ISUP Grade Prediction of Prostate Nodules on T2WI Acquisitions Using Clinical Features, Textural Parameters and Machine Learning-Based Algorithms.
Simple Summary: Prostate cancer is the most frequent and impactful malignancy in male patients. Although multi-parametric magnetic resonance imaging (mpMRI) of the prostate has enabled notable progress in terms of image resolution and prostate cancer detection, it does not hold a one-to-one correspo...
| Publicado en: | Cancers Vol. 17; no. 12; pp. 2035 - 2054 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images protocol research tables/charts Journal Article |
| Publicado: |
MDPI
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186205667&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186205667 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jun2025 vid: 17 iid: 12 pid: 97109 pub: MDPI artinfo: ui: 186205667 186205667 186205667 10.3390/cancers17122035 186205667 ppf: 2035 ppct: 19 formats: tig: atl: ISUP Grade Prediction of Prostate Nodules on T2WI Acquisitions Using Clinical Features, Textural Parameters and Machine Learning-Based Algorithms. aug: au: Telecan, Teodora Chiorean, Alexandra Sipos-Lascu, Roxana Caraiani, Cosmin Boca, Bianca Hendea, Raluca Maria Buliga, Teodor Andras, Iulia Crisan, Nicolae Lupsor-Platon, Monica affil: Department of Anatomy and Embryology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania sug: subj: Prostatic Neoplasms Diagnosis Adenocarcinoma Diagnosis Neoplasm Grading Machine Learning Algorithms Magnetic Resonance Imaging Methods Prostatic Neoplasms Radiography Prostatic Neoplasms Symptoms Biopsy Human Male Female Middle Age Aged Cancer Patients Severity of Illness Artificial Intelligence Radiomics Random Forest Support Vector Machine Logistic Regression Descriptive Statistics Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Simple Summary: Prostate cancer is the most frequent and impactful malignancy in male patients. Although multi-parametric magnetic resonance imaging (mpMRI) of the prostate has enabled notable progress in terms of image resolution and prostate cancer detection, it does not hold a one-to-one correspondence with the pathological report; thus, a prostate biopsy is still needed in order to confirm the suspicion of malignancy. Recent advancements in textural analysis and machine learning (ML) classification models have attempted to create non-invasive diagnostic surrogates, bypassing the need for confirmatory biopsy. Starting from this hypothesis, we aimed to create an ML-based classification algorithm that can accurately predict the histology of prostate nodules, based on their textural characteristics derived from the mpMRI scans and integrated clinical data. Background: Prostate cancer (PCa) represents a matter at the forefront of healthcare, being divided into clinically significant (csPCa) and indolent PCa based on prognostic and treatment options. Although multi-parametric magnetic resonance imaging (mpMRI) has enabled significant advances, it cannot differentiate between the aforementioned categories; therefore, in order to render the initial diagnosis, invasive procedures such as transrectal prostate biopsy are still necessary. In response to these challenges, artificial intelligence (AI)-based algorithms combined with radiomics features offer the possibility of creating a textural pixel pattern-based surrogate, which has the potential of correlating the medical imagery with the pathological report in a one-to-one manner. Objective: The aim of the present study was to develop a machine learning model that can differentiate indolent from csPCa lesions, as well as individually classifying each nodule into corresponding ISUP grades prior to prostate biopsy, using textural features derived from mpMRI T2WI acquisitions. Materials and Methods: The study was conducted in 154 patients and 201 individual prostatic lesions. All cases were scanned using the same 1.5 Tesla mpMRI machine, employing a standard protocol. Each nodule was manually delineated using the 3D Slicer platform (version 5.2.2) and textural parameters were derived using the PyRadiomics database (version 3.1.0). We compared three machine learning classification models (Random Forest, Support Vector Machine, and Logistic Regression) in full, partial and no correlation settings, in order to differentiate between indolent and csPCa, as well as between ISUP 2 and ISUP 3 lesions. Results: The median age was 65 years (IQR: 61–69), the mean PSA value was 10.27 ng/mL, and 76.61% of the segmented lesions had a PI-RADS score of 4 or higher. Overall, the highest performance was registered for the Random Forest model in the partial correlation setting, differentiating between indolent and csPCa and between ISUP 2 versus ISUP 3 lesions, with accuracies of 88.13% and 82.5%, respectively. When the models were trained on combined clinical data and radiomic signatures, these accuracies increased to 91.11% and 91.39%, respectively. Conclusions: We developed a machine learning decision support tool that accurately predicts the ISUP grade prior to prostate biopsy, based on the textural features extracted from T2 MRI acquisitions. pubtype: Academic Journal doctype: diagnostic images protocol research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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