Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients

Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients | 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 Article Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients linli LIU, Qiong JIN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4677808/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Endometrial cancer (EC) is one of the most frequent gynecologic cancers, approximately 20% of patients are regarded as high-risk with poor prognosis. However, more details of patients with second primary endometrial cancer (SPEC) after colorectal cancer (CRC) remain poorly understood.We therefore purposed to construct two nomograms to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates to facilitate clinical application. Nomograms for predicting OS and CSS were constructed and validated. The receiver operating characteristic curves, calibration plot, decision curve analysis, C-index, net reclassification improvement, and integrated discrimination improvement were applied to evaluate the predictive performance. Finally, the Prognostic index was calculated and used for risk stratification of Kaplan-Meier survival analysis based on different treatment options. Nomograms of OS and CSS were formulated based on the independent prognostic factors utilizing the training set. The 3- and 5- years of OS nomogram demonstrated good discrimination (AUC = 0.840 and 0.829, respectively), well-calibrated power, and excellent clinical effectiveness. Our nomograms of predicting OS and CSS had a concordance index of 0.801 and 0.866 compared with 0.676 and 0.746 for the AJCC staging system, and more importantly, demonstrated a better forecast accuracy. Chemoradiotherapy displayed a significant survival benefit in the high-risk groups, but proceeding to surgery plus chemotherapy showed a favorable survival for the low groups based on all patients. We developed and internally validated multivariable models that predict OS and CSS risk of SPEC in patients with a CRC to help clinicians make applicable clinical decisions for patients. Biological sciences/Cancer Health sciences/Medical research Endometrial neoplasms Colorectal carcinoma Nomogram Overall survival Cancer-specific survival Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Endometrial carcinoma (EC) is the most common gynecologic cancer in developed countries and accounts for more than 2% of deaths due to cancer in women worldwide, with the American Joint Committee on Cancer (AJCC) stage and histological type being associated with the treatment options and final prognosis[ 1 , 2 ]. Recent cohort studies have identified an approximately 3-fold increased risk for uterine corpus cancer in women with previous rectal cancer who underwent radiotherapy[ 3 , 4 ]. However, the current research on the prognosis of second primary endometrial cancer (SPEC) among colorectal cancer (CRC) is limited. CRC is the third most prevalent diagnosed cancer and the second leading cause of cancer-related death worldwide, with approximately 10% of all newly diagnosed malignant tumors per year[ 5 ]. Studies have found that there is a very obvious imbalance of intestinal flora and disruption of barrier function in the intestines of people with CRC, that is, dysbiosis of intestinal microbiota may contribute to CRC[ 6 ]. In addition, gut microbiota may also lead to insulin resistance[ 7 ], abnormal estrogen metabolism[ 8 ], or chronic inflammation via multiple pathways[ 8 ], and thus involved in EC occurrence and progression. Recently, a Mendelian randomization study supported that gut microbiota may be causally associated with both CRC and EC[ 9 ]. Hence, these previous results may indicate an underlying bidirectional link between CRC, intestinal dysbacteriosis, and EC. Moreover, the incidence of CRC and EC increases significantly with global aging, the treatment decisions of SPEC among CRC have received increasing attention from clinicians, but effective regimens remain elusive. Although several clinical prediction models have been reported for EC[ 10 , 11 ], there is limited data on the concordance of these, shedding light on the prognosis of EC varies significantly between patients with different characteristics. However, compared to primary EC, due to surgical, radio- and chemotherapy or a combination of these therapies was used for the treatment of CRC, further aggravating intestinal microbiota dysbiosis, which might lead to worse physical and psychological conditions. The advice of options on clinical treatment in SPEC in CRC has still been uncertain. Thus, it is imperative to identify appropriate prognostic factors to establish survival prediction models for making better clinical decisions. Our objective was to use the Surveillance, Epidemiology, and End Results (SEER) database to construct and verify two nomogram prognostic models for predicting 3­ and 5-year survival rates of SPEC patients in CRC, which may be useful for prognostic prediction, treatment strategy selection, and follow-up management of these patients. MATERIALS AND METHODS Study populations and data collection Data were obtained from the Surveillance, Epidemiology and End Results (SEER) database (SEER*Stat software 8.4.2), including 1975–2020 and 2010–2020, which serves as an authoritative, federally funded cancer reporting system. Patient personal information is not identifiable, and SEER database information is publicly available, so we do not need to obtain ethical approval and informed consent from patients. All primary cancer sites were coded according to the International Classification of Diseases for Oncology, Third Edition . This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies. We first identified individuals with an initial primary cancer diagnosed CRC based on the ICD-O-3 codes/WHO 2008(colon and rectum) and sequence number (1st of 2 or more primaries). Then, SPEC patients were further recognized depending on the ‘person selection’ function of SEER records of the primary site labeled (C54.0–C54.9, C55.9) and sequence number (2nd of 2 or more primaries). Duplication cases, leiomyocarcinosarcoma of myometrium or corpus uteri, and patients with unknown survival time were excluded. Finally, 1631 patients were screened in this study and randomly divided into training and validation groups (7:3 ratio) for the development and validation of the nomogram, respectively. The specific flowchart is shown in Supplementary Fig. 1. The primary endpoints consisted of overall survival (OS) and cancer-specific survival (CSS) rates at 3- and 5–5 years respectively. OS events were death from any cause. CSS events were deaths resulting from EC. Study variables and outcomes The following 19 clinicopathological variables of SPEC in CRC patients were downloaded from the SEER database: age, race, marital status, median household income, previous CRC-related variables (histology, location, radiation, and chemotherapy), interval time between CRC and SPEC, SEER summary stage, histologic types, grade, treatment type, lymph node-positive (pelvic or para-aortic), distant lymph nodes, metastasis outside the pelvic reproductive system (including bone, lung, liver, brain, bladder, or vulva, et al.), months to treat, tumor size, date of last follow-up visit, and patient status at last visit. The reclassification stage was recorded according to the 8th edition American Joint Committee on Cancer (AJCC) and categorized as stages I to IV[ 12 ]. Additionally, the histologic types were classified as endometrioid, and non-endometrioid and were designated as grade I, II, or III[ 13 , 14 ]. Statistical analysis Variables based on previous reports or clinical consensus were all included in the comparison between the training and validation groups. Categorical variables were expressed in percentages (95% confidence interval, 95% CI) and compared using chi-square tests. The least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis were devoted to constructing OS- and CSS-associated prognostic nomograms. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC), concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) in comparison with the traditional AJCC stage system. Calibration curves evaluated the concordance between observed and predicted survival probability. Decision curve analysis (DCA) was implemented to illustrate the clinical performance of the model. We derived the prognostic index (PI) for each patient from the regression coefficients found in the final multivariable Cox regression model. Kaplan-Meier (K-M) survival analysis was performed to evaluate the clinical effectiveness of the risk stratification system, and the significance was evaluated by a log-rank test. Statistical analyses were performed using R software (version 4.3.1) and STATA software (Stata 16.0, College Station, Texas 77845, USA). A p -value of less than 0.05 was considered statistically significant. RESULTS participants basic characteristics The median follow-up time was 78 months (73–86 months), and 711 patients died during follow-up. The median OS was 95 months (88–101 months), and the 3- and 5-year OS rates were 67% (64%-69%) and 59% (57%-62%), respectively. The median CSS was 52 months (46–56 months), and the 3- and 5-year CSS rates were 83% (81%-85%) and 80% (78%-82%), respectively. The distribution of white race and age over 60 years old was 78.6% and 1202 (73.70%) respectively. 