Performance of Node-RADS for Standardized Diagnosis of Cervical Lymph Nodes in Oral Squamous Cell Carcinoma

Performance of Node-RADS for Standardized Diagnosis of Cervical Lymph Nodes in Oral Squamous Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance of Node-RADS for Standardized Diagnosis of Cervical Lymph Nodes in Oral Squamous Cell Carcinoma Jitao Zhu, Wenyi Zhang, Junru Zhao, Bo Feng, Lisha Sun, Zhipeng Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8205525/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 Objectives Current imaging techniques for diagnosing cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC) lack consensus. The Node-RADS (Node Reporting and Data System) offers a criterion to evaluate LNM based on size and configuration. This study aimed to explore the correlation between Node-RADS scores and LNM rates in OSCC, and assess diagnostic performance. Materials and Methods A retrospective analysis was conducted on 200 patients (average age, 57.8 years, 73.5% were male) diagnosed with OSCC at the Peking University School of Stomatology, who underwent neck dissection, preoperative contrast enhanced CT scans, and had definitive postoperative pathological results. The correlation between Node-RADS scores and LNM rates was examined, various cutoff points (> 1,>2,>3,>4) was applied to evaluate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). The ROC and AUC were used to assess diagnostic efficacy. Additionally, the optimal short-axis diameter length and each configuration criterion were determined, while Kappa statistics evaluated inter-rater reliability. Results There was a notable linear correlation between Node-RADS scores and LNM rates. With increasing Node-RADS cutoff values, specificity and PPV increased from 5.6% to 97.2% and 65.3% to 97.5%, sensitivity and NPV decreased from 100% to 60.2% and 100% to 57.9%. The most effective Node-RADS cutoff values for individual patient, lymph node laterality and lymph node level dimension were respectively identified as > 3/>3/>2, with corresponding AUCs of 0.86/0.86/0.82. The Kappa consistency test yielded a value of 0.814. Conclusions This study establishes a groundwork for implementing the Node-RADS in assessing LNM in OSCC, also suggests it has moderate to high accuracy and robust diagnostic performance. Clinical Relevance: The results of this study suggest that Node Reporting and Data System is a reliable tool for guiding clinical decisions in the management of oral squamous cell carcinoma. Node-RADS Oral squamous cell carcinoma Cervical Lymph Node Metastasis Imaging Reporting and Data Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction OSCC is the most common malignant tumor in the oral and maxillofacial region[ 1 , 2 ]. According to the 2022 Global Cancer Statistics, there were approximately 389,485 new cases of oral cancer worldwide, accounting for roughly 2% of all malignancies, with 188,230 reported deaths[ 3 ]. A notable characteristic of OSCC is its high tendency for cervical LNM, with the metastasis rate ranging from 50% to 59%[ 4 , 5 ]. More than 30% patients with OSCC still experience cervical LNM even if their clinical manifestations show negative[ 6 ]. A large number of studies have confirmed that cervical LNM is an important reason for the high recurrence rate and low survival rate of patients with OSCC[ 7 , 8 ]. Survival rates in patients with cervical LNM are approximately 50% lower than in patients without cervical LNM[ 9 ]. Consequently, the early diagnosis of such metastasis in OSCC patients is vital. Timely diagnosis significantly impacts tumor staging, determination of therapeutic regimen, and ultimately, the prognosis of the patients. Traditional imaging reports rely on pathological examination as the gold standard. In OSCC, radiologists typically classify cervical LNM risk dichotomously—either positive or negative. However, discrepancies between imaging and pathological diagnoses are common in clinical practice. For instance, imaging may suggest no LNM while pathology confirms metastasis, or vice versa. This has led to a widespread perception that imaging examinations have inherent limitations, creating a sense of uncertainty. In the TNM staging of oral cancer, N1 denotes a single metastatic lymph node ≤ 3 cm, whereas N0 indicates no metastasis. Clinically, metastatic lymph nodes > 3 cm are rare, further complicating clinical interpretation. The root of this issue lies in the language of imaging reports. As Wittgenstein posited, "Language is the world." Binary classifications fail to capture the nuanced reality of imaging findings. The Imaging Reporting and Data Systems (RADS) framework has mitigated this by structuring imaging data descriptively, rather than demanding perfect concordance with pathology. Over the past three decades, RADS has been successfully applied across multiple malignant tumors[ 10 – 12 ], demonstrating its clinical utility. In 2021, Elsholtz[ 13 ] et al. first introduced the Node-RADS 1.0 to solve the problem of lack of consensus in the radiological diagnosis of LNM in cancer. Node-RADS classifies the suspicion level of LNM through a comprehensive evaluation of imaging findings. For lymph nodes that appear suspicious, scores are allocated based on their size and configuration. These scores accumulate to indicate the level of suspicion, on a scale from 1 (very low probability) to 5 (very high probability). Node-RADS is applicable for evaluating suspicious lymph nodes in CT and MRI scans across various anatomical regions. Since Node-RADS 1.0's introduction in 2021, it has been validated in cancers such as prostate, bladder, nasopharyngeal, perihilar cholangiocarcinoma and gastric cancer, with encouraging outcomes[ 14 – 18 ]. However, its application in cervical LNM in OSCC remains unexplored. To address this gap, we reviewed preoperative enhanced CT scans of OSCC patients who underwent neck dissection at our institution. We hypothesized that an elevated Node-RADS score correlates with an increased risk of cervical LNM. Our study aimed to evaluate the overall diagnostic efficacy of Node-RADS scores at the cervical lymph node level, laterality, and individual patient dimensions. 2. Materials and Methods 2.1 Study Design This retrospective and observational study was granted ethical approval by the Ethics Review Committee of Peking University School and Hospital of Stomatology (PKUSSIRB-202391121). It encompassed a cohort of 200 patients diagnosed with primary oral cancer, who underwent neck dissection at the Peking University School of Stomatology within the timeframe of September 2013 to March 2023. All patients underwent preoperative contrast enhanced CT scans, and their postoperative pathological diagnoses were subsequently confirmed. The study excluded certain patient groups to maintain data integrity. This exclusion criteria included patients who had received radiotherapy prior to their surgery, individuals presenting poor quality CT images characterized by significant artifacts in the region of interest, those diagnosed with other inflammatory diseases affecting the maxillofacial region, and patients with other lymph node-related diseases that had been previously diagnosed. This rigorous selection process was essential to ensure the reliability and validity of the study's findings. The flowchart of this study is Fig. 1 . 2.2 Surgical and pathological procedures In this study, all patients underwent surgical resection of oral cancer and cervical lymph node dissection. The surgeon meticulously conducted cervical lymph node dissection guided by anatomical landmarks during the operation. Following resection, all lymph node specimens were processed using standardized pathological protocols, including tissue fixation, dehydration, embedding, sectioning, and hematoxylin-eosin staining. During histopathological evaluation, the long and short diameters of each lymph node were measured. The extent of neck dissection varied across the cohort: 137 patients (68.5%) received unilateral neck dissection, while the remaining 63 patients (31.5%) underwent bilateral neck dissection. The dissection scope encompassed levels I-III to I-V, depending on clinical indications. Postoperatively, all patients were pathologically staged using to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM Staging System (2017)[ 19 ], and histological grading was performed according to the 4th edition of the World Health Organization (WHO) Classification of Tumors of the Head and Neck[ 20 ]. 2.3 Imaging technique Prior to surgery, all patients underwent contrast enhanced CT scans of the oral-maxillofacial region and neck. These scans were conducted using a GE Optima CT520 scanner in a supine position. For these scans, following an initial plain scan, a contrast agent (Iopamidol, 370 mgI/100ml) was administered intravenously at the elbow. The scanning parameters were set as follows: tube voltage at 120–140 kV, tube current between 200–380 mA, and a slice thickness of 1.25 mm with a pitch of 1.65:1. The image reconstruction parameters included a standard reconstruction mode with a slice thickness and inter-slice spacing of 1.25 mm each, and a reconstruction field of view measuring 20*20 cm. All imaging data were stored in the Digital Imaging and Communications in Medicine (DICOM) format on the Picture Archiving and Communication System (PACS). 