymphocyte count, monocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and CKC histopathological results.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Variables and Detection Methods\u003c/h3\u003e\n\u003cp\u003eThe high-risk human papillomavirus (hr-HPV) status was determined using the HybriMax HPV genotyping method (HybriBio Ltd, Chaozhou, China). This assay identifies 15 high-risk HPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, and 68). A positive result was defined as the presence of at least one high-risk HPV type, while the absence of detectable high-risk types was considered negative. Genotyping specifically identified HPV 16 and 18, along with other high-risk genotypes including 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68.\u003c/p\u003e\u003cp\u003eCytological evaluation was conducted according to The Bethesda System. Results were classified as negative for intraepithelial lesion or malignancy, atypical squamous cells of undetermined significance, atypical squamous cells\u003cb\u003e\u0026mdash;\u003c/b\u003ecannot exclude high-grade squamous intraepithelial lesion, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, squamous cell carcinoma, or adenocarcinoma.\u003c/p\u003e\u003cp\u003eColposcopic examination and classification were performed by experienced colposcopists. Colposcopic impressions were documented as normal, low-grade lesion, high-grade lesion, or suspected invasive cancer. Aberrant vascular patterns observed during colposcopy included punctation, mosaic, and atypical vessels. The transformation zone was categorized into three types based on the visibility of the squamocolumnar junction: Type 1, entirely ectocervical and fully visible; Type 2, partially extending into the endocervical canal but fully visible with instrumental assistance; Type 3, predominantly or entirely within the endocervical canal and not fully visible.\u003c/p\u003e\u003cp\u003eHistopathological assessment included endocervical curettage, which was performed independently before conization. Endocervical curettage results were reported as negative (no pathological changes) or positive (presence of cervical intraepithelial neoplasia or more severe lesions). Cone biopsy specimens, which served as the primary pathological outcome of this study, were independently evaluated by two senior pathologists. The pathological evaluation comprised final diagnoses (chronic inflammation, cervical intraepithelial neoplasia 1, 2, 3, adenocarcinoma \u003cem\u003ein situ\u003c/em\u003e, microinvasive carcinoma [Stage\u0026thinsp;\u0026le;\u0026thinsp;IA1], or invasive carcinoma [Stage\u0026thinsp;\u0026ge;\u0026thinsp;IA2]), margin status (endocervical, ectocervical, or basal margins reported as positive [involved by lesion] or negative [free of lesion]), and gland involvement (specifically indicating whether high-grade squamous intraepithelial lesion [cervical intraepithelial neoplasia 2/3] involved endocervical glands, recorded as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;).\u003c/p\u003e\n\u003ch3\u003eNomogram Development\u003c/h3\u003e\n\u003cp\u003eThe nomogram was developed using multivariate logistic regression analysis to predict the risk of pathological discrepancies between CDB and CKC findings. Univariate analysis was first used to identify statistically significant variables (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which were subsequently incorporated into a multivariate model through backward stepwise selection, with the Akaike Information Criterion being the stopping rule. The nomogram was constructed using the rms package in R software (version 4.1.2), with points assigned to each predictor according to their regression coefficients. Internal validation was performed via bootstrap resampling with 1,000 iterations. The discriminative ability of the model was evaluated using the area under the receiver operating characteristic curve (AUC), while calibration was assessed through calibration plots and the Hosmer-Lemeshow goodness\u003cb\u003e-\u003c/b\u003eof-fit test.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData analyses were performed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, U.S.A.). Statistical significance was set at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The mean imputation method was used to estimate missing values. Logistic regression modelling with a forward stepwise approach was used for multivariate analyses. The RStudio Desktop (2022.02.2\u0026thinsp;+\u0026thinsp;485) was used to establish a nomogram prediction model. The AUC was used to assess the predictive performance of the model in terms of the calibration curve to determine the predictive consistency.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePatient and Public Involvement\u003c/strong\u003e\u003cp\u003eThe patients or the public were not involved in the design, conduct, reporting, or dissemination plans of our research.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eIn accordance with the journal\u0026rsquo;s guidelines, we will provide our data for independent analysis through the team selected by the Editorial Team for additional data analysis or for the reproducibility of this study in other centers if such is requested.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 607 patients. Table 1 summarizes the baseline clinical characteristics of the study population. The median age was 44 (range: 21\u0026ndash;77) years, with 456 and 151 non-menopausal and menopausal patients, respectively. The median number of pregnancies and vaginal deliveries was 3.0 and 2.0, respectively. The cytological finding was negative for intraepithelial lesions or malignancy in 143 (23.6%), atypical squamous cells of undetermined significance in 154 (25.4%), low-grade squamous intraepithelial lesion in 116 (19.1%), high-grade squamous intraepithelial lesions in 166 (27.3%), atypical squamous cells cannot exclude an high-grade squamous intraepithelial lesions in 26 (4.3%), and atypical glandular cells in 2 (0.3%) patients. Of the total 607 patients, 546 underwent endocervical curettage, among whom 254 had inaccurate results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eCharacteristics of the study group (n=607)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNo.(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAge (yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e＜35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e126 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e44(21-77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e35-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e210 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e＞45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e271 (44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMenopausal status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eNo menopause\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e456 (75.