268 (16.43%) and 673 (41.26%) had received radiation therapy and chemotherapy respectively during previous CRC, but the minority of the tumors (22.44%) were located in the rectum. The majority type of SPEC cases (66.95%) was endometrioid histology. Among patients with information on AJCC stage available, 69.59% of patients were early stage (I–II), while 22.5% were late stage (III-IV). In addition, most patients underwent surgery. The clinical and disease characteristics of patients in the model training and validation samples were similar and summarized in Table 1 . Table 1 Baseline characteristics of the included patients. Characteristic Total (n = 1631) Training group (n = 1143) Validation group (n = 488) p -value Age (%) 0.893 20–49 0.07(0.06–0.08) 0.07(0.05–0.08) 0.07(0.05–0.10) 50–69 0.48(0.46–0.50) 0.48(0.45–0.51) 0.48(0.44–0.52) ≥ 70 0.45(0.43–0.47) 0.45(0.42–0.48) 0.45(0.40–0.49) Race (%) 0.122 black 0.12(0.10–0.13) 0.12(0.10–0.14) 0.11(0.08–0.14) white 0.79(0.77–0.81) 0.77(0.75–0.80) 0.82(0.78–0.85) others 0.10(0.08–0.11) 0.11(0.09–0.13) 0.08(0.06–0.11) Marital status (%) 0.858 Single 0.13(0.12–0.15) 0.14(0.12–0.16) 0.13(0.1–0.16) Married or Domestic Partner 0.46(0.43–0.48) 0.45(0.42–0.48) 0.47(0.43–0.52) Widowed or Divorced or Separated 0.35(0.33–0.37) 0.35(0.32–0.38) 0.34(0.3–0.39) Unknown 0.06(0.05–0.07) 0.06(0.05–0.08) 0.06(0.04–0.08) Median household income (%) 0.528 < $ 70000 0.45(0.43–0.48) 0.46(0.43–0.49) 0.44(0.4–0.49) ≥ $ 70000 0.55(0.52–0.57) 0.54(0.51–0.57) 0.56(0.51–0.6) Interval time (%) 0.852 < 1 0.17(0.15–0.19) 0.17(0.15–0.19) 0.17(0.14–0.20) ≥ 1,<5 0.44(0.42–0.47) 0.44(0.41–0.47) 0.45(0.41–0.50) ≥ 5 0.39(0.37–0.41) 0.39(0.37–0.42) 0.38(0.34–0.42) Colorectal cancer histology (%) 0.919 Adenocarcinoma 0.83(0.81–0.85) 0.83(0.81–0.85) 0.84(0.8–0.87) Mucous tumor 0.09(0.08–0.11) 0.09(0.08–0.11) 0.09(0.07–0.12) Others 0.08(0.06–0.09) 0.08(0.06–0.09) 0.07(0.05–0.10) Colorectal cancer location (%) 0.399 Colon 0.78(0.75–0.8) 0.77(0.74–0.79) 0.79(0.75–0.82) Rectum 0.22(0.2–0.25) 0.23(0.21–0.26) 0.21(0.18–0.25) Colorectal cancer Radiation 0.750 Yes 0.16(0.15–0.18) 0.17(0.15–0.19) 0.16(0.13–0.20) No 0.84(0.82–0.85) 0.83(0.81–0.85) 0.84(0.80–0.87) Colorectal cancer Chemotherapy (%) 0.068 Yes 0.41(0.39–0.44) 0.40(0.37–0.43) 0.45(0.4–0.49) No 0.59(0.56–0.61) 0.6(0.57–0.63) 0.55(0.51–0.60) Grade (%) 0.070 I 0.28(0.26–0.30) 0.27(0.25–0.30) 0.28(0.24–0.32) II 0.20(0.18–0.22) 0.18(0.16–0.21) 0.23(0.19–0.27) III 0.28(0.26–0.30) 0.28(0.26–0.31) 0.28(0.24–0.32) Unknown 0.25(0.22–0.27) 0.26(0.24–0.29) 0.21(0.18–0.25) Histology (%) 0.439 Endometrioid 0.67(0.65–0.69) 0.68(0.65–0.70) 0.66(0.61–0.70) Non Endometrioid 0.33(0.31–0.35) 0.32(0.3–0.35) 0.34(0.3–0.39) Summary stage (%) 0.698 Location 0.62(0.6–0.64) 0.61(0.59–0.64) 0.64(0.6–0.68) Regional 0.22(0.2–0.24) 0.22(0.2–0.25) 0.22(0.19–0.26) Distant 0.09(0.07–0.10) 0.09(0.08–0.11) 0.08(0.06–0.11) Unknown 0.07(0.06–0.08) 0.07(0.06–0.09) 0.06(0.04–0.09) AJCC Stage (%) 0.647 I/II 0.70(0.68–0.72) 0.69(0.66–0.72) 0.72(0.67–0.75) III/IV 0.23(0.21–0.25) 0.23(0.21–0.26) 0.21(0.18–0.25) Unknown 0.08(0.06–0.09) 0.08(0.06–0.09) 0.07(0.05–0.10) Treatment (%) 0.908 Surgery only 0.54(0.51–0.56) 0.53(0.5–0.56) 0.56(0.51–0.60) Surgery and chemotherapy 0.12(0.1–0.13) 0.11(0.1–0.13) 0.12(0.09–0.15) Surgery and radiation 0.12(0.1–0.14) 0.12(0.1–0.14) 0.11(0.09–0.14) Surgery, radiation, and chemotherapy 0.08(0.06–0.09) 0.08(0.06–0.09) 0.08(0.06–0.10) Chemotherapy only 0.02(0.02–0.03) 0.02(0.02–0.03) 0.02(0.01–0.03) Radiation only 0.02(0.01–0.03) 0.02(0.01–0.03) 0.02(0.01–0.03) Radiation and chemotherapy 0.01(0-0.01) 0.01(0-0.02) 0.01(0-0.02) Unknown 0.10(0.09–0.12) 0.11(0.09–0.13) 0.10(0.07–0.13) Months to treatment (%) 0.857 ≤ 1 0.66(0.63–0.68) 0.65(0.62–0.68) 0.67(0.62–0.71) > 1 0.25(0.23–0.27) 0.25(0.23–0.28) 0.24(0.21–0.28) Unknown 0.10(0.08–0.11) 0.10(0.08–0.12) 0.09(0.07–0.12) Regional Nodes positive (%) 0.100 Yes 0.09(0.08–0.11) 0.09(0.08–0.11) 0.09(0.07–0.12) No 0.75(0.73–0.77) 0.74(0.71–0.76) 0.78(0.74–0.81) Unknown 0.16(0.14–0.18) 0.17(0.15–0.19) 0.13(0.1–0.16) Distant lymph nodes (%) 0.784 Yes 0.02(0.02–0.03) 0.02(0.01–0.03) 0.02(0.01–0.04) No 0.38(0.36–0.41) 0.38(0.35–0.41) 0.39(0.35–0.43) Unknown 0.60(0.57–0.62) 0.60(0.57–0.63) 0.59(0.54–0.63) Metastasis outside the pelvic reproductive system (%) 0.816 Yes 0.07(0.06–0.08) 0.07(0.06–0.09) 0.06(0.05–0.09) No 0.83(0.81–0.85) 0.83(0.81–0.85) 0.84(0.81–0.87) Unknown 0.10(0.09–0.11) 0.10(0.09–0.12) 0.09(0.07–0.12) Tumor size (%) 0.542 < 2 0.12(0.11–0.14) 0.13(0.11–0.15) 0.11(0.08–0.14) ≥ 2 0.50(0.48–0.53) 0.50(0.47–0.53) 0.51(0.47–0.55) Unknown 0.38(0.36–0.40) 0.38(0.35–0.40) 0.38(0.34–0.43) OS 0.936 No 0.56(0.54–0.59) 0.56(0.53–0.59) 0.57(0.52–0.61) Yes 0.44(0.41–0.46) 0.44(0.41–0.47) 0.43(0.39–0.48) CSS 0.335 No 0.84(0.82–0.85) 0.83(0.81–0.85) 0.85(0.82–0.88) Yes 0.16(0.15–0.18) 0.17(0.15–0.19) 0.15(0.12–0.18) Values are percentages (95% confidence interval, 95% CI). Abbreviations: OS, overall survival; CSS, cancer-specific survival; HR, hazard ratio; AJCC, American Joint Committee on Cancer. Race others include American Indian/Alaska Native and Asian/Pacific Islander. Univariate KM survival analysis K-M analysis was used to evaluate the association between each variable in the baseline data table and OS in the whole patient, which demonstrated that SPEC patients who underwent surgery combined with radiotherapy and chemotherapy had better survivorship. More details of this analysis are illustrated in Supplementary Fig. 2. Independent Prognostic Factors in the Training Cohort LASSO regression (Supplementary Fig. 3A-D) was performed to further identify possible predictors that were statistically significant ( p < 0.05) under univariate Cox regression analysis within the training set (Supplementary Table 1). Finally, by multivariate Cox regression analysis, those factors, age, CRC radiation, grade, histology, summary stage, treatment, and tumor size were significant predictors for OS; while grade, histology, AJCC stage, treatment, and tumor size were independent predictors for CSS in SPEC patients following CRC (Fig. 1 A-B). Prognostic Nomogram for OS and CSS The prognostic nomogram that integrated all significant independent factors for OS and CSS in the training cohort is shown in Fig. 2 A and 2 B respectively. Next, by combining each possible point total, the predicted risks of OS and CSS at 3 and 5 years were calculated. In addition, ROC curves were used to assess the discriminated ability of the 3- and 5­year OS for the training groups (AUC = 0.840 and AUC = 0.843, respectively) (Fig. 3 A) and the validation groups (AUC = 0.829 and AUC = 0.804, respectively) (Fig. 5C), the 3- and 5­ year CSS for the training groups (AUC = 0.817 and AUC = 0.700) (Fig. 5B) and the validation groups of (AUC = 0.936 and AUC = 0.804) (Fig. 5D). Moreover, the calibration plots of the training group and the validation group for the probability of OS and CSS at 3 or 5 years demonstrated an optimal agreement between prediction survival and actual survival (Supplementary Fig. 4A-4H). Overall, the nomograms exhibited considerable discriminative and calibrating abilities. Furthermore, DCA revealed that our models were useful for threshold probabilities between 1% and 70%, which had high predictive value and clinical utility (Supplementary Fig. 5A-5H). Clinical value comparison between nomograms and AJCC stage system We estimated the C-index, IDI, and NRI by comparing the nomograms with the 8th edition of the AJCC staging system alone. Concordance was higher for nomogram (c -index = 0.801(95% CI, 0.779–0.822) and 0.866(95% CI, 0.841–0.891) for OS and CSS, respectively) than for AJCC staging system (c-index = 0.676(95% CI, 0.654–0.698) and 0.746(95% CI, 0.715–0.777), respectively) in the training set. The IDI for the 3- and 5-year OS of the training group were 0.153 (95%CI:0.114–0.196) and 0.149 (95%CI:0.108–0.195), as well as 0.381 (95%CI:0.313–0.466) and 0.403 (95%CI:0.306–0.468) for NRI values, respectively (P < 0.001) (Supplementary Table 2). Results of these values for the validation group were also certificated and presented in Supplementary Table 2, indicating that our nomogram was superior in predicting accuracy prognosis than the traditional AJCC staging system. Risk stratification for SPEC in CRC patients The PI value for each patient from the independent predictor of the nomogram was developed, and based on this value, the patients were stratified into low- and high-risk groups. The risk stratification system showed that the 3- and 5-year OS of the low-risk patients were significantly higher than those of the high-risk patients in both training and validation groups (Supplementary Fig. 6A-6D). A similar performance on the ability to predict CSS was achieved (Supplementary Fig. 6E-6H). We also performed KM analysis and log-rank tests for 3-year OS to clarify the efficacy of treatment in the two-risk stratification subgroup (low-risk PI < 1.88 and high-risk PI ≥ 1.88) with the whole patient. Our results showed that CRC-associated SPEC patients receiving chemoradiotherapy had a better prognosis in the high-risk group, while for the low-risk group, surgery combined with chemotherapy was incapable of improving the outcome (Fig. 4 A-B). Similar results were also seen in the CSS nomogram (low-risk PI < 2.89 and high-risk PI ≥ 2.89) (Fig. 4 C-D). Thus, the method described above could help clinicians improve prognosis prediction and make better therapeutic decisions. DISCUSSION The major results of this study showed that the nomograms demonstrated good performance and accuracy in both training and validation cohorts, and their prediction was supported by C-index, calibration, ROC curves, and DCA. When compared with the AJCC staging systems, the nomograms showed a better