Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study

Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study | 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 Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study Jia He, Xianzheng Tan, Huashan Lin, Jing Fang, Diejuan Liu, Jiabei Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7634936/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2026 Read the published version in Cancer Imaging → Version 1 posted 7 You are reading this latest preprint version Abstract Objectives To develop and validate a magnetic resonance imaging (MRI)-based radiomics model of the mesorectum for predicting extramural venous invasion (EMVI) in patients with rectal cancer (RC). Methods A retrospective study included 238 patients with RC from two hospitals between May 2020 and March 2023. Patients were divided into a training set (n = 114, from institution 1), an internal validation set (n = 48, from institution 1), and an external validation set (n = 76, from institution 2). A total of 963 radiomics features were extracted from the mesorectum region using T2-weighted imaging (T2WI). The radiomics model was developed using the methods of the minimum redundancy of the maximum relevance (mRMR) and the least absolute shrinkage (LASSO) regression. After univariate and multivariate logistic analysis, a clinical model was constructed based on clinical characteristics. A combined model was built and demonstrated as a nomogram. These models were evaluated by discrimination, calibration, and clinical application. Results Among 238 patients, 98 (41.1%) were EMVI-positive. The Area Under the Curve (AUC) values for the clinical, radiomics, and combined models, respectively, were 0.65, 0.85, and 0.88 for the training set (95% CI: 0.81–0.94); 0.60, 0.81, and 0.81 for the internal validation set (95% CI: 0.68–0.95); and 0.60, 0.78, and 0.82 for the external validation set (95% CI: 0.72–0.91). Conclusion This study presents a model for predicting the EMVI status in patients with rectal cancer. The combined model, which incorporates both a mesorectal radiomics signature from T2WI and the clinical predictor of serum neutrophil count, demonstrated superior discrimination, calibration, and clinical utility compared to models based on either clinical or radiomics features alone. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making. Rectal cancer Artificial intelligence Extramural venous invasion Mesorectum Magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Point A combined model was developed for preoperative EMVI prediction using radiomics scores from T2W features and clinical information. The combined model demonstrated superior diagnostic performance compared to either the radiomics model or clinical model alone for predicting EMVI in patients with RC. 1. Introduction Rectal cancer (RC) ranks among the most prevalent malignancies of the digestive system, with an increasing incidence in younger demographic of patients 1 . Among its various biological characteristics, extramural venous invasion (EMVI) is recognized as a significant prognostic indicator and a key driver of local recurrence 2 3 . Historically, EMVI was defined by Talbot et al. 4 as malignant cells within endothelium-lined vessels extending beyond the muscularis propria. In 2003, MRI-detected extramural vascular invasion (mrEMVI) was defined by Brown et al. 5 as “serpentine extension of tumor signal within a vascular structure.” Subsequently, Smith et al. 6 developed a standardized scoring system for assessing mrEMVI status, which was further popularized by the MERCURY study group as a reproducible and prognostically validated method for risk stratification in rectal cancer 7 . Typically, the gold standard for EMVI assessment is post-surgical pathology examination, which cannot help preoperative treatment decisions for patients. However, identifying EMVI status in smaller veins (< 3 mm) remains challenging due to standard MRI spatial resolution limitations. In recent years, radiomics has emerged as a non-invasive, convenient tool for extracting thousands of high-throughput quantitative features from digital images, offering a new dimension in personalized treatment 8 9 . Previous studies 10 – 12 in RC have mostly focused on the intratumoral region, which would provide important but easily overlooked information on EMVI. Recently, many radiomics features resulting from the peritumoral area have also shown a good predictive power in response assessment in other carcinomas. For instance, Sun et al. 13 and Hu et al. 14 built radiomics models based on intratumoral and peritumoral radiomics, which showed a dependable ang high predictive ability of assessing the response to neoadjuvant chemoradiation in patients with cervical cancer and esophageal squamous cell carcinoma. To our knowledge, there are no researches on the assessment of the status of EMVI by using radiomics of the entire mesorectum in patients with RC. In this study, we aim to construct and validate an MRI-based radiomics model of the mesorectum in a bi-centric database to predict the status of EMVI in patients with RC. 2. Material and methods 2.1. Patients This retrospective study enrolled consecutive patients with rectal cancer from Hunan Provincial People’s Hospital (institution 1) between May 2020 and October 2022 and Hunan Cancer Hospital (institution 2) between May 2022 and March 2023. Patients from institution 1 were used as the training and internal validation set, and those from institution 2 were used as the external validation set. The inclusion criteria were as follows: (a) histopathological confirmed rectal cancer; (b) pelvic MRI examination performed within 2 months prior to surgery; (c) complete clinical data. The exclusion criteria were: (a) incomplete clinical data; (b) poor MRI quality prohibiting accurate mesorectal fascia segmentation; (c) any prior chemotherapy or other treatments before surgery. The details and baseline characteristics of the study are summarized in Table 1. Additionally, the process of patient selection flowchart was shown in Fig. 1 . 2.2. Clinical characteristics and pathology information Clinical characteristics, including sex, age, and preoperative laboratory test results such as carcinoembryonic antigen (CEA) level, carbohydrate antigen 19 − 9 (CA19-9) level, and neutrophil count, were collected from the hospital information system (HIS) database. The gold standard for EMVI status was determined by postoperative histopathological evaluation. All surgically resected specimens were evaluated by pathologists who defined EMVI as a rounded mass of tumor tissue within an endothelium-lined space, which contained red blood cells or was surrounded by a rim of smooth muscle. In addition, a radiologist with 10 years of experience, who was blinded to the histopathologic and clinical information, retrospectively assessed the mrEMVI status on T2-weighted images using Smith's 5-point scoring system. Pathological information also included lymph node metastasis and tumor stage according to the 8th edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) Staging System. 