Radiology. Imaging cancer最新文献

{"title":"Time to Consider Diffusion Time: Exploring the Potential of Time-Dependent Diffusion MRI in Breast Cancer Imaging.","authors":"Masako Kataoka, Mami Iima","doi":"10.1148/rycan.250129","DOIUrl":"10.1148/rycan.250129","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e250129"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Metastasis-directed Therapy Guided by Different PET/CT Radiotracers on Distant and Local Disease Control in Oligorecurrent Hormone-sensitive Prostate Cancer: A Secondary Analysis of the PRECISE-MDT Study.","authors":"Francesco Lanfranchi, Liliana Belgioia, Domenico Albano, Luca Triggiani, Flavia Linguanti, Luca Urso, Rosario Mazzola, Alessio Rizzo, Elisa D'Angelo, Francesco Dondi, Eneida Mataj, Gloria Pedersoli, Elisabetta Maria Abenavoli, Luca Vaggelli, Beatrice Detti, Naima Ortolan, Antonio Malorgio, Alessia Guarneri, Federico Garrou, Matilde Fiorini, Serena Grimaldi, Pietro Ghedini, Giuseppe Carlo Iorio, Antonella Iudicello, Guido Rovera, Giuseppe Fornarini, Diego Bongiovanni, Michela Marcenaro, Filippo Maria Pazienza, Giorgia Timon, Matteo Salgarello, Manuela Racca, Mirco Bartolomei, Stefano Panareo, Umberto Ricardi, Francesco Bertagna, Filippo Alongi, Salvina Barra, Silvia Morbelli, Gianmario Sambuceti, Matteo Bauckneht","doi":"10.1148/rycan.240150","DOIUrl":"10.1148/rycan.240150","url":null,"abstract":"<p><p>Prospective trials suggest that metastasis-directed therapy (MDT) is an effective treatment for patients with oligometastatic prostate cancer (PCa). Gallium 68 (⁶⁸Ga) prostate-specific membrane antigen (PSMA)-11 PET/CT-guided MDT seems to improve the oncologic outcome in these patients compared with fluorine 18 (¹⁸F)-fluorocholine and ¹⁸F-PSMA-1007 PET/CT-guided MDT, but the effects in terms of local or distant disease control remain unclear. Thus, the present subanalysis of the PRECISE-MDT study analyzed patients with hormone-sensitive PCa who underwent MDT guided by PET/CT for nodal or bone oligorecurrent disease and were restaged with the same imaging modality in case of biochemical recurrence after MDT. Among 340 lesions detected in 241 male patients (median age, 74 [IQR, 9] years), ¹⁸F-fluorocholine, ⁶⁸Ga-PSMA-11, and ¹⁸F-PSMA-1007 PET/CT-guided MDT was performed in 179, 81, and 80 lesions, respectively. At restaging imaging, the PET/CT imaging modality used to guide MDT was not significantly associated with local recurrence-free survival (LRFS), with median LRFS not reached for ⁶⁸Ga-PSMA-11 PET/CT, ¹⁸F-PSMA-11 PET/CT, and ¹⁸F-fluorocholine PET/CT (<i>P</i> = .73). However, the detection rate of a new metastasis was significantly higher if MDT was guided by ¹⁸F-fluorocholine PET/CT (119 of 179 lesions, 66.5%) compared with ⁶⁸Ga-PSMA-11 or ¹⁸F-PSMA-1007 PET/CT (23 of 81 lesions, 28%, and 27 of 80, 34%, respectively; <i>P</i> < .001 for both). Moreover, MDT guided by ⁶⁸Ga-PSMA-11 PET/CT led to an improved median metastasis-free survival (MFS) (not reached) compared with ¹⁸F-PSMA-1007 (median MFS, 24.9 months; <i>P</i> < .001) or ¹⁸F-fluorocholine PET/CT (median MFS, 18 months; <i>P</i> < .001). These findings suggest that using different PET/CT imaging modalities to guide MDT might impact the distant disease control in this clinical scenario. <b>Keywords:</b> Radiation Therapy, Oncology, Urinary, Prostate, PET/CT <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240150"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Ablation Confirmation Methods in Percutaneous Thermal Ablation of Malignant Liver Tumors: Technical Insights, Clinical Evidence, and Future Outlook.","authors":"Iwan Paolucci, Jessica Albuquerque Marques Silva, Yuan-Mao Lin, Alexander Shieh, Anna Maria Ierardi, Gianpaolo Caraffiello, Carlo Gazzera, Kyle A Jones, Paolo Fonio, Reto Bale, Kristy K Brock, Marco Calandri, Bruno C Odisio","doi":"10.1148/rycan.240293","DOIUrl":"10.1148/rycan.240293","url":null,"abstract":"<p><p>Percutaneous image-guided thermal ablation is an established local curative-intent treatment technique for the treatment of primary and secondary malignant liver tumors. Whereas margin assessment after surgical resection can be accomplished with microscopic examination of the resected specimen, margin assessment after percutaneous thermal ablation relies on cross-sectional imaging. The critical measure of technical success is the minimal ablative margin (MAM), defined as the minimum distance between the tumor and the edge of the ablation zone. Traditionally, the MAM has been assessed qualitatively using anatomic