{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:18:16Z","timestamp":1788153496309,"version":"build-2803163510"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"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>Introduction<\/jats:title>\n                    <jats:p>Reliable differentiation between malignant and benign prostate lesions remains a critical challenge in clinical practice, particularly in settings with limited access to expert interpretation of multiparametric MRI (mpMRI). We investigated whether a streamlined radiomics framework based solely on apparent diffusion coefficient (ADC) imaging could provide accurate, reproducible, and interpretable diagnostic support for prostate cancer detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed ADC images from 198 men, including 121 patients with histologically confirmed prostate cancer and 77 patients without malignancy, acquired between 2020 and 2025. Following image resampling and standardization, 107 radiomic features were extracted and subjected to hierarchical feature selection using recursive feature elimination followed by LASSO regression. Four discriminative features were retained to train support vector machine, random forest, logistic regression, and gradient boosting decision tree (GBDT) classifiers. Model performance was evaluated in independent training and testing cohorts using accuracy, AUC, precision, recall, F1 score, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The GBDT model demonstrated the most robust performance, achieving AUC of 0.81 (95% CI: 0.75\u20130.87) in the training cohort and 0.78 (95% CI: 0.72\u20130.85) in the testing cohort. In the testing cohort, the model achieved an accuracy of 0.76 (95% CI: 0.69\u20130.84), a recall of 1.00 (95% CI: 1.00\u20131.00), an F1 score of 0.86 (95% CI: 0.80\u20130.92), and a specificity of 0.73 (95% CI: 0.62\u20130.88). The selected radiomic features reflected image energy, textural irregularity, and intralesional heterogeneity and were consistently associated with malignant pathology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>A minimal-feature radiomics model derived exclusively from ADC images showed promising diagnostic performance for differentiating malignant from benign prostate lesions. By avoiding reliance on multiparametric imaging, this streamlined approach may provide a practical, reproducible, and resource-efficient strategy for supporting prostate cancer diagnosis, particularly in settings where expert mpMRI interpretation is limited. Further multicenter validation, integration of clinical variables, and incorporation of explainable artificial intelligence approaches are warranted to establish its robustness and enhance its translational potential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1885686","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:27:57Z","timestamp":1788150477000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ADC-based radiomics for risk stratification in prostate cancer: a clinical decision support study"],"prefix":"10.3389","volume":"16","author":[{"given":"Kun","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology, The Third Affiliated Hospital (The Affiliated Luohu Hospital) of Shenzhen University","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangguang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Third Affiliated Hospital (The Affiliated Luohu Hospital) of Shenzhen University","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijing","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Third Affiliated Hospital (The Affiliated Luohu Hospital) of Shenzhen University","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Third Affiliated Hospital (The Affiliated Luohu Hospital) of Shenzhen University","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanlin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Third Affiliated Hospital (The Affiliated Luohu Hospital) of Shenzhen University","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21763","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA: A Cancer J For Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2018.07.005","article-title":"Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018","volume":"103","author":"Ferlay","year":"2018","journal-title":"Eur J Cancer"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol30020178","article-title":"Underlying features of prostate cancer\u2014statistics, risk factors, and emerging methods for its diagnosis","volume":"30","author":"Berenguer","year":"2023","journal-title":"Curr Oncol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2025.0228","article-title":"Prostate cancer: a review","volume":"333","author":"Raychaudhuri","year":"2025","journal-title":"JAMA"},{"key":"B5","doi-asserted-by":"publisher","first-page":"109118","DOI":"10.1016\/j.cmpb.2025.109118","article-title":"NN-PCP: Screening phenotype-related core pathways to construct a prostate cancer metastasis prediction model based on multiple types of mutation data","volume":"273","author":"Zhou","year":"2026","journal-title":"Comput Methods Programs BioMed"},{"key":"B6","doi-asserted-by":"crossref","DOI":"10.1080\/21681805.2022.2057585","article-title":"Swedish national guidelines on urothelial cancer echo the EAU guidelines but with some regional dialect","volume-title":"Scand J Urol","author":"Gontero","year":"2022"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13215378","article-title":"Significant inter-and intralaboratory variation in gleason grading of prostate cancer: a nationwide study of 35,258 patients in the Netherlands","volume":"13","author":"Flach","year":"2021","journal-title":"Cancers"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17030441","article-title":"Heterogeneity in cancer","volume":"17","author":"MacDonald","year":"2025","journal-title":"Cancers"},{"key":"B9","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.euo.2019.08.001","article-title":"Concordance between biopsy and radical prostatectomy pathology in the era of targeted biopsy: a systematic review and meta-analysis","volume":"3","author":"Goel","year":"2020","journal-title":"Eur Urol Oncol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s10334-022-01006-6","article-title":"The role of MRI in prostate cancer: current and future directions","volume":"35","author":"Fernandes","year":"2022","journal-title":"Magn Reson Mater Phys Biol Med"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2022.0063","article-title":"NCCN Guidelines\u00ae insights: prostate cancer, version 1.2023: featured updates to the NCCN guidelines","volume":"20","author":"Schaeffer","year":"2022","journal-title":"J Natl Compr Cancer Network"},{"key":"B12","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1097\/ju.0000000000003491","article-title":"Early detection of prostate cancer: AUA\/SUO guideline part I: prostate cancer screening","volume":"210","author":"Wei","year":"2023","journal-title":"J Urol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11020369","article-title":"A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: high grade vs. low grade","volume":"11","author":"Castillo","year":"2021","journal-title":"Diagnostics"},{"key":"B14","doi-asserted-by":"publisher","first-page":"108804","DOI":"10.1016\/j.cmpb.2025.108804","article-title":"A systematic review of AI as a digital twin for prostate cancer