{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:08:18Z","timestamp":1782630498248,"version":"3.54.5"},"reference-count":30,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop a novel transparent and lightweight machine learning model, the Green Learning (GL), for automated prostate segmentation (PS) and clinically significant prostate cancer (csPCa) detection on magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>Men who underwent 3\u2010T MRI and prostate biopsy (PBx) were identified. MRI was acquired and interpreted according to the Prostate Imaging\u2010Reporting and Data System (PI\u2010RADS), version 2 or 2.1. The GL was created to automate PS and csPCa detection on biparametric MRI. The performance was compared to the standard\u2010of\u2010care radiologists using PI\u2010RADS, and a conventional deep learning (DL) U\u2010Net model as benchmarking. The PS performance was evaluated by the Dice similarity coefficient (DSC). The area under the curve (AUC) for patient\u2010level csPCa detection was assessed. Model size and computational workload, measured by floating point operations (FLOPs), were reported.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 602 MRIs were randomly divided for training (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009483) and testing (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009119). Overall, 224 patients had csPCa on PBx. The median DSC for PS was higher for GL than U\u2010Net (0.91 vs 0.88,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). The AUC for csPCa detection of GL was similar to PI\u2010RADS (0.75 vs 0.76,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.8) and U\u2010Net (vs 0.74,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.3). A combination of GL and PI\u2010RADS showed a higher AUC of 0.81 than PI\u2010RADS alone (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.02). Compared with U\u2010Net, the GL had smaller magnitude parameters (1.21\u00d7\u200910\n                      <jats:sup>6<\/jats:sup>\n                      vs 177\u00d7\u200910\n                      <jats:sup>6<\/jats:sup>\n                      ) and less computational workload (9.8\u00d7\u200910\n                      <jats:sup>9<\/jats:sup>\n                      vs 1027\u00d7\u200910\n                      <jats:sup>9<\/jats:sup>\n                      FLOPs).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A novel GL model fully automatically detects csPCa on prostate biparametric MRI with comparable performance to PI\u2010RADS and DL. Combined with PI\u2010RADS, GL significantly improves csPCa detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.70203","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T17:12:58Z","timestamp":1772989978000},"page":"1014-1025","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A transparent, lightweight and sustainable Green Learning AI model for prostate cancer detection on\n                    <scp>MRI<\/scp>"],"prefix":"10.1111","volume":"137","author":[{"given":"Masatomo","family":"Kaneko","sequence":"first","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Yang","sequence":"additional","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasileios","family":"Magoulianitis","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"},{"name":"Ming Hsieh Department of Electrical and Computer Engineering University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni E.","family":"Cacciamani","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"},{"name":"Radiomics Lab, Department of Radiology, Keck School of Medicine University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jintang","family":"Xue","sequence":"additional","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Passant","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, Keck School of Medicine University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Cai","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karanvir","family":"Gill","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo Storino","family":"Ramacciotti","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manju","family":"Aron","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"},{"name":"Department of Pathology, Keck School of Medicine University of Southern California  Los Angeles CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinay","family":"Duddalwar","sequence":"additional","affiliation":[{"name":"Center for Image\u2010Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer USC Institute of Urology  Los Angeles CA USA"},{"name":"Radiomics Lab, Department of Radiology, Keck School of Medicine University of Southern California  Los Angeles CA USA"},{"name":"Department of Radiology Los Angeles General Medical Center  Los Angeles CA USA"},{"name":"Alfred E. 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