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;Menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e151 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMenopause age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e＜50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e59 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e92 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAbnormal vaginal bleeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e445 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e162 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eNo. of pregnancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e3(0，11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e68 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e131 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e169 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e228 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eNo. of vaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e96 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2(0，8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e173 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e218 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e120 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHR-HPV type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e444 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e139 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCytological results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eNILM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e143 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eASC-US\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;154 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eLSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e116 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eHSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e166 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eASC-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eImpression of colposcopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026le;LSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e143 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e＞HSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e464 (76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAcetowhite changes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;thin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e139 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003edense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e446 (73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAbnormal vessels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e378 (62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e229 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eTransformation zone type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eTZI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e79 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eTZII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e478 (78.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eTZIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e50 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDiagnosis of\u0026nbsp;colposcopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e205 (33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e402 (66.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eInvolvement of glands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e307 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e300 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eECC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e61 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e546 (90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e292 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e254 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDiagnosis of conization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eNILM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e39 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCINI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e64 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e187 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e255 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eAIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e60 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eInterval between colposcopy and\u0026nbsp;\u003cbr\u003econization (day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e＜30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e456 (75.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e16(1,183)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e116 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e＞60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e35 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ediscrepancy of pathologic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003ecolposcopy underestimation \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e103(17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003ecolposcopy overestimation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e62(10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eLYM(\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.87(0.57,4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMON(\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.4(0.15,1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.643(0.324,10.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e128.571(41.538,350.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e4.714(1.426,14.783)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 presents the differences in the histopathological results obtained via CDB and CKC. Among those with CDB-diagnosed high-grade squamous intraepithelial lesions, the overall incidence of diagnostic concordance/correct diagnosis was 72.8% (442 of 607). The incidence of colposcopic underestimation was 10.2% (62 of 607), and that of colposcopy overestimation was 17.0% (103 of 607).