Nomogram Model Based on MRI and Clinical Characteristics for Predicting Lymph Node Metastasis in Rectal Cancer

Nomogram Model Based on MRI and Clinical Characteristics for Predicting Lymph Node Metastasis in Rectal Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nomogram Model Based on MRI and Clinical Characteristics for Predicting Lymph Node Metastasis in Rectal Cancer Lu Chen, Zhao Yang, Qi lin Niu, Zhi zhen Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7015877/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 Objective To explore the diagnostic value of a predictive model that combines MRI features and clinical features for detecting lymph node metastasis (LNM) in patients with rectal cancer (RC). Methods A retrospective analysis was conducted on 170 patients who had been pathologically diagnosed with RC by a pathologist. Of these, 74 were in the LNM group and 96 were in the non-metastatic group. The relationship between LNM and clinical and MRI features was analyzed using univariate and multivariate binary logistic regression. Based on these results, a clinical prediction model was constructed, and its diagnostic effectiveness was analyzed using a receiver operating characteristic (ROC) curve. Results Univariate binary logistic regression analysis showed statistically significant differences in MR T-stage, CEA, CA199, lymphovascular invasion (LVI), perineural invasion (PNI) and circumferential resection margin (CRM) between the LNM group and the non-metastasis group. Multivariate regression analysis revealed that CEA levels greater than 5 ng/mL, positive LVI and positive CRM were independent risk factors for LNM in RC. ROC curve analysis revealed an area under the curve (AUC) of 0.850 (95% CI: 0.791–0.909) for the model assessing LNM in RC. Conclusions The clinical prediction model, which was constructed using a combination of MRI and clinical features (including CEA, LVI and CRM), has high diagnostic value. rectal cancer magnetic resonance imaging lymph node metastasis lymphovascular invasion nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction According to the 2022 Global Cancer Statistics Report, over 1.9 million new cases of colorectal cancer (CRC) were diagnosed, resulting in 904,000 CRC-related deaths worldwide [ 1 ] . In China specifically, 510,000 new CRC cases were reported, with CRC ranking second and fourth in terms of incidence and mortality, respectively, among all malignant tumors nationally [ 2 ] . Rectal cancer (RC) accounts for more than one-third of all CRC cases. The presence of lymph node metastasis (LNM) in RC patients is strongly associated with metastatic disease and a poor clinical prognosis. Total mesorectal excision (TME) is currently the standard surgical approach for RC, and lymph node dissection is required for patients exhibiting LNM. Studies indicate that patients with LNM have a 5-year survival rate of 50%-68%, and are at a significantly higher risks of local recurrence than patients without LNM [ 3 ] . Therefore, accurate preoperative identification of LNM is critical for guiding treatment decisions and predicting survival outcomes. Recognised for its superior soft tissue resolution, magnetic resonance imaging (MRI) is recommended as the primary preoperative imaging modality for RC in China's CRC diagnosis and treatment guidelines. Although MRI demonstrates high diagnostic accuracy (80%-85%) for preoperative T-staging [ 4 – 5 ] , its capability to determine nodal status is limited. The current diagnosis of metastatic lymph nodes using MRI relies on morphological criteria (e.g., size, border characteristics and internal signal patterns) [ 6 ] . However, reactive lymph node hyperplasia can mimic structural changes, making it difficult to distinguish between metastatic and non-metastatic nodes based solely on signal intensity variations. These limitations emphasise the need for more accurate methods of predicting LNM. Recent research suggests that clinical characteristics may be significant predictors of LNM [ 7 ] . Integrating these features with MRI findings could enable a more comprehensive assessment of patient and inform personalized treatment strategies. This study aims to develop a predictive model combining MRI features and clinical risk factors to improve the accuracy of metastatic lymph node diagnosis. Materials and Methods This retrospective study was approved by the Institutional Review Board. Patient approval or informed consent was not required to review medical records or images. We also complied with the Declaration of Helsinki with regard to the ethical conduct of research involving human subjects. Patients A retrospective analysis was conducted on cases of RC that met the following criteria. The inclusion criteria were: (1) no preoperative radiotherapy or chemotherapy; (2) radical surgery performed one to two weeks after an MRI scan; and (3) complete clinical data and corresponding test results available. The exclusion criteria were: (1) lesions could not be visualized on MRI scans; and (2) severe image artifacts that compromised lymph node assessment. MRI examinations MRI examinations were performed using a Philips Achieva 3.0T dual-gradient superconducting system (Netherlands) with an abdominal phased-array coil. Patients fasted for four hours prior to scanning and received a glycerol enema for bowel preparation 30 minutes before imaging. The scanning parameters were as follows: (1) axial T2WI: TR = 3000 ms, TE = 80 ms, FOV 260×260 mm, matrix 256×320, slice thickness 5 mm; (2) Diffusion-weighted imaging (DWI): TR 3500 ms, TE 55 ms, matrix 256×256, FOV 289×289 mm, slice thickness 5 mm; (3) Contrast-enhanced axial T1WI: TR 600 ms, TE 29 ms, matrix 640×432, FOV 225×225 mm, slice thickness 5mm. Gadopentetate dimeglumine was administered intravenously at 2.0 mL/s (0.1mmol/kg), followed by a saline flush. Dynamic phase imaging was performed at 25 s (arterial), 55 s (venous) and 180 s (delayed). Image interpretation All MR images were independently reviewed by two radiologists with 10 and 20 years’ experience in abdominal imaging, respectively. They evaluated tumor location, depth of invasion (MR T stage), LNM (MR N stage), lymphovascular invasion (LVI), perineural invasion (PNI) and circumferential resection margin (CRM), in accordance with the 2018 Chinese Society of Clinical Oncology colorectal cancer guidelines. Each radiologist was blinded to the pathology results. Any discrepancies were adjudicated by a third gastrointestinal radiologist with 30 years' experience. MRI images of RC are shown in Figs. 1 and 2 . Statistical Analysis Statistical analyses were performed using SPSS version 26.0 software (SPSS 26.0, IBM, Chicago, USA). Quantitative data are expressed as the mean ± standard deviation and qualitative data as the number of cases and percentage. Where appropriate, Fisher’s exact tests and chi-squared tests were used to compare single factors between the LNM and non-metastatic categories. Univariate and multivariate logistic regression analyses were performed to analyze independent risk factors. A p-value of less than 0.05 was considered to indicate a statistically significant difference. A nomogram was created to predict the likelihood of LNM in patients based on the predictors included in the final multivariate model. Receiver operating characteristic (ROC) curves were plotted to evaluate and compare the model's discriminatory ability using the area under the ROC curve (AUC) and 95% confidence interval (CI). The calibration curve and Hosmer–Lemeshow goodness-of-fit test were used to assess the calibration of the model. A decision curve analysis (DCA) was performed to demonstrate the clinical utility of the prediction model by quantifying the net benefit across a range of threshold probabilities. Results Overall clinical Data This study enrolled 170 RC patients, comprising 96 cases without LNM (mean age 65.4 ± 11.9 years) and 74 cases with LNM (mean age 62.8 ± 10.2 years). All patients underwent preoperative rectal MRI examinations and had postoperative pathological confirmation of rectal adenocarcinoma. The clinical characteristics of the patients are detailed in Table 1 . Table 1 Clinical and MRI characteristics of patients with RC Non-LNM(n = 96) LNM(n = 74) P value Gender Male 65(67.7) 48(64.9) 0.697 Female 31(32.3) 26(35.1) Age 65.448 ± 11.919 62.759 ± 10.171 0.246 MR T stage T1-T2 11(11.5) 1(1.4) 0.011 T3-T4 85(88.5) 73(98.6) MR N stage N0 10(10.4) 6(8.1) 0.609 N+ 86(89.6) 68(91.9) CEA ≤ 5(ng/ mL) 74(77.1) 31(41.9) 5(ng/ mL) 22(22.9) 43(58.1) CA199 ≤ 35(U/ mL) 88(91.7) 56(75.7) 0.004 >35(U/ mL) 8(8.3) 18(24.3) LVI Absent 81(84.4) 32(43.2) <0.001 Present 15(15.6) 42(56.8) PNI Absent 83(86.5) 55(74.3) 0.045 Present 13(13.5) 19(25.7) CRM Negative 27(28.1) 10(13.5) 0.022 Positive 69(71.9) 64(86.5) Location Low 30(31.3) 22(29.7) 0.403 Mid 60(62.5) 43(58.1) High 6(6.2) 9(12.2) Association between LNM and clinical/MRI characteristics Univariate binary logistic regression analysis revealed statistically significant associations between LNM, MR T stage, CEA, CA199, LVI, PNI and CRM status ( P < 0.05). By contrast, gender, age, MR N stage and tumor location showed no statistical significance ( P = 0.697, 0.246, 0.609 and 0.403, respectively). Multivariate logistic regression analysis identified three independent risk factors for LNM: CEA > 5 ng/mL (OR: 6.179; 95% CI: 2.660–14.352), LVI positivity (OR: 11.277; 95% CI: 4.620–27.524), and CRM positivity (OR: 2.860; 95% CI: 1.010–8.098), all with P < 0.05 (see Table 2 ). Table 2 Logistic regression analysis of imaging factors for LNM in RC Univariate analysis Multivariate analysis Odds Ratio P β Odds Ratio P β MR T stage (95%CI) (95%CI) T1-T2 Reference T3-T4 9.447 0.034 2.246 2.625 0.406 0.965 (1.191–74.931) (0.269–25.611) CEA ≤ 5(ng/ mL) Reference >5(ng/ mL) 4.666 <0.001 1.540 6.179 35(U/ mL) 3.536 0.006 1.263 2.038 0.208 0.712 (1.441–8.676) (0.672–6.176) LVI Absent Reference Present 7.078 <0.001 1.958 11.277 <0.001 2.423 (3.458–14.527) (4.620-27.524) PNI Absent Reference Present 2.206 0.048 0.791 2.176 0.109 0.777 (1.008–4.828) (0.840–5.636) CRM Negative Reference Positive 2.504 0.025 0.918 2.860 0.048 1.051 (1.124–5.581) (1.010–8.098) Model construction and evaluation A nomogram incorporating three independent predictors: LVI, CEA > 5 ng/mL and CRM positivity, was constructed to predict LNM risk (Fig. 3). The model's ability to discriminate was evaluated using a RUC curve, which yielded an area under the curve (AUC) of 0.850 (95% CI: 0.791–0.909) (Fig. 4). Calibration performance was assessed using calibration curves and the Hosmer-Lemeshow test (χ² = 5.82, P = 0.684), which demonstrated optimal agreement between the predicted and observed LNM probabilities (Fig. 5). Decision curve analysis quantified the clinical utility by calculating net benefits across threshold probabilities (Fig. 6), confirming the model's robust clinical applicability. Discussion Recent advances in multimodal therapeutic interventions have significantly improved the prognosis of patients with RC. Accurate lymph node staging is essential for making treatment decisions and predicting outcomes. According to the National Comprehensive Cancer Network (NCCN) guidelines, RC patients with T1–2N0M0 should undergo endoscopic mucosal resection or TME. However, those with suspected LNM require neoadjuvant chemoradiotherapy. Consequently, precise preoperative nodal staging is of substantial clinical significance, since lymph node enlargement may be caused by inflammation or reactive hyperplasia, while smaller nodes may harbor metastatic tumor cells. Therefore, size alone is inadequate for determining nodal status [ 8 ] . Our study developed a predictive model to identify LNM in RC patients before surgery. This model integrates MRI features and clinical risk factors. We constructed a hybrid clinical-radiomics nomogram to visualize the model and provide clinical utility. This study integrated imaging and clinical features to analyze LNM (LNM). Univariate logistic regression identified the following statistically significant associations ( P < 0.05): MR T stage, CEA, CA199, LVI, PNI and CRM status. These findings are consistent with the report by Sumii et al. that CEA, CRM involvement and PNI are risk factors for occult LNM [ 9 ] . The