accuracy for survival prediction. Age, CRC radiation, grade, histology, SEER stage, treatment, and tumor size consisted of the OS prediction model. In comparison with most other nomograms of FPEC, our findings firstly supported CRC-based radiation was negatively correlated with the OS of SPEC patients. Especially, evaluations of the underlying risk of second primary cancer (SPC) after radiotherapy for pelvic cancers, including prostate[ 15 ], bladder[ 16 ], cervix uteri[ 17 ], and rectum[ 3 , 4 ], have substantiated the idea of an increased incidence of SPEC in patients undergoing pelvic radiotherapy, regardless of 1-, 5- or 10-year incubation period[ 3 , 18 ]. Additionally, Guan et al indicated that 10-year survival rates in patients with radiotherapy-associated SPC are less than that in matched patients with first primary cancer[ 3 ]. Collectively, the variable of CRC radiation played a significant role in the nomogram of OS. Other than CRC radiation, other significant predictors of our OS nomogram, one or more, were adopted for various previous studies in different populations of EC. This phenomenon seemed to be partially attributed to the similar clinicopathological features between FPEC and SPEC in Japanese patients reported by Keiichiro Nakamura et al, which also implied the reliability of clinical interpretation of the OS nomogram from another aspect[ 19 ]. Unlike the large sample model with emphasizing a major impact of age at diagnosis on patient survival constructed by Sun et al[ 20 ], our study did not adjust for age at diagnosis. To minimize the offset due to the impact of the survival rate of patients with EC with time over and medical technology advances, we reclassified the tumor stage of all cases according to the 8th edition of the AJCC staging system, added the SEER summary stage, and modified pathological grading based on previous reports, because the period of case selection spanned two SEER databases from 1973 to 2020. A little controversial finding was that age did not remain an independent predictor of CSS in this study, in contrast to many studies on EC where known age is a crucial prognostic factor. However, the present study participants were predominantly over 60 years old, above the average age from 58–61 years of FPEC[ 21 ], compared with these similar age distribution-based studies. Consistent with the work by Fleming et al., which showed that age greater than 70 in patients with EC was not a statistically significant predictor of poor outcomes after adjusting for other poor prognostic variables[ 22 ]. Previous research on patients with low CLDN6 expression also supports our finding[ 23 ]. On the other hand, the CSS model did not incorporate CRC-based radiation. One likely explanation is that patients who died from EC had a more aggressive histological type and more advanced disease, thereby masking the true underlying impact. A significant similarity of both nomograms was the equal importance of treatment features supporting survival benefits, although treatment data solely relied on broad categories. Additionally, the survival outcomes of surgery combined with radiotherapy and chemotherapy appeared to be greatest by unadjusted survival analysis. Nevertheless, further risk stratification tests suggested not all patients can benefit from multimodality therapy. we discovered that patients who received radiotherapy plus chemotherapy had higher survival rates than other treatments in the high-risk group, but for the low-risk group, surgery plus chemotherapy was the better choice. This finding might at first look appear to be incongruent with the traditional therapeutic methods mainly by surgery, supplemented by radiotherapy and chemotherapy. However, the strategies of treatment for EC have been alterable due to the heterogeneity of high-risk populations. Gynaecologic Oncology Group (GOG) 122 is a pivotal study that changed the way we think about EC and chemotherapy[ 24 ]. A randomized trial included women with early-stage high-risk and stage III EC and reported significant improvements in recurrence-free survival and OS trends from chemotherapy plus radiation compared with chemotherapy alone [ 25 ]. On the contrary, Professor Daniela Matei’s team showed that chemotherapy plus radiation did not significantly improve relapse-free survival rate in terminal EC, but distant metastasis controlled[ 26 ]. Briefly, adjuvant radiotherapy might have an impact on those patients who had chemotherapy as primary treatment, which seemed likely to explain our findings that high-risk EC patients benefited most from chemoradiotherapy. Moreover, radiotherapy has been the standard adjuvant treatment in endometrial cancer historically. But lately, chemotherapy has emerged as a primary treatment alternative in early endometrial cancer[ 27 ]. Adjuvant radiation use is greatly reduced on account of the results of postoperative radiotherapy in endometrial carcinoma (ORTEC)-1[ 28 ]. The GOG-122 study showed that postoperative adjuvant chemotherapy was superior to whole adjuvant abdominal irradiation for EC patients, consistent with multiple randomized controlled trials[ 29 – 31 ]. Even, adjuvant chemotherapy could delay metastases, and the side effects could be acceptable, although chemotherapy failed to prevent local recurrence, also pointed out by these trials. Notably, our results could partially be attributed to the population differences between the target patient with SPEC in CRC and the FPEC although randomized studies on comparison of the treatments of patients with SPEC are worth further exploration. More individualized treatment was therefore suggested for EC patients after CRC. There are several limitations of this study. First, since our nomogram was based on the data of Western countries, it may not be generalizable to all countries. Second, the prognostic model had not been externally validated, and practical advantages for prospective clinical use remain unclear. Third, this current study ignored the effect of diagnosis year, which may have an unexpected impact on patient outcomes with the advancement in medical technology over time. Finally, the relatively small sample size may limit statistical power, further research with large sample sizes is necessary to verify. In summary, in this study, two objective and accurate prediction nomograms as drawn up informed patients about their prognosis and provided oncologists some reference for clinical decision-making based on the subpopulation at different risks. More works are required to confirm whether it has potential generalization ability in other patient groups. Declarations Acknowledgments The authors are deeply grateful to the Surveillance, Epidemiology, and End Results database for providing high-quality clinical data to undertake our study. Funding This research was supported by the Natural Science Foundation of Fujian Province (grant no. 2021J011303) and the Fuzhou Science and Technology Program (2023-S-019). The funding source had no role in study design, data collection, analysis, interpretation, writing of the manuscript, or publication decision. Patient consent statement This study was considered exempt from review and thus no patient written informed consent was required based on the publicly available data. Author contributions Material preparation, data collection, and analysis were performed by Linli LIU. The first draft of the manuscript was written by Linli LIU and constructive comments that helped us to improve the manuscript were provided by Qiong JIN. Data availability This research data can be openly available at https://seer.cancer.gov/seerstat/. Conflict of interest statement None of the authors have any conflicts of interest associated with this research. References Vinuesa L, Webster RM. The endometrial carcinoma market. Nat Rev Drug Discov. 2022;21(4):255–256. doi.org/10.1038/d41573-022-00016-2 Dou Y, Kawaler EA, Cui Zhou D, et al. Proteogenomic Characterization of Endometrial Carcinoma. Cell. 2020;180(4):729–748.e726. doi.org/10.1016/j.cell.2020.01.026 Guan X, Wei R, Yang R, et al. Association of Radiotherapy for Rectal Cancer and Second Gynecological Malignant Neoplasms. JAMA Netw Open. 2021;4(1):e2031661. doi.org/10.1001/jamanetworkopen.2020.31661 Wu M, Huang M, He C, et al. Risk of Second Primary Malignancies Based on the Histological Subtypes of Colorectal Cancer. Front Oncol. 2021;11:650937. doi.org/10.3389/fonc.2021.650937 Siegel RL, Wagle NS, Cercek A, et al. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023;73(3):233–254. doi.org/10.3322/caac.21772 Tilg H, Adolph TE, Gerner RR, et al. The Intestinal Microbiota in Colorectal Cancer. Cancer Cell. 2018;33(6):954–964. doi.org/10.1038/s41586-023-06466-x Takeuchi T, Kubota T, Nakanishi Y, et al. Gut microbial carbohydrate metabolism contributes to insulin resistance. Nature. 2023;621(7978):389–395. doi.org/10.1038/s41586-023-06466-x Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2021;19(1):55–71. doi.org/10.1038/s41579-020-0433-9 Long Y, Tang L, Zhou Y, et al. Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study. BMC Med. 2023;21(1):66. doi.org/10.1186/s12916-023-02761-6 Li R, Yue Q. A nomogram for predicting overall survival in patients with endometrial carcinoma: A SEER-based study. Int J Gynaecol Obstet. 