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of colposcopy-directed biopsy with cold knife conization pathological diagnosis (cases)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003eCDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 355px;\"\u003e\n \u003cp\u003eConization (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(P-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eNILM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eCINI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eAIS/SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e34(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e90(43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e56(27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e205(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e76.016\u003c/p\u003e\n \u003cp\u003e(＜0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e17(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e30(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e97(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e199(49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e59(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e402(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTables 3 and 4 present the univariate analyses. The CDB underdiagnosis and correct or overdiagnosis groups exhibited significant differences concerning age and peripheral blood lymphocyte count, with patients in the underdiagnosis group being older and having a lower peripheral blood lymphocyte count (P-value \u0026lt;0.05). Furthermore, menopause, \u0026ge;3 vaginal deliveries, cytological diagnosis of high-grade squamous intraepithelial lesions, abnormal vessels, CDB diagnosis of cervical intraepithelial neoplasia 3, and abnormal endocervical curettage were significantly higher in the underdiagnosis group (P-value\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt;0.05). The monocyte count was significantly higher in the CDB overdiagnosis group than in the correct or underdiagnosis groups. Moreover, the cytological diagnosis of high-grade squamous intraepithelial lesions, abnormal vessels, CDB diagnosis of cervical intraepithelial neoplasia 3, involvement of glands, and positive endocervical curettage were significantly lower in the CDB overdiagnosis group than in the other two groups (P-value\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Univariate analysis of colposcopy-directed biopsy underestimation\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ecorrectly or over-diagnosed group\u003c/p\u003e\n \u003cp\u003e(n=545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eunder-diagnosed group\u003c/p\u003e\n \u003cp\u003e(n=62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eZ/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAge (yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e43(36，50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e48(41.75，56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLYM (\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.890(1.545，2.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.700(1.430，2.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMON (\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.400(0.330,0.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0.400(0.320.0.450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.630(1.260,2.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.775(1.470,2.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e127.481(102.564,158.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e139.617(111.701,170.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4.733(3.823,5.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e4.472(3.877，5.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMenopausal status\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e418(76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e38(61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e7.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e127(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e24(38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNo. of pregnancies\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e343(62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e36(58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＞3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e202(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e26(41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNo. of vaginal delivery\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＜3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e444(81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e43(69.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e101(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e19(30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAbnormal vaginal bleeding n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e401(73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e44(71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e144(26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e18(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHR-HPV\u003csup\u003ea\u0026nbsp;\u003c/sup\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e5(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.985\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e526(96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e57(91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHR-HPV type\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e415(76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e53(85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.749\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＞1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e130(23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e9(14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ecytological results\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNILM/ASC-US/LSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e383(70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e30(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e12.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHSIL/ASC-H/AGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e162(29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e32(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eImpression of colposcopy n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;LSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e133(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e10(16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＞HSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e412(75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e52(83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAbnormal vessels n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e347(63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e31(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e198(36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e31(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTZ type\u003csup\u003ea\u0026nbsp;\u003c/sup\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e504(92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e56(90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e41(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e6(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eDiagnosis of\u0026nbsp;CDB n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e202(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e3(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e25.