results of the multivariate logistic regression analysis identified CEA > 5 ng/mL, CRM positivity and LVI positivity as independent risk factors for LNM in RC. LVI, which is defined histologically as tumor cell clusters or individual cells within or disrupting endothelial-lined spaces, facilitates metastatic dissemination via lymphatic or venous channels. This represents a critical step in early LNM [ 10 ] . There is substantial evidence linking LVI to poor prognosis and post-endoscopic recurrence, as well as its role as a stage-independent prognostic determinant [ 11 – 12 ] . Radiologically, CRM positivity is defined as the presence of tumour, metastatic lymph nodes, or tumour deposits within 5 mm of the mesorectal fascia on MRI (pathologically equivalent to 1 mm or less). Wei et al. demonstrated that CRM status and CEA levels were significantly associated with LNM in patients with locally advanced cancer in univariate logistic regression analysis. However, multivariate logistic regression analysis revealed that neither CRM nor CEA levels were independent risk factors for LNM [ 13 ] . While these findings suggest a lack of independence, a concurrent meta-analysis identified CEA levels greater than 5 ng/mL, LVI and CRM positivity as significant risk factors for LNM ( P < 0.05) [ 14 ] . These findings are consistent with those of the present study, which identified CRM status as an independent risk factor for LNM. CRM positivity was found to confer an adjusted risk probability of 2.860 (95% CI: 1.010–8.098). Elevated CEA levels have been shown to significantly correlate with the risk of LNM. Hu et al. established that preoperative elevation of tumor markers is an independent predictor of LNM [ 15 ] , with CEA and CA199 being the most clinically relevant biomarkers in CRC. A study of 130 CRC patients confirmed their association with nodal involvement [ 16 ] . Our results specifically implicate CEA elevation, which is consistent with the identification of CEA as an independent risk factor for LNM by Ma et al. [ 17 ] , reflecting advanced tumour stage and metastatic propensity. A recent meta-analysis has shown that CEA is an independent risk factor for LNM [ 18 ][ 19 ] . Although Niu et al. demonstrated that combining CEA with imaging features enhances pathological LNM prediction [ 20 ] , their model omitted the integration of CRM and LVI. Our combined clinical-imaging model demonstrated superior discriminative performance (AUC: 0.850; 95% CI: 0.791–0.909) and was operationalized through a clinically applicable nomogram. This visualization tool synthesises multivariate risk predictors, significantly strengthening the predictive capacity of LNM. However, limitations include the retrospective design, the inability to correlate individual MRI-detected lymph nodes pathologically, the limited sample size, and the absence of quantitative parameters from dynamic contrast-enhanced MRI. Conclusions CEA levels greater than 5 ng/mL, LVI and CRM positivity are all independent risk factors for LNM in RC. Using these factors together improves the accuracy of predicting preoperative LNM. Declarations Author Contribution Lu Chen and Zhi-zhen Gao wrote the main manuscript text. Zhao Yang and Qi-lin Niu prepared figures. All authors reviewed the manuscript. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin. 2024 May-Jun;74(3):229–263. Ji YT, Liu SW, Zhang YM, et al. [Comparison of the latest cancer statistics, cancer epidemic trends and determinants between China and the United States]. Zhonghua Zhong Liu Za Zhi. 2024;46(7):646–656. Chinese. Ishihara S, Kawai K, Tanaka T, et al. Oncological Outcomes of Lateral Pelvic Lymph Node Metastasis in Rectal Cancer Treated With Preoperative Chemoradiotherapy[J]. Dis Colon Rectum. 2017, 60(5):469–476. Stijns RCH, de Graaf EJR, Punt CJA, et al. Long-term Oncological and Functional Outcomes of Chemoradiotherapy Followed by Organ-Sparing Transanal Endoscopic Microsurgery for Distal Rectal Cancer: The CARTS Study[J]. JAMA Surg. 2019, 154(1):47–54. Nougaret S, Reinhold C, Mikhael HW, et al. The use of MR imaging in treatment planning for patients with rectal carcinoma: have you checked the "DISTANCE"[J]? Radiology. 2013, 268(2):330–44. Moreno CC, Sullivan PS, Mittal PK. MRI Evaluation of Rectal Cancer: Staging and Restaging[J]. Curr Probl Diagn Radiol. 2017, 46(3):234–241. Xu H, Zhao W, Guo W, et al. Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer[J]. J Magn Reson Imaging. 2021, 53(3):874–883. Liu Y, Wan L, Peng W, et al. A magnetic resonance imaging (MRI)-based nomogram for predicting lymph node metastasis in rectal cancer: a node-for-node comparative study of MRI and histopathology[J]. Quant Imaging Med Surg. 2021, 11(6):2586–2597. Sumii A, Hida K, Sakai Y, et al. Establishment and validation of a nomogram for predicting potential lateral pelvic lymph node metastasis in low rectal cancer[J]. Int J Clin Oncol. 2022, 27(7):1173–1179. Abe T, Yasui M, Imamura H, et al. Combination of extramural venous invasion and lateral lymph node size detected with magnetic resonance imaging is a reliable biomarker for lateral lymph node metastasis in patients with rectal cancer[J]. World J Surg Oncol. 2022, 20(1):5. Liu L, Liu M, Yang Z, et al. Correlation of MRI-detected extramural vascular invasion with regional lymph node metastasis in rectal cancer[J]. Clin Imaging. 2016, 40(3):456–60. Lin Y, You Z, Lin Z, et al. Association of clinicopathological factor with lymph node metastasis in rectal cancer patients: a retrospective cohort study[J]. BMC Gastroenterol. 2025, 25(1):358. Zhao W, Xu H, Zhao R, et al. MRI-based Radiomics Model for Preoperative Prediction of Lateral Pelvic Lymph Node Metastasis in Locally Advanced Rectal Cancer[J]. Acad Radiol. 2024, 31(7):2753–2772. Zeng DX, Yang Z, Tan L, et al. Risk factors for lateral pelvic lymph node metastasis in patients with lower rectal cancer: a systematic review and meta-analysis[J]. Front Oncol. 2023,13:1219608. Xu M, Wang Z, Qiao XF, et al. A nomogram model for predicting lymph node metastasis of rectal cancer by combining preoperative magnetic resonance imaging signs and tumour markers[J]. Pol J Radiol. 