2023;161(3):744–750. doi.org/10.1002/ego.14580 . Ren X, Wang MM, Wang G, et al. A nomogram for predicting overall survival in patients with type II endometrial carcinoma: a retrospective analysis and multicenter validation study. Eur Rev Med Pharmacol Sci. 2023;27(1):233–247. doi.org/10.26355/eurrev_202301_30904 Manjelievskaia J, Brown D, McGlynn KA, et al. Chemotherapy Use and Survival Among Young and Middle-Aged Patients With Colon Cancer. JAMA Surg. 2017;152(5):452–459. doi.org/10.1016/j.radonc.2017.02.007 Park AB, Darcy KM, Tian C, et al. Racial disparities in survival among women with endometrial cancer in an equal access system. Gynecol Oncol. 2021;163(1):125–129. doi.org/10.1016/j.ygyno.2021.07.022 Chan JK, Sherman AE, Kapp DS, et al. Influence of gynecologic oncologists on the survival of patients with endometrial cancer. J Clin Oncol. 2011;29(7):832–838. doi.org/10.1200/jco.2010.31.2124 Hinnen KA, Schaapveld M, van Vulpen M, et al. Prostate brachytherapy and second primary cancer risk: a competitive risk analysis. J Clin Oncol. 2011;29(34):4510–4515. doi.org/10.1200/jco.2011.35.0991 Chen R, Zhan X, Jiang H, et al. Risk and prognosis of secondary malignant neoplasms after radiation therapy for bladder cancer: A large population-based cohort study. Front Oncol. 2022;12:953615. doi.org/10.3389/fonc.2022.953615 Wu Y, Chong Y, Han C, et al. Second primary malignancies associated with radiation therapy in cervical cancer patients diagnosed between 1975 and 2011: a population-based competing-risk study. Ann Transl Med. 2021;9(17):1375. doi.org/10.21037/atm-21-1393 Warschkow R, Güller U, Cerny T, et al. Secondary malignancies after rectal cancer resection with and without radiation therapy: A propensity-adjusted, population-based SEER analysis. Radiother Oncol. 2017;123(1):139–146. doi.org/10.1016/j.radonc.2017.02.007 Haraga J, Nakamura K, Haruma T, et al. Molecular Characterization of Second Primary Endometrial Cancer. Anticancer Res. 2020;40(7):3811–3818. doi.org/10.21873/anticanres.14370 Zhu L, Sun X, Bai W. Nomograms for Predicting Cancer-Specific and Overall Survival Among Patients With Endometrial Carcinoma: A SEER Based Study. Front Oncol. 2020;10:269. doi.org/10.3389/fonc.2020.00269 Rhoades J, Vetter MH, Fisher JL, et al. The association between histological subtype of a first primary endometrial cancer and second cancer risk. Int J Gynecol Cancer. 2019;29(2):290–298. doi.org/10.1136/ijgc-2018-000014 Fleming ND, Lentz SE, Cass I, et al. Is older age a poor prognostic factor in stage I and II endometrioid endometrial adenocarcinoma? Gynecol Oncol. 2011;120(2):189–192. doi.org/10.1016/j.ygyno.2010.10.038 Endo Y, Sugimoto K, Kobayashi M, et al. Claudin–9 is a novel prognostic biomarker for endometrial cancer. Int J Oncol. 2022;61(5). doi.org/10.3892/ijo.2022.5425 Randall ME, Filiaci VL, Muss H, et al. Randomized phase III trial of whole-abdominal irradiation versus doxorubicin and cisplatin chemotherapy in advanced endometrial carcinoma: a Gynecologic Oncology Group Study. J Clin Oncol. 2006;24(1):36–44. doi.org/10.1200/jco.2004.00.7617 de Boer SM, Powell ME, Mileshkin L, et al. Adjuvant chemoradiotherapy versus radiotherapy alone for women with high-risk endometrial cancer (PORTEC-3): final results of an international, open-label, multicentre, randomised, phase 3 trial. Lancet Oncol. 2018;19(3):295–309. doi.org/10.1016/s1470-2045(18)30079-2 Matei D, Filiaci V, Randall ME, et al. Adjuvant Chemotherapy plus Radiation for Locally Advanced Endometrial Cancer. N Engl J Med. 2019;380(24):2317–2326. doi.org/10.1056/NEJMoa1813181 Hogberg T. What is the role of chemotherapy in endometrial cancer? Curr Oncol Rep. 2011;13(6):433–441. doi.org/10.1007/s11912-011-0192-x Nout RA, van de Poll-Franse LV, Lybeert ML, et al. Long-term outcome and quality of life of patients with endometrial carcinoma treated with or without pelvic radiotherapy in the post operative radiation therapy in endometrial carcinoma 1 (PORTEC-1) trial. J Clin Oncol. 2011;29(13):1692–1700. doi: 10.1200/jco.2010.32.4590 Morrow CP, Bundy BN, Homesley HD, et al. Doxorubicin as an adjuvant following surgery and radiation therapy in patients with high-risk endometrial carcinoma, stage I and occult stage II: a Gynecologic Oncology Group Study. Gynecol Oncol. 1990;36(2):166–171. doi.org/10.1016/0090-8258(90)90166-i Maggi R, Lissoni A, Spina F, et al. Adjuvant chemotherapy vs radiotherapy in high-risk endometrial carcinoma: results of a randomised trial. Br J Cancer. 2006;95(3):266–271. doi.org/10.1038/sj.bjc.6603279 Susumu N, Sagae S, Udagawa Y, et al. Randomized phase III trial of pelvic radiotherapy versus cisplatin-based combined chemotherapy in patients with intermediate- and high-risk endometrial cancer: a Japanese Gynecologic Oncology Group study. Gynecol Oncol. 2008;108(1):226–233. doi.org/10.1016/j.ygyno.2007.09.029 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureandtable.zip Supplementarymaterials.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4677808","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331234091,"identity":"e45743dc-7480-441e-a418-a832d207fdfe","order_by":0,"name":"linli LIU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACPmYGxgMJDBY8DAzMBw58+EGEFjZmBgagFgmgFrbEgzN7iNECxAcYGCSAFI/xYQ42YrSw8xgceLhDQkZ+Rs6Hwww8DPL8YgcIOQyoJfGMBI/BjdwNhwssGAxnzk4gRksbUIsEUMsMHoYEg9vEagE67MFhHjZStDDcyGEgVgtbAcRhZ54ZAANZgrBf+PkPb3z4s83GXr49+fGHDz9s5PmlCWhBAAGwSglilYPtO0CK6lEwCkbBKBhJAADOhD+rROR9MgAAAABJRU5ErkJggg==","orcid":"","institution":"Fuzhou First General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"linli","middleName":"","lastName":"LIU","suffix":""},{"id":331234092,"identity":"c8cf9039-c922-4892-8b22-dc29e3a17816","order_by":1,"name":"Qiong JIN","email":"","orcid":"","institution":"Fuzhou First General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"JIN","suffix":""}],"badges":[],"createdAt":"2024-07-03 05:40:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4677808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4677808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61348029,"identity":"9bf2ad2a-0022-4991-a9d8-2937ca47f0f2","added_by":"auto","created_at":"2024-07-29 18:20:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239399,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariable Cox regression model illustrating the significative prognostic factors on OS and CSS respectively. (A) Forest plot for OS; (B) Forest plot for CSS. HR, hazard ratio.CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/8b67f7e77c22dae56bda1962.jpg"},{"id":61348027,"identity":"f1b93fa5-197d-4879-8618-1855817ef303","added_by":"auto","created_at":"2024-07-29 18:20:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114379,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the probability of OS and CSS at 3- and 5- years for elderly endometrial cancer (EC) patients following colorectal cancer (CRC). (A) Nomogram for OS; (B) Nomogram for CSS. Each clinical characteristic is translated into a risk score. The individual risk scores are summed together by the reader. The total scores can correspond to predicted 3- and 5-year OS and CSS probabilities.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/75ed53e3db92ec4ca874884c.jpg"},{"id":61349077,"identity":"c36ec416-e4dd-476e-aa48-f98e7095c199","added_by":"auto","created_at":"2024-07-29 18:28:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":252869,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves of the nomogram for 5- and 10-year predictions. AUC for predicting OS in the training (A) and validation set (C); AUC for predicting CSS in the training (B) and validation cohort (D), respectively. AUC, an area under the curve; ROC, receiver operator characteristic.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/af84539b2b63aa239c7f4508.jpg"},{"id":61348031,"identity":"e475b368-b9ae-4583-b496-6b6974908fbf","added_by":"auto","created_at":"2024-07-29 18:20:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":236974,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of the treatment methods of EC patients following CRC by risk stratification. (A) 3- year OS by high-risk patient; (B) 3- year OS by low-risk patient;(C) 3- year CSS by high-risk patient;(D) 3- year CSS by low-risk patient.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/92780c1092029179e2fff8f6.jpg"},{"id":66055667,"identity":"3ef619f5-b76f-4dbd-b7ee-649e0235f9d5","added_by":"auto","created_at":"2024-10-07 09:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1571706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/b280e086-7239-4866-a595-59715ec58bd4.pdf"},{"id":61349079,"identity":"bd0f6bc2-0a48-4d29-9bd2-8dc97e5eb518","added_by":"auto","created_at":"2024-07-29 18:28:01","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3820527,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureandtable.zip","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/f88611324a9993e08322e5ac.zip"},{"id":61348032,"identity":"05d03671-aeb8-407f-9183-af024738cd4b","added_by":"auto","created_at":"2024-07-29 18:20:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4446825,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4677808/v1/9ad3f0ac5176e75c6ec5ffff.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEndometrial carcinoma (EC) is the most common gynecologic cancer in developed countries and accounts for more than 2% of deaths due to cancer in women worldwide, with the American Joint Committee on Cancer (AJCC) stage and histological type being associated with the treatment options and final prognosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent cohort studies have identified an approximately 3-fold increased risk for uterine corpus cancer in women with previous rectal cancer who underwent radiotherapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the current research on the prognosis of second primary endometrial cancer (SPEC) among colorectal cancer (CRC) is limited.