2.4 Node-RADS Assessment The contrast enhanced CT images were retrospectively evaluated by two veteran radiologists with 15 years of experience in oral and maxillofacial imaging diagnosis. The radiologists were kept unaware of the patients' postoperative pathological findings to ensure unbiased analysis. The image assessment adhered to Node-RADS guidelines, utilizing a structured three-tiered flowchart (illustrated in Fig. 2 ). The evaluation criteria focused on the size and configuration of lymph nodes, with each subcategory receiving a corresponding score. The cumulative score, ranging from 1 (very low) to 5 (very high), indicated the probability of LNM (as shown in Fig. 3 ). Radiologists evaluated lymph nodes in levels I-V bilaterally for all patients and assigned a RADS score. Within each level, the lymph node deemed most suspicious for metastasis was designated as the target lymph node, and its long and short axis diameters were recorded. Lymph node matching was performed by an independent investigator according to the following protocol: First, all non-dissected levels were excluded from analysis. For levels without pathologically confirmed LNM, the RADS score provided by the radiologist was directly applied. For levels with pathologically confirmed LNM, the sampled lymph nodes were matched to their corresponding target lymph nodes identified on contrast-enhanced CT, with successful matching defined as cases where both the long and short axis diameters discrepancies between the target and sampled lymph nodes were within 2 mm. 2.5 Statistical Analyses Our statistical analysis was conducted at three distinct dimensions: lymph node level, lymph node laterality, and overall individual patient assessment. At the lymph node level dimension, the Node-RADS score for each dissected area in each patient was compared with the final pathological status (pN0 or pN ≥ 1). At the lymph node laterality dimension, we matched the highest Node-RADS score from each dissected area on each side of the patient with the corresponding side's final pathological result. For the individual patient dimension analysis, the highest Node-RADS score across all dissected areas was compared with the overall final pathological outcome for each patient. Initially, a Linear-by-Linear Association test was employed to examine trends in LNM rates across Node-RADS scores from one to five. The diagnostic effectiveness of Node-RADS scores for LNM was then assessed using ROC analysis and the AUC. The sensitivity, specificity, PPV, and NPV were calculated for various Node-RADS score thresholds (> 1 vs. >2 vs. >3 vs. >4). Additionally, the cutoff values were varied to determine the optimal short-axis diameter length for diagnosing LNM. Lastly, the consistency of Node-RADS scoring between two different radiologists was evaluated to test inter-rater reliability. All analyses were performed at the individual patient, lymph node laterality, and lymph node level dimensions, utilizing two-sided tests with a significance threshold set at p < 0.05. For these analyses, both SPSS software (version 27.0) and Python (version 3.12) was employed. 3. Results 3.1 Study Population Characteristics This study included 200 patients with primary oral cancer who underwent cervical lymph node dissection. The median age was 57.8 years (Interquartile Range, IQR 51.75-65), and 73.5% of the patients were male. The primary tumors were located in six different sites: lip (1%), tongue (43.5%), gingiva (21.5%), floor of the mouth (12%), cheek (20.5%), and palate (1.5%). The postoperative pathological staging is shown in (Table 1 ). Table 1 The clinical information of the OSCC patients. Characteristic Overall, N = 200 Age (years) Median(IQR) 57.8(51.75, 65) Gender Female 53(26.5%) Male 147(73.5%) Primary tumor Lip 2(1.0%) Tongue 87(43.5%) Gingiva 43(21.5%) Floor of mouth 24(12.0%) Buccal mucosa 41(20.5%) Palate 3(1.5%) Pathologic T stage 1 28(14.0%) 2 80(40.0%) 3 74(37.0%) 4a 18(9.0%) 4b 0(0%) Pathologic N stage 0 69(34.5%) 1 33(16.5%) 2a 7(3.5%) 2b 52(26.0%) 2c 21(10.5%) 3a 0(0%) 3b 18(9.0%) Pathologic M stage 0 200(100.0%) 1 0(0%) TNM stage Ⅰ 14(7.0%) Ⅱ 29(14.5%) Ⅲ 56(28.0%) ⅣA 83(41.5%) ⅣB 18(9.0%) ⅣC 0(0%) 3.2 Lymph Node Metastasis Rates According to the Node-RADS Score At the individual patient dimension, the overall LNM rate was 64% (128/200). Based on blinded CT image evaluation, the distribution of Node-RADS scores for the patients was as follows: Score 1–4 patients (2%), Score 2–29 patients (14.5%), Score 3–46 patients (23%), Score 4–42 patients (21%), and Score 5–79 patients (39.5%). As shown in (Fig. 4 A, Table 2 ), it is evident that with increasing Node-RADS scores, the detection rate of positive LNM also increased, ranging from 0% to 97.5% (p < 0.001). At the laterality dimension of lymph nodes, the overall LNM rate was 59.9% (156/263). Based on blinded CT image evaluation, the distribution of Node-RADS scores for different sides was as follows: Score 1–6 sides (2.3%), Score 2–56 sides (21.3%), Score 3–58 sides (22.1%), Score 4–54 sides (20.5%), and Score 5–89 sides (33.8%). As shown in (Fig. 4 B, Table 2 ), with increasing Node-RADS scores, the detection rate of positive LNM also increased, ranging from 0% to 97.8% (p < 0.001). At the lymph node level dimension, the overall LNM rate was 26.5% (258/973). Based on blinded CT image evaluation, the distribution of Node-RADS scores for different regions was as follows: Score 1–354 levels (36.4%), Score 2–275 levels (28.3%), Score 3–140 levels (14.4%), Score 4–91 levels (9.3%), and Score 5–113 levels (11.6%). As illustrated in (Fig. 4 C, Table 2 ), with increasing Node-RADS scores, the detection rate of positive LNM also increased, ranging from 3.7% to 90.3% (p < 0.001). Table 2 The rates of LNM corresponding to different grades at these three different dimensions. Dimension LNM Overall Node-RADS 1 Node-RADS 2 Node-RADS 3 Node-RADS 4 Node-RADS 5 P 1 Individual Patients Sum 200(100%) 4(2%) 29(14.5%) 46(23%) 42(21%) 79(39.5%) < 0.001 Negative 72(36%) 4(100%) 26(90.0%) 32(69.6%) 8(19.0%) 2(2.5%) Positive 128(64%) 0(0%) 3(10%) 14(30.4%) 43(81.0%) 77(97.5%) Lymph Node Laterality Sum 263(100%) 6(2.3%) 56(21.3%) 58(22.1%) 54(20.5%) 89(33.8%) < 0.001 Negative 107(40.1%) 6(100%) 48(85.8%) 40(69.0% 11(20.4%) 2(2.2%) Positive 156(59.9%) 0(0%) 8(14.2%) 18(31.0%) 43(79.6%) 87(97.8%) Lymph Node Level Sum 973(100%) 354(36.4%) 275(28.3%) 140(14.4%) 91(9.3) 113(11.6%) < 0.001 Negative 715(73.5%) 341(96.3%) 243(88.4%) 93(66.4%) 27(29.7%) 11(9.7%) Positive 258(26.5%) 13(3.7%) 32(11.6%) 47(33.6%) 64(70.3%) 102(90.3%) 1 Linear-by-Linear association for tend in proportion. In a univariable logistic regression analysis, Node-RADS score correlated with LNM at the lymph node level dimension (OR 4.033, 95% CI 6.77–33.23, p < 0.001). The variables “Age”, “Sex”, “Primary tumor” and “pT stage” were also examined in univariate logistic regression to assess their potential confounding effects. The results indicated an association between “pT stage” and LNM. Following multivariable adjustments for essential confounders, Node-RADS score was found to be an independent predictor of LNM (Table 3 ). Table 3 Univariate and multivariate logistic regression analysis of predictors in predicting pN status. Predictor Univariate logistic regression Multivariate logistic regression OR 95%CI P OR 95%CI P Age 1.001 0.987–1.014 0.914 Gender 0.865 0.624–1.199 0.383 Primary tumor 1.092 0.956–1.249 0.196 pT stage 1.268 1.067–1.508 0.007* 1.155 0.911–1.464 0.235 Node-RADS score 4.033 3.403–4.780 < 0.001*** 4.006 3.380–4.748 1 vs. >2 vs. >3 vs. >4), as shown in (Fig. 5 ), it is apparent that, at the individual patient dimension and the laterality dimension of lymph nodes, a Node-RADS score of > 3 is the optimal diagnostic cut-off value, while at the lymph node level dimension, a Node-RADS score of > 2 is the optimal diagnostic cut-off value. At the individual patient dimension, by setting higher Node-RADS cut-off values (from > 1 to > 4), the specificity and PPV increased from 5.6% and 65.3% to 97.2% and 97.5%, respectively (Table 4 ). Conversely, the sensitivity and NPV decreased from 100% to 60.2% and from 100% to 57.9%, respectively. Similar trends were observed at the lymph node laterality dimension and the lymph node level dimension (Table 4 ). Table 4 The Sensitivity, Specificity, PPV and NPV are reported for different Node-RADS cut-off values at the individual patient, lymph node laterality and lymph node level dimensions. Dimension Node-Rads Cut-off n(%) Above Cut-off n(%) Below Cut-off n(%) Sensitivity (%) Specificity (%) PPV (%) NPV (%) Individual Patients > 4 79(39.5) 121(60.5) 60.2 97.2 97.5 57.9 > 3 121(60.5) 79(39.5) 86.7 86.1 91.8 78.4 > 2 167(83.5) 33(16.5) 97.7 41.7 74.9 91.0 > 1 196(98.0) 4(2.0) 100.0 5.6 65.3 100.0 Lymph Node Laterality > 4 89(33.9) 174(66.1) 55.8 98.1 97.8 60.3 > 3 143(54.4) 120(45.6) 83.3 87.9 91.0 78.3 > 2 201(76.4) 62(23.6) 94.9 50.5 73.6 87.1 > 1 257(97.7) 6(2.3) 100.0 5.6 60.8 100.0 Lymph Node Level > 4 113(11.6) 860(88.4) 40.0 98.5 90.3 81.9 > 3 204(21.0) 769(79.0) 64.3 94.7 81.4 88.0 > 2 344(35.4) 629(64.6) 82.6 81.7 62.0 92.9 > 1 619(63.6) 354(36.4) 100.0 55.6 40.0 96.3 3.4 Diagnostic Performance of the Different Size and Configuration Criterion To determine the optimal diagnostic criterion for LNM, we evaluated various cutoff values for the short-axis diameter and assessed each configuration criterion at the lymph node level dimension. Figure 6 shows the percentage of patients with (pN +) and without (pN-) LNM in whom a lymph node meeting each short-axis diameter cut-off (5–15 mm) was detectable on preoperative CT. Table 5 presents sensitivity, specificity, and Youden’s index for each diameter cut-off from 5 to 15 mm. Lymph nodes with short-axis diameters of ≤ 6 mm were frequently seen in patients without LNM (pN-), giving rise to low specificity (< 63.6%). Lymph nodes with short-axis diameters ≥ 11 mm were rarely seen in either group of patients, giving rise to poor sensitivity (< 40.0%). The size criterion performed best with a short-axis diameter cut-off of 8 mm, resulting in 58.1% sensitivity, 86.6% specificity, and Youden’s index of 0.447. Table 5 Results for short-axis diameter. Sensitivity, specificity, and Youden’s index for cut-offs from 5 to 15 mm. Cutoff with best diagnostic performance is shown in bold. Size cut-off Sensitivity Specificity Youden’s Index 5mm 84.9% 53.1% 0.380 6mm 76.4% 63.6% 0.400 7mm 68.2% 75.7% 0.439 8mm 58.1% 86.6% 0.447 9mm 49.2% 91.5% 0.407 10mm 44.2% 95.4% 0.396 11mm 35.3% 98.6% 0.339 12mm 29.8% 98.7% 0.285 13mm 24.4% 99.0% 0.234 14mm 20.2% 99.3% 0.195 15mm 17.4% 99.3% 0.167 Figure 7 shows the percentage of patients with (pN+) and without (pN-) LNM in whom a lymph node was visible on preoperative CT according to its configuration. Table 6 compiles sensitivities, specificities, and Youden’s indices for these configuration criteria. Focal necrosis, border-change, and spherical—were highly specific (> 90%) but rarely seen in either group of patients, resulting in sensitivities < 25% and Youden’s indices 90% of patients with (pN+) with resulting Youden’s indices of 0.489, respectively. Best performance for the configuration criterion was found for “Gross necrosis” with 53.1% sensitivity, 96.4% specificity, and Youden’s index of 0.495. Table 6 Results for configuration criterion. Sensitivity, specificity, and Youden’s index for the respective morphological criterion. Best diagnostic performance is shown in bold. Configuration criterion Sensitivity Specificity Youden’s Index Texture-any change 92.6% 56.2% 0.489 Heterogeneous 21.3% 65.3% -0.134 Focal necrosis 18.2% 94.5% 0.128 Gross necrosis 53.1% 96.4% 0.495 Border-change 21.3% 91.5% 0.128 Spherical 19.4% 91.2% 0.106 3.5 Inter-reader agreement To evaluate the practicality and ease of implementing the Node-RADS scoring in real-world settings, we engaged two stomatology postgraduate students, each with a year of experience in radiological diagnosis, to independently score patients. During this scoring process, both readers were unaware of the postoperative pathological results. As shown in Table 7 , there were some discrepancies between Reader 1 and Reader 2 at lower Node-RADS scores, while the differences diminished at higher Node-RADS scores. The results of the Kappa consistency test showed a Kappa value of 0.814 and a U value of 19.918, with p < 0.001. Table 7 At the individual patient dimension, a comparison of Node-RADS scores between Reader 1 and Reader 2 was conducted. Node-RADS score Reader Number of cases scored Histopathologically confirmed positive 1 1 4 0 (0%) 2 5 0 (0%) 2 1 29 2 (6.9%) 2 29 4 (13.8%) 3 1 45 15 (33.3%) 2 42 15 (35.7%) 4 1 43 34 (79.1%) 2 45 32 (71.1%) 5 1 79 77 (97.5%) 2 79 77 (97.5%) 4. Discussion In this study, we assessed the diagnostic performance of Node-RADS 1.0[ 13 ], a system that evaluates lymph nodes based on their size and configuration, for structured reporting of cervical lymph node status in contrast enhanced CT scans of OSCC patients. We used histopathology as the reference standard. Our findings indicate that Node-RADS 1.0 exhibits commendable diagnostic accuracy in assessing cervical LNM. We initially hypothesized that a higher Node-RADS score would correlate with an increased risk of cervical LNM. Our statistical analysis, involving a linear-by-linear trend test at lymph node level, lymph node laterality, and individual patient dimensions, demonstrated a linear increase in lymph node metastasis rates with varying Node-RADS scores. We then established different cutoff values (> 1,>2,>3, and > 4) and calculated the sensitivity, specificity, PPV, and NPV for each threshold. At the individual patient dimension and lymph node laterality dimension, a Node-RADS score > 3 emerged as the optimal cutoff, showing moderate to high sensitivity and specificity. In contrast, at the lymph node level dimension, a Node-RADS score > 2 was identified as the optimal cutoff. Additionally, we plotted ROC curves for these cutoff values. At the individual patient and lymph node laterality dimensions, a Node-RADS score > 3 demonstrated good diagnostic efficacy, evidenced by an AUC of 0.86. However, at the lymph node level dimension, a Node-RADS score > 2 exhibited a slightly lower but still favorable diagnostic performance, with an AUC of 0.82. Furthermore, our findings indicate that a short-axis diameter of 8mm is optimal for diagnosing LNM, as opposed to the 10mm threshold suggested by Node-RADS 1.0. And "Gross necrosis" exhibited optimal performance in terms of the configuration criterion. In our study, two junior radiologists independently performed Node-RADS scoring at the individual patient dimension. The high consistency revealed in the Kappa test results suggests that Node-RADS scoring effectively addresses the variability and lack of consensus inherent in traditional imaging reporting models. For OSCC patients, accurately diagnosing cervical LNM prior to treatment is crucial for effective clinical staging, treatment planning, and prognostic assessment. Performing unnecessary neck dissections in patients without metastatic cervical lymph nodes can lead to detrimental aesthetic and functional consequences. Conversely, delaying surgery in cases with metastatic nodes may lead to cancer progression and reduced survival rates[ 21 ]. The current criteria for enhanced CT diagnosis of cervical metastatic lymph nodes include factors such as size, border, density, internal structure, shape, number, and the presence of extracapsular spread[ 22 ]. However, there is a lack of consensus on the specifics of these criteria, and opinions vary regarding their inclusion in diagnostic processes. Our research differentiated between three analytical dimensions: cervical lymph node level, laterality, and individual patient. This distinction is critical considering the head and neck region contains 40% of the body's lymph nodes[ 23 ], and AJCC classifies cervical lymph nodes into regional levels I to VII. Given the complexity of cervical lymph nodes, accurately differentiating each level during lymphadenectomy and pathological examination poses a challenge in clinical practice, often leading to discrepancies between surgical findings and actual diagnoses. Consequently, we separately analyzed different regional levels, the left/right laterality, and individual patient dimensions to identify the most appropriate diagnostic settings. This disparity is likely due to the increased complexity and variability at the lymph node level dimension. For example, a lymph node located on the boundary between two regional levels might be classified differently by surgeons during lymphadenectomy or by radiologists during imaging diagnosis. This variability can affect the diagnostic performance of Node-RADS in regional analysis. Despite our promising results in applying Node-RADS to evaluate cervical LNM in OSCC, our study is not without limitations. (i) The findings are based on a limited sample size from a single institution and may lack broader applicability. Encouraged by our positive outcomes, further research, incorporating retrospective or prospective data from multiple institutions, is necessary for validation. For instance, our study revealed that the optimal diameter of the short axis measures 8mm, which deviates from the size standard recommended by Node-RADS 1.0. Additionally, variations in the optimal diameter of the short axis have been observed in other studies[ 16 , 24 ]. These findings underscore the heterogeneity in Node-RADS assessment criteria across different cancer classifications. Consequently, future assessments may necessitate the adoption of distinct criteria tailored to specific cancer types. Additionally, (ii) we exclusively used enhanced CT scans in assessing Node-RADS scores, as it is the most prevalent preoperative imaging diagnostic technique. Nonetheless, MRI might offer greater accuracy in LNM assessment. Therefore, future studies should consider incorporating MRI, or a combination of enhanced CT and MRI, in applying Node-RADS for evaluating cervical lymph node status in OSCC. In the context of other tumors, the application of Node-RADS to MRI has yielded superior outcomes, with the current results demonstrating an AUC ranging from 0.93 to 0.97 for diagnosing LNM[ 18 , 25 ].(iii) Since Node-RADS was proposed in 2021, it has only been applied in individual studies, and experience is still limited. It is possible that with the continuous improvement of the version, the diagnostic performance of Node-RADS will be higher and the application of Node-RADS will be more extensive in the future. The present study establishes a foundational basis for the adoption of the Node-RADS scoring system in assessing cervical lymph nodes in patients diagnosed with OSCC. Our analysis revealed a clear linear relationship between the rate of LNM and varying Node-RADS scores, with the Node-RADS scores emerging as an independent predictor of LNM. Significantly, the Node-RADS scoring system demonstrated moderate to high accuracy in evaluating the status of cervical lymph nodes. Importantly, this system exhibited excellent diagnostic efficacy both at the individual patient dimension and when considering the laterality of lymph node involvement. This suggests that Node-RADS is a reliable tool for guiding clinical decisions in the management of OSCC. Declarations Acknowledgements The authors declare that no acknowledgements are required for this manuscript. Author contributions Zhi-peng Sun and Li-sha Sun contributed to the study conception, design, and final revision of the manuscript. Material preparation, data collection and analysis were performed by Ji-tao Zhu, Wen-yi Zhang, Jun-ru Zhao and Bo Feng. The first draft of the manuscript was written by Ji-tao Zhu, Wen-yi Zhang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This work was supported by Beijing Research Ward Excellence Program, BRWEP (BRWEP2024W194100100) and Beijing Natural Science Foundation (L252202). Data availability The datasets generated and analyzed during the current study are not publicly available due to the fact that they contain information that could compromise research participant privacy and confidentiality. However, the de-identified data are available from the corresponding author on reasonable request. Supplementary Information No supplementary information is available for this article. Ethics approval This study was conducted with the approval of the local ethics committee and in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration. 