2025, 90:e114-e123. Zhou C, Liu HS, Liu XH, et al. Preoperative assessment of lymph node metastasis in clinically node-negative rectal cancer patients based on a nomogram consisting of five clinical factors[J]. Ann Transl Med. 2019, 7(20):543. Ma S, Lu H, Jing G, et al . Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study[J]. Front Med (Lausanne). 2023, 10:1276672. Wang Q, Zhu FX, Shi M. Clinical and pathological features of advanced rectal cancer with submesenteric root lymph node metastasis: Meta-analysis[J]. World J Gastrointest Oncol. 2024, 16(7):3299–3307. Zeng DX, Yang Z, Tan L, et al. Risk factors for lateral pelvic lymph node metastasis in patients with lower rectal cancer: a systematic review and meta-analysis[J]. Front Oncol. 2023, 13:1219608. Niu Y, Yu S, Chen P, et al. Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer[J]. Abdom Radiol (NY). 2025, 50(1):38–48. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7015877","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482142412,"identity":"fb916603-92c6-4e8f-a1e6-7b9342c1fff0","order_by":0,"name":"Lu Chen","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Chen","suffix":""},{"id":482142414,"identity":"4c45181c-ee7b-445d-b153-e61096483046","order_by":1,"name":"Zhao Yang","email":"","orcid":"","institution":"Bengbu First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Yang","suffix":""},{"id":482142415,"identity":"32d04139-bdd6-474b-bd4f-9996ff06944e","order_by":2,"name":"Qi lin Niu","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"lin","lastName":"Niu","suffix":""},{"id":482142416,"identity":"444479e9-fca1-4f37-83df-d4b090e5528d","order_by":3,"name":"Zhi zhen Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYJACZgYDhgQgdfDBhwoJOXkStLAlG844Y2Fs2ECUFgaQFh4zYd62ikSGAwSUy7sfPvy5oKAuj1+6wYyZd55EAmMD88NHN/BoMTyTlmA8w4CtWHLOgbSHc7dJ5LEzsBkb5+DT0pBjkMxjwJO44UbCcYO32ySKGRt42KTxaul/Y3CYx0Aicf+NxDYJ3jkSiQ0HCGiRl8gxbOYxMEjcIJHMJsnbQIQWA4lnycw8BgmJM26kMRvOOCZhbNhMwC/y/cmHP/P8qUvsn5H/8cGHmjo5efbmh4/x2nIAQ4gZj3KwLQ0EFIyCUTAKRsEoYAAA37RLCyuSIuEAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhi","middleName":"zhen","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-07-01 04:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7015877/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7015877/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86389138,"identity":"c2aac7d8-e0de-4c44-abd8-eafb7deff973","added_by":"auto","created_at":"2025-07-10 06:27:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130394,"visible":true,"origin":"","legend":"\u003cp\u003eA and B show T2WI images in the transverse and sagittal planes, respectively. C and D show the enhanced images. The CRM is positive and LVI is visible around the lesion. CEA level is 13.03 ng/mL and CA199 level is 126.35 U/mL. Postoperative pathology confirmed the presence of LNM.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/5b2f2e1932488582d6b05a5d.jpg"},{"id":86386857,"identity":"8d933f85-e90e-4894-9aed-ccfe75d5cd80","added_by":"auto","created_at":"2025-07-10 06:03:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110136,"visible":true,"origin":"","legend":"\u003cp\u003eImages A and B show axial and sagittal T2WI, respectively. C and D are axial and sagittal post-contrast images, respectively. The CRM is negative for lymphovascular and peripheral nerve invasion. CEA is 1.27 ng/mL and CA199 is 3.3 U/mL. Postoperative pathology confirmed the absence of LNM.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/53b8f041cc09a5b6263f9ffd.jpg"},{"id":86386852,"identity":"cfb2a084-8b3d-4acd-90c5-b6f08428fd31","added_by":"auto","created_at":"2025-07-10 06:03:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram of the prediction model.\u003c/strong\u003e Each risk factor is assigned a corresponding \"points\" value on the designated axis. The \"total points,\" derived from the sum of the points for each factor, aligns with the \"RISK\" scale to estimate the probability of LNM.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/a16b66575dc5c21001de3631.jpg"},{"id":86386860,"identity":"be05ab72-b1bd-4b57-9d00-9bb6b2913f2b","added_by":"auto","created_at":"2025-07-10 06:03:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the predictive model.\u003c/strong\u003e\u003cbr\u003e\nThe nomogram achieved an AUC of 0.850 (95% CI: 0.791–0.909), indicating excellent discriminative ability.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/5ba3062a68d7e455bdfdba96.jpg"},{"id":86387863,"identity":"987a50e4-b5b4-4cbd-a758-9ab27c02993d","added_by":"auto","created_at":"2025-07-10 06:11:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the nomogram\u003c/strong\u003e. This graphical representation shows the agreement between the predicted and observed probabilities of LNM.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/39dff864e11f436a015488a7.jpg"},{"id":86386861,"identity":"8b985d93-f924-42c3-b809-9ab6bdce6d91","added_by":"auto","created_at":"2025-07-10 06:03:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis of the nomogram.\u003c/strong\u003e The gray line represents the scenario in which all patients are assumed to have LNM, while the red line represents the hypothesis in which none of the patients have metastasis. The X-axis indicates the threshold probability and the Y-axis shows the net benefit.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/783f07e20d6ca2a8dc51632d.jpg"},{"id":93712429,"identity":"9201be43-7da1-4e82-865f-022c07b47eb2","added_by":"auto","created_at":"2025-10-16 18:31:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1181233,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7015877/v1/e0239e9a-1d55-4047-ac1c-3cb0c7b6f927.