ArticleBMC medicine2023
Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study.
Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07631806 (Prospective Validation of an AI-Assisted Multimodal Imaging-Pathology Fusion System for Precision Diagnosis and Biopsy Guidance in Patients With Suspected Prostate Cancer), which is not on this map. Cited by 15 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Prospective Validation of an AI-Assisted Multimodal Imaging-Pathology Fusion System for Precision Diagnosis and Biopsy Guidance in Patients With Suspected Prostate Cancer
Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Establishment of the China Elderly Comorbidity Medical Database (CECMed) and its application in machine learning-based prediction.BMC geriatrics · 2026Article
- Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis.Scientific reports · 2026Article
- Privacy-Preserving Linkage of Distributed Biological, Clinical, and Imaging Data Supporting Artificial Intelligence in Pediatric Oncology.Methods of information in medicine · 2026Article
- Predicting inguinal lymph node metastasis in penile squamous cell carcinoma, from imaging, molecular biomarkers to multimodal AI: a narrative review.Translational andrology and urology · 2026Review
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Primary healthcare-friendly prostate cancer prediction model using routine clinical parameters: a multicenter study.Frontiers in oncology · 2026Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Tracking and analysis of benign prostatic hyperplasia and prostate cancer burden globally: 1990-2021 epidemiological trends.Translational andrology and urology · 2025Article
- Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework.Scientific reports · 2025Article
- Machine Learning-Based Prediction of Prostate Biopsy Necessity Using PSA, MRI, and Hematologic Parameters.Journal of clinical medicine · 2024Article
- Review
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- A Systematic Review of Artificial Intelligence-Based Clinical Decision Support Systems in Prostate Cancer Management.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
29 authors at 13 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML).
methodsThis study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots.
resultsThe random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively.
conclusionsThe PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study.
trial registrationChinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.