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Eur Radiol 12:727–738. 10.1007/s003300101102 Niu Y, Wen L, Yang Y, Zhang Y, Fu Y, Lu Q, Wang Y, Yu X, Yu X Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT Pediconi F, Maroncelli RA-O, Pasculli M, Galati F, Moffa G, Marra A Polistena A and Rizzo V Performance of MRI for standardized lymph nodes assessment in breast cancer: are we ready for Node-RADS? Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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00:12:12","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170060,"visible":true,"origin":"","legend":"","description":"","filename":"b11deab41bc7466fb0b31c881fc32a5f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/f8e3d5aa4fcb3184015d669d.xml"},{"id":97395651,"identity":"35c043c2-b093-4a38-97d6-8e81997dfc58","added_by":"auto","created_at":"2025-12-04 00:12:11","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177434,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/0c37be738f4bd06c03741c94.html"},{"id":97395611,"identity":"a854d95b-426c-49df-9ffd-251c331d3f60","added_by":"auto","created_at":"2025-12-04 00:12:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1470151,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/faa97ebec9cafd8994a7a939.jpg"},{"id":97395618,"identity":"72fc2aed-5258-48b5-ba36-f3fea6ca11b0","added_by":"auto","created_at":"2025-12-04 00:12:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4495117,"visible":true,"origin":"","legend":"\u003cp\u003eExplanation of the Node-RADS scoring system, adapted from the original publication.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/5fd97b8f55ba5d9aa1f56e34.jpg"},{"id":97395612,"identity":"7e90f63c-611f-45d3-9421-74a313901bef","added_by":"auto","created_at":"2025-12-04 00:12:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1110295,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images for criterion “configuration” in contrast enhanced CT. \u003cstrong\u003ea1\u003c/strong\u003ehomogeneous of texture; \u003cstrong\u003ea2\u003c/strong\u003e heterogeneous of texture; \u003cstrong\u003ea3\u003c/strong\u003e focal necrosis of texture; \u003cstrong\u003ea4\u003c/strong\u003e gross necrosis of texture; \u003cstrong\u003eb1\u003c/strong\u003e smooth of border; \u003cstrong\u003eb2\u003c/strong\u003e irregular/ill-defined of border; \u003cstrong\u003ec1\u003c/strong\u003e any shape with fatty hilum in shape; \u003cstrong\u003ec2\u003c/strong\u003e kidney-bean-like or oval without fatty hilum in shape; \u003cstrong\u003ec3\u003c/strong\u003e spherical without fatty hilum in shape.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/b6150fa08252e181dfbad562.jpg"},{"id":97666206,"identity":"23879fc2-e14e-4e0d-9671-ce6202fad10b","added_by":"auto","created_at":"2025-12-08 09:20:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3195109,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram depicting the LNM rates at the final pathologic examination, according to the preoperative Node-RADS score at the individual patient dimension (A), lymph node laterality dimension (B) and lymph node level dimension (C).\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/f328b9803e7334a0d7bc5c04.jpg"},{"id":97395610,"identity":"3a1f001f-c51c-4c1a-bbbd-8a1dc201624b","added_by":"auto","created_at":"2025-12-04 00:12:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e represents the ROC and AUC at the individual patient dimension; \u003cstrong\u003eB\u003c/strong\u003e represents the ROC and AUC at the lymph node laterality dimension; and \u003cstrong\u003eC\u003c/strong\u003e represents the ROC and AUC at the lymph node level dimension.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/c13df76766a8afd2120af6a2.jpg"},{"id":97395624,"identity":"51265630-e456-4428-b229-b2a2aedb56f0","added_by":"auto","created_at":"2025-12-04 00:12:11","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178003,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of patients with (pN+) and without (pN-) LNM in whom a lymph node of respective short-axis diameter cut-off was visible on preoperative CT (5-15 mm). n = 973 lymph nodes in a total of 200 patients.\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/b8f281f44bfe49c1fcc50851.jpg"},{"id":97665477,"identity":"660b401a-c70d-485c-8a1e-f076fe3c5724","added_by":"auto","created_at":"2025-12-08 09:18:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":123017,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of patients with (pN+) and without (pN-) LNM in whom a lymph node was visible on preoperative CT according to its configuration. n = 973 lymph nodes in a total of 200 patients.\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/e38d49c81a475d22b0726f75.jpg"},{"id":100366470,"identity":"5348481c-f6c7-4b2b-bc11-f664872dbbaf","added_by":"auto","created_at":"2026-01-16 07:56:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11878749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8205525/v1/3424e421-98bd-4778-ab56-317836f6185e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance of Node-RADS for Standardized Diagnosis of Cervical Lymph Nodes in Oral Squamous Cell Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOSCC is the most common malignant tumor in the oral and maxillofacial region[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the 2022 Global Cancer Statistics, there were approximately 389,485 new cases of oral cancer worldwide, accounting for roughly 2% of all malignancies, with 188,230 reported deaths[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A notable characteristic of OSCC is its high tendency for cervical LNM, with the metastasis rate ranging from 50% to 59%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. More than 30% patients with OSCC still experience cervical LNM even if their clinical manifestations show negative[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A large number of studies have confirmed that cervical LNM is an important reason for the high recurrence rate and low survival rate of patients with OSCC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Survival rates in patients with cervical LNM are approximately 50% lower than in patients without cervical LNM[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, the early diagnosis of such metastasis in OSCC patients is vital. Timely diagnosis significantly impacts tumor staging, determination of therapeutic regimen, and ultimately, the prognosis of the patients.\u003c/p\u003e\u003cp\u003eTraditional imaging reports rely on pathological examination as the gold standard. In OSCC, radiologists typically classify cervical LNM risk dichotomously\u0026mdash;either positive or negative. However, discrepancies between imaging and pathological diagnoses are common in clinical practice. For instance, imaging may suggest no LNM while pathology confirms metastasis, or vice versa. This has led to a widespread perception that imaging examinations have inherent limitations, creating a sense of uncertainty. In the TNM staging of oral cancer, N1 denotes a single metastatic lymph node\u0026thinsp;\u0026le;\u0026thinsp;3 cm, whereas N0 indicates no metastasis. Clinically, metastatic lymph nodes\u0026thinsp;\u0026gt;\u0026thinsp;3 cm are rare, further complicating clinical interpretation.\u003c/p\u003e\u003cp\u003eThe root of this issue lies in the language of imaging reports. As Wittgenstein posited, \"Language is the world.\" Binary classifications fail to capture the nuanced reality of imaging findings. The Imaging Reporting and Data Systems (RADS) framework has mitigated this by structuring imaging data descriptively, rather than demanding perfect concordance with pathology. Over the past three decades, RADS has been successfully applied across multiple malignant tumors[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], demonstrating its clinical utility.\u003c/p\u003e\u003cp\u003eIn 2021, Elsholtz[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] et al. first introduced the Node-RADS 1.0 to solve the problem of lack of consensus in the radiological diagnosis of LNM in cancer. Node-RADS classifies the suspicion level of LNM through a comprehensive evaluation of imaging findings. For lymph nodes that appear suspicious, scores are allocated based on their size and configuration. These scores accumulate to indicate the level of suspicion, on a scale from 1 (very low probability) to 5 (very high probability). Node-RADS is applicable for evaluating suspicious lymph nodes in CT and MRI scans across various anatomical regions.