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram Model Based on MRI and Clinical Characteristics for Predicting Lymph Node Metastasis in Rectal Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the 2022 Global Cancer Statistics Report, over 1.9\u0026nbsp;million new cases of colorectal cancer (CRC) were diagnosed, resulting in 904,000 CRC-related deaths worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In China specifically, 510,000 new CRC cases were reported, with CRC ranking second and fourth in terms of incidence and mortality, respectively, among all malignant tumors nationally\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Rectal cancer (RC) accounts for more than one-third of all CRC cases. The presence of lymph node metastasis (LNM) in RC patients is strongly associated with metastatic disease and a poor clinical prognosis. Total mesorectal excision (TME) is currently the standard surgical approach for RC, and lymph node dissection is required for patients exhibiting LNM. Studies indicate that patients with LNM have a 5-year survival rate of 50%-68%, and are at a significantly higher risks of local recurrence than patients without LNM \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, accurate preoperative identification of LNM is critical for guiding treatment decisions and predicting survival outcomes.\u003c/p\u003e\u003cp\u003e Recognised for its superior soft tissue resolution, magnetic resonance imaging (MRI) is recommended as the primary preoperative imaging modality for RC in China's CRC diagnosis and treatment guidelines. Although MRI demonstrates high diagnostic accuracy (80%-85%) for preoperative T-staging\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, its capability to determine nodal status is limited. The current diagnosis of metastatic lymph nodes using MRI relies on morphological criteria (e.g., size, border characteristics and internal signal patterns)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, reactive lymph node hyperplasia can mimic structural changes, making it difficult to distinguish between metastatic and non-metastatic nodes based solely on signal intensity variations.\u003c/p\u003e\u003cp\u003eThese limitations emphasise the need for more accurate methods of predicting LNM. Recent research suggests that clinical characteristics may be significant predictors of LNM\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Integrating these features with MRI findings could enable a more comprehensive assessment of patient and inform personalized treatment strategies. This study aims to develop a predictive model combining MRI features and clinical risk factors to improve the accuracy of metastatic lymph node diagnosis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e This retrospective study was approved by the Institutional Review Board. Patient approval or informed consent was not required to review medical records or images. We also complied with the Declaration of Helsinki with regard to the ethical conduct of research involving human subjects.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA retrospective analysis was conducted on cases of RC that met the following criteria. The inclusion criteria were: (1) no preoperative radiotherapy or chemotherapy; (2) radical surgery performed one to two weeks after an MRI scan; and (3) complete clinical data and corresponding test results available. The exclusion criteria were: (1) lesions could not be visualized on MRI scans; and (2) severe image artifacts that compromised lymph node assessment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMRI examinations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMRI examinations were performed using a Philips Achieva 3.0T dual-gradient superconducting system (Netherlands) with an abdominal phased-array coil. Patients fasted for four hours prior to scanning and received a glycerol enema for bowel preparation 30 minutes before imaging. The scanning parameters were as follows: (1) axial T2WI: TR\u0026thinsp;=\u0026thinsp;3000 ms, TE\u0026thinsp;=\u0026thinsp;80 ms, FOV 260\u0026times;260 mm, matrix 256\u0026times;320, slice thickness 5 mm; (2) Diffusion-weighted imaging (DWI): TR 3500 ms, TE 55 ms, matrix 256\u0026times;256, FOV 289\u0026times;289 mm, slice thickness 5 mm; (3) Contrast-enhanced axial T1WI: TR 600 ms, TE 29 ms, matrix 640\u0026times;432, FOV 225\u0026times;225 mm, slice thickness 5mm. Gadopentetate dimeglumine was administered intravenously at 2.0 mL/s (0.1mmol/kg), followed by a saline flush. Dynamic phase imaging was performed at 25 s (arterial), 55 s (venous) and 180 s (delayed).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage interpretation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll MR images were independently reviewed by two radiologists with 10 and 20 years\u0026rsquo; experience in abdominal imaging, respectively. They evaluated tumor location, depth of invasion (MR T stage), LNM (MR N stage), lymphovascular invasion (LVI), perineural invasion (PNI) and circumferential resection margin (CRM), in accordance with the 2018 Chinese Society of Clinical Oncology colorectal cancer guidelines. Each radiologist was blinded to the pathology results. Any discrepancies were adjudicated by a third gastrointestinal radiologist with 30 years' experience. MRI images of RC are shown in \u003cb\u003eFigs.\u0026nbsp;1 and 2\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS version 26.0 software (SPSS 26.0, IBM, Chicago, USA). Quantitative data are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and qualitative data as the number of cases and percentage. Where appropriate, Fisher\u0026rsquo;s exact tests and chi-squared tests were used to compare single factors between the LNM and non-metastatic categories. Univariate and multivariate logistic regression analyses were performed to analyze independent risk factors. A p-value of less than 0.05 was considered to indicate a statistically significant difference. A nomogram was created to predict the likelihood of LNM in patients based on the predictors included in the final multivariate model. Receiver operating characteristic (ROC) curves were plotted to evaluate and compare the model's discriminatory ability using the area under the ROC curve (AUC) and 95% confidence interval (CI). The calibration curve and Hosmer\u0026ndash;Lemeshow goodness-of-fit test were used to assess the calibration of the model. A decision curve analysis (DCA) was performed to demonstrate the clinical utility of the prediction model by quantifying the net benefit across a range of threshold probabilities.