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e343(62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e59(95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eInvolvement of glands n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e277(50.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e30(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e268(49.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e32(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eECC results n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e340(62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e13(21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e39.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e205(37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e49(79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Univariate analysis of colposcopy-directed biopsy overestimation\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eCorrectly or under-diagnosed group\u003c/p\u003e\n \u003cp\u003e(n=504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eover-diagnosed group\u003c/p\u003e\n \u003cp\u003e(n=103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eZ/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAge (yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e44(36，50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e45(34，51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLYM(\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.845(1.513，2.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.980(1.550，2.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMON (\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.390(0.320，0.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0.420(0.350，0.500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.675(1.284，2.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.604(1.210，2.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e129.470(103.309,162.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e122.786(101.333，155.811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4.727(3.848,5.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e4.526(3.677，5.735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMenopausal status\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e384(76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e72(69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e120(23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e31(30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNo. of pregnancies\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e317(62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e62(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＞3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e187(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e41(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNo. of vaginal delivery\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＜3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e399(79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e88(85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e105(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e15(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAbnormal vaginal bleeding n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e364(72.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e81(78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e140(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e22(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHR-HPV\u003csup\u003ea\u0026nbsp;\u003c/sup\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e16(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e8(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3.617\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e488(96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e95(92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHR-HPV type\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e389(77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e79(76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＞1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e115(22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e24(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ecytological results\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNILM/ASC-US/LSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e326(64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e87(84.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e15.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHSIL/ASC-H/AGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e178(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e16(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eImpression of colposcopy n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;LSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e112(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e31(30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e＞HSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e392(77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e72(69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAbnormal vessels n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e303(60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e75(72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e201(39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e28(27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTZ type\u003csup\u003ea\u0026nbsp;\u003c/sup\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e467(92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e93(90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e37(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e10(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eDiagnosis of\u0026nbsp;CDB n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e149(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e56(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e23.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e355(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e47(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eInvolvement of glands n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e243(48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e64(62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e261(51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e39(37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eECC results n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e269(53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e84(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e27.