\u003c/p\u003e\u003cp\u003eSince Node-RADS 1.0's introduction in 2021, it has been validated in cancers such as prostate, bladder, nasopharyngeal, perihilar cholangiocarcinoma and gastric cancer, with encouraging outcomes[\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, its application in cervical LNM in OSCC remains unexplored. To address this gap, we reviewed preoperative enhanced CT scans of OSCC patients who underwent neck dissection at our institution. We hypothesized that an elevated Node-RADS score correlates with an increased risk of cervical LNM. Our study aimed to evaluate the overall diagnostic efficacy of Node-RADS scores at the cervical lymph node level, laterality, and individual patient dimensions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003e This retrospective and observational study was granted ethical approval by the Ethics Review Committee of Peking University School and Hospital of Stomatology (PKUSSIRB-202391121). It encompassed a cohort of 200 patients diagnosed with primary oral cancer, who underwent neck dissection at the Peking University School of Stomatology within the timeframe of September 2013 to March 2023. All patients underwent preoperative contrast enhanced CT scans, and their postoperative pathological diagnoses were subsequently confirmed.\u003c/p\u003e\u003cp\u003eThe study excluded certain patient groups to maintain data integrity. This exclusion criteria included patients who had received radiotherapy prior to their surgery, individuals presenting poor quality CT images characterized by significant artifacts in the region of interest, those diagnosed with other inflammatory diseases affecting the maxillofacial region, and patients with other lymph node-related diseases that had been previously diagnosed. This rigorous selection process was essential to ensure the reliability and validity of the study's findings. The flowchart of this study is Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Surgical and pathological procedures\u003c/h2\u003e\u003cp\u003eIn this study, all patients underwent surgical resection of oral cancer and cervical lymph node dissection. The surgeon meticulously conducted cervical lymph node dissection guided by anatomical landmarks during the operation. Following resection, all lymph node specimens were processed using standardized pathological protocols, including tissue fixation, dehydration, embedding, sectioning, and hematoxylin-eosin staining. During histopathological evaluation, the long and short diameters of each lymph node were measured.\u003c/p\u003e\u003cp\u003eThe extent of neck dissection varied across the cohort: 137 patients (68.5%) received unilateral neck dissection, while the remaining 63 patients (31.5%) underwent bilateral neck dissection. The dissection scope encompassed levels I-III to I-V, depending on clinical indications.\u003c/p\u003e\u003cp\u003ePostoperatively, all patients were pathologically staged using to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM Staging System (2017)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and histological grading was performed according to the 4th edition of the World Health Organization (WHO) Classification of Tumors of the Head and Neck[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Imaging technique\u003c/h2\u003e\u003cp\u003ePrior to surgery, all patients underwent contrast enhanced CT scans of the oral-maxillofacial region and neck. These scans were conducted using a GE Optima CT520 scanner in a supine position. For these scans, following an initial plain scan, a contrast agent (Iopamidol, 370 mgI/100ml) was administered intravenously at the elbow. The scanning parameters were set as follows: tube voltage at 120\u0026ndash;140 kV, tube current between 200\u0026ndash;380 mA, and a slice thickness of 1.25 mm with a pitch of 1.65:1. The image reconstruction parameters included a standard reconstruction mode with a slice thickness and inter-slice spacing of 1.25 mm each, and a reconstruction field of view measuring 20*20 cm. All imaging data were stored in the Digital Imaging and Communications in Medicine (DICOM) format on the Picture Archiving and Communication System (PACS).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Node-RADS Assessment\u003c/h2\u003e\u003cp\u003eThe contrast enhanced CT images were retrospectively evaluated by two veteran radiologists with 15 years of experience in oral and maxillofacial imaging diagnosis. The radiologists were kept unaware of the patients' postoperative pathological findings to ensure unbiased analysis. The image assessment adhered to Node-RADS guidelines, utilizing a structured three-tiered flowchart (illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The evaluation criteria focused on the size and configuration of lymph nodes, with each subcategory receiving a corresponding score. The cumulative score, ranging from 1 (very low) to 5 (very high), indicated the probability of LNM (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Radiologists evaluated lymph nodes in levels I-V bilaterally for all patients and assigned a RADS score. Within each level, the lymph node deemed most suspicious for metastasis was designated as the target lymph node, and its long and short axis diameters were recorded.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLymph node matching was performed by an independent investigator according to the following protocol: First, all non-dissected levels were excluded from analysis. For levels without pathologically confirmed LNM, the RADS score provided by the radiologist was directly applied. For levels with pathologically confirmed LNM, the sampled lymph nodes were matched to their corresponding target lymph nodes identified on contrast-enhanced CT, with successful matching defined as cases where both the long and short axis diameters discrepancies between the target and sampled lymph nodes were within 2 mm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analyses\u003c/h2\u003e\u003cp\u003eOur statistical analysis was conducted at three distinct dimensions: lymph node level, lymph node laterality, and overall individual patient assessment. At the lymph node level dimension, the Node-RADS score for each dissected area in each patient was compared with the final pathological status (pN0 or pN\u0026thinsp;\u0026ge;\u0026thinsp;1). At the lymph node laterality dimension, we matched the highest Node-RADS score from each dissected area on each side of the patient with the corresponding side's final pathological result. For the individual patient dimension analysis, the highest Node-RADS score across all dissected areas was compared with the overall final pathological outcome for each patient.\u003c/p\u003e\u003cp\u003eInitially, a Linear-by-Linear Association test was employed to examine trends in LNM rates across Node-RADS scores from one to five. The diagnostic effectiveness of Node-RADS scores for LNM was then assessed using ROC analysis and the AUC. The sensitivity, specificity, PPV, and NPV were calculated for various Node-RADS score thresholds (\u0026gt;\u0026thinsp;1 vs. \u0026gt;2 vs. \u0026gt;3 vs. \u0026gt;4). Additionally, the cutoff values were varied to determine the optimal short-axis diameter length for diagnosing LNM. Lastly, the consistency of Node-RADS scoring between two different radiologists was evaluated to test inter-rater reliability.\u003c/p\u003e\u003cp\u003eAll analyses were performed at the individual patient, lymph node laterality, and lymph node level dimensions, utilizing two-sided tests with a significance threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For these analyses, both SPSS software (version 27.0) and Python (version 3.12) was employed.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Study Population Characteristics\u003c/h2\u003e\u003cp\u003eThis study included 200 patients with primary oral cancer who underwent cervical lymph node dissection. The median age was 57.8 years (Interquartile Range, IQR 51.75-65), and 73.5% of the patients were male. The primary tumors were located in six different sites: lip (1%), tongue (43.5%), gingiva (21.5%), floor of the mouth (12%), cheek (20.5%), and palate (1.5%). The postoperative pathological staging is shown 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\u003eThe clinical information of the OSCC patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;200\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.8(51.75, 65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53(26.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147(73.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003ePrimary tumor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLip\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(1.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTongue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87(43.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGingiva\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43(21.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFloor of mouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(12.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuccal mucosa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41(20.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePalate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(1.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003ePathologic T stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28(14.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80(40.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74(37.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003ePathologic N stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69(34.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33(16.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(3.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52(26.