2.3.MRI Protocol MRI were performed with 4 MRI scanners from two institutions. For institution 1, patients were examined with 3.0-T (Magnetom Tim Trio, Siemens Healthineers), 1.5-T (Achieva, Philips Healthcare), and 3.0-T MRI systems (Ingenia, Philips Healthcare). For institution 2, patients were scanned on a 3.0-T MRI scanner (Discovery 750W®, GE Healthcare, Waukesha, WI). The axial T2-weighted imaging (T2WI) (perpendicular to the long axis of the rectum): TR/TE, 2700–5500/80–120; field of view (FOV), 180 × 180 to 240 × 240; matrix size, 320 × 320 to 580 × 365; slice thickness, 3 mm without an interslice gap. For hospital 2, the axial T2WI: TR/TE 3800–5500/80–120; FOV, 180 × 180 to 240 × 240; matrix size, 320 × 320; slice thickness, 3 mm without an interslice gap. 2.4.MRI Radiomic Analysis 2.4.1 Image segmentation. The axial T2WI was derived from the picture archiving and communication system (PACS). Two radiologists (with 3 and 5 years of experience in abdominal radiological diagnosis, respectively) manually segmented the entire mesorectum on each slice using 3D Slicer version 5.0.3 ( www.slicer.org ) , an open-source and freely available software platform. The international consensus definition of the rectum is the “sigmoid take-off” which identifies the junction of the sigmoid mesocolon and the mesorectum, and therefore the sigmoid colon with the rectum 15 . The radiologists were blinded to histopathological results and clinical outcomes. Furthermore, to evaluate inter-observer reproducibility, both radiologists delineated the volume of interest (VOI) on a randomly selected subset of 30 patients from the training set. The intraclass correlation coefficient (ICC) was calculated for each feature. Features with an ICC greater than 0.75 were considered reproducible and retained for further analysis 16 . 2.4.2 Radiomic feature extraction. The radiomics features of the mesorectum based on T2WI were extracted using Pyradiomics ( https://pyradiomics.readthedocs.io/en/latest/ ). After the mesorectum of the patients with rectal cancer was reconstructed and segmented, VOI images were processed through SlicerRadiomics code for feature extraction. All radiomics features of the VOI images were extracted, including: (1) first-order statistics, (2) 3D shape-based features, (3) texture features, and (4) wavelet features. In total, 963 radiomics features were extracted from each patient's mesorectum region on T2WI. 2.4.3 Feature selection and model building Patients from Institution 1 were randomly divided into the training and internal validation set at a 7:3 ratio. A two-step feature selection process was employed. First, mRMR was performed to eliminate redundant features, reducing the feature set to the top 20. Second, LASSO regression with 10-fold cross-validation was used to select the most informative and predictive features from the remaining 20. The final radiomics signature was constructed based on these selected features and their corresponding LASSO coefficients. 3. Statistical Analyses Continuous variables were compared using the Mann-Whitney U test or t test, and the categorical variables were compared using the χ2 test or Fisher’s exact test. Univariate and multivariate logistic regression analyses were used to identify independent predictors of EMVI. The area under the receiver operating curve (AUC) values of different models were compared by using the DeLong test. Decision curve analysis (DCA) was used to evaluate the clinical utility. And calibration curves indicated the goodness-of-fit of the combined model. The optimal cutoff values of sensitivity, specificity and accuracy values were acquired by the Youden index. The interobserver agreement between the two radiologists was analyzed by calculating the intraclass correlation coefficient (ICC). All statistical analyses were performed with R software (version 4.0.4; http://www.r-project.org/ ). p < 0.05 was considered statistically significant. 4. Results 4.1 Patient Characteristics A total of 238 consecutive patients (mean age, 63 ± 10 years; 153 male) were included in the study. From institution 1, 162 patients were included and randomly divided into a training set (n = 114) and an internal validation set (n = 48). The external validation set from institution 2 consisted of 76 patients. Overall, 98 of 238 patients (41.2%) were pathologically confirmed to be EMVI-positive. Significant differences were observed in T stage, N stage, and mrEMVI score between the EMVI-positive and EMVI-negative groups across all three sets (P < 0.05). In the training set, univariable analysis revealed that only mrEMVI score (P < 0.001) and serum neutrophil count (P < 0.05) were significantly associated with EMVI status. These were included in the multivariate analysis, which confirmed them as independent predictors. Detailed patient characteristics are presented in Table 1. In the training set, univariable analysis of variables revealed mrEMVI (P < 0.05) and serum neutrophils (P < 0.05) were associated with higher odds for patients of EMVI-positive and were thus included in the multivariable analysis. However, there was no statistically significant difference in other variables (P = 0.07 to P = 0.75) between the EMVI-negative and EMVI-positive groups (Table 2). 4.2 Models Building and Performance Testing Following mRMR and LASSO regression analysis, seven predictive radiomics features were selected to construct the radiomics signature (Fig. 2 ). The diagnostic performance of the clinical, radiomics, and combined models is detailed in Table 3 and Fig. 3 . The clinical model, which included mrEMVI score and serum neutrophil count, yielded AUCs of 0.65, 0.60, and 0.60 in the training, internal validation, and external validation sets, respectively. In comparison, the radiomics model showed significantly better performance, with corresponding AUCs of 0.85, 0.81, and 0.78. The combined model, which integrated the radiomics signature with the two clinical predictors, demonstrated the highest discrimination capability, with an AUC of 0.88 (95% CI: 0.81, 0.94) in the training set, 0.81 (95% CI: 0.68, 0.95) in the internal validation set, and 0.82 (95% CI: 0.72, 0.91) in the external validation set (Fig. 3 ). The DeLong test showed that the combined and radiomics models were both significantly superior to the clinical model (P 0.05), although the combined model showed a consistently higher net benefit in DCA. A nomogram was developed based on the combined model to provide an individualized tool for predicting EMVI probability (Fig. 4 ). The calibration curves demonstrated good agreement between the nomogram's predictions and actual observations in all three sets (Fig. 5 A). DCA showed that the combined model provided a greater net benefit across a wide range of threshold probabilities compared to either the radiomics or clinical model alone, indicating superior clinical usefulness (Fig. 5 B). 4.3 Smith’s 5-point scoring system performance The diagnostic accuracy of the Smith's 5-point scoring system was evaluated against the pathological gold standard (Table 4 ). In terms of EMVI status, patients with score 1 had the highest accuracy (83%), and score 4 (46%) was associated with higher rates of misdiagnose than other scores. Notably, the scoring system demonstrated a tendency to overestimate EMVI status. 5. Discussion In this study, we developed and validated an MRI-based radiomics model using features from the mesorectum to predict EMVI status in patients with RC. Our main finding is that the combined model, which integrated the mesorectal radiomics signature with clinical risk factors, achieved the best predictive performance, with AUCs of 0.88, 0.81, and 0.82 in the training, internal validation, and external validation sets, respectively. A nomogram incorporating the radiomics signature and clinical predictors was also developed to provide a more precise method for predicting EMVI status. A key finding was that an elevated serum neutrophils count was an independent predictor of EMVI. This is consistent with previous research suggesting that neutrophils can promote tumor progression 17 . Neutrophil extracellular traps (NETs), released during a process called NETosis, can disrupt endothelial cell junctions, compromise vascular integrity, and subsequently mobilize tumor cells, potentially leading to venous thromboembolism. And nomogram was developed that incorporate the radiomic feature and the clinical signature, providing a more precise method for predicting the status of EMVI. Preoperative assessment of EMVI is crucial for clinical decision-making, as it is a known independent risk factor for poor survival and recurrence in RC. However, the traditional diagnosis relies on postoperative surgical specimens, which cannot guide preoperative therapy. Some previous studies have used different MRI sequences to predict EMVI status, Bae et al. 18 showed an