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e235(46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e19(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe multivariate logistic regression analysis indicated cervical intraepithelial neoplasia 3 (odds ratio [OR]=9.455, P-value \u0026lt;0.001), abnormal endocervical curettage (OR=5.407, P\u003cem\u003e-\u003c/em\u003evalue \u0026lt;0.001), high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude an high-grade squamous intraepithelial lesions/atypical glandular cells\u0026nbsp;(OR=1.791, P\u003cem\u003e-\u003c/em\u003evalue =0.044), and\u0026nbsp;peripheral blood lymphocyte count (OR=0.523, P-value\u003cem\u003e\u0026nbsp;\u003c/em\u003e=0.018) as factors associated with CDB underestimation (Table 5). In contrast, cervical intraepithelial neoplasia 2 (OR=2.369,\u0026nbsp;P-value\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt;0.001), normal endocervical curettage (OR=3.271, P-value \u0026lt;0.001), negative for intraepithelial lesions or malignancy/atypical squamous cells of undetermined significance/low-grade squamous intraepithelial lesion (OR=2.362, P-value=0.004), and monocyte count (OR=7.989, P-value =0.016) were associated with CDB overestimation (Table 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e Multifactorial logistic regression analysis of colposcopy-directed biopsy underestimation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eWald\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eOR(95% Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eCINIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e13.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e9.455(2.882～31.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eECC positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e25.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.407(2.813～10.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eHSIL/ASC-H/AGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.791(1.014～3.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eLYM (\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e5.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.523(0.306～0.896)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e Multifactorial logistic regression analysis of colposcopy-directed biopsy overestimation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eWald\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eOR(95% Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eCINII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e14.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.369(1.511～3.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eECC negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;-1.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e18.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.271(1.905～5.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNILM/LSIL/ASC-US\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.362(1.319～4.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eMON (\u0026times;10\u003csup\u003e9\u003c/sup\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e2.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e5.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.989(1.482～43.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe variables from the final multivariate model were used to construct predictive nomograms (Fig. 1a and b). Each nomogram included four risk factors; the nomogram for estimating underestimation risk included the results of CDB, endocervical curettage, cytology, and peripheral blood lymphocyte count, while that for estimating overestimation risk included the results of CDB, endocervical curettage, cytology, and monocyte count. The regression weight of each predictor was the length of each predictor\u0026rsquo;s line segment, and the total score was equivalent to the linear predictor. The receiver operator characteristic curves of the nomograms are shown in Figure 2a and b. The AUC of the nomogram for estimating underestimation risk was 0.815 (95% confidence interval [CI]: 0.767\u0026minus;0.863), and its sensitivity and specificity were 77.4% and 75.6%, respectively. The AUC of the nomogram for estimating overestimation risk was 0.742 (95% CI: 0.690\u0026minus;0.793), and its sensitivity and specificity were 60.2% and 79.2%, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInternal validation was performed through 10-fold cross-validation. The C-indices for the underestimation and overestimation models were 0.813 and 0.727, respectively. The calibration plots exhibited excellent consistency between the actual and predicted discrepancy probability with an additional 1,000 bootstraps ( Fig. 3a, mean absolute error = 0.006; Fig. 3b, mean absolute error = 0.009).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of Main Results:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated the concordance of the pathological findings between CDB and CKC and found that CDB may overestimate and underestimate cervical lesions. In addition, we identified the factors associated with pathological discordance and used them to develop nomogram models to predict the possibility of CDB underestimation and overestimation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults in the Context of Published Literature:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColposcopic impression has been used to identify areas with the highest degree of visual abnormality, and CDB from those areas has become the standard diagnostic procedure for precancerous cervical lesions. However, the concordance between CDB and conization diagnoses varies substantially within countries and hospitals. Consequently, women are usually misdiagnosed or submitted to nonessential biopsies. Therefore, we aimed to address this gap by identifying the factors influencing CDB accuracy and improving the preoperative accuracy of cervical lesion diagnosis. The reported rates of biopsy underestimation are 75.0% for normal findings, 24.7% for cervical intraepithelial neoplasia 1, 23.4% for cervical intraepithelial neoplasia 2, and 24.2% for cervical intraepithelial neoplasia 3, with an overall rate of 23.1%.\u003csup\u003e10\u003c/sup\u003e Conversely, those of biopsy overestimation are 30.1% for cervical intraepithelial neoplasia 1, 42.6% for cervical intraepithelial neoplasia 2, 26.6% for cervical intraepithelial neoplasia 3, and 46.0% for MIC/squamous cell carcinoma, with an overall rate of 33.6%.\u003csup\u003e10\u003c/sup\u003e In the present study, colposcopic and conization diagnoses were concordant in 72.8% of cases, comparable to previously reported findings in China.\u003csup\u003e11\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile some investigators have identified the inaccuracy inherent to CDB as the primary cause of the difference, others have examined factors that may influence the consistency between CDB and conization biopsy results. Fan et al. suggested that old age (\u0026ge;50), postmenopausal status, and transformation zone type 3 are positively associated with CDB underdiagnosis, while three or more biopsies and cone width \u0026ge;21 mm might improve CDB accuracy.