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eOverall clinical Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study enrolled 170 RC patients, comprising 96 cases without LNM (mean age 65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9 years) and 74 cases with LNM (mean age 62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 years). All patients underwent preoperative rectal MRI examinations and had postoperative pathological confirmation of rectal adenocarcinoma. The clinical characteristics of the patients are detailed 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\u003eClinical and MRI characteristics of patients with RC\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-LNM(n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLNM(n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65(67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48(64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26(35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\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\u003e65.448\u0026thinsp;\u0026plusmn;\u0026thinsp;11.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.759\u0026thinsp;\u0026plusmn;\u0026thinsp;10.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMR T stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1-T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11(11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1(1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3-T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85(88.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73(98.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMR N stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15(15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42(56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83(86.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55(74.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19(25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27(28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69(71.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64(86.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30(31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22(29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43(58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6(6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9(12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between LNM and clinical/MRI characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnivariate binary logistic regression analysis revealed statistically significant associations between LNM, MR T stage, CEA, CA199, LVI, PNI and CRM status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By contrast, gender, age, MR N stage and tumor location showed no statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.697, 0.246, 0.609 and 0.403, respectively). Multivariate logistic regression analysis identified three independent risk factors for LNM: CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL (OR: 6.179; 95% CI: 2.660\u0026ndash;14.352), LVI positivity (OR: 11.277; 95% CI: 4.620\u0026ndash;27.524), and CRM positivity (OR: 2.860; 95% CI: 1.010\u0026ndash;8.098), all with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eLogistic regression analysis of imaging factors for LNM in RC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMR T stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1-T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3-T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1.191\u0026ndash;74.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(0.269\u0026ndash;25.611)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5(ng/ mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;5(ng/ mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(2.404\u0026ndash;9.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(2.660-14.352)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;35(U/ mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;35(U/ mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1.441\u0026ndash;8.676)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(0.672\u0026ndash;6.176)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.423\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(3.458\u0026ndash;14.527)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(4.620-27.524)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1.008\u0026ndash;4.828)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(0.840\u0026ndash;5.636)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1.124\u0026ndash;5.581)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(1.010\u0026ndash;8.098)\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\u003cb\u003eModel construction and evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA nomogram incorporating three independent predictors: LVI, CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL and CRM positivity, was constructed to predict LNM risk (Fig.\u0026nbsp;3). The model's ability to discriminate was evaluated using a RUC curve, which yielded an area under the curve (AUC) of 0.850 (95% CI: 0.791\u0026ndash;0.909) (Fig.\u0026nbsp;4). Calibration performance was assessed using calibration curves and the Hosmer-Lemeshow test (χ\u0026sup2; = 5.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.684), which demonstrated optimal agreement between the predicted and observed LNM probabilities (Fig.\u0026nbsp;5). Decision curve analysis quantified the clinical utility by calculating net benefits across threshold probabilities (Fig.\u0026nbsp;6), confirming the model's robust clinical applicability.