inexperienced reviewer detected the status of EMVI on MRI using a 5-point grading system with poor performance (AUC of 0.658). Crimì et al. 19 detected EMVI in patients with rectal cancer using T2-weighted, diffusion weighted imaging (DWI) and contrast-enhanced sequences, DWI (AUC of 0.729) showed better performance for EMVI detection compared to contrast-enhanced (AUC of 0.624) and T2-weighted sequences (AUC of 0.610). In recent year, some researchers have found that radiomics provide a noninvasive and reproducible method to elicit undiscovered features from imaging data that are not accessible by radiologist perception or conventional image analysis 20 . Shu et al. 21 reported that the Bayes-based radiomics signature predicted EMVI with an AUC of 0.744. Yu et al. 22 also showed that the dynamic contrast-enhanced magnetic resonance imaging-based radiomics (AUC, 0.812) had good prediction of assessing EMVI. These studies 23 can assess the status of EMVI by MRI-based radiomics feature, to create a standardized definition and normalized reference for radiomics research, the image biomarker standardization initiative criteria and radiomics quality score were established 24 . However, these studies only focused on the associations between tumor imaging and radiomics features to predict EMVI. Recently, a few studies 25 indicated that the surrounding areas can provide additional information about tumor heterogeneity in other cancers such as esophageal squamous cell carcinoma and cervical cancer. Actually, the mesorectum is the place where EMVI occurs. Therefore, we proposed a widely available, noninvasive, and MRI-based radiomics model with a favorable predictive value using mesorectum radiomics features to predict the status of EMVI in patients with rectal cancer. The strong performance of our radiomics-only model (AUCs of 0.85, 0.81, and 0.78 across the three sets) supports this hypothesis. To our knowledge, this is the first study to investigate the association between mesorectal radiomic features and EMVI status. This study has several limitations. First, its retrospective design is susceptible to inherent selection bias. Second, correlating EMVI on a preoperative MRI with the exact location in the postoperative surgical specimen can be challenging. Third, the manual segmentation of the mesorectum was time-consuming and labor-intensive; fully automated segmentation using artificial intelligence is a goal for future work. Fourth, the data from multiple centers were acquired using different MRI scanners, which may influence radiomic features. Finally, the study was limited to a single external validation cohort due to data availability. In conclusion, this study establishes that a model combining clinical data with radiomic features from the mesorectum can accurately predict EMVI status in patients with RC. This noninvasive approach may serve as a promising imaging marker to enhance preoperative risk stratification and guide clinical practice. Further validation in larger, prospective studies is required to confirm its clinical utility. Conclusion In summary, this study presents a model for predicting the EMVI status in patients with rectal cancer. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making. Declarations Acknowledgements Not applicable. Funding This study has received funding by the Changsha Municipal Natural Science Fundation [grant numbers: kq2014201]. Authors and Affiliations Hunan Provincial People's Hospital, Changsha, China, Department of Radiology Jia He, Xianzheng Tan, Jing Fang, Diejuan Liu, Jiabei Liu, Peng Liu. Hunan Cancer Hospital, Changsha, China, Department of Radiolog Xiaoping Yu Hunan Women and Children's Hospital, Changsha, China, Department of Radiology Xiang Feng GE Healthcare, Hangzhou, China Huashan Lin Authors' contributions Peng Liu and Xiaoping Yu conceived of and designed the study and polished the language of the manuscript; Jia He wrote the initial draft of the paper; Jia He, Xiang Feng, Jiabei Liu and Diejuan Liu collected and analysed the data; Jia He, Huashan Lin and Jing Fang conducted the statistical analysis; All authors approved of the final version before submission. Corresponding authors Correspondence to Xiaoping Yu ( [email protected] ) or Peng Liu ( [email protected] ). Ethical Approval and Consent to participate Ethics license was received from the Science Ethics Committee. This study is a retrospective study and informed consents were waived. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Keller DS, Berho M, Perez RO, et al. The multidisciplinary management of rectal cancer. Nat Rev Gastroenterol Hepatol. 2020;17(7):414–29. 10.1038/s41575-020-0275-y . [published Online First: 2020/03/24]. Zhang XY, Wang S, Li XT et al. MRI of Extramural Venous Invasion in Locally Advanced Rectal Cancer: Relationship to Tumor Recurrence and Overall Survival. Radiology 2018;289(3):677 – 85. 10.1148/radiol.2018172889 [published Online First: 2018/08/29]. Brown PJ, Hyland R, Quyn AJ, et al. Current concepts in imaging for local staging of advanced rectal cancer. Clin Radiol. 2019;74(8):623–36. 10.1016/j.crad.2019.03.023 . [published Online First: 2019/05/01]. Talbot IC, Ritchie S, Leighton MH, et al. The clinical significance of invasion of veins by rectal cancer. Br J Surg. 1980;67(6):439–42. 10.1002/bjs.1800670619 . [published Online First: 1980/06/01]. Brown G, Radcliffe AG, Newcombe RG, et al. Preoperative assessment of prognostic factors in rectal cancer using high-resolution magnetic resonance imaging. Br J Surg. 2003;90(3):355–64. 10.1002/bjs.4034 . [published Online First: 2003/02/21]. Smith NJ, Shihab O, Arnaout A, et al. MRI for detection of extramural vascular invasion in rectal cancer. AJR Am J Roentgenol. 2008;191(5):1517–22. 10.2214/ajr.08.1298 . [published Online First: 2008/10/23]. Battersby NJ, How P, Moran B, et al. Prospective Validation of a Low Rectal Cancer Magnetic Resonance Imaging Staging System and Development of a Local Recurrence Risk Stratification Model: The MERCURY II Study. Ann Surg. 2016;263(4):751–60. 10.1097/sla.0000000000001193 . [published Online First: 2015/03/31]. Gillies RJ, Kinahan PE, Hricak H, Radiomics. Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563–77. 10.1148/radiol.2015151169 . [published Online First: 2015/11/19]. Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev. 2022;42(1):426–40. 10.1002/med.21846 . [published Online First: 2021/07/27]. Shaish H, Aukerman A, Vanguri R, et al. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur Radiol. 2020;30(11):6263–73. 10.1007/s00330-020-06968-6 . [published Online First: 2020/06/06]. Shin J, Seo N, Baek SE, et al. MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy. Radiology. 2022;303(2):351–58. 10.1148/radiol.211986 . [published Online First: 2022/02/09]. Liu Z, Zhang XY, Shi YJ, et al. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017;23(23):7253–62. 10.1158/1078-0432.Ccr-17-1038 . [published Online First: 2017/09/25]. Sun C, Tian X, Liu Z, et al. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study. EBioMedicine. 2019;46:160–69. 10.1016/j.ebiom.2019.07.049 . [published Online First: 2019/08/10]. Hu Y, Xie C, Yang H, et al. Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma. JAMA Netw Open. 2020;3(9):e2015927. 10.1001/jamanetworkopen.2020.15927 . [published Online First: 2020/09/11]. DʼSouza N, de Neree Tot Babberich MPM, d'Hoore A, et al. Definition of the Rectum: An International, Expert-based Delphi Consensus. Ann Surg. 2019;270(6):955–59. 10.1097/sla.0000000000003251 . [published Online First: 2019/04/12]. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74. [published Online First: 1977/03/01]. Amulic B, Cazalet C, Hayes GL, et al. Neutrophil function: from mechanisms to disease. Annu Rev Immunol. 2012;30:459–89. 10.1146/annurev-immunol-020711-074942 . [published Online First: 2012/01/10]. Jiang ZZ, Peng ZP, Liu XC, et al. Neutrophil extracellular traps induce tumor metastasis through dual effects on cancer and endothelial cells. Oncoimmunology. 