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This multicentre validation evaluates whether deep learning can enable accurate breast cancer diagnosis using only diffusion-weighted imaging (DWI). A four-centre dataset of 2493 pathologically confirmed patients trains, validates, and tests a DWI-only model and compares it with an abbreviated enhanced model and expert radiologists across external and prospective cohorts.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ai-driven-diffusion-weighted-imaging-based-non-contrast-protocol-for-breast-cancer-diagnosis-a-multicentre-multidimensional-validation-study/434805/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ai-driven-diffusion-weighted-imaging-based-non-contrast-protocol-for-breast-cancer-diagnosis-a-multicentre-multidimensional-validation-study/434805.png","ImageObject",300,407,{"name":92,"@type":93},"Terk","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in breast MRI diagnosis?","Question",{"text":112,"@type":113},"It targets limitations of contrast-enhanced breast MRI, including complexity, long acquisition times, and reliance on gadolinium-based contrast agents. 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Noncontrast diffusion-weighted imaging (DWI) offers an ultrafast alternative, but its diagnostic accuracy has been insufficient for standalone use. We investigated whether a deep learning (DL) model could enable accurate breast cancer diagnosis using only DWI.  \nMethods This four-centre study included 2493 patients with pathologically confirmed breast lesions. A DWI-based model (DWI-DL) was developed using data from 1286 patients at Peking University People’s Hospital (PKUPH; January 2015 to July 2021), who were randomly divided into three cohorts at a ratio of 6:2:2 (train n = 774, validation n = 256, test n = 256) . Independent testing used three external cohorts (n = 661) and a prospective cohort from PKUPH that was retrospectively analysed (n = 546; August 2021 to September 2022) . The diagnostic performance of DWI-DL model was compared to that of an abbreviated enhanced-based (AE-DL) model and two expert radiologists. Subgroup analysis assessed performance across external and prospective cohorts. Amultireader multicase validation, using DW-DL model and selective contrast-enhanced workflow, was conducted by 12 radiologists from ten institutions to evaluate the clinical utility of DWI-DL in assisting diagnosis. Performance was assessed using the area under the curve (AUC) of receiver operating characteristics. The multireader multicase study was registered with the China Clinical Trial Registry (ChiCTR2500095953) .  \nFindings DWI-DL model demonstrated diagnostic performance across all cohorts that was comparable to the AE-DL model (AUC: 0.771–0.912 vs. 0.780–0.898, p = 0.36–0.98). DWI-DL outperformed two expert radiologists interpreting DWI alone (AUC: 0.781–0.858 vs. 0.714–0.770, p range \u003C0.0001–0.023) . In the multireader multicase validation, the AI-guided selective sequence protocol was non-inferior to the full protocol (Protocol E vs. C, AUC: 0.834 [95% CI: 0.785, 0.883] vs. 0.835 [95% CI: 0.789, 0.881], difference: −0.001 [95% CI: −0.029, 0.027], p = 0.94), while simultaneously reduced interpretation time by 55.5%(Protocol E vs. C, mean time: 59.6 [43.5] vs. 134.0 [72.4] seconds, p \u003C 0.0001) .  \nInterpretation A DL model using only non-contrast DWI can accurately diagnose breast cancer. The DWI-DL model demonstrated robust performance, comparable to the AE-DL model and surpassing human experts in DWI  \n*Corresponding author.  \n**Corresponding author.  \n***Corresponding author.  \n****Cor","cbCaiuwWSBrufaEO","https://ap.wps.com/l/cbCaiuwWSBrufaEO","pdf",4318447,16,"English","# Summary\n# Background\n# Methods\n## Study design and cohorts\n## Comparators and validation\n## Evaluation metrics\n# Findings\n# Interpretation\n# Funding\n# Research in context","[{\"question\":\"What problem does the study address in breast MRI diagnosis?\",\"answer\":\"It targets limitations of contrast-enhanced breast MRI, including complexity, long acquisition times, and reliance on gadolinium-based contrast agents. It tests whether a DWI-only approach can replace contrast for accurate diagnosis.\"},{\"question\":\"How was the DWI-based deep learning model evaluated?\",\"answer\":\"The study developed a DWI-DL model using one hospital cohort, validated and tested it internally, and then assessed performance on external cohorts plus a prospective cohort retrospectively analyzed. It compared results to an abbreviated enhanced-based model and two expert radiologists.\"},{\"question\":\"What were the key outcomes of using DWI-DL and an AI-guided selective protocol?\",\"answer\":\"DWI-DL showed diagnostic performance comparable to the abbreviated enhanced-based model across cohorts and outperformed expert radiologists when interpreting DWI alone. In multireader multicase validation, the AI-guided selective contrast-enhanced workflow was non-inferior to the full protocol while reducing interpretation time by 55.5%.\"}]","AI-driven diffusion weighted imaging-based non-contrast protocol for breast cancer diagnosis - a multicentre, multidimensional validation study | PDF",1790670643]