{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:07:39Z","timestamp":1784696859675,"version":"3.55.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and internally validate a parsimonious logistic regression model for clinically significant prostate cancer (CSPCa) specifically in patients with PI-RADS 3 lesions, integrating multiparametric clinical indicators to guide biopsy decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively enrolled 193 patients with PI-RADS 3 lesions who underwent mpMRI and prostate biopsy. A nested five-fold cross-validation framework was used, with LASSO regression performed independently in each training fold to select predictors from nine clinical variables. Four modeling approaches were compared, and logistic regression was chosen as the final model based on performance, calibration, and interpretability. Model discrimination, calibration, and clinical utility were evaluated internally.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LASSO selected age, PHI, and PHID as core predictors. Logistic regression achieved the highest cross-validated AUROC (0.840, 95% CI: 0.754\u20130.916) with acceptable calibration (slope 0.917, intercept \u22120.122). The model outperformed PSA and PSAD alone, and showed numerically higher discrimination than PHI or PHID alone. However, the incremental AUROC gain over PHI or PHID alone did not reach statistical significance. Exploratory risk stratification identified low-, intermediate-, and high-risk groups with CSPCa detection rates of 8.1%, 38.5%, and 83.3%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A three-variable logistic model incorporating age, PHI, and PHID demonstrated stable internal performance for predicting CSPCa in PI-RADS 3 patients. External validation is needed before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1883622","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:49:04Z","timestamp":1784695744000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and internal validation of a parsimonious logistic regression model integrating multiparametric clinical indicators for predicting prostate cancer in the PI-RADS 3 cohort"],"prefix":"10.3389","volume":"16","author":[{"given":"Yongheng","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Urology, Qilu Hospital of Shandong University","place":["Jinan, China"]},{"name":"University of Health and Rehabilitation Sciences","place":["Qingdao, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meikai","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Urology, Qilu Hospital of Shandong University","place":["Jinan, China"]},{"name":"University of Health and Rehabilitation Sciences","place":["Qingdao, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Urology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Urology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaofeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Urology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2024.11.013","article-title":"Recent patterns and trends in global prostate cancer incidence and mortality: An update","volume":"87","author":"Schafer","year":"2025","journal-title":"Eur Urol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s00345-024-04991-6","article-title":"Predicting clinically significant prostate cancer following suspicious mpMRI: Analyses from a high-volume center","volume":"42","author":"Jahnen","year":"2024","journal-title":"World J Urol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019182946","article-title":"PI-RADS Steering Committee: The PI-RADS multiparametric MRI and MRI-directed biopsy pathway","volume":"292","author":"Padhani","year":"2019","journal-title":"Radiology"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2019.06.014","article-title":"Prostate imaging reporting and data system 3 category cases at multiparametric magnetic resonance for prostate cancer: A systematic review and meta-analysis","volume":"6","author":"Maggi","year":"2020","journal-title":"Eur Urol Focus"},{"key":"B5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.21037\/tau.2017.12.31","article-title":"MRI in early prostate cancer detection: How to manage indeterminate or equivocal PI-RADS 3 lesions","volume":"7","author":"Schoots","year":"2018","journal-title":"Transl Androl Urol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2020.09.042","article-title":"EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: Screening, diagnosis, and local treatment with curative intent","volume":"79","author":"Mottet","year":"2021","journal-title":"Eur Urol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2023.0050","article-title":"Prostate cancer, version 4.2023, NCCN clinical practice guidelines in oncology","volume":"21","author":"Schaeffer","year":"2023","journal-title":"J Natl Compr Canc Netw"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1097\/js9.0000000000003269","article-title":"The role of apparent diffusion coefficient values in diagnosing prostate cancer for patients with equivocal PI-RADS 3 lesions: A multicenter retrospective study","volume":"111","author":"Wang","year":"2025","journal-title":"Int J Surg"},{"key":"B9","doi-asserted-by":"publisher","first-page":"370 e379","DOI":"10.1016\/j.urolonc.2024.05.025","article-title":"Molecular and diffusion features for identification of clinically significant prostate cancer in PI-RADS 3 lesions","volume":"42","author":"Ajami","year":"2024","journal-title":"Urol Oncol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.euo.2020.12.004","article-title":"Positive predictive value of prostate imaging reporting and data system version 2 for the detection of clinically significant prostate cancer: A systematic review and meta-analysis","volume":"4","author":"Mazzone","year":"2021","journal-title":"Eur Urol