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecent advances in multimodal therapeutic interventions have significantly improved the prognosis of patients with RC. Accurate lymph node staging is essential for making treatment decisions and predicting outcomes. According to the National Comprehensive Cancer Network (NCCN) guidelines, RC patients with T1\u0026ndash;2N0M0 should undergo endoscopic mucosal resection or TME. However, those with suspected LNM require neoadjuvant chemoradiotherapy. Consequently, precise preoperative nodal staging is of substantial clinical significance, since lymph node enlargement may be caused by inflammation or reactive hyperplasia, while smaller nodes may harbor metastatic tumor cells. Therefore, size alone is inadequate for determining nodal status\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Our study developed a predictive model to identify LNM in RC patients before surgery. This model integrates MRI features and clinical risk factors. We constructed a hybrid clinical-radiomics nomogram to visualize the model and provide clinical utility. This study integrated imaging and clinical features to analyze LNM (LNM).\u003c/p\u003e\u003cp\u003eUnivariate logistic regression identified the following statistically significant associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05): MR T stage, CEA, CA199, LVI, PNI and CRM status. These findings are consistent with the report by Sumii et al. that CEA, CRM involvement and PNI are risk factors for occult LNM\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The results of the multivariate logistic regression analysis identified CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL, CRM positivity and LVI positivity as independent risk factors for LNM in RC. LVI, which is defined histologically as tumor cell clusters or individual cells within or disrupting endothelial-lined spaces, facilitates metastatic dissemination via lymphatic or venous channels. This represents a critical step in early LNM\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. There is substantial evidence linking LVI to poor prognosis and post-endoscopic recurrence, as well as its role as a stage-independent prognostic determinant\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRadiologically, CRM positivity is defined as the presence of tumour, metastatic lymph nodes, or tumour deposits within 5 mm of the mesorectal fascia on MRI (pathologically equivalent to 1 mm or less). Wei et al. demonstrated that CRM status and CEA levels were significantly associated with LNM in patients with locally advanced cancer in univariate logistic regression analysis. However, multivariate logistic regression analysis revealed that neither CRM nor CEA levels were independent risk factors for LNM\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. While these findings suggest a lack of independence, a concurrent meta-analysis identified CEA levels greater than 5 ng/mL, LVI and CRM positivity as significant risk factors for LNM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. These findings are consistent with those of the present study, which identified CRM status as an independent risk factor for LNM. CRM positivity was found to confer an adjusted risk probability of 2.860 (95% CI: 1.010\u0026ndash;8.098).\u003c/p\u003e\u003cp\u003eElevated CEA levels have been shown to significantly correlate with the risk of LNM. Hu et al. established that preoperative elevation of tumor markers is an independent predictor of LNM\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, with CEA and CA199 being the most clinically relevant biomarkers in CRC. A study of 130 CRC patients confirmed their association with nodal involvement\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Our results specifically implicate CEA elevation, which is consistent with the identification of CEA as an independent risk factor for LNM by Ma et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, reflecting advanced tumour stage and metastatic propensity. A recent meta-analysis has shown that CEA is an independent risk factor for LNM\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Although Niu et al. demonstrated that combining CEA with imaging features enhances pathological LNM prediction \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, their model omitted the integration of CRM and LVI. Our combined clinical-imaging model demonstrated superior discriminative performance (AUC: 0.850; 95% CI: 0.791\u0026ndash;0.909) and was operationalized through a clinically applicable nomogram.\u003c/p\u003e\u003cp\u003eThis visualization tool synthesises multivariate risk predictors, significantly strengthening the predictive capacity of LNM. However, limitations include the retrospective design, the inability to correlate individual MRI-detected lymph nodes pathologically, the limited sample size, and the absence of quantitative parameters from dynamic contrast-enhanced MRI.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCEA levels greater than 5 ng/mL, LVI and CRM positivity are all independent risk factors for LNM in RC. Using these factors together improves the accuracy of predicting preoperative LNM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLu Chen and Zhi-zhen Gao wrote the main manuscript text. Zhao Yang and Qi-lin Niu prepared figures. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin. 2024 May-Jun;74(3):229\u0026ndash;263.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJi YT, Liu SW, Zhang YM, et al. [Comparison of the latest cancer statistics, cancer epidemic trends and determinants between China and the United States]. Zhonghua Zhong Liu Za Zhi. 2024;46(7):646\u0026ndash;656. Chinese.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIshihara S, Kawai K, Tanaka T, et al. Oncological Outcomes of Lateral Pelvic Lymph Node Metastasis in Rectal Cancer Treated With Preoperative Chemoradiotherapy[J]. Dis Colon Rectum. 2017, 60(5):469\u0026ndash;476.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStijns RCH, de Graaf EJR, Punt CJA, et al. Long-term Oncological and Functional Outcomes of Chemoradiotherapy Followed by Organ-Sparing Transanal Endoscopic Microsurgery for Distal Rectal Cancer: The CARTS Study[J]. JAMA Surg. 2019, 154(1):47\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNougaret S, Reinhold C, Mikhael HW, et al. The use of MR imaging in treatment planning for patients with rectal carcinoma: have you checked the \"DISTANCE\"[J]? Radiology. 