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(10.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePathologic M stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200(100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eTNM stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(7.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29(14.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(28.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅣA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83(41.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅣB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅣC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Lymph Node Metastasis Rates According to the Node-RADS Score\u003c/h2\u003e\u003cp\u003eAt the individual patient dimension, the overall LNM rate was 64% (128/200). Based on blinded CT image evaluation, the distribution of Node-RADS scores for the patients was as follows: Score 1\u0026ndash;4 patients (2%), Score 2\u0026ndash;29 patients (14.5%), Score 3\u0026ndash;46 patients (23%), Score 4\u0026ndash;42 patients (21%), and Score 5\u0026ndash;79 patients (39.5%). As shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), it is evident that with increasing Node-RADS scores, the detection rate of positive LNM also increased, ranging from 0% to 97.5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eAt the laterality dimension of lymph nodes, the overall LNM rate was 59.9% (156/263). Based on blinded CT image evaluation, the distribution of Node-RADS scores for different sides was as follows: Score 1\u0026ndash;6 sides (2.3%), Score 2\u0026ndash;56 sides (21.3%), Score 3\u0026ndash;58 sides (22.1%), Score 4\u0026ndash;54 sides (20.5%), and Score 5\u0026ndash;89 sides (33.8%). As shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with increasing Node-RADS scores, the detection rate of positive LNM also increased, ranging from 0% to 97.8% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eAt the lymph node level dimension, the overall LNM rate was 26.5% (258/973). Based on blinded CT image evaluation, the distribution of Node-RADS scores for different regions was as follows: Score 1\u0026ndash;354 levels (36.4%), Score 2\u0026ndash;275 levels (28.3%), Score 3\u0026ndash;140 levels (14.4%), Score 4\u0026ndash;91 levels (9.3%), and Score 5\u0026ndash;113 levels (11.6%). As illustrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with increasing Node-RADS scores, the detection rate of positive LNM also increased, ranging from 3.7% to 90.3% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe rates of LNM corresponding to different grades at these three different dimensions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLNM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNode-RADS 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNode-RADS 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNode-RADS 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNode-RADS 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNode-RADS 5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIndividual Patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29(14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46(23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42(21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e79(39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72(36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26(90.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32(69.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8(19.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2(2.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128(64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14(30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43(81.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e77(97.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLymph Node Laterality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e263(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56(21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58(22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54(20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89(33.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107(40.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48(85.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40(69.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11(20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2(2.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156(59.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18(31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43(79.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87(97.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLymph Node Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e973(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e354(36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e275(28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140(14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e91(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e113(11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e715(73.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e341(96.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e243(88.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93(66.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27(29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11(9.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258(26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13(3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32(11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47(33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64(70.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e102(90.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eLinear-by-Linear association for tend in proportion.\u003c/p\u003e\u003cp\u003eIn a univariable logistic regression analysis, Node-RADS score correlated with LNM at the lymph node level dimension (OR 4.033, 95% CI 6.77\u0026ndash;33.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The variables \u0026ldquo;Age\u0026rdquo;, \u0026ldquo;Sex\u0026rdquo;, \u0026ldquo;Primary tumor\u0026rdquo; and \u0026ldquo;pT stage\u0026rdquo; were also examined in univariate logistic regression to assess their potential confounding effects. The results indicated an association between \u0026ldquo;pT stage\u0026rdquo; and LNM. Following multivariable adjustments for essential confounders, Node-RADS score was found to be an independent predictor of LNM (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate logistic regression analysis of predictors in predicting pN status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate logistic regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariate logistic regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.987\u0026ndash;1.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.624\u0026ndash;1.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.956\u0026ndash;1.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epT stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.067\u0026ndash;1.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.911\u0026ndash;1.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNode-RADS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.403\u0026ndash;4.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.380\u0026ndash;4.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Diagnostic Performance of the Node-RADS Score According to a Different Cut-off\u003c/h2\u003e\u003cp\u003eIntegrating the ROC and the AUC for different cut-off values (\u0026gt;\u0026thinsp;1 vs. \u0026gt;2 vs. \u0026gt;3 vs. \u0026gt;4), as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), it is apparent that, at the individual patient dimension and the laterality dimension of lymph nodes, a Node-RADS score of \u0026gt;\u0026thinsp;3 is the optimal diagnostic cut-off value, while at the lymph node level dimension, a Node-RADS score of \u0026gt;\u0026thinsp;2 is the optimal diagnostic cut-off value.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the individual patient dimension, by setting higher Node-RADS cut-off values (from \u0026gt;\u0026thinsp;1 to \u0026gt;\u0026thinsp;4), the specificity and PPV increased from 5.6% and 65.3% to 97.2% and 97.5%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, the sensitivity and NPV decreased from 100% to 60.2% and from 100% to 57.9%, respectively. Similar trends were observed at the lymph node laterality dimension and the lymph node level dimension (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Sensitivity, Specificity, PPV and NPV are reported for different Node-RADS cut-off values at the individual patient, lymph node laterality and lymph node level dimensions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNode-Rads Cut-off n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbove Cut-off n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBelow Cut-off n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPV (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eIndividual Patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79(39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121(60.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e57.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121(60.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79(39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e91.