2022;11(1):2052418. 10.1080/2162402x.2022.2052418 . [published Online First: 2022/03/22]. Bae JS, Kim SH, Hur BY, et al. Prognostic value of MRI in assessing extramural venous invasion in rectal cancer: multi-readers' diagnostic performance. Eur Radiol. 2019;29(8):4379–88. 10.1007/s00330-018-5926-9 . [published Online First: 2019/01/09]. Crimì F, Angelone R, Corso A, et al. Diagnostic accuracy of state-of-the-art rectal MRI sequences for the diagnosis of extramural vascular invasion in locally advanced rectal cancer after preoperative chemoradiotherapy: dos or maybes? Eur Radiol. 2023;33(10):6852–60. 10.1007/s00330-023-09655-4 . [published Online First: 2023/04/28]. Aerts HJ. The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. JAMA Oncol. 2016;2(12):1636–42. 10.1001/jamaoncol.2016.2631 . [published Online First: 2016/08/20]. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. 10.1038/nrclinonc.2017.141 . [published Online First: 2017/10/05]. Shu Z, Mao D, Song Q, et al. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur Radiol. 2022;32(2):1002–13. 10.1007/s00330-021-08242-9 . [published Online First: 2021/09/06]. Yu X, Song W, Guo D, et al. Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Front Oncol. 2020;10:459. 10.3389/fonc.2020.00459 . [published Online First: 2020/04/25]. Zwanenburg A, Vallières M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328–38. 10.1148/radiol.2020191145 . [published Online First: 2020/03/11]. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2026 Read the published version in Cancer Imaging → Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 24 Sep, 2025 Submission checks completed at journal 23 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7634936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528294647,"identity":"5b9db120-8559-4a27-a598-b25093790b02","order_by":0,"name":"Jia He","email":"","orcid":"","institution":"Hunan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"He","suffix":""},{"id":528294648,"identity":"bc3fcf10-e399-4dc0-9f0a-c7ef4926b5b9","order_by":1,"name":"Xianzheng Tan","email":"","orcid":"","institution":"Hunan Provincial People's 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17:02:36","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78904,"visible":true,"origin":"","legend":"","description":"","filename":"a97de53acaa7453c9ffeca2f6d2ef6db1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/f7f48cd410491e00d62eb24c.xml"},{"id":93616861,"identity":"45fef70d-16f3-4815-88af-e43538f9c861","added_by":"auto","created_at":"2025-10-15 17:02:36","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91923,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/d20551c57e9b46ac2553d0c8.html"},{"id":93617688,"identity":"46cbde0b-f027-4104-a745-c8f23a2b6692","added_by":"auto","created_at":"2025-10-15 17:10:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135319,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart shows the patient selection from two hospitals in this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/7082a48519f1c9ffd296ebf3.png"},{"id":93617691,"identity":"4116e1ca-15da-41c5-b53e-68da2408e8b0","added_by":"auto","created_at":"2025-10-15 17:10:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269364,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of the construction and internal verification of a radiomics model.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/a642920b2c9326838968c409.png"},{"id":93618124,"identity":"d0557b46-f55a-4807-9a4b-4ac9c1cccb69","added_by":"auto","created_at":"2025-10-15 17:18:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141809,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of models for the ability to predict extramural venous invasion status. Areas under the receiver operating characteristic curve (AUCs) for the (A) training set (n = 114), (B) internal test set (n = 48), and (C) external test set (n = 76). AUCs are reported with 95% CI in parentheses.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/84c9439454bb33aea44076d8.png"},{"id":93616843,"identity":"ee47d0d3-dc9b-4fa2-9bf3-84912786af7b","added_by":"auto","created_at":"2025-10-15 17:02:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87592,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of the combined model for predicting extramural venous invasion status (EMVI) in patients with rectal cancer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/4e897e72eb0a65ed4bec0ac8.png"},{"id":93616865,"identity":"7d6e2ae1-8812-428e-be6d-f4553de29cf8","added_by":"auto","created_at":"2025-10-15 17:02:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102797,"visible":true,"origin":"","legend":"\u003cp\u003eA) The calibration curves of the training, internal test, and external test sets indicated the goodness-of-fit of the clinical-radiomic nomogram. The dotted line represents the performance of the nomogram in different sets and a closer fit to the diagonal solid line represents a better prediction. B) Decision curve analysis of these models to investigate the clinical usefulness in predicting EMVI. The blue line represents the decision curve for the radiomics model. The red line represents the clinical model and the green line represents the combined model. Using the combined model to predict the EMVI status added more benefit than using either the treat-all plan or the treat-none plan at any given threshold probability.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/a19e7b5cfab6f680bc9e6095.png"},{"id":107352479,"identity":"4767268d-2ce4-4122-b76f-d090c0906d21","added_by":"auto","created_at":"2026-04-20 16:14:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/0645ad97-2234-406d-b7b1-a65112825734.pdf"},{"id":93616838,"identity":"ee26e609-658c-42df-92d1-d11c22805697","added_by":"auto","created_at":"2025-10-15 17:02:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":457634,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7634936/v1/d827dd0bd7738b22090e8a66.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study","fulltext":[{"header":"Key Point","content":"\u003cul start=\"50\"\u003e\n \u003cli\u003eA combined model was developed for preoperative EMVI prediction using radiomics scores from T2W features and clinical information.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe combined model demonstrated superior diagnostic performance compared to either the radiomics model or clinical model alone for predicting EMVI in patients with RC.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eRectal cancer (RC) ranks among the most prevalent malignancies of the digestive system, with an increasing incidence in younger demographic of patients\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among its various biological characteristics, extramural venous invasion (EMVI) is recognized as a significant prognostic indicator and a key driver of local recurrence\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Historically, EMVI was defined by Talbot et al.\u003csup\u003e4\u003c/sup\u003e as malignant cells within endothelium-lined vessels extending beyond the muscularis propria. In 2003, MRI-detected extramural vascular invasion (mrEMVI) was defined by Brown et al.\u003csup\u003e5\u003c/sup\u003e as \u0026ldquo;serpentine extension of tumor signal within a vascular structure.\u0026rdquo; Subsequently, Smith et al.\u003csup\u003e6\u003c/sup\u003e developed a standardized scoring system for assessing mrEMVI status, which was further popularized by the MERCURY study group as a reproducible and prognostically validated method for risk stratification in rectal cancer\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTypically, the gold standard for EMVI assessment is post-surgical pathology examination, which cannot help preoperative treatment decisions for patients. However, identifying EMVI status in smaller veins (\u0026lt;\u0026thinsp;3 mm) remains challenging due to standard MRI spatial resolution limitations. In recent years, radiomics has emerged as a non-invasive, convenient tool for extracting thousands of high-throughput quantitative features from digital images, offering a new dimension in personalized treatment\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e in RC have mostly focused on the intratumoral region, which would provide important but easily overlooked information on EMVI. Recently, many radiomics features resulting from the peritumoral area have also shown a good predictive power in response assessment in other carcinomas. For instance, Sun et al.