Oncol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1038\/s41391-021-00417-1","article-title":"Cancer detection rates of the PI-RADSv2.1 assessment categories: Systematic review and meta-analysis on lesion level and patient level","volume":"25","author":"Oerther","year":"2022","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"B12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1097\/mou.0000000000001188","article-title":"Liquid biomarkers in prostate cancer: Recent advancements and future directions","volume":"35","author":"Wong","year":"2025","journal-title":"Curr Opin Urol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cca.2022.04.992","article-title":"Value of PHI and PHID in the detection of intermediate- and high-risk prostate cancer","volume":"531","author":"Filella","year":"2022","journal-title":"Clin Chim Acta"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1016\/j.prnil.2025.08.004","article-title":"Utility of the prostate health index density for detecting clinically significant prostate cancer in patients with negative magnetic resonance imaging findings","volume":"13","author":"Oshinomi","year":"2025","journal-title":"Prostate Int"},{"key":"B15","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","article-title":"How machine learning will transform biomedicine","volume":"181","author":"Goecks","year":"2020","journal-title":"Cell"},{"key":"B16","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat Rev Mol Cell Biol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12880-023-01002-9","article-title":"Machine learning-based radiomics model to predict benign and Malignant PI-RADS v2.1 category 3 lesions: A retrospective multi-center study","volume":"23","author":"Jin","year":"2023","journal-title":"BMC Med Imaging"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J Stat Software"},{"key":"B19","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s40644-024-00667-x","article-title":"The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy","volume":"24","author":"Hu","year":"2024","journal-title":"Cancer Imaging: Off Publ Int Cancer Imaging Soc"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.4111\/icu.20230060","article-title":"A prospective study of the prostate health index density and multiparametric magnetic resonance imaging in diagnosing clinically significant prostate cancer","volume":"64","author":"Chen","year":"2023","journal-title":"Investig Clin Urol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1007\/s00345-020-03585-2","article-title":"PHI density prospectively improves prostate cancer detection","volume":"39","author":"Stephan","year":"2021","journal-title":"World J Urol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.21037\/tau-23-80","article-title":"Comparisons of the diagnostic accuracy across prostate health index, prostate health index density, and percentage free prostate-specific antigen for clinically significant prostate cancer: A prospective diagnostic study","volume":"12","author":"Wu","year":"2023","journal-title":"Transl Androl Urol"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1343999","article-title":"BioPrev-C - development and validation of a contemporary prostate cancer risk calculator","volume":"14","author":"Hermanns","year":"2024","journal-title":"Front Oncol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s41512-020-00084-1","article-title":"Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: A systematic review","volume":"4","author":"Miles","year":"2020","journal-title":"Diagn Progn Res"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1002\/pros.70168","article-title":"Development of machine learning models for predicting prostate cancer in biopsy candidates using prostate-specific antigen, magnetic resonance imaging, and hematologic parameters","volume":"86","author":"\u00d6zl\u00fc","year":"2026","journal-title":"Prostate"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1687745","article-title":"Development of machine learning models for predicting postoperative hyperglycemia in non-diabetic gastric cancer patients: A retrospective cohort study analysis","volume":"16","author":"Wang","year":"2025","journal-title":"Front Endocrinol"},{"key":"B27","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1186\/s12894-025-01986-2","article-title":"To biopsy or not biopsy, that is the question - PI-RADS 3 prostate lesions - validation of clinical and radiological parameters for biopsy decision-making","volume":"25","author":"Franz","year":"2025","journal-title":"BMC Urol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.4103\/Singaporemedj.smj-2024-017","article-title":"Determinants of decision-making in biopsy of PI-RADS 3 transition zone lesions","author":"Wee","year":"2024","journal-title":"Singapore Med J"},{"key":"B29","doi-asserted-by":"publisher","first-page":"34","DOI":"10.4103\/aja202342","article-title":"Developing a diagnostic model for predicting prostate cancer: A retrospective study based on Chinese multicenter clinical data","volume":"26","author":"Wang","year":"2024","journal-title":"Asian J Androl"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.825429","article-title":"Development and validation of a radiomics nomogram for predicting clinically significant prostate cancer in PI-RADS 3 lesions","volume":"11","author":"Li","year":"2021","journal-title":"Front Oncol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1883622\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:49:08Z","timestamp":1784695748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1883622\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":30,"alternative-id":["10.3389\/fonc.2026.1883622"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1883622","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]},"article-number":"1883622"}}