{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T01:40:06Z","timestamp":1778722806972,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01844-5","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T05:29:40Z","timestamp":1753939780000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation"],"prefix":"10.1186","volume":"25","author":[{"given":"Hyun Bin","family":"Kim","sequence":"first","affiliation":[]},{"given":"Hong Qi","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Wen Long","family":"Nei","sequence":"additional","affiliation":[]},{"given":"Ying Cong Ryan Shea","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Yiyu","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Fuqiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"1844_CR1","doi-asserted-by":"publisher","unstructured":"Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. May 2023;73(3):233\u201354. https:\/\/doi.org\/10.3322\/caac.21772.","DOI":"10.3322\/caac.21772"},{"key":"1844_CR2","doi-asserted-by":"publisher","unstructured":"Bhudia J, Glynne-Jones R, Smith T, Hall M. Neoadjuvant chemotherapy without radiation in colorectal cancer. Clin Colon Rectal Surg. 2020;33(05):287\u2013297. https:\/\/doi.org\/10.1055\/s-0040-1713746","DOI":"10.1055\/s-0040-1713746"},{"key":"1844_CR3","doi-asserted-by":"publisher","unstructured":"Bismuth H et al. Resection of nonresectable liver metastases from colorectal cancer after neoadjuvant chemotherapy. Ann Surg. 1996;224(4):509\u201320. discussion 520-2. https:\/\/doi.org\/10.1097\/00000658-199610000-00009","DOI":"10.1097\/00000658-199610000-00009"},{"key":"1844_CR4","doi-asserted-by":"publisher","unstructured":"Shao Y-C, et al. Neoadjuvant chemotherapy can improve outcome of colorectal cancer patients with unresectable metastasis. Int J Colorectal Dis. Oct. 2013;28(10):1359\u201365. https:\/\/doi.org\/10.1007\/s00384-013-1713-x.","DOI":"10.1007\/s00384-013-1713-x"},{"key":"1844_CR5","doi-asserted-by":"publisher","unstructured":"Habr-Gama A, S\u00e3o Juli\u00e3o GP, Perez RO. Nonoperative management of rectal cancer. Hematol Oncol Clin North Am. Feb. 2015;29(1):135\u201351. https:\/\/doi.org\/10.1016\/j.hoc.2014.09.004.","DOI":"10.1016\/j.hoc.2014.09.004"},{"key":"1844_CR6","doi-asserted-by":"publisher","unstructured":"Habr-Gama A et al. Watch and wait approach following extended neoadjuvant chemoradiation for distal rectal cancer. Dis Colon Rectum. 2013;56(10):1109\u20131117. https:\/\/doi.org\/10.1097\/DCR.0b013e3182a25c4e","DOI":"10.1097\/DCR.0b013e3182a25c4e"},{"key":"1844_CR7","doi-asserted-by":"publisher","unstructured":"Trakarnsanga A, et al. Comparison of tumor regression grade systems for locally advanced rectal cancer after multimodality treatment. JNCI: J Natl Cancer Inst. Oct. 2014;106(10). https:\/\/doi.org\/10.1093\/jnci\/dju248.","DOI":"10.1093\/jnci\/dju248"},{"key":"1844_CR8","doi-asserted-by":"publisher","unstructured":"George TJ, Allegra CJ, Yothers G. Neoadjuvant rectal (NAR) score: a new surrogate endpoint in rectal cancer clinical trials. Curr Colorectal Cancer Rep. Oct. 2015;11(5):275\u201380. https:\/\/doi.org\/10.1007\/s11888-015-0285-2.","DOI":"10.1007\/s11888-015-0285-2"},{"key":"1844_CR9","doi-asserted-by":"publisher","unstructured":"Ucar G et al. Prognostic and predictive value of NAR score in gastric cancer. J Gastrointest Cancer. 2021;52(3):1054\u20131060. https:\/\/doi.org\/10.1007\/s12029-020-00537-2","DOI":"10.1007\/s12029-020-00537-2"},{"key":"1844_CR10","doi-asserted-by":"publisher","unstructured":"Mukai T et al. Importance of the neoadjuvant rectal (NAR) score to the outcome of neoadjuvant chemotherapy alone for locally advanced rectal cancer. Surg Today. 