\u003csup\u003e13\u003c/sup\u003e and Hu et al. \u003csup\u003e14\u003c/sup\u003ebuilt radiomics models based on intratumoral and peritumoral radiomics, which showed a dependable ang high predictive ability of assessing the response to neoadjuvant chemoradiation in patients with cervical cancer and esophageal squamous cell carcinoma.\u003c/p\u003e\u003cp\u003eTo our knowledge, there are no researches on the assessment of the status of EMVI by using radiomics of the entire mesorectum in patients with RC. In this study, we aim to construct and validate an MRI-based radiomics model of the mesorectum in a bi-centric database to predict the status of EMVI in patients with RC.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Patients\u003c/h2\u003e\u003cp\u003eThis retrospective study enrolled consecutive patients with rectal cancer from Hunan Provincial People\u0026rsquo;s Hospital (institution 1) between May 2020 and October 2022 and Hunan Cancer Hospital (institution 2) between May 2022 and March 2023. Patients from institution 1 were used as the training and internal validation set, and those from institution 2 were used as the external validation set.\u003c/p\u003e\u003cp\u003eThe inclusion criteria were as follows: (a) histopathological confirmed rectal cancer; (b) pelvic MRI examination performed within 2 months prior to surgery; (c) complete clinical data. The exclusion criteria were: (a) incomplete clinical data; (b) poor MRI quality prohibiting accurate mesorectal fascia segmentation; (c) any prior chemotherapy or other treatments before surgery. The details and baseline characteristics of the study are summarized in Table\u0026nbsp;1. Additionally, the process of patient selection flowchart was shown in 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. Clinical characteristics and pathology information\u003c/h2\u003e\u003cp\u003eClinical characteristics, including sex, age, and preoperative laboratory test results such as carcinoembryonic antigen (CEA) level, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) level, and neutrophil count, were collected from the hospital information system (HIS) database.\u003c/p\u003e\u003cp\u003eThe gold standard for EMVI status was determined by postoperative histopathological evaluation. All surgically resected specimens were evaluated by pathologists who defined EMVI as a rounded mass of tumor tissue within an endothelium-lined space, which contained red blood cells or was surrounded by a rim of smooth muscle. In addition, a radiologist with 10 years of experience, who was blinded to the histopathologic and clinical information, retrospectively assessed the mrEMVI status on T2-weighted images using Smith's 5-point scoring system. Pathological information also included lymph node metastasis and tumor stage according to the 8th edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) Staging System.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3.MRI Protocol\u003c/h2\u003e\u003cp\u003eMRI were performed with 4 MRI scanners from two institutions. For institution 1, patients were examined with 3.0-T (Magnetom Tim Trio, Siemens Healthineers), 1.5-T (Achieva, Philips Healthcare), and 3.0-T MRI systems (Ingenia, Philips Healthcare). For institution 2, patients were scanned on a 3.0-T MRI scanner (Discovery 750W\u0026reg;, GE Healthcare, Waukesha, WI). The axial T2-weighted imaging (T2WI) (perpendicular to the long axis of the rectum): TR/TE, 2700\u0026ndash;5500/80\u0026ndash;120; field of view (FOV), 180 \u0026times; 180 to 240 \u0026times; 240; matrix size, 320 \u0026times; 320 to 580 \u0026times; 365; slice thickness, 3 mm without an interslice gap. For hospital 2, the axial T2WI: TR/TE 3800\u0026ndash;5500/80\u0026ndash;120; FOV, 180 \u0026times; 180 to 240 \u0026times; 240; matrix size, 320 \u0026times; 320; slice thickness, 3 mm without an interslice gap.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4.MRI Radiomic Analysis\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Image segmentation.\u003c/h2\u003e\u003cp\u003eThe axial T2WI was derived from the picture archiving and communication system (PACS). Two radiologists (with 3 and 5 years of experience in abdominal radiological diagnosis, respectively) manually segmented the entire mesorectum on each slice using 3D Slicer version 5.0.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.slicer.org\" target=\"_blank\"\u003ewww.slicer.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.slicer.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, an open-source and freely available software platform. The international consensus definition of the rectum is the \u0026ldquo;sigmoid take-off\u0026rdquo; which identifies the junction of the sigmoid mesocolon and the mesorectum, and therefore the sigmoid colon with the rectum\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The radiologists were blinded to histopathological results and clinical outcomes.\u003c/p\u003e\u003cp\u003eFurthermore, to evaluate inter-observer reproducibility, both radiologists delineated the volume of interest (VOI) on a randomly selected subset of 30 patients from the training set. The intraclass correlation coefficient (ICC) was calculated for each feature. Features with an ICC greater than 0.75 were considered reproducible and retained for further analysis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Radiomic feature extraction.\u003c/h2\u003e\u003cp\u003eThe radiomics features of the mesorectum based on T2WI were extracted using Pyradiomics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyradiomics.readthedocs.io/en/latest/\u003c/span\u003e\u003cspan address=\"https://pyradiomics.readthedocs.io/en/latest/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After the mesorectum of the patients with rectal cancer was reconstructed and segmented, VOI images were processed through SlicerRadiomics code for feature extraction. All radiomics features of the VOI images were extracted, including: (1) first-order statistics, (2) 3D shape-based features, (3) texture features, and (4) wavelet features. In total, 963 radiomics features were extracted from each patient's mesorectum region on T2WI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Feature selection and model building\u003c/h2\u003e\u003cp\u003ePatients from Institution 1 were randomly divided into the training and internal validation set at a 7:3 ratio. A two-step feature selection process was employed. First, mRMR was performed to eliminate redundant features, reducing the feature set to the top 20. Second, LASSO regression with 10-fold cross-validation was used to select the most informative and predictive features from the remaining 20. The final radiomics signature was constructed based on these selected features and their corresponding LASSO coefficients.