\u003csup\u003e12\u003c/sup\u003e Using the database of the Gardasil clinical trials, Stoler et al.\u003csup\u003e8\u003c/sup\u003e included 594 cases and identified patients\u0026apos; age, number of biopsies, lesion sizes, presence of HPV 16/18 genotypes, and geographic region as factors affecting the agreement between\u0026nbsp;CDB\u0026nbsp;and excisional biopsy results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present study indicated that cervical intraepithelial neoplasia 3, abnormal endocervical curettage, high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude high-grade squamous intraepithelial lesions/atypical glandular cells, and peripheral blood lymphocyte count were the independent factors associated with biopsy underestimation. However, cervical intraepithelial neoplasia 2, negative endocervical curettage, negative for intraepithelial lesions or malignancy/atypical squamous cells of undetermined significance/low-grade squamous intraepithelial lesion, and monocyte count were independently associated with biopsy overestimation. Furthermore, the nomogram models developed using these factors showed good discriminative capability. The models predict whether colposcopy biopsy is overestimated or underestimated. If the model shows that colposcopy biopsy is highly overestimated, a follow-up or loop electrosurgical excision procedure is recommended, particularly for women with fertility requirements. If the model shows that colposcopy biopsy is likely to be underestimated, thorough examinations before operation, such as magnetic resonance imaging and squamous cell carcinoma antigen, are essential. Clinicians may appropriately enlarge the conization scope according to the predicted results to reduce the chance of a positive margin and residual lesion and avoid second operations.\u003c/p\u003e\n\u003cp\u003ePatients with cytological findings of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells were previously found to have a 33-fold higher risk for cervical cancer than did those with low-grade squamous intraepithelial lesions/atypical squamous cells of undetermined significance/negative for intraepithelial lesions or malignancy.\u003csup\u003e13\u003c/sup\u003e In the current study, we found a higher missed diagnosis rate in women with high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells. Hence, in patients with atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion and high-grade squamous intraepithelial lesions, CDB may not represent a good diagnostic or reliable management method, and immediate conization without punch biopsy may prevent overlooking high-grade lesions.\u003csup\u003e10\u003c/sup\u003e Furthermore, our multivariate logistic analysis showed that cytology results were an independent factor influencing biopsy overestimation and that patients with cytological findings suggestive of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells were less likely to have an overestimated diagnosis. This implies that patients with cytological findings suggestive of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude a high-grade squamous intraepithelial lesion/atypical glandular cells are more likely to have high-grade squamous intraepithelial lesion findings, while those with\u0026nbsp;cytological findings suggestive of low-grade squamous intraepithelial lesion/atypical squamous cells of undetermined significance/negative for intraepithelial lesions or malignancy are more likely to be negative for intraepithelial lesions or malignancy/low-grade squamous intraepithelial lesion findings. We suggest that, in clinical settings, it is essential to individually interpret the pathological report of cervical biopsy according to the results of cervical cytology for patients who are diagnosed with high-grade squamous intraepithelial lesions via colposcopic biopsy. This would provide an early warning to high-risk patients whose conditions are progressing to cervical cancer, enhancing doctor-patient communication.\u003c/p\u003e\n\u003cp\u003eThe present study indicated CDB diagnosis and endocervical curettage results as independent factors influencing pathological discordance between CDB and conization. Patients with a CDB diagnosis of cervical intraepithelial neoplasia 2 and normal endocervical curettage were at a higher risk of biopsy overestimation. However, patients with a CDB diagnosis of cervical intraepithelial neoplasia 3 and abnormal endocervical curettage were more likely to have an underestimated biopsy. The risk of biopsy underestimation was 8.253 times higher in patients with a preoperative histological diagnosis of cervical intraepithelial neoplasia 3 than in those with cervical intraepithelial neoplasia 2. Patients with cervical intraepithelial neoplasia 3 are at a higher risk of progression to cervical cancer; the lesions tend to extend into the cervical canal, and some of them are hopper or multicentric, making them susceptible to missed diagnosis via colposcopy. A higher rate of biopsy-missed diagnoses was reported in patients with abnormal endocervical curettage. An abnormal endocervical curettage indicates the presence of lesions in the cervical canal; however, the specimens obtained through endocervical curettage are usually unduly small to assess the cervical canal adequately, and some lesions may be missed. Although endocervical curettage results independently influence pathological differences between CDB and conization, the endocervical curettage test is not feasible in some individuals, and the specific indications for this test remain debated.\u003csup\u003e14\u003c/sup\u003e There is no clear and refined expert consensus or guideline regarding the selection of endocervical curettage patients globally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe presence of inflammatory cells in tumor tissue was first suggested by the German pathologist Rudolf Virchow in 1863, who speculated that cancer susceptibility and severity are associated with functional polymorphisms of inflammatory cytokine genes.\u003csup\u003e15\u003c/sup\u003e Subsequently, the role of inflammatory cytokines in the tumor microenvironment has been focused on in the field of tumor immune response, and many studies have suggested inflammatory immune indicators as predictors of early diagnosis, staging, and prognosis of cervical cancer.\u003csup\u003e16-19\u003c/sup\u003e Tas et al. reported that the platelet-to-lymphocyte ratio was significantly higher in patients with cervical cancer than in those with low-grade squamous intraepithelial lesion and high-grade squamous intraepithelial lesion diagnoses or healthy individuals (P-value \u0026lt;0.001), and their logistic regression analysis revealed that the neutrophil-to-lymphocyte ratio (OR: 1.643, 95% CI: 1.009\u0026ndash;3.142, P-value\u003cem\u003e\u0026nbsp;\u003c/em\u003e=0.047) and platelet-to-lymphocyte ratio (OR: 1.032, 95% CI: 1.003\u0026ndash;1.062, P-value =0.029) were predictors of squamous cell carcinoma.