landmarks, which has suboptimal accuracy and reproducibility and is prone to operator bias. Consequently, specialized software-based methods have been developed to standardize and automate MAM quantification. In this review, the authors discuss the technical components of such methods, including image acquisition, segmentation, registration, and MAM computation, define the sources of measurement error, describe available software solutions in terms of image processing techniques and modes of integration, and outline the current clinical evidence, which strongly supports the use of such dedicated software. Finally, the authors discuss current logistical and financial barriers to widespread use of ablation confirmation methods as well as potential solutions. <b>Keywords:</b> Ablation Techniques, CT, Image Postprocessing, Liver <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240293"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework.","authors":"Xiaoxia Wang, Xiaofei Hu, Churan Wang, Hua Yang, Yan Hu, Xiaosong Lan, Yao Huang, Ying Cao, Lijun Yan, Fandong Zhang, Yizhou Yu, Jiuquan Zhang","doi":"10.1148/rycan.240184","DOIUrl":"10.1148/rycan.240184","url":null,"abstract":"<p><p>Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-proven invasive breast cancer between January 2015 and January 2021. An automatic breast lesion segmentation model was developed using three-dimensional (3D) ResU-Net as the backbone, and its accuracy was evaluated in an internal and two external testing datasets using the Dice score. An ensemble model for classification of breast cancer into four molecular subtypes (Ensemble ResNet) was then developed by combining both two-dimensional and 3D lesion features. The performance of Ensemble ResNet was evaluated in the three testing datasets using the area under the receiver operating characteristic curve (AUC). Results A total of 687 female patients (mean age ± SD, 48.70 years ± 8.97) were included, with 289, 61, 73, and 264 patients included in the training, internal testing, and two external testing datasets, respectively. The proposed segmentation model achieved high accuracy in internal testing dataset 1, external testing dataset 2, and external testing dataset 3 (Dice scores: 0.86, 0.82, 0.85) and luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative breast cancer (TNBC) subtypes (Dice scores: 0.8571, 0.8323, 0.8199, 0.8481). Ensemble ResNet demonstrated high performance for the prediction of luminal A subtypes (AUC range, 0.74-0.84), luminal B subtypes (AUC range, 0.68-0.72), HER2-enriched subtypes (AUC range, 0.73-0.82), and TNBC (AUC range, 0.80-0.81) in the three testing datasets. Conclusion The proposed novel deep learning framework based on MRI achieved high, robust performance in fully automatic classification of breast cancer molecular subtypes. <b>Keywords:</b> MR-Imaging, Breast, Oncology, Breast Cancer, Molecular Subtype, Deep Learning Framework <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240184"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Automated Tools for Lesion Detection on ¹⁸F Fluoroestradiol PET/CT Images and Assessment of Concordance with Standard-of-Care Imaging in Metastatic Breast Cancer.","authors":"Renee Miller, Mark Battle, Kristen Wangerin, Daniel T Huff, Amy J Weisman, Song Chen, Timothy G Perk, Gary A Ulaner","doi":"10.1148/rycan.240253","DOIUrl":"10.1148/rycan.240253","url":null,"abstract":"<p><p>Purpose To evaluate two automated tools for detecting lesions on fluorine 18 (¹⁸F) fluoroestradiol (FES) PET/CT images and assess concordance of ¹⁸F-FES PET/CT with standard diagnostic CT and/or ¹⁸F fluorodeoxyglucose (FDG) PET/CT in patients with breast cancer. Materials and Methods This retrospective analysis of a prospective study included participants with breast cancer who underwent ¹⁸F-FES PET/CT examinations (<i>n</i> = 52), ¹⁸F-FDG PET/CT examinations (<i>n</i> = 13 of 52), and diagnostic CT examinations (<i>n</i> = 37 of 52). A convolutional neural network was trained for lesion detection using manually contoured lesions. Concordance in lesions labeled by a nuclear medicine physician between ¹⁸F-FES and ¹⁸F-FDG PET/CT and between ¹⁸F-FES PET/CT and diagnostic CT was assessed using an automated software medical device. Lesion detection performance was evaluated using sensitivity and false positives per participant. Wilcoxon tests were used for statistical comparisons. Results The study included 52 participants. The lesion detection algorithm achieved a median sensitivity of 62% with 0 