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e78.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167(83.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33(16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e91.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e196(98.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4(2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e65.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLymph Node Laterality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89(33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e174(66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e98.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e120(45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e91.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e78.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e201(76.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62(23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e94.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e73.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e257(97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLymph Node Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e113(11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e860(88.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e98.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e81.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e204(21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e769(79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e81.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e88.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e344(35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e629(64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e92.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e619(63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e354(36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.4 Diagnostic Performance of the Different Size and Configuration Criterion\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo determine the optimal diagnostic criterion for LNM, we evaluated various cutoff values for the short-axis diameter and assessed each configuration criterion at the lymph node level dimension. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the percentage of patients with (pN +) and without (pN-) LNM in whom a lymph node meeting each short-axis diameter cut-off (5\u0026ndash;15 mm) was detectable on preoperative CT. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents sensitivity, specificity, and Youden\u0026rsquo;s index for each diameter cut-off from 5 to 15 mm. Lymph nodes with short-axis diameters of \u0026le;\u0026thinsp;6 mm were frequently seen in patients without LNM (pN-), giving rise to low specificity (\u0026lt;\u0026thinsp;63.6%). Lymph nodes with short-axis diameters\u0026thinsp;\u0026ge;\u0026thinsp;11 mm were rarely seen in either group of patients, giving rise to poor sensitivity (\u0026lt;\u0026thinsp;40.0%). The size criterion performed best with a short-axis diameter cut-off of 8 mm, resulting in 58.1% sensitivity, 86.6% specificity, and Youden\u0026rsquo;s index of 0.447.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults for short-axis diameter. Sensitivity, specificity, and Youden\u0026rsquo;s index for cut-offs from 5 to 15 mm. Cutoff with best diagnostic performance is shown in bold.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize cut-off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYouden\u0026rsquo;s Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8mm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e58.1%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e86.6%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.447\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the percentage of patients with (pN+) and without (pN-) LNM in whom a lymph node was visible on preoperative CT according to its configuration. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e compiles sensitivities, specificities, and Youden\u0026rsquo;s indices for these configuration criteria. Focal necrosis, border-change, and spherical\u0026mdash;were highly specific (\u0026gt;\u0026thinsp;90%) but rarely seen in either group of patients, resulting in sensitivities\u0026thinsp;\u0026lt;\u0026thinsp;25% and Youden\u0026rsquo;s indices\u0026thinsp;\u0026lt;\u0026thinsp;0.15. A \u0026ldquo;texture-any change\u0026rdquo; lymph node was seen in \u0026gt;\u0026thinsp;90% of patients with (pN+) with resulting Youden\u0026rsquo;s indices of 0.489, respectively. Best performance for the configuration criterion was found for \u0026ldquo;Gross necrosis\u0026rdquo; with 53.1% sensitivity, 96.4% specificity, and Youden\u0026rsquo;s index of 0.495.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults for configuration criterion. Sensitivity, specificity, and Youden\u0026rsquo;s index for the respective morphological criterion. Best diagnostic performance is shown in bold.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfiguration criterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYouden\u0026rsquo;s Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTexture-any change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterogeneous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFocal necrosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGross necrosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e53.1%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e96.4%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.495\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorder-change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpherical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Inter-reader agreement\u003c/h2\u003e\u003cp\u003eTo evaluate the practicality and ease of implementing the Node-RADS scoring in real-world settings, we engaged two stomatology postgraduate students, each with a year of experience in radiological diagnosis, to independently score patients. During this scoring process, both readers were unaware of the postoperative pathological results. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, there were some discrepancies between Reader 1 and Reader 2 at lower Node-RADS scores, while the differences diminished at higher Node-RADS scores. The results of the Kappa consistency test showed a Kappa value of 0.814 and a U value of 19.918, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAt the individual patient dimension, a comparison of Node-RADS scores between Reader 1 and Reader 2 was conducted.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNode-RADS score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of cases scored\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHistopathologically confirmed positive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (79.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (71.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (97.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (97.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we assessed the diagnostic performance of Node-RADS 1.0[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], a system that evaluates lymph nodes based on their size and configuration, for structured reporting of cervical lymph node status in contrast enhanced CT scans of OSCC patients. We used histopathology as the reference standard. Our findings indicate that Node-RADS 1.0 exhibits commendable diagnostic accuracy in assessing cervical LNM. We initially hypothesized that a higher Node-RADS score would correlate with an increased risk of cervical LNM. Our statistical analysis, involving a linear-by-linear trend test at lymph node level, lymph node laterality, and individual patient dimensions, demonstrated a linear increase in lymph node metastasis rates with varying Node-RADS scores. We then established different cutoff values (\u0026gt;\u0026thinsp;1,\u0026gt;2,\u0026gt;3, and \u0026gt;\u0026thinsp;4) and calculated the sensitivity, specificity, PPV, and NPV for each threshold. At the individual patient dimension and lymph node laterality dimension, a Node-RADS score\u0026thinsp;\u0026gt;\u0026thinsp;3 emerged as the optimal cutoff, showing moderate to high sensitivity and specificity. In contrast, at the lymph node level dimension, a Node-RADS score\u0026thinsp;\u0026gt;\u0026thinsp;2 was identified as the optimal cutoff. Additionally, we plotted ROC curves for these cutoff values. At the individual patient and lymph node laterality dimensions, a Node-RADS score\u0026thinsp;\u0026gt;\u0026thinsp;3 demonstrated good diagnostic efficacy, evidenced by an AUC of 0.86. However, at the lymph node level dimension, a Node-RADS score\u0026thinsp;\u0026gt;\u0026thinsp;2 exhibited a slightly lower but still favorable diagnostic performance, with an AUC of 0.82. Furthermore, our findings indicate that a short-axis diameter of 8mm is optimal for diagnosing LNM, as opposed to the 10mm threshold suggested by Node-RADS 1.0. And \"Gross necrosis\" exhibited optimal performance in terms of the configuration criterion. In our study, two junior radiologists independently performed Node-RADS scoring at the individual patient dimension. The high consistency revealed in the Kappa test results suggests that Node-RADS scoring effectively addresses the variability and lack of consensus inherent in traditional imaging reporting models.