\u003csup\u003e25\u003c/sup\u003e In addition, the measurement of systemic inflammatory indicators is simple, cost-effective, and readily available. Therefore, five indicators, namely peripheral blood lymphocyte count, monocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio, were investigated in our study for their predictive value for pathological discordance between CDB and CKC. The multivariate logistic regression analysis revealed peripheral blood lymphocyte count as an independent factor for predicting biopsy underestimation and monocyte count as an independent factor for predicting biopsy overestimation, while neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio were not significantly associated with pathological discordance. These findings may be owing to the small sample size; therefore, further research is necessary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Weaknesses:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, this is the first study to associate inflammatory indicators with pathological discordance between biopsy and conization in China. Furthermore, we developed nomogram models to visualize the risk of pathological discordance. Nonetheless, this study had some limitations. First, the retrospective study design has inherent drawbacks, such as selection bias. Second, this was a single-institution study, and the sample size might have been insufficient to define the diagnostic performance of colposcopy with high accuracy. Finally, the nomogram models were constructed using a small sample size and lack external validation. Therefore, multi-center studies with large sample sizes are warranted to confirm our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Practice and Future Research:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is the first to associate inflammatory indicators with the pathological discrepancies between biopsy and conization and to advance predictive nomogram models. We believe these models hold significant potential for clinical application. Integrating routinely available parameters\u0026mdash;including cervical biopsy histology, endocervical curettage findings, cytological classification, and peripheral blood lymphocyte or monocyte counts\u0026mdash;facilitates the quantitative assessment of the individualized risk of CDB underestimation or overestimation. For patients predicted to have a high underestimation risk, more comprehensive preoperative evaluations (such as magnetic resonance imaging or squamous cell carcinoma antigen testing) may be recommended, along with appropriately extending the cone margins to reduce the risk of positive margins and residual lesions. Conversely, for those with a high probability of overestimation, particularly young patients with fertility desires, more conservative management strategies (e.g., active surveillance or loop electrosurgical excision procedure) could be considered to avoid overtreatment. This tool facilitates precise and individualized clinical decision-making, addressing limitations in current diagnostic workflows. However, future multi-center studies with larger sample sizes are warranted to validate and optimize the clinical utility of the models.\u003c/p\u003e\n\u003cp\u003eOur nomogram models may help estimate the probability of CDB underestimation and overestimation, enhancing individualized treatment plans.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we found discordance in the pathological findings between CDB and CKC. After identifying the factors influencing this discordance, we developed nomogram models for predicting the possibility of biopsy underestimation and overestimation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis project is supported by the National Natural Science Foundation of China (Grant No.81702583). Research Project Supported by Shanxi Scholarship Council of China (grant no.2022-195), Shanxi Graduate Education Teaching Reform Project in 2022 (107)\u0026nbsp;(grant no.2022YJJG105), the Outstanding Youth Fund Project of Shanxi Province (grant no.201901D211506) and China Postdoctoral Science Foundation (grant no.2019M651072) to Dr.\u0026nbsp;Weihong Zhao, and is supported by the Nature Science Foundation of Shanxi Province (grant no.201901D111364) and the Shanxi Province Key National Science and Technology Cooperation Projects (grant no.202104041101006) to Dr. Jingfang Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eI have nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eIn accordance with the journal’s guidelines, we will provide our data for independent analysis through the team selected by the Editorial Team for additional data analysis or the reproducibility of this study in other centers if such is requested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Ethics Committees of the Second Hospital of Shanxi Medical University (IRB/112/2022). The requirement for informed consent was waived, as the study was observational, and personal information was anonymous. The exemption of the informed consent procedure was approved by the acknowledged ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eJF W,YY M, JR L, SL W, SQ W, QL, and WH Z contributed conception and design of the study. JF W and YY M drafted the manuscript. JF W and JR L participated in data analysis. SL W, YB and WH Z participated in data acquisition. WH Z critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantesso N, Mustafa RA, Sch\u0026uuml;nemann HJ, et al. World Health Organization Guidelines for treatment of cervical intraepithelial neoplasia 2\u0026ndash;3 and screen-and-treat strategies to prevent cervical cancer. Int J Gynaecol Obstet. 2016;132(3):252\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastellsagu\u0026eacute; X. Natural history and epidemiology of HPV infection and cervical cancer. Gynecol Oncol. 2008;110(3 Suppl 2):S4\u0026ndash;07.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang WJ, Wang D, Zhao M, et al. Serum lncRNAs (CCAT2, LINC01133, LINC00511) with Squamous Cell Carcinoma Antigen Panel as Novel Non-Invasive Biomarkers for Detection of Cervical Squamous Carcinoma. Cancer Manag Res. 2020;12:9495\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan A, Wang C, Zhang L, et al. Diagnostic value of the 2011 International Federation for Cervical Pathology and Colposcopy Terminology in predicting cervical lesions. Oncotarget. 2018;9(10):9166\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue P, Ng MTA, Qiao YL. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence. Bmc Med. 2020;18(1):169.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStoler MH, Vichnin MD, Ferenczy A, et al. The accuracy of colposcopic biopsy: analyses from the placebo arm of the Gardasil clinical trials. Int J Cancer. 