care","volume":"268","author":"John","year":"2025","journal-title":"Comput Methods Programs BioMed"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-20388-4","article-title":"Insights into radiomics: impact of feature selection and classification","volume":"84","author":"Perniciano","year":"2025","journal-title":"Multimedia Tools Appl"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging11080250","article-title":"The role of radiomic analysis and different machine learning models in prostate cancer diagnosis","volume":"11","author":"Bekou","year":"2025","journal-title":"J Imaging"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging12050213","article-title":"MRI-derived biomarkers and radiomic signatures for early, dose-dependent evaluation of prostate cancer radiotherapy: an exploratory study","volume":"12","author":"Bekou","year":"2026","journal-title":"J Imaging"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J Nucl Med"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2025.05.537","article-title":"Enhanced risk stratification for stage II colorectal cancer using deep learning-based CT classifier and pathological markers to optimize adjuvant therapy decision","volume":"36","author":"Huang","year":"2025","journal-title":"Ann Oncol"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00377-0","article-title":"Radiological tumour classification across imaging modality and histology","volume":"3","author":"Wu","year":"2021","journal-title":"Nat Mach Intell"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2024.111314","article-title":"Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review","volume":"171","author":"Qian","year":"2024","journal-title":"Eur J Radiol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2025.103551","article-title":"AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study","volume":"89","author":"Ye","year":"2025","journal-title":"EClinicalMedicine"},{"key":"B23","doi-asserted-by":"crossref","DOI":"10.1109\/ICIPTM54933.2022.9753940","article-title":"Analysis of textural variations in cerebellum in brain to identify Alzheimers by using Haralicks in comparison with gray level co-occurrence matrix (GLRLM)","author":"Venkatesh","year":"2022"},{"key":"B24","first-page":"2987","article-title":"Automated glaucoma detection from fundus eye images using grey level based feature extraction methods and supervised learning classification","volume":"12","author":"Rayen","year":"2021","journal-title":"Turkish Online J Qual Inq"},{"key":"B25","doi-asserted-by":"publisher","first-page":"46","DOI":"10.22514\/jomh.2025.067","article-title":"Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video","volume":"21","author":"Wu","year":"2025","journal-title":"J Men's Health"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13243640","article-title":"A critical analysis of the robustness of radiomics to variations in segmentation methods in 18F-PSMA-1007 PET images of patients affected by prostate cancer","volume":"13","author":"Pasini","year":"2023","journal-title":"Diagnostics"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-025-01585-5","article-title":"Radiological and biological dictionary of radiomics features: Addressing understandable AI issues in personalized prostate cancer, dictionary version PM1.0","volume":"39","author":"Salmanpour","year":"2026","journal-title":"J Imaging Inform Med"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","article-title":"The regression analysis of binary sequences","volume":"20","author":"Cox","year":"1958","journal-title":"J R Stat Soc Ser B Stat Method"},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"B30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann Stat"},{"key":"B32","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.eururo.2023.04.021","article-title":"2022 update on prostate cancer epidemiology and risk factors\u2014a systematic review","volume":"84","author":"Bergengren","year":"2023","journal-title":"Eur Urol"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.792456","article-title":"MRI based radiomics compared with the PI-RADS V2.1 in the prediction of clinically significant prostate cancer: biparametric vs multiparametric MRI","volume":"11","author":"Chen","year":"2022","journal-title":"Front Oncol"},{"key":"B34","first-page":"83","article-title":"Prostate cancer detection: performance of radiomics analysis in multiparametric MRI","author":"Ali","year":"2023"},{"key":"B35","volume-title":"The Role of Multiparametric MRI in Patients on Active Surveillance for Prostate Cancer: Assessment and Validation of the Precise Recommendations","author":"Giganti","year":"2021"},{"key":"B36","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/jmrs.841","article-title":"A two\u2010stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach","volume":"72","author":"Zou","year":"2025","journal-title":"J Med Radiat Sci"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1020317","article-title":"Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4\u223c 10 ng\/mL","volume":"12","author":"Lu","year":"2022","journal-title":"Front Oncol"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.3390\/s21082586","article-title":"Role of AI and histopathological images in detecting prostate cancer: a survey","volume":"21","author":"Ayyad","year":"2021","journal-title":"Sensors"},{"key":"B39","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13244-024-01865-8","article-title":"Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study","volume":"16","author":"Wang","year":"2025","journal-title":"Insights into Imaging"},{"key":"B40","volume-title":"Explainable artificial intelligence for differential diagnosis and intervention of prostate carcinoma using multiparametric MRI","author":"Gunashekar","year":"2025"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1491848","article-title":"An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer","volume":"15","author":"Wu","year":"2025","journal-title":"Front Oncol"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejro.2023.100496","article-title":"Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer","volume":"10","author":"Li","year":"2023","journal-title":"Eur J Radiol Open"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00940","article-title":"Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer","volume":"10","author":"Xu","year":"2020","journal-title":"Front Oncol"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.3390\/cancers12071767","article-title":"Multiparametric MRI for prostate cancer characterization: combined use of radiomics model with PI-RADS and clinical parameters","volume":"12","author":"Wo\u017anicki","year":"2020","journal-title":"Cancers"},{"key":"B45","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1186\/s13244-024-01783-9","article-title":"Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences","volume":"15","author":"Mylona","year":"2024","journal-title":"Insights into Imaging"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1885686\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:27:59Z","timestamp":1788150479000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1885686\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":45,"alternative-id":["10.3389\/fonc.2026.1885686"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1885686","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"article-number":"1885686"}}