false positives per participant. Compared with sensitivity in overall lesion detection, the sensitivity was higher for detection of high-uptake lesions (maximum standardized uptake value > 1.5, <i>P</i> = .002) and similar for detection of large lesions (volume > 0.5 cm³, <i>P</i> = .15). The artificial intelligence (AI) lesion detection tool was combined with a standardized uptake value threshold to demonstrate a fully automated method of labeling patients as having FES-avid metastases. Additionally, automated concordance analysis showed that 17 of 25 participants (68%) had over half of the detected lesions across two modalities present on ¹⁸F-FES PET/CT images. Conclusion An AI model was trained to detect lesions on ¹⁸F-FES PET/CT images and an automated concordance tool measured heterogeneity between ¹⁸F-FES PET/CT and standard-of-care imaging. <b>Keywords:</b> Molecular Imaging-Cancer, Neural Networks, PET/CT, Breast, Computer Applications-General (Informatics), Segmentation, ¹⁸F-FES PET, Metastatic Breast Cancer, Lesion Detection, Artificial Intelligence, Lesion Matching <i>Supplemental material is available for this article.</i> Clinical Trials Identifier: NCT04883814 Published under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240253"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12134609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective Evaluation of Contrast-enhanced Mammography for Early Prediction of Pathologic Response after Neoadjuvant Therapy.","authors":"Lígia Pires-Gonçalves, Ana Teresa Aguiar, Conceição Leal, António Guimarães-Santos, Miguel Abreu, Rui Henrique","doi":"10.1148/rycan.240117","DOIUrl":"10.1148/rycan.240117","url":null,"abstract":"<p><p>Purpose To assess whether changes in contrast-enhanced mammography (CEM)-derived lesion measurements after the first cycle of neoadjuvant therapy (NAT) can predict pathologic complete response (pCR) in individuals with breast cancer. Materials and Methods This prospective single-center pilot study enrolled consecutive participants with breast cancer treated with NAT who underwent CEM at baseline (May 2018 to December 2018). CEM was performed before and after the first cycle of NAT. Two breast radiologists independently evaluated the percentage change in the longest dimension of the lesion (CLD) and Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria at CEM. Multivariable logistic regression was used to identify independent predictors of pCR, and predictive performance was assessed using area under the receiver operating characteristic curve (AUC). Results Thirty-six participants (mean age ± SD, 48 years ± 10.3) were included; 11 (30.5%) participants achieved pCR. A CLD of at least 20.93% independently predicted pCR (odds ratio, 9.52; 95% CI: 1.34, 67.23; <i>P</i> = .02), achieving a sensitivity of 73% (eight of 11) and a specificity of 88% (22 of 25). Response according to RECIST 1.1 criteria was not associated with pCR (odds ratio, 3.22; 95% CI: 0.46, 22.53; <i>P</i> = .24). In participants with hormone-receptor negative breast cancer, a CLD of at least 20.93% was associated with a higher likelihood of pCR (odds ratio, 40.00; 95% CI: 2.01, 794.27; <i>P</i> = .005) and had an AUC of 0.86 (95% CI: 0.65, >0.99; <i>P</i> = .005). Conclusion CLD at CEM after the first cycle of NAT may be an early predictor of pCR in individuals with breast cancer. <b>Keywords:</b> Breast, Tumor Response, Mammography, Oncology, Neoadjuvant Therapy, Radiographic Image Enhancement, Pathologic Complete Response, Breast Tumor <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240117"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiation-induced Osteosarcoma of the Calvarium.","authors":"Sunil Kumar, Smily Sharma, Abhishek Nayak, Deepti An, Bejoy Thomas","doi":"10.1148/rycan.240502","DOIUrl":"10.1148/rycan.240502","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240502"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the Lifetime Cancer Risk Associated with CT Imaging.","authors":"Saumya Gurbani, Meagan A Bechel","doi":"10.1148/rycan.259011","DOIUrl":"10.1148/rycan.259011","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e259011"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Diagnostic Breast Imaging in Symptomatic Pregnant and Lactating Patients: Systematic Review and Meta-Analysis.","authors":"Benjamin W Weber, Lu Mao, Kelley Salem, Mary Hitchcock, Abigail H Keller, Mai A Elezaby, Lonie R Salkowski, Laura M Bozzuto, Amy M Fowler","doi":"10.1148/rycan.240281","DOIUrl":"10.1148/rycan.240281","url":null,"abstract":"<p><p>Purpose