2020;50(8):912\u2013919. https:\/\/doi.org\/10.1007\/s00595-020-01964-1","DOI":"10.1007\/s00595-020-01964-1"},{"key":"1844_CR11","doi-asserted-by":"publisher","unstructured":"MAEDA K, et al. Prognostic significance of neoadjuvant rectal score and indication for postoperative adjuvant therapy in rectal cancer patients after neoadjuvant chemoradiotherapy. In Vivo (Brooklyn). 2020;34(1):283\u2013289. https:\/\/doi.org\/10.21873\/invivo.11772","DOI":"10.21873\/invivo.11772"},{"key":"1844_CR12","doi-asserted-by":"publisher","unstructured":"Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","DOI":"10.1038\/323533a0"},{"key":"1844_CR13","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. May 2017;60:84\u201390. https:\/\/doi.org\/10.1145\/3065386.","DOI":"10.1145\/3065386"},{"key":"1844_CR14","doi-asserted-by":"publisher","unstructured":"Russakovsky O, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. Dec. 2015;115(3):211\u201352. https:\/\/doi.org\/10.1007\/s11263-015-0816-y.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"1844_CR15","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014."},{"key":"1844_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2015.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1844_CR17","unstructured":"Vaswani A, et al. Attention is all you need. 2017."},{"key":"1844_CR18","unstructured":"Dosovitskiy A, et al. An image is worth 16x16 words: Transformers for image recognition at scale. 2020."},{"key":"1844_CR19","unstructured":"Brown TB, et al. Language models are few-shot learners. 2020."},{"key":"1844_CR20","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. 2018."},{"key":"1844_CR21","doi-asserted-by":"publisher","unstructured":"Bychkov D, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8(1):3395. https:\/\/doi.org\/10.1038\/s41598-018-21758-3.","DOI":"10.1038\/s41598-018-21758-3"},{"key":"1844_CR22","doi-asserted-by":"publisher","unstructured":"Xu L, et al. Colorectal cancer detection based on deep learning. J Pathol Inf. Jan. 2020;11(1):28. https:\/\/doi.org\/10.4103\/jpi.jpi_68_19.","DOI":"10.4103\/jpi.jpi_68_19"},{"key":"1844_CR23","doi-asserted-by":"publisher","unstructured":"Khumsi AF, Almezhghwi K, Adweb K. Deep learning based analysis in oncological studies. Colorectal Cancer Staging\u2019. 2020;573\u20139. https:\/\/doi.org\/10.1007\/978-3-030-35249-3_73.","DOI":"10.1007\/978-3-030-35249-3_73"},{"key":"1844_CR24","doi-asserted-by":"publisher","unstructured":"Clusmann J, et al. The future landscape of large language models in medicine. Commun Med. 2023;3(1):141. https:\/\/doi.org\/10.1038\/s43856-023-00370-1","DOI":"10.1038\/s43856-023-00370-1"},{"key":"1844_CR25","doi-asserted-by":"crossref","unstructured":"Chang C-H, Lucas MM, Lu-Yao G, Yang CC. Classifying cancer stage with open-source clinical large language models. 2024.","DOI":"10.1109\/ICHI61247.2024.00018"},{"key":"1844_CR26","doi-asserted-by":"publisher","unstructured":"Huo B, et al. Dr. GPT will see you now: the ability of large Language model-linked chatbots to provide colorectal cancer screening recommendations. Health Technol (Berl). May 2024;14(3):463\u20139. https:\/\/doi.org\/10.1007\/s12553-024-00836-9.","DOI":"10.1007\/s12553-024-00836-9"},{"key":"1844_CR27","doi-asserted-by":"crossref","unstructured":"Alsentzer E, et al. Publicly available clinical BERT embeddings. 2019.","DOI":"10.18653\/v1\/W19-1909"},{"key":"1844_CR28","doi-asserted-by":"publisher","unstructured":"Yang X, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194. https:\/\/doi.org\/10.1038\/s41746-022-00742-2","DOI":"10.1038\/s41746-022-00742-2"},{"key":"1844_CR29","doi-asserted-by":"publisher","unstructured":"Gu Y, et al. Domain-Specific Language model pretraining for biomedical natural Language processing. ACM Trans Comput Healthc. Jan. 2022;3(1):1\u201323. https:\/\/doi.org\/10.1145\/3458754.","DOI":"10.1145\/3458754"},{"key":"1844_CR30","doi-asserted-by":"publisher","unstructured":"Lee J, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. 2019. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"1844_CR31","unstructured":"Wang G, Yang G, Du Z, Fan L, Li X. ClinicalGPT: Large language models finetuned with diverse medical data and comprehensive evaluation. 2023."},{"key":"1844_CR32","unstructured":"Singhal K, et al. Towards expert-level medical question answering with large language models. 2023."},{"key":"1844_CR33","doi-asserted-by":"publisher","unstructured":"Yuan Z et al. Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy. Rep Pract Oncol Radioth. 2021;26(1):29\u201334. https:\/\/doi.org\/10.5603\/RPOR.a2021.0004","DOI":"10.5603\/RPOR.a2021.0004"},{"key":"1844_CR34","doi-asserted-by":"publisher","unstructured":"Wang F, et al. Predicting outcomes for locally advanced rectal cancer treated with neoadjuvant chemoradiation with CT-based radiomics. Sci Rep. Apr. 2022;12(1):6167. https:\/\/doi.org\/10.1038\/s41598-022-10175-2.","DOI":"10.1038\/s41598-022-10175-2"},{"key":"1844_CR35","doi-asserted-by":"publisher","unstructured":"Shaish H, 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\u20136273. https:\/\/doi.org\/10.1007\/s00330-020-06968-6","DOI":"10.1007\/s00330-020-06968-6"},{"key":"1844_CR36","doi-asserted-by":"publisher","unstructured":"Tan RSYC, et al. Inferring cancer disease response from radiology reports using large language models with data augmentation and prompting. J American Med Inform Associa. 2023;30(10):1657\u20131664. https:\/\/doi.org\/10.1093\/jamia\/ocad133","DOI":"10.1093\/jamia\/ocad133"},{"key":"1844_CR37","doi-asserted-by":"publisher","unstructured":"Masoudi S, et al. Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. J Med Imaging. Jan. 2021;8(01). https:\/\/doi.org\/10.1117\/1.JMI.8.1.010901.","DOI":"10.1117\/1.JMI.8.1.010901"},{"key":"1844_CR38","doi-asserted-by":"publisher","unstructured":"Mei X, et al. RadImageNet: an open radiologic deep learning research dataset for effective transfer learning. Radiol Artif Intell. Sep. 2022;4(5). https:\/\/doi.org\/10.1148\/ryai.210315.","DOI":"10.1148\/ryai.210315"},{"key":"1844_CR39","doi-asserted-by":"publisher","unstructured":"Serte S, Demirel H. Deep learning for diagnosis of COVID-19 using 3D CT scans. Comput Biol Med. May 2021;132:104306. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104306.","DOI":"10.1016\/j.compbiomed.2021.104306"},{"key":"1844_CR40","unstructured":"Kataoka H, Wakamiya T, Hara K, Satoh Y. Would mega-scale datasets further enhance spatiotemporal 3D CNNs? 2020."},{"key":"1844_CR41","unstructured":"Kim Y, Oh J, Kim S, Yun S-Y. How to fine-tune models with few samples: Update, data augmentation, and test-time augmentation. 2022."},{"key":"1844_CR42","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Oct., \u2018Grad-CAM: visual explanations from deep networks via Gradient-based localization\u2019, 2016, https:\/\/doi.org\/10.1007\/s11263-019-01228-7","DOI":"10.1007\/s11263-019-01228-7"},{"key":"1844_CR43","doi-asserted-by":"publisher","unstructured":"Dif N, Elberrichi Z. A new deep learning model selection method for colorectal cancer classification. Internat J Swarm Intelli Res. 2020;11(3):72\u201388. https:\/\/doi.org\/10.4018\/IJSIR.2020070105","DOI":"10.4018\/IJSIR.2020070105"},{"key":"1844_CR44","doi-asserted-by":"publisher","unstructured":"Tsai M-J, Tao Y-H. Deep learning techniques for the classification of colorectal cancer tissue. Electronics (Basel). 