2011;128(6):1354\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKyrgiou M, Athanasiou A, Paraskevaidi M, et al. Adverse obstetric outcomes after local treatment for cervical preinvasive and early invasive disease according to cone depth: systematic review and meta-analysis. BMJ. 2016;354:i3633.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung Y, Lee AR, Lee SJ, et al. Clinical factors that affect diagnostic discrepancy between colposcopically directed biopsies and loop electrosurgical excision procedure conization of the uterine cervix. Obstet Gynecol Sci. 2018;61(4):477\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim SI, Kim SJ, Suh DH, et al. Pathologic discrepancies between colposcopy-directed biopsy and loop electrosurgical excision procedure of the uterine cervix in women with cytologic high-grade squamous intraepithelial lesions. J Gynecol Oncol. 2020;31(2):e13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan A, Zhang L, Wang C, et al. Analysis of clinical factors correlated with the accuracy of colposcopically directed biopsy. Arch Gynecol Obstet. 2017;296(5):965\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePretorius RG, Belinson JL, Peterson P, Burchette RJ. Factors That Virtually Exclude Cervical Cancer at Colposcopy. J Low Genit Tract Dis. 2015;19(4):319\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Luo H, Zhang X, et al. Development and validation of a clinical prediction model for endocervical curettage decision-making in cervical lesions. BMC Cancer. 2021;21(1):804.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357(9255):539\u0026thinsp;\u0026ndash;\u0026thinsp;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLima P, Mantoani P, Murta E, Nomelini RS. Laboratory parameters as predictors of prognosis in uterine cervical neoplasia. Eur J Obstet Gynecol Reprod Biol. 2021;256:391\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrinh H, Dzul SP, Hyder J, et al. Prognostic value of changes in neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and lymphocyte-to-monocyte ratio (LMR) for patients with cervical cancer undergoing definitive chemoradiotherapy (dCRT). Clin Chim Acta. 2020;510:711\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrabawa I, Bhargah A, Liwang F, et al. Pretreatment Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) as a Predictive Value of Hematological Markers in Cervical Cancer. Asian Pac J Cancer Prev. 2019;20(3):863\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTas M, Yavuz A, Ak M, Ozcelik B. Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Discriminating Precancerous Pathologies from Cervical Cancer. J Oncol. 2019;2019:2476082.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"High-grade squamous intraepithelial lesions, colposcopy-directed biopsy, pathological discordance, nomograms","lastPublishedDoi":"10.21203/rs.3.rs-7599091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7599091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eWe aimed to develop a nomogram for predicting the probability of discordance between colposcopy-directed biopsy and cold knife conization pathological findings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis was a quantitative research involving a case-control study. We retrospectively reviewed the records of patients diagnosed with high-grade squamous intraepithelial lesions through colposcopy-directed biopsy, who underwent cold knife conization at the Second Hospital of Shanxi Medical University between September 2018 and September 2021. The nomogram was developed using multivariate logistic regression analysis to predict the risk of pathological discrepancies between colposcopy-directed biopsy and cold knife conization findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe colposcopy-directed biopsy accuracy rate for identifying high-grade squamous intraepithelial lesions was 72.8%. Multivariate analysis showed that cervical intraepithelial neoplasia Grade 3 (odds ratio [OR]\u0026thinsp;=\u0026thinsp;9.455, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), positive endocervical curettage (OR\u0026thinsp;=\u0026thinsp;5.407, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), findings of high-grade squamous intraepithelial lesions/atypical squamous cells cannot exclude high-grade squamous intraepithelial lesions/atypical glandular cells (OR\u0026thinsp;=\u0026thinsp;1.791, P-value\u0026thinsp;=\u0026thinsp;0.044), and peripheral blood lymphocyte count (OR\u0026thinsp;=\u0026thinsp;0.523, P-value\u0026thinsp;=\u0026thinsp;0.018) were associated with colposcopy-directed biopsy underestimation. Cervical intraepithelial neoplasia 2 (OR\u0026thinsp;=\u0026thinsp;2.369, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), negative endocervical curettage (OR\u0026thinsp;=\u0026thinsp;3.271, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), negative for intraepithelial lesions or malignancy/atypical squamous cells of undetermined significance/low-grade squamous intraepithelial lesion (OR\u0026thinsp;=\u0026thinsp;2.362, P-value\u0026thinsp;=\u0026thinsp;0.004), and peripheral blood monocyte count (OR\u0026thinsp;=\u0026thinsp;7.989, P-value\u0026thinsp;=\u0026thinsp;0.016) were associated with colposcopy-directed biopsy overestimation per the multivariate analysis. The above factors were used to construct nomograms for predicting colposcopy-directed biopsy underestimation or overestimation, which had area under the curve values of 0.815 (95% confidence interval [CI]: 0.767\u0026minus;0.863) and 0.742 (95% CI: 0.690\u0026minus;0.793) for underestimation and overestimation, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur results suggest a significant discordance between colposcopy-directed biopsy and cold knife conization pathological results, which could prompt nonessential conization or delayed treatment, particularly for fertile women. Our nomogram models may help estimate the probability of colposcopy-directed biopsy underestimation and overestimation, enhancing individualized treatment plans.\u003c/p\u003e","manuscriptTitle":"Nomogram for Predicting Pathological Discordance between Colposcopy- directed Biopsies and Cold Knife Conization Findings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:24:42","doi":"10.21203/rs.3.rs-7599091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-09T06:27:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T06:34:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T06:28:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T06:27:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-09-12T09:01:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bf63b17-9855-4996-9bdf-3f44a9dd3c8b","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T19:24:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:24:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7599091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7599091","identity":"rs-7599091","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}