2013, 268(2):330\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoreno CC, Sullivan PS, Mittal PK. MRI Evaluation of Rectal Cancer: Staging and Restaging[J]. Curr Probl Diagn Radiol. 2017, 46(3):234\u0026ndash;241.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu H, Zhao W, Guo W, et al. Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer[J]. J Magn Reson Imaging. 2021, 53(3):874\u0026ndash;883.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Wan L, Peng W, et al. A magnetic resonance imaging (MRI)-based nomogram for predicting lymph node metastasis in rectal cancer: a node-for-node comparative study of MRI and histopathology[J]. Quant Imaging Med Surg. 2021, 11(6):2586\u0026ndash;2597.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSumii A, Hida K, Sakai Y, et al. Establishment and validation of a nomogram for predicting potential lateral pelvic lymph node metastasis in low rectal cancer[J]. Int J Clin Oncol. 2022, 27(7):1173\u0026ndash;1179.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbe T, Yasui M, Imamura H, et al. Combination of extramural venous invasion and lateral lymph node size detected with magnetic resonance imaging is a reliable biomarker for lateral lymph node metastasis in patients with rectal cancer[J]. World J Surg Oncol. 2022, 20(1):5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu L, Liu M, Yang Z, et al. Correlation of MRI-detected extramural vascular invasion with regional lymph node metastasis in rectal cancer[J]. Clin Imaging. 2016, 40(3):456\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Y, You Z, Lin Z, et al. Association of clinicopathological factor with lymph node metastasis in rectal cancer patients: a retrospective cohort study[J]. BMC Gastroenterol. 2025, 25(1):358.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao W, Xu H, Zhao R, et al. MRI-based Radiomics Model for Preoperative Prediction of Lateral Pelvic Lymph Node Metastasis in Locally Advanced Rectal Cancer[J]. Acad Radiol. 2024, 31(7):2753\u0026ndash;2772.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng DX, Yang Z, Tan L, et al. Risk factors for lateral pelvic lymph node metastasis in patients with lower rectal cancer: a systematic review and meta-analysis[J]. Front Oncol. 2023,13:1219608.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu M, Wang Z, Qiao XF, et al. A nomogram model for predicting lymph node metastasis of rectal cancer by combining preoperative magnetic resonance imaging signs and tumour markers[J]. Pol J Radiol. 2025, 90:e114-e123.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou C, Liu HS, Liu XH, et al. Preoperative assessment of lymph node metastasis in clinically node-negative rectal cancer patients based on a nomogram consisting of five clinical factors[J]. Ann Transl Med. 2019, 7(20):543.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa S, Lu H, Jing G, \u003cem\u003eet al\u003c/em\u003e. Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study[J]. Front Med (Lausanne). 2023, 10:1276672.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, Zhu FX, Shi M. Clinical and pathological features of advanced rectal cancer with submesenteric root lymph node metastasis: Meta-analysis[J]. World J Gastrointest Oncol. 2024, 16(7):3299\u0026ndash;3307.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng DX, Yang Z, Tan L, et al. Risk factors for lateral pelvic lymph node metastasis in patients with lower rectal cancer: a systematic review and meta-analysis[J]. Front Oncol. 2023, 13:1219608.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu Y, Yu S, Chen P, et al. Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer[J]. Abdom Radiol (NY). 2025, 50(1):38\u0026ndash;48.\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":"rectal cancer, magnetic resonance imaging, lymph node metastasis, lymphovascular invasion, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7015877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7015877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the diagnostic value of a predictive model that combines MRI features and clinical features for detecting lymph node metastasis (LNM) in patients with rectal cancer (RC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA retrospective analysis was conducted on 170 patients who had been pathologically diagnosed with RC by a pathologist. Of these, 74 were in the LNM group and 96 were in the non-metastatic group. The relationship between LNM and clinical and MRI features was analyzed using univariate and multivariate binary logistic regression. Based on these results, a clinical prediction model was constructed, and its diagnostic effectiveness was analyzed using a receiver operating characteristic (ROC) curve.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnivariate binary logistic regression analysis showed statistically significant differences in MR T-stage, CEA, CA199, lymphovascular invasion (LVI), perineural invasion (PNI) and circumferential resection margin (CRM) between the LNM group and the non-metastasis group. Multivariate regression analysis revealed that CEA levels greater than 5 ng/mL, positive LVI and positive CRM were independent risk factors for LNM in RC. ROC curve analysis revealed an area under the curve (AUC) of 0.850 (95% CI: 0.791\u0026ndash;0.909) for the model assessing LNM in RC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe clinical prediction model, which was constructed using a combination of MRI and clinical features (including CEA, LVI and CRM), has high diagnostic value.\u003c/p\u003e","manuscriptTitle":"Nomogram Model Based on MRI and Clinical Characteristics for Predicting Lymph Node Metastasis in Rectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 06:03:48","doi":"10.21203/rs.3.rs-7015877/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":"c71bd698-ba91-4c90-b4d6-5dc08656640a","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T18:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 06:03:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7015877","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7015877","identity":"rs-7015877","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}