2021;10(14):1662. https:\/\/doi.org\/10.3390\/electronics10141662","DOI":"10.3390\/electronics10141662"},{"key":"1844_CR45","doi-asserted-by":"publisher","unstructured":"Foersch S, et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med. Feb. 2023;29(2):430\u20139. https:\/\/doi.org\/10.1038\/s41591-022-02134-1.","DOI":"10.1038\/s41591-022-02134-1"},{"key":"1844_CR46","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.rcro.2023.100004","volume":"1","author":"WC Chong","year":"2023","unstructured":"Chong WC, et al. A comprehensive evaluation of MR-radiomics role in NAR score prediction in locally advanced rectal cancer. Royal Coll Radiol Open. 2023;1:100004. https:\/\/doi.org\/10.1016\/j.rcro.2023.100004.","journal-title":"Royal Coll Radiol Open"},{"key":"1844_CR47","doi-asserted-by":"publisher","unstructured":"Liu J, Wang C, Liu S. Utility of ChatGPT in clinical practice. J Med Internet Res. Jun. 2023;25:e48568. https:\/\/doi.org\/10.2196\/48568.","DOI":"10.2196\/48568"},{"key":"1844_CR48","unstructured":"Li M, Blaes A, Johnson S, Liu H, Xu H, Zhang R. CancerLLM: a large language model in cancer domain. 2024."},{"key":"1844_CR49","doi-asserted-by":"publisher","unstructured":"Sorin V, et al. Large language model (ChatGPT) as a support tool for breast tumor board. NPJ Breast Cancer. 2023;9(1):44. https:\/\/doi.org\/10.1038\/s41523-023-00557-8","DOI":"10.1038\/s41523-023-00557-8"},{"key":"1844_CR50","doi-asserted-by":"publisher","unstructured":"Sorin V, et al. Utilizing large Language models in breast cancer management: systematic review. J Cancer Res Clin Oncol. Mar. 2024;150(3):140. https:\/\/doi.org\/10.1007\/s00432-024-05678-6.","DOI":"10.1007\/s00432-024-05678-6"},{"key":"1844_CR51","doi-asserted-by":"publisher","unstructured":"Beiderwellen K, et al. Whole-body [18F]FDG PET\/MRI vs. PET\/CT in the assessment of bone lesions in oncological patients: initial results. Eur Radiol. 2014;24(8):2023\u20132030. https:\/\/doi.org\/10.1007\/s00330-014-3229-3.","DOI":"10.1007\/s00330-014-3229-3"},{"key":"1844_CR52","doi-asserted-by":"publisher","unstructured":"Riola-Parada C, Garc\u00eda-Ca\u00f1amaque L, P\u00e9rez-Due\u00f1as V, Garcerant-Tafur M, Carreras-Delgado JL. PET\/RM simult\u00e1nea vs. PET\/TC En oncolog\u00eda. Una revisi\u00f3n Sistem\u00e1tica. Rev Esp Med Nucl Imagen Mol. Sep. 2016;35(5):306\u201312. https:\/\/doi.org\/10.1016\/j.remn.2016.06.001.","DOI":"10.1016\/j.remn.2016.06.001"},{"key":"1844_CR53","unstructured":"Lundberg S, Lee S-I. A unified approach to interpreting model predictions. 2017."},{"key":"1844_CR54","doi-asserted-by":"publisher","unstructured":"Sorin V, Barash Y, Konen E, Klang E. Deep learning for natural Language processing in Radiology\u2014Fundamentals and a systematic review. J Am Coll Radiol. May 2020;17(5):639\u201348. https:\/\/doi.org\/10.1016\/j.jacr.2019.12.026.","DOI":"10.1016\/j.jacr.2019.12.026"},{"key":"1844_CR55","doi-asserted-by":"publisher","unstructured":"Sugimoto K, et al. Extracting clinical terms from radiology reports with deep learning. J Biomed Inf. Apr. 2021;116:103729. https:\/\/doi.org\/10.1016\/j.jbi.2021.103729.","DOI":"10.1016\/j.jbi.2021.103729"},{"key":"1844_CR56","doi-asserted-by":"crossref","unstructured":"Li C, et al. LLaVA-Med: Training a large language-and-vision assistant for biomedicine in one day. 2023.","DOI":"10.32388\/VLXB6M"},{"key":"1844_CR57","unstructured":"Eslami S, de Melo G, Meinel C. Does CLIP benefit visual question answering in the medical domain as much as it does in the general domain? 2021."}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01844-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01844-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01844-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T05:29:46Z","timestamp":1753939786000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01844-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1844"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01844-5","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"7 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved with waiver of informed consent from SingHealth centralized institution review board and all methods were performed in accordance with relevant guidelines and regulations. This study adhered to the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"306"}}