\u003c/p\u003e \u003cp\u003eCRC is the third most prevalent diagnosed cancer and the second leading cause of cancer-related death worldwide, with approximately 10% of all newly diagnosed malignant tumors per year[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies have found that there is a very obvious imbalance of intestinal flora and disruption of barrier function in the intestines of people with CRC, that is, dysbiosis of intestinal microbiota may contribute to CRC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, gut microbiota may also lead to insulin resistance[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], abnormal estrogen metabolism[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], or chronic inflammation via multiple pathways[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and thus involved in EC occurrence and progression. Recently, a Mendelian randomization study supported that gut microbiota may be causally associated with both CRC and EC[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Hence, these previous results may indicate an underlying bidirectional link between CRC, intestinal dysbacteriosis, and EC. Moreover, the incidence of CRC and EC increases significantly with global aging, the treatment decisions of SPEC among CRC have received increasing attention from clinicians, but effective regimens remain elusive. Although several clinical prediction models have been reported for EC[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], there is limited data on the concordance of these, shedding light on the prognosis of EC varies significantly between patients with different characteristics. However, compared to primary EC, due to surgical, radio- and chemotherapy or a combination of these therapies was used for the treatment of CRC, further aggravating intestinal microbiota dysbiosis, which might lead to worse physical and psychological conditions. The advice of options on clinical treatment in SPEC in CRC has still been uncertain. Thus, it is imperative to identify appropriate prognostic factors to establish survival prediction models for making better clinical decisions.\u003c/p\u003e \u003cp\u003eOur objective was to use the Surveillance, Epidemiology, and End Results (SEER) database to construct and verify two nomogram prognostic models for predicting 3\u0026shy; and 5-year survival rates of SPEC patients in CRC, which may be useful for prognostic prediction, treatment strategy selection, and follow-up management of these patients.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations and data collection\u003c/h2\u003e \u003cp\u003eData were obtained from the Surveillance, Epidemiology and End Results (SEER) database (SEER*Stat software 8.4.2), including 1975\u0026ndash;2020 and 2010\u0026ndash;2020, which serves as an authoritative, federally funded cancer reporting system. Patient personal information is not identifiable, and SEER database information is publicly available, so we do not need to obtain ethical approval and informed consent from patients. All primary cancer sites were coded according to the \u003cem\u003eInternational Classification of Diseases for Oncology, Third Edition\u003c/em\u003e. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.\u003c/p\u003e \u003cp\u003eWe first identified individuals with an initial primary cancer diagnosed CRC based on the ICD-O-3 codes/WHO 2008(colon and rectum) and sequence number (1st of 2 or more primaries). Then, SPEC patients were further recognized depending on the \u0026lsquo;person selection\u0026rsquo; function of SEER records of the primary site labeled (C54.0\u0026ndash;C54.9, C55.9) and sequence number (2nd of 2 or more primaries). Duplication cases, leiomyocarcinosarcoma of myometrium or corpus uteri, and patients with unknown survival time were excluded.\u003c/p\u003e \u003cp\u003eFinally, 1631 patients were screened in this study and randomly divided into training and validation groups (7:3 ratio) for the development and validation of the nomogram, respectively. The specific flowchart is shown in Supplementary Fig.\u0026nbsp;1. The primary endpoints consisted of overall survival (OS) and cancer-specific survival (CSS) rates at 3- and 5\u0026ndash;5 years respectively. OS events were death from any cause. CSS events were deaths resulting from EC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables and outcomes\u003c/h2\u003e \u003cp\u003eThe following 19 clinicopathological variables of SPEC in CRC patients were downloaded from the SEER database: age, race, marital status, median household income, previous CRC-related variables (histology, location, radiation, and chemotherapy), interval time between CRC and SPEC, SEER summary stage, histologic types, grade, treatment type, lymph node-positive (pelvic or para-aortic), distant lymph nodes, metastasis outside the pelvic reproductive system (including bone, lung, liver, brain, bladder, or vulva, et al.), months to treat, tumor size, date of last follow-up visit, and patient status at last visit. The reclassification stage was recorded according to the 8th edition American Joint Committee on Cancer (AJCC) and categorized as stages I to IV[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the histologic types were classified as endometrioid, and non-endometrioid and were designated as grade I, II, or III[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eVariables based on previous reports or clinical consensus were all included in the comparison between the training and validation groups. Categorical variables were expressed in percentages (95% confidence interval, 95% CI) and compared using chi-square tests. The least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis were devoted to constructing OS- and CSS-associated prognostic nomograms. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC), concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) in comparison with the traditional AJCC stage system. Calibration curves evaluated the concordance between observed and predicted survival probability. Decision curve analysis (DCA) was implemented to illustrate the clinical performance of the model. We derived the prognostic index (PI) for each patient from the regression coefficients found in the final multivariable Cox regression model. Kaplan-Meier (K-M) survival analysis was performed to evaluate the clinical effectiveness of the risk stratification system, and the significance was evaluated by a log-rank test. Statistical analyses were performed using R software (version 4.3.1) and STATA software (Stata 16.0, College Station, Texas 77845, USA). A \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eparticipants basic characteristics\u003c/h2\u003e \u003cp\u003eThe median follow-up time was 78 months (73\u0026ndash;86 months), and 711 patients died during follow-up. The median OS was 95 months (88\u0026ndash;101 months), and the 3- and 5-year OS rates were 67% (64%-69%) and 59% (57%-62%), respectively. The median CSS was 52 months (46\u0026ndash;56 months), and the 3- and 5-year CSS rates were 83% (81%-85%) and 80% (78%-82%), respectively.\u003c/p\u003e \u003cp\u003eThe distribution of white race and age over 60 years old was 78.6% and 1202 (73.70%) respectively. 268 (16.43%) and 673 (41.26%) had received radiation therapy and chemotherapy respectively during previous CRC, but the minority of the tumors (22.44%) were located in the rectum. The majority type of SPEC cases (66.95%) was endometrioid histology. Among patients with information on AJCC stage available, 69.59% of patients were early stage (I\u0026ndash;II), while 22.5% were late stage (III-IV). In addition, most patients underwent surgery. The clinical and disease characteristics of patients in the model training and validation samples were similar and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the included patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1631)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining group (n\u0026thinsp;=\u0026thinsp;1143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation group (n\u0026thinsp;=\u0026thinsp;488)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.06\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07(0.05\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07(0.05\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48(0.46\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48(0.45\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48(0.44\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45(0.43\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45(0.42\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45(0.40\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.10\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12(0.10\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11(0.08\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.77\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77(0.75\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.78\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11(0.09\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13(0.12\u0026ndash;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14(0.12\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13(0.1\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or Domestic Partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46(0.43\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45(0.42\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47(0.43\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed or Divorced or Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35(0.33\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35(0.32\u0026ndash;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34(0.3\u0026ndash;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06(0.05\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06(0.05\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06(0.04\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian household income (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e70000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45(0.43\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46(0.43\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44(0.4\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u003cspan\u003e$\u003c/span\u003e70000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55(0.52\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54(0.51\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56(0.51\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval time (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17(0.15\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17(0.15\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17(0.14\u0026ndash;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1,\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44(0.42\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44(0.41\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45(0.41\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39(0.37\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39(0.37\u0026ndash;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38(0.34\u0026ndash;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer histology (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.81\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83(0.81\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.8\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucous tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07(0.05\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer location (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78(0.75\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77(0.74\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.75\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22(0.2\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23(0.21\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21(0.18\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer Radiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16(0.15\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17(0.15\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16(0.13\u0026ndash;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84(0.82\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83(0.81\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.80\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer Chemotherapy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41(0.39\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40(0.37\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45(0.4\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59(0.56\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6(0.57\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55(0.51\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.26\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27(0.25\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28(0.24\u0026ndash;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20(0.18\u0026ndash;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18(0.16\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23(0.19\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.26\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28(0.26\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28(0.24\u0026ndash;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25(0.22\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26(0.24\u0026ndash;0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21(0.18\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67(0.65\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.65\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66(0.61\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon Endometrioid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33(0.31\u0026ndash;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32(0.3\u0026ndash;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34(0.3\u0026ndash;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummary stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62(0.6\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61(0.59\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64(0.6\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22(0.2\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22(0.2\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22(0.19\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.06\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06(0.04\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70(0.68\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.66\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72(0.67\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23(0.21\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23(0.21\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21(0.18\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07(0.05\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54(0.51\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53(0.5\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56(0.51\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery and chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.1\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11(0.1\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12(0.09\u0026ndash;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery and radiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.1\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12(0.1\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11(0.09\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery, radiation, and chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08(0.06\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02(0.02\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.02\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02(0.01\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02(0.01\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.01\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02(0.01\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation and chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01(0-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01(0-0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01(0-0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10(0.09\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11(0.09\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10(0.07\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonths to treatment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66(0.63\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65(0.62\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67(0.62\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25(0.23\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25(0.23\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24(0.21\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10(0.08\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional Nodes positive (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75(0.73\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74(0.71\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78(0.74\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16(0.14\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17(0.15\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13(0.1\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant lymph nodes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02(0.02\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.01\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02(0.01\u0026ndash;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38(0.36\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38(0.35\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39(0.35\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60(0.57\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60(0.57\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59(0.54\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis outside the pelvic reproductive system (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.06\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07(0.06\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06(0.05\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.81\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83(0.81\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.81\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10(0.09\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10(0.09\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.11\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13(0.11\u0026ndash;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11(0.08\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50(0.48\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50(0.47\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51(0.47\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38(0.36\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38(0.35\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38(0.34\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56(0.54\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56(0.53\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57(0.52\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44(0.41\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44(0.41\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43(0.39\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84(0.82\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83(0.81\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85(0.82\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16(0.15\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17(0.15\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15(0.12\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are percentages (95% confidence interval, 95% CI). Abbreviations: OS, overall survival; CSS, cancer-specific survival; HR, hazard ratio; AJCC, American Joint Committee on Cancer. Race others include American Indian/Alaska Native and Asian/Pacific Islander.