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Statistical Analyses","content":"\u003cp\u003eContinuous variables were compared using the Mann-Whitney U test or \u003cem\u003et\u003c/em\u003e test, and the categorical variables were compared using the χ2 test or Fisher\u0026rsquo;s exact test. Univariate and multivariate logistic regression analyses were used to identify independent predictors of EMVI. The area under the receiver operating curve (AUC) values of different models were compared by using the DeLong test. Decision curve analysis (DCA) was used to evaluate the clinical utility. And calibration curves indicated the goodness-of-fit of the combined model. The optimal cutoff values of sensitivity, specificity and accuracy values were acquired by the Youden index. The interobserver agreement between the two radiologists was analyzed by calculating the intraclass correlation coefficient (ICC). All statistical analyses were performed with R software (version 4.0.4; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Patient Characteristics\u003c/h2\u003e\u003cp\u003eA total of 238 consecutive patients (mean age, 63\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years; 153 male) were included in the study. From institution 1, 162 patients were included and randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;114) and an internal validation set (n\u0026thinsp;=\u0026thinsp;48). The external validation set from institution 2 consisted of 76 patients. Overall, 98 of 238 patients (41.2%) were pathologically confirmed to be EMVI-positive. Significant differences were observed in T stage, N stage, and mrEMVI score between the EMVI-positive and EMVI-negative groups across all three sets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the training set, univariable analysis revealed that only mrEMVI score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and serum neutrophil count (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly associated with EMVI status. These were included in the multivariate analysis, which confirmed them as independent predictors. Detailed patient characteristics are presented in Table\u0026nbsp;1. In the training set, univariable analysis of variables revealed mrEMVI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and serum neutrophils (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were associated with higher odds for patients of EMVI-positive and were thus included in the multivariable analysis. However, there was no statistically significant difference in other variables (P\u0026thinsp;=\u0026thinsp;0.07 to P\u0026thinsp;=\u0026thinsp;0.75) between the EMVI-negative and EMVI-positive groups (Table\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Models Building and Performance Testing\u003c/h2\u003e\u003cp\u003eFollowing mRMR and LASSO regression analysis, seven predictive radiomics features were selected to construct the radiomics signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The diagnostic performance of the clinical, radiomics, and combined models is detailed in Table\u0026nbsp;3 and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The clinical model, which included mrEMVI score and serum neutrophil count, yielded AUCs of 0.65, 0.60, and 0.60 in the training, internal validation, and external validation sets, respectively. In comparison, the radiomics model showed significantly better performance, with corresponding AUCs of 0.85, 0.81, and 0.78. The combined model, which integrated the radiomics signature with the two clinical predictors, demonstrated the highest discrimination capability, with an AUC of 0.88 (95% CI: 0.81, 0.94) in the training set, 0.81 (95% CI: 0.68, 0.95) in the internal validation set, and 0.82 (95% CI: 0.72, 0.91) in the external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe DeLong test showed that the combined and radiomics models were both significantly superior to the clinical model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no statistically significant difference between the combined and radiomics models (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), although the combined model showed a consistently higher net benefit in DCA. A nomogram was developed based on the combined model to provide an individualized tool for predicting EMVI probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The calibration curves demonstrated good agreement between the nomogram's predictions and actual observations in all three sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). DCA showed that the combined model provided a greater net benefit across a wide range of threshold probabilities compared to either the radiomics or clinical model alone, indicating superior clinical usefulness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Smith\u0026rsquo;s 5-point scoring system performance\u003c/h2\u003e\u003cp\u003eThe diagnostic accuracy of the Smith's 5-point scoring system was evaluated against the pathological gold standard (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of EMVI status, patients with score 1 had the highest accuracy (83%), and score 4 (46%) was associated with higher rates of misdiagnose than other scores. Notably, the scoring system demonstrated a tendency to overestimate EMVI status.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eIn this study, we developed and validated an MRI-based radiomics model using features from the mesorectum to predict EMVI status in patients with RC. Our main finding is that the combined model, which integrated the mesorectal radiomics signature with clinical risk factors, achieved the best predictive performance, with AUCs of 0.88, 0.81, and 0.82 in the training, internal validation, and external validation sets, respectively. A nomogram incorporating the radiomics signature and clinical predictors was also developed to provide a more precise method for predicting EMVI status.\u003c/p\u003e\u003cp\u003eA key finding was that an elevated serum neutrophils count was an independent predictor of EMVI. This is consistent with previous research suggesting that neutrophils can promote tumor progression\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Neutrophil extracellular traps (NETs), released during a process called NETosis, can disrupt endothelial cell junctions, compromise vascular integrity, and subsequently mobilize tumor cells, potentially leading to venous thromboembolism.\u003c/p\u003e\u003cp\u003eAnd nomogram was developed that incorporate the radiomic feature and the clinical signature, providing a more precise method for predicting the status of EMVI.\u003c/p\u003e\u003cp\u003ePreoperative assessment of EMVI is crucial for clinical decision-making, as it is a known independent risk factor for poor survival and recurrence in RC. However, the traditional diagnosis relies on postoperative surgical specimens, which cannot guide preoperative therapy. Some previous studies have used different MRI sequences to predict EMVI status, Bae et al.\u003csup\u003e18\u003c/sup\u003e showed an inexperienced reviewer detected the status of EMVI on MRI using a 5-point grading system with poor performance (AUC of 0.658). Crim\u0026igrave; et al.\u003csup\u003e19\u003c/sup\u003e detected EMVI in patients with rectal cancer using T2-weighted, diffusion weighted imaging (DWI) and contrast-enhanced sequences, DWI (AUC of 0.729) showed better performance for EMVI detection compared to contrast-enhanced (AUC of 0.624) and T2-weighted sequences (AUC of 0.610).\u003c/p\u003e\u003cp\u003eIn recent year, some researchers have found that radiomics provide a noninvasive and reproducible method to elicit undiscovered features from imaging data that are not accessible by radiologist perception or conventional image analysis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Shu et al.\u003csup\u003e21\u003c/sup\u003e reported that the Bayes-based radiomics signature predicted EMVI with an AUC of 0.744. Yu et al.\u003csup\u003e22\u003c/sup\u003e also showed that the dynamic contrast-enhanced magnetic resonance imaging-based radiomics (AUC, 0.812) had good prediction of assessing EMVI. These studies \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003ecan assess the status of EMVI by MRI-based radiomics feature, to create a standardized definition and normalized reference for radiomics research, the image biomarker standardization initiative criteria and radiomics quality score were established\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, these studies only focused on the associations between tumor imaging and radiomics features to predict EMVI. Recently, a few studies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e indicated that the surrounding areas can provide additional information about tumor heterogeneity in other cancers such as esophageal squamous cell carcinoma and cervical cancer. Actually, the mesorectum is the place where EMVI occurs. Therefore, we proposed a widely available, noninvasive, and MRI-based radiomics model with a favorable predictive value using mesorectum radiomics features to predict the status of EMVI in patients with rectal cancer. The strong performance of our radiomics-only model (AUCs of 0.85, 0.81, and 0.78 across the three sets) supports this hypothesis. To our knowledge, this is the first study to investigate the association between mesorectal radiomic features and EMVI status.