To perform a systematic review of the literature and meta-analysis to summarize the diagnostic performance of breast imaging modalities for cancer detection in pregnant and lactating patients. Materials and Methods A systematic review of the literature in PubMed, Scopus, Web of Science, and Cochrane Library databases published up until March 3, 2023, was conducted. Included studies evaluated patients of any age who underwent breast imaging during pregnancy or lactation. The primary outcome of this review was sensitivity and specificity of each imaging modality. Meta-analysis was performed using a bivariate modeling approach, and summary receiver operating characteristic (ROC) analysis was used to generate a summary area under the ROC curve (AUC). Results Twenty-five studies met the eligibility criteria and included 1681 female patients (mean age, 33 years; range, 18-49 years). For US, seven of 24 studies had complete data yielding an AUC of 0.90 (95% CI: 0.85, 0.93), a sensitivity of 81% (95% CI: 56, 94), and a specificity of 85% (95% CI: 71, 92). For mammography, three of 21 studies had complete data yielding an AUC of 0.93 (95% CI: 0.75, 0.97), a sensitivity of 72% (95% CI: 47, 88), and a specificity of 93% (95% CI: 86, 97). For MRI, two of eight studies had complete data yielding an AUC of 95% (95% CI: 59, 96), a sensitivity of 91% (95% CI: 56, 99), and a specificity of 88% (95% CI: 48, 98). Conclusion US, mammography, and breast MRI showed high diagnostic performance for detection of pregnancy-associated breast cancer in symptomatic pregnant or lactating patients. <b>Keywords:</b> Meta-Analysis, Breast, Oncology, Pregnancy, Mammography, MR-Dynamic Contrast Enhanced, Ultrasound <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240281"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI.","authors":"Zonglin Liu, Runqi Meng, Qiong Ma, Zhen Guan, Rong Li, Caixia Fu, Yanfen Cui, Yiqun Sun, Tong Tong, Dinggang Shen","doi":"10.1148/rycan.240359","DOIUrl":"10.1148/rycan.240359","url":null,"abstract":"<p><p>Purpose To develop and validate a deep multitask network, MultiRecNet, for fully automatic prediction of disease-free survival (DFS) in patients with neoadjuvant chemoradiotherapy (nCRT)-treated locally advanced rectal cancer (LARC). Materials and Methods This retrospective study collected clinical information and baseline multimodal MRI (T2, apparent diffusion coefficient [ADC], <i>D</i>_app, and <i>K</i>_app) data from patients with LARC after nCRT at three centers between October 2011 and May 2019. Patients from centers 1 and 2 were divided into training, validation, and internal testing sets, while patients from center 3 served as the external testing set. MultiRecNet is capable of simultaneously performing segmentation, classification, and survival prediction tasks within a single framework. Multiple combinations of data from different clinical stages (pretreatment and postoperative) were input into MultiRecNet to generate different models and identify the model with optimal performance. Evaluation metrics included the Dice similarity coefficient (DSC), the area under the receiver operating characteristic curve (AUC), and the Harrell concordance index (C-index) for the segmentation, classification, and survival prediction tasks, respectively. Results The study included 445 patients: 261 in the training set (median age, 60 years [IQR, 53-67 years]; 172 male), 37 in the validation set (median age, 61 years [IQR, 55-68 years]; 30 male), 75 in the internal testing set (median age, 60 years [IQR, 51-67 years]; 45 male), and 72 in the external testing set (median age, 55 years [IQR, 49-61 years]; 38 male). In the internal testing set, the best model based on MultiRecNet (the All model, with T2-weighted imaging, ADC, <i>D</i>_app, <i>K</i>_app, pretreatment clinical indicators, and postoperative pathologic indicators) achieved a DSC of 0.72 for tumor segmentation, an AUC of 0.97 (95% CI: 0.92, >.99) for recurrence or metastasis classification at 3 years, and a C-index of 0.92 for DFS prediction. In the external testing set, the model continued to perform well for survival prediction (C-index = 0.81, <i>P</i> < .001). Conclusion The MultiRecNet-based model enabled prognostic prediction in a fully automated end-to-end manner in patients with LARC following nCRT. <b>Keywords:</b> MR-Imaging, Abdomen/GI, Rectum, Oncology <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240359"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}