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate KM survival analysis\u003c/h2\u003e \u003cp\u003eK-M analysis was used to evaluate the association between each variable in the baseline data table and OS in the whole patient, which demonstrated that SPEC patients who underwent surgery combined with radiotherapy and chemotherapy had better survivorship. More details of this analysis are illustrated in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Prognostic Factors in the Training Cohort\u003c/h2\u003e \u003cp\u003eLASSO regression (Supplementary Fig.\u0026nbsp;3A-D) was performed to further identify possible predictors that were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) under univariate Cox regression analysis within the training set (Supplementary Table\u0026nbsp;1). Finally, by multivariate Cox regression analysis, those factors, age, CRC radiation, grade, histology, summary stage, treatment, and tumor size were significant predictors for OS; while grade, histology, AJCC stage, treatment, and tumor size were independent predictors for CSS in SPEC patients following CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003ePrognostic Nomogram for OS and CSS\u003c/h2\u003e \u003cp\u003eThe prognostic nomogram that integrated all significant independent factors for OS and CSS in the training cohort is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB respectively. Next, by combining each possible point total, the predicted risks of OS and CSS at 3 and 5 years were calculated. In addition, ROC curves were used to assess the discriminated ability of the 3- and 5\u0026shy;year OS for the training groups (AUC\u0026thinsp;=\u0026thinsp;0.840 and AUC\u0026thinsp;=\u0026thinsp;0.843, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and the validation groups (AUC\u0026thinsp;=\u0026thinsp;0.829 and AUC\u0026thinsp;=\u0026thinsp;0.804, respectively) (Fig.\u0026nbsp;5C), the 3- and 5\u0026shy; year CSS for the training groups (AUC\u0026thinsp;=\u0026thinsp;0.817 and AUC\u0026thinsp;=\u0026thinsp;0.700) (Fig.\u0026nbsp;5B) and the validation groups of (AUC\u0026thinsp;=\u0026thinsp;0.936 and AUC\u0026thinsp;=\u0026thinsp;0.804) (Fig.\u0026nbsp;5D). Moreover, the calibration plots of the training group and the validation group for the probability of OS and CSS at 3 or 5 years demonstrated an optimal agreement between prediction survival and actual survival (Supplementary Fig.\u0026nbsp;4A-4H). Overall, the nomograms exhibited considerable discriminative and calibrating abilities. Furthermore, DCA revealed that our models were useful for threshold probabilities between 1% and 70%, which had high predictive value and clinical utility (Supplementary Fig.\u0026nbsp;5A-5H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical value comparison between nomograms and AJCC stage system\u003c/h2\u003e \u003cp\u003eWe estimated the C-index, IDI, and NRI by comparing the nomograms with the 8th edition of the AJCC staging system alone. Concordance was higher for nomogram (c -index\u0026thinsp;=\u0026thinsp;0.801(95% CI, 0.779\u0026ndash;0.822) and 0.866(95% CI, 0.841\u0026ndash;0.891) for OS and CSS, respectively) than for AJCC staging system (c-index\u0026thinsp;=\u0026thinsp;0.676(95% CI, 0.654\u0026ndash;0.698) and 0.746(95% CI, 0.715\u0026ndash;0.777), respectively) in the training set. The IDI for the 3- and 5-year OS of the training group were 0.153 (95%CI:0.114\u0026ndash;0.196) and 0.149 (95%CI:0.108\u0026ndash;0.195), as well as 0.381 (95%CI:0.313\u0026ndash;0.466) and 0.403 (95%CI:0.306\u0026ndash;0.468) for NRI values, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table\u0026nbsp;2). Results of these values for the validation group were also certificated and presented in Supplementary Table\u0026nbsp;2, indicating that our nomogram was superior in predicting accuracy prognosis than the traditional AJCC staging system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk stratification for SPEC in CRC patients\u003c/h2\u003e \u003cp\u003eThe PI value for each patient from the independent predictor of the nomogram was developed, and based on this value, the patients were stratified into low- and high-risk groups. The risk stratification system showed that the 3- and 5-year OS of the low-risk patients were significantly higher than those of the high-risk patients in both training and validation groups (Supplementary Fig.\u0026nbsp;6A-6D). A similar performance on the ability to predict CSS was achieved (Supplementary Fig.\u0026nbsp;6E-6H).\u003c/p\u003e \u003cp\u003eWe also performed KM analysis and log-rank tests for 3-year OS to clarify the efficacy of treatment in the two-risk stratification subgroup (low-risk PI\u0026thinsp;\u0026lt;\u0026thinsp;1.88 and high-risk PI\u0026thinsp;\u0026ge;\u0026thinsp;1.88) with the whole patient. Our results showed that CRC-associated SPEC patients receiving chemoradiotherapy had a better prognosis in the high-risk group, while for the low-risk group, surgery combined with chemotherapy was incapable of improving the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Similar results were also seen in the CSS nomogram (low-risk PI\u0026thinsp;\u0026lt;\u0026thinsp;2.89 and high-risk PI\u0026thinsp;\u0026ge;\u0026thinsp;2.89) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). Thus, the method described above could help clinicians improve prognosis prediction and make better therapeutic decisions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe major results of this study showed that the nomograms demonstrated good performance and accuracy in both training and validation cohorts, and their prediction was supported by C-index, calibration, ROC curves, and DCA. When compared with the AJCC staging systems, the nomograms showed a better accuracy for survival prediction.\u003c/p\u003e \u003cp\u003eAge, CRC radiation, grade, histology, SEER stage, treatment, and tumor size consisted of the OS prediction model. In comparison with most other nomograms of FPEC, our findings firstly supported CRC-based radiation was negatively correlated with the OS of SPEC patients. Especially, evaluations of the underlying risk of second primary cancer (SPC) after radiotherapy for pelvic cancers, including prostate[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], bladder[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], cervix uteri[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and rectum[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], have substantiated the idea of an increased incidence of SPEC in patients undergoing pelvic radiotherapy, regardless of 1-, 5- or 10-year incubation period[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, Guan et al indicated that 10-year survival rates in patients with radiotherapy-associated SPC are less than that in matched patients with first primary cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Collectively, the variable of CRC radiation played a significant role in the nomogram of OS. Other than CRC radiation, other significant predictors of our OS nomogram, one or more, were adopted for various previous studies in different populations of EC. This phenomenon seemed to be partially attributed to the similar clinicopathological features between FPEC and SPEC in Japanese patients reported by Keiichiro Nakamura et al, which also implied the reliability of clinical interpretation of the OS nomogram from another aspect[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Unlike the large sample model with emphasizing a major impact of age at diagnosis on patient survival constructed by Sun et al[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], our study did not adjust for age at diagnosis. To minimize the offset due to the impact of the survival rate of patients with EC with time over and medical technology advances, we reclassified the tumor stage of all cases according to the 8th edition of the AJCC staging system, added the SEER summary stage, and modified pathological grading based on previous reports, because the period of case selection spanned two SEER databases from 1973 to 2020.