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, its retrospective design is susceptible to inherent selection bias. Second, correlating EMVI on a preoperative MRI with the exact location in the postoperative surgical specimen can be challenging. Third, the manual segmentation of the mesorectum was time-consuming and labor-intensive; fully automated segmentation using artificial intelligence is a goal for future work. Fourth, the data from multiple centers were acquired using different MRI scanners, which may influence radiomic features. Finally, the study was limited to a single external validation cohort due to data availability.\u003c/p\u003e\u003cp\u003eIn conclusion, this study establishes that a model combining clinical data with radiomic features from the mesorectum can accurately predict EMVI status in patients with RC. This noninvasive approach may serve as a promising imaging marker to enhance preoperative risk stratification and guide clinical practice. Further validation in larger, prospective studies is required to confirm its clinical utility.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study presents a model for predicting the EMVI status in patients with rectal cancer. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received funding by the Changsha Municipal Natural Science Fundation [grant numbers: kq2014201].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHunan Provincial People\u0026apos;s Hospital, Changsha, China, Department of Radiology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJia He, Xianzheng Tan, Jing Fang, Diejuan Liu, Jiabei Liu, Peng Liu.\u003c/p\u003e\n\u003cp\u003eHunan Cancer Hospital, Changsha, China, Department of Radiolog\u003c/p\u003e\n\u003cp\u003eXiaoping Yu\u003c/p\u003e\n\u003cp\u003eHunan Women and Children\u0026apos;s Hospital, Changsha, China, Department of Radiology\u003c/p\u003e\n\u003cp\u003eXiang Feng\u003c/p\u003e\n\u003cp\u003eGE Healthcare, Hangzhou, China\u003c/p\u003e\n\u003cp\u003eHuashan Lin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeng Liu and Xiaoping Yu conceived of and designed the study and polished the language of the manuscript; Jia He wrote the initial draft of the paper; Jia He, Xiang Feng, Jiabei Liu and Diejuan Liu collected and analysed the data; Jia He, Huashan Lin and Jing Fang conducted the statistical analysis; All authors approved of the final version before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xiaoping Yu ([email protected]) or Peng Liu ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics license was received from the Science Ethics Committee. This study is a retrospective study and informed consents were waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKeller DS, Berho M, Perez RO, et al. The multidisciplinary management of rectal cancer. Nat Rev Gastroenterol Hepatol. 2020;17(7):414\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41575-020-0275-y\u003c/span\u003e\u003cspan address=\"10.1038/s41575-020-0275-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2020/03/24].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang XY, Wang S, Li XT et al. MRI of Extramural Venous Invasion in Locally Advanced Rectal Cancer: Relationship to Tumor Recurrence and Overall Survival. \u003cem\u003eRadiology\u003c/em\u003e 2018;289(3):677\u0026thinsp;\u0026ndash;\u0026thinsp;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2018172889\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2018172889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [published Online First: 2018/08/29].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown PJ, Hyland R, Quyn AJ, et al. Current concepts in imaging for local staging of advanced rectal cancer. Clin Radiol. 2019;74(8):623\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.crad.2019.03.023\u003c/span\u003e\u003cspan address=\"10.1016/j.crad.2019.03.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2019/05/01].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalbot IC, Ritchie S, Leighton MH, et al. The clinical significance of invasion of veins by rectal cancer. Br J Surg. 1980;67(6):439\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/bjs.1800670619\u003c/span\u003e\u003cspan address=\"10.1002/bjs.1800670619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 1980/06/01].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown G, Radcliffe AG, Newcombe RG, et al. Preoperative assessment of prognostic factors in rectal cancer using high-resolution magnetic resonance imaging. Br J Surg. 2003;90(3):355\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/bjs.4034\u003c/span\u003e\u003cspan address=\"10.1002/bjs.4034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2003/02/21].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith NJ, Shihab O, Arnaout A, et al. MRI for detection of extramural vascular invasion in rectal cancer. AJR Am J Roentgenol. 2008;191(5):1517\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2214/ajr.08.1298\u003c/span\u003e\u003cspan address=\"10.2214/ajr.08.1298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2008/10/23].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBattersby NJ, How P, Moran B, et al. Prospective Validation of a Low Rectal Cancer Magnetic Resonance Imaging Staging System and Development of a Local Recurrence Risk Stratification Model: The MERCURY II Study. Ann Surg. 2016;263(4):751\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/sla.0000000000001193\u003c/span\u003e\u003cspan address=\"10.1097/sla.0000000000001193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2015/03/31].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGillies RJ, Kinahan PE, Hricak H, Radiomics. Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2015151169\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2015151169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2015/11/19].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev. 2022;42(1):426\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/med.21846\u003c/span\u003e\u003cspan address=\"10.1002/med.21846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2021/07/27].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaish H, Aukerman A, Vanguri R, et al. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur Radiol. 2020;30(11):6263\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-020-06968-6\u003c/span\u003e\u003cspan address=\"10.1007/s00330-020-06968-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2020/06/06].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin J, Seo N, Baek SE, et al. MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy. Radiology. 2022;303(2):351\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.211986\u003c/span\u003e\u003cspan address=\"10.1148/radiol.211986\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2022/02/09].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Zhang XY, Shi YJ, et al. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017;23(23):7253\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.Ccr-17-1038\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.Ccr-17-1038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2017/09/25].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun C, Tian X, Liu Z, et al. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study. EBioMedicine. 