\u003c/p\u003e\u003cp\u003eFor OSCC patients, accurately diagnosing cervical LNM prior to treatment is crucial for effective clinical staging, treatment planning, and prognostic assessment. Performing unnecessary neck dissections in patients without metastatic cervical lymph nodes can lead to detrimental aesthetic and functional consequences. Conversely, delaying surgery in cases with metastatic nodes may lead to cancer progression and reduced survival rates[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The current criteria for enhanced CT diagnosis of cervical metastatic lymph nodes include factors such as size, border, density, internal structure, shape, number, and the presence of extracapsular spread[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, there is a lack of consensus on the specifics of these criteria, and opinions vary regarding their inclusion in diagnostic processes. Our research differentiated between three analytical dimensions: cervical lymph node level, laterality, and individual patient. This distinction is critical considering the head and neck region contains 40% of the body's lymph nodes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and AJCC classifies cervical lymph nodes into regional levels I to VII. Given the complexity of cervical lymph nodes, accurately differentiating each level during lymphadenectomy and pathological examination poses a challenge in clinical practice, often leading to discrepancies between surgical findings and actual diagnoses. Consequently, we separately analyzed different regional levels, the left/right laterality, and individual patient dimensions to identify the most appropriate diagnostic settings. This disparity is likely due to the increased complexity and variability at the lymph node level dimension. For example, a lymph node located on the boundary between two regional levels might be classified differently by surgeons during lymphadenectomy or by radiologists during imaging diagnosis. This variability can affect the diagnostic performance of Node-RADS in regional analysis.\u003c/p\u003e\u003cp\u003eDespite our promising results in applying Node-RADS to evaluate cervical LNM in OSCC, our study is not without limitations. (i) The findings are based on a limited sample size from a single institution and may lack broader applicability. Encouraged by our positive outcomes, further research, incorporating retrospective or prospective data from multiple institutions, is necessary for validation. For instance, our study revealed that the optimal diameter of the short axis measures 8mm, which deviates from the size standard recommended by Node-RADS 1.0. Additionally, variations in the optimal diameter of the short axis have been observed in other studies[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings underscore the heterogeneity in Node-RADS assessment criteria across different cancer classifications. Consequently, future assessments may necessitate the adoption of distinct criteria tailored to specific cancer types. Additionally, (ii) we exclusively used enhanced CT scans in assessing Node-RADS scores, as it is the most prevalent preoperative imaging diagnostic technique. Nonetheless, MRI might offer greater accuracy in LNM assessment. Therefore, future studies should consider incorporating MRI, or a combination of enhanced CT and MRI, in applying Node-RADS for evaluating cervical lymph node status in OSCC. In the context of other tumors, the application of Node-RADS to MRI has yielded superior outcomes, with the current results demonstrating an AUC ranging from 0.93 to 0.97 for diagnosing LNM[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].(iii) Since Node-RADS was proposed in 2021, it has only been applied in individual studies, and experience is still limited. It is possible that with the continuous improvement of the version, the diagnostic performance of Node-RADS will be higher and the application of Node-RADS will be more extensive in the future.\u003c/p\u003e\u003cp\u003eThe present study establishes a foundational basis for the adoption of the Node-RADS scoring system in assessing cervical lymph nodes in patients diagnosed with OSCC. Our analysis revealed a clear linear relationship between the rate of LNM and varying Node-RADS scores, with the Node-RADS scores emerging as an independent predictor of LNM. Significantly, the Node-RADS scoring system demonstrated moderate to high accuracy in evaluating the status of cervical lymph nodes. Importantly, this system exhibited excellent diagnostic efficacy both at the individual patient dimension and when considering the laterality of lymph node involvement. This suggests that Node-RADS is a reliable tool for guiding clinical decisions in the management of OSCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The authors declare that no acknowledgements are required for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e Zhi-peng Sun and Li-sha Sun contributed to the study conception, design, and final revision of the manuscript. Material preparation, data collection and analysis were performed by Ji-tao Zhu, Wen-yi Zhang, Jun-ru Zhao and Bo Feng. The first draft of the manuscript was written by Ji-tao Zhu, Wen-yi Zhang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by Beijing Research Ward Excellence Program, BRWEP (BRWEP2024W194100100) and Beijing Natural Science Foundation (L252202).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets generated and analyzed during the current study are not publicly available due to the fact that they contain information that could compromise research participant privacy and confidentiality. However, the de-identified data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e No supplementary information is available for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e This study was conducted with the approval of the local ethics committee and in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFunk GF, Karnell LH, Robinson RA, Zhen WK, Trask DK, Hoffman HT (2002) Presentation, treatment, and outcome of oral cavity cancer: a National Cancer Data Base report. 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Eur Radiol 12:727\u0026ndash;738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s003300101102\u003c/span\u003e\u003cspan address=\"10.1007/s003300101102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu Y, Wen L, Yang Y, Zhang Y, Fu Y, Lu Q, Wang Y, Yu X, Yu X Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePediconi F, Maroncelli RA-O, Pasculli M, Galati F, Moffa G, Marra A Polistena A and Rizzo V Performance of MRI for standardized lymph nodes assessment in breast cancer: are we ready for Node-RADS?\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":"Node-RADS, Oral squamous cell carcinoma, Cervical Lymph Node Metastasis, Imaging Reporting and Data Systems","lastPublishedDoi":"10.21203/rs.3.rs-8205525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8205525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eCurrent imaging techniques for diagnosing cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC) lack consensus. The Node-RADS (Node Reporting and Data System) offers a criterion to evaluate LNM based on size and configuration. This study aimed to explore the correlation between Node-RADS scores and LNM rates in OSCC, and assess diagnostic performance.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003eA retrospective analysis was conducted on 200 patients (average age, 57.8 years, 73.5% were male) diagnosed with OSCC at the Peking University School of Stomatology, who underwent neck dissection, preoperative contrast enhanced CT scans, and had definitive postoperative pathological results. The correlation between Node-RADS scores and LNM rates was examined, various cutoff points (\u0026gt;\u0026thinsp;1,\u0026gt;2,\u0026gt;3,\u0026gt;4) was applied to evaluate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). The ROC and AUC were used to assess diagnostic efficacy. Additionally, the optimal short-axis diameter length and each configuration criterion were determined, while Kappa statistics evaluated inter-rater reliability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThere was a notable linear correlation between Node-RADS scores and LNM rates. With increasing Node-RADS cutoff values, specificity and PPV increased from 5.6% to 97.2% and 65.3% to 97.5%, sensitivity and NPV decreased from 100% to 60.2% and 100% to 57.9%. The most effective Node-RADS cutoff values for individual patient, lymph node laterality and lymph node level dimension were respectively identified as \u0026gt;\u0026thinsp;3/\u0026gt;3/\u0026gt;2, with corresponding AUCs of 0.86/0.86/0.82. The Kappa consistency test yielded a value of 0.814.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study establishes a groundwork for implementing the Node-RADS in assessing LNM in OSCC, also suggests it has moderate to high accuracy and robust diagnostic performance.\u003c/p\u003e\u003ch2\u003eClinical Relevance:\u003c/h2\u003e\u003cp\u003eThe results of this study suggest that Node Reporting and Data System is a reliable tool for guiding clinical decisions in the management of oral squamous cell carcinoma.\u003c/p\u003e","manuscriptTitle":"Performance of Node-RADS for Standardized Diagnosis of Cervical Lymph Nodes in Oral Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 00:12:06","doi":"10.21203/rs.3.rs-8205525/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":"49a95af8-54d5-407a-bee4-7eadc6ca334b","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T08:54:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 00:12:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8205525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8205525","identity":"rs-8205525","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}