\u003c/p\u003e \u003cp\u003eA little controversial finding was that age did not remain an independent predictor of CSS in this study, in contrast to many studies on EC where known age is a crucial prognostic factor. However, the present study participants were predominantly over 60 years old, above the average age from 58\u0026ndash;61 years of FPEC[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], compared with these similar age distribution-based studies. Consistent with the work by Fleming et al., which showed that age greater than 70 in patients with EC was not a statistically significant predictor of poor outcomes after adjusting for other poor prognostic variables[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous research on patients with low CLDN6 expression also supports our finding[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. On the other hand, the CSS model did not incorporate CRC-based radiation. One likely explanation is that patients who died from EC had a more aggressive histological type and more advanced disease, thereby masking the true underlying impact.\u003c/p\u003e \u003cp\u003eA significant similarity of both nomograms was the equal importance of treatment features supporting survival benefits, although treatment data solely relied on broad categories. Additionally, the survival outcomes of surgery combined with radiotherapy and chemotherapy appeared to be greatest by unadjusted survival analysis. Nevertheless, further risk stratification tests suggested not all patients can benefit from multimodality therapy. we discovered that patients who received radiotherapy plus chemotherapy had higher survival rates than other treatments in the high-risk group, but for the low-risk group, surgery plus chemotherapy was the better choice. This finding might at first look appear to be incongruent with the traditional therapeutic methods mainly by surgery, supplemented by radiotherapy and chemotherapy. However, the strategies of treatment for EC have been alterable due to the heterogeneity of high-risk populations. Gynaecologic Oncology Group (GOG) 122 is a pivotal study that changed the way we think about EC and chemotherapy[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A randomized trial included women with early-stage high-risk and stage III EC and reported significant improvements in recurrence-free survival and OS trends from chemotherapy plus radiation compared with chemotherapy alone [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. On the contrary, Professor Daniela Matei\u0026rsquo;s team showed that chemotherapy plus radiation did not significantly improve relapse-free survival rate in terminal EC, but distant metastasis controlled[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Briefly, adjuvant radiotherapy might have an impact on those patients who had chemotherapy as primary treatment, which seemed likely to explain our findings that high-risk EC patients benefited most from chemoradiotherapy. Moreover, radiotherapy has been the standard adjuvant treatment in endometrial cancer historically. But lately, chemotherapy has emerged as a primary treatment alternative in early endometrial cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Adjuvant radiation use is greatly reduced on account of the results of postoperative radiotherapy in endometrial carcinoma (ORTEC)-1[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The GOG-122 study showed that postoperative adjuvant chemotherapy was superior to whole adjuvant abdominal irradiation for EC patients, consistent with multiple randomized controlled trials[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Even, adjuvant chemotherapy could delay metastases, and the side effects could be acceptable, although chemotherapy failed to prevent local recurrence, also pointed out by these trials. Notably, our results could partially be attributed to the population differences between the target patient with SPEC in CRC and the FPEC although randomized studies on comparison of the treatments of patients with SPEC are worth further exploration. More individualized treatment was therefore suggested for EC patients after CRC.\u003c/p\u003e \u003cp\u003eThere are several limitations of this study. First, since our nomogram was based on the data of Western countries, it may not be generalizable to all countries. Second, the prognostic model had not been externally validated, and practical advantages for prospective clinical use remain unclear. Third, this current study ignored the effect of diagnosis year, which may have an unexpected impact on patient outcomes with the advancement in medical technology over time. Finally, the relatively small sample size may limit statistical power, further research with large sample sizes is necessary to verify.\u003c/p\u003e \u003cp\u003eIn summary, in this study, two objective and accurate prediction nomograms as drawn up informed patients about their prognosis and provided oncologists some reference for clinical decision-making based on the subpopulation at different risks. More works are required to confirm whether it has potential generalization ability in other patient groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e The authors are deeply grateful to the Surveillance, Epidemiology, and End Results database for providing high-quality clinical data to undertake our study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This research was supported by the Natural Science Foundation of Fujian Province (grant no. 2021J011303) and the Fuzhou\u0026nbsp;Science\u0026nbsp;and\u0026nbsp;Technology\u0026nbsp;Program\u0026nbsp;(2023-S-019).\u0026nbsp;The funding source had\u0026nbsp;no\u0026nbsp;role\u0026nbsp;in study design,\u0026nbsp;data\u0026nbsp;collection, analysis,\u0026nbsp;interpretation, writing\u0026nbsp;of\u0026nbsp;the\u0026nbsp;manuscript,\u0026nbsp;or publication decision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u0026nbsp;\u003c/strong\u003eThis\u0026nbsp;study\u0026nbsp;was\u0026nbsp;considered\u0026nbsp;exempt from review and thus no\u0026nbsp;patient\u0026nbsp;written\u0026nbsp;informed\u0026nbsp;consent\u0026nbsp;was\u0026nbsp;required based on the publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e Material preparation, data collection, and analysis were performed by Linli LIU. The first draft of the manuscript was written by Linli LIU and\u0026nbsp;constructive comments that helped us to improve the manuscript were provided by\u0026nbsp;Qiong JIN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e This\u0026nbsp;research data can be openly available\u0026nbsp;at https://seer.cancer.gov/seerstat/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003eNone of the authors have any conflicts of interest associated with this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVinuesa L, Webster RM. The endometrial carcinoma market. 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Gynecol Oncol. 2008;108(1):226\u0026ndash;233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.ygyno.2007.09.029\u003c/span\u003e\u003cspan address=\"10.1016/j.ygyno.2007.09.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometrial neoplasms, Colorectal carcinoma, Nomogram, Overall survival, Cancer-specific survival","lastPublishedDoi":"10.21203/rs.3.rs-4677808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4677808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometrial cancer (EC) is one of the most frequent gynecologic cancers, approximately 20% of patients are regarded as high-risk with poor prognosis. However, more details of patients with second primary endometrial cancer (SPEC) after colorectal cancer (CRC) remain poorly understood.We therefore purposed to construct two nomograms to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates to facilitate clinical application. Nomograms for predicting OS and CSS were constructed and validated. The receiver operating characteristic curves, calibration plot, decision curve analysis, C-index, net reclassification improvement, and integrated discrimination improvement were applied to evaluate the predictive performance. Finally, the Prognostic index was calculated and used for risk stratification of Kaplan-Meier survival analysis based on different treatment options. Nomograms of OS and CSS were formulated based on the independent prognostic factors utilizing the training set. The 3- and 5- years of OS nomogram demonstrated good discrimination (AUC\u0026thinsp;=\u0026thinsp;0.840 and 0.829, respectively), well-calibrated power, and excellent clinical effectiveness. Our nomograms of predicting OS and CSS had a concordance index of 0.801 and 0.866 compared with 0.676 and 0.746 for the AJCC staging system, and more importantly, demonstrated a better forecast accuracy. Chemoradiotherapy displayed a significant survival benefit in the high-risk groups, but proceeding to surgery plus chemotherapy showed a favorable survival for the low groups based on all patients. We developed and internally validated multivariable models that predict OS and CSS risk of SPEC in patients with a CRC to help clinicians make applicable clinical decisions for patients.\u003c/p\u003e","manuscriptTitle":"Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 18:19:56","doi":"10.21203/rs.3.rs-4677808/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2cdf311-65eb-4e0a-82ef-0fb6fa6d335f","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35068569,"name":"Biological sciences/Cancer"},{"id":35068570,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-10-07T09:09:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 18:19:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4677808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4677808","identity":"rs-4677808","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}