2019;46:160\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2019.07.049\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2019.07.049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2019/08/10].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu Y, Xie C, Yang H, et al. Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma. JAMA Netw Open. 2020;3(9):e2015927. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2020.15927\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2020.15927\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2020/09/11].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDʼSouza N, de Neree Tot Babberich MPM, d'Hoore A, et al. Definition of the Rectum: An International, Expert-based Delphi Consensus. Ann Surg. 2019;270(6):955\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/sla.0000000000003251\u003c/span\u003e\u003cspan address=\"10.1097/sla.0000000000003251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2019/04/12].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159\u0026ndash;74. [published Online First: 1977/03/01].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmulic B, Cazalet C, Hayes GL, et al. Neutrophil function: from mechanisms to disease. Annu Rev Immunol. 2012;30:459\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-immunol-020711-074942\u003c/span\u003e\u003cspan address=\"10.1146/annurev-immunol-020711-074942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2012/01/10].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang ZZ, Peng ZP, Liu XC, et al. Neutrophil extracellular traps induce tumor metastasis through dual effects on cancer and endothelial cells. Oncoimmunology. 2022;11(1):2052418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/2162402x.2022.2052418\u003c/span\u003e\u003cspan address=\"10.1080/2162402x.2022.2052418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2022/03/22].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBae JS, Kim SH, Hur BY, et al. Prognostic value of MRI in assessing extramural venous invasion in rectal cancer: multi-readers' diagnostic performance. Eur Radiol. 2019;29(8):4379\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-018-5926-9\u003c/span\u003e\u003cspan address=\"10.1007/s00330-018-5926-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2019/01/09].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrim\u0026igrave; F, Angelone R, Corso A, et al. Diagnostic accuracy of state-of-the-art rectal MRI sequences for the diagnosis of extramural vascular invasion in locally advanced rectal cancer after preoperative chemoradiotherapy: dos or maybes? Eur Radiol. 2023;33(10):6852\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-023-09655-4\u003c/span\u003e\u003cspan address=\"10.1007/s00330-023-09655-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2023/04/28].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAerts HJ. The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. JAMA Oncol. 2016;2(12):1636\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2016.2631\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2016.2631\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2016/08/20].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrclinonc.2017.141\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2017.141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2017/10/05].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShu Z, Mao D, Song Q, et al. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur Radiol. 2022;32(2):1002\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-021-08242-9\u003c/span\u003e\u003cspan address=\"10.1007/s00330-021-08242-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2021/09/06].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu X, Song W, Guo D, et al. Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Front Oncol. 2020;10:459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2020.00459\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2020.00459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2020/04/25].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZwanenburg A, Valli\u0026egrave;res M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2020191145\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2020191145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [published Online First: 2020/03/11].\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rectal cancer, Artificial intelligence, Extramural venous invasion, Mesorectum, Magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-7634936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo develop and validate a magnetic resonance imaging (MRI)-based radiomics model of the mesorectum for predicting extramural venous invasion (EMVI) in patients with rectal cancer (RC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective study included 238 patients with RC from two hospitals between May 2020 and March 2023. Patients were divided into a training set (n\u0026thinsp;=\u0026thinsp;114, from institution 1), an internal validation set (n\u0026thinsp;=\u0026thinsp;48, from institution 1), and an external validation set (n\u0026thinsp;=\u0026thinsp;76, from institution 2). A total of 963 radiomics features were extracted from the mesorectum region using T2-weighted imaging (T2WI). The radiomics model was developed using the methods of the minimum redundancy of the maximum relevance (mRMR) and the least absolute shrinkage (LASSO) regression. After univariate and multivariate logistic analysis, a clinical model was constructed based on clinical characteristics. A combined model was built and demonstrated as a nomogram. These models were evaluated by discrimination, calibration, and clinical application.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 238 patients, 98 (41.1%) were EMVI-positive. The Area Under the Curve (AUC) values for the clinical, radiomics, and combined models, respectively, were 0.65, 0.85, and 0.88 for the training set (95% CI: 0.81\u0026ndash;0.94); 0.60, 0.81, and 0.81 for the internal validation set (95% CI: 0.68\u0026ndash;0.95); and 0.60, 0.78, and 0.82 for the external validation set (95% CI: 0.72\u0026ndash;0.91).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study presents a model for predicting the EMVI status in patients with rectal cancer. The combined model, which incorporates both a mesorectal radiomics signature from T2WI and the clinical predictor of serum neutrophil count, demonstrated superior discrimination, calibration, and clinical utility compared to models based on either clinical or radiomics features alone. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 17:02:31","doi":"10.21203/rs.3.rs-7634936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T14:46:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T13:37:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216037739471318701111418064216793848839","date":"2025-10-23T13:25:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T10:03:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T05:50:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T03:52:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Imaging","date":"2025-09-17T02:40:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27a9d356-b26f-4c61-808f-3ed6aee72429","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:11:57+00:00","versionOfRecord":{"articleIdentity":"rs-7634936","link":"https://doi.org/10.1186/s40644-026-01020-0","journal":{"identity":"cancer-imaging","isVorOnly":false,"title":"Cancer Imaging"},"publishedOn":"2026-04-15 15:58:35","publishedOnDateReadable":"April 15th, 2026"},"versionCreatedAt":"2025-10-15 17:02:31","video":"","vorDoi":"10.1186/s40644-026-01020-0","vorDoiUrl":"https://doi.org/10.1186/s40644-026-01020-0","workflowStages":[]},"version":"v1","identity":"rs-7634936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634936","identity":"rs-7634936","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}