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Accurate staging, restaging, invasiveness assessment, and treatment efficacy evaluation are crucial for its clinical management. Magnetic resonance imaging (MRI) plays a significant role in these processes. However, standard MRI techniques, including T2-weighted and diffusion-weighted imaging, have uncertainties in identifying early-stage tumors, high-risk nodules, extramural vascular invasion, and treatment efficacy, potentially leading to inappropriate treatment. Recent advances suggest that the integration of traditional MRI methods, including diffusion-weighted imaging, opposed-phase or contrast-enhanced T1-weighted imaging, as well as emerging synthetic MRI, could address these challenges. Additionally, improvements in imaging technology have spurred research into advanced functional MRI techniques such as diffusion kurtosis imaging and amide proton transfer weighted MRI, yielding promising results in RC assessment. Total neoadjuvant therapy has emerged as a new treatment paradigm for locally advanced RC, with neoadjuvant immunotherapy and chemotherapy offering viable alternatives to neoadjuvant chemoradiotherapy. However, the lack of standards for the early prediction of patient survival and tumor response to neoadjuvant therapy highlights a critical unmet need in matching therapies to suitable patients. Furthermore, organ preservation strategies after neoadjuvant therapy provide personalized options based on tumor response and patient preferences, yet traditional MRI assessments show significant variability. Radiomics and artificial intelligence hold promise for revealing complex patterns in MRI images associated with patient prognosis and treatment response. This review provides an overview of current MRI advancements in RC assessment and emphasizes how future research can refine tailored treatment strategies to improve patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The accurate diagnosis of early-stage rectal tumors, high-risk nodules, treatment responses, and the early prediction of patient survival and therapeutic outcomes remain an unmet need<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Visual MRI has improved staging, restaging, and invasiveness evaluation. Advanced MRI, radiomics and artificial intelligence provide significant potential for tumor characterization and outcome prediction<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Advances in visual MRI are improving routine imaging protocols and radiomics and artificial intelligence show promise in enhancing treatment decisions through precise tumor characterization and outcome prediction<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11463-x","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T19:12:04Z","timestamp":1741201924000},"page":"5714-5732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction"],"prefix":"10.1007","volume":"35","author":[{"given":"Xiaoling","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7007-6367","authenticated-orcid":false,"given":"Bin","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"key":"11463_CR1","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1136\/gutjnl-2022-327736","volume":"72","author":"E Morgan","year":"2023","unstructured":"Morgan E, Arnold M, Gini A et al (2023) Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. 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