Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

Abstract

Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

Assessment of the optimal number of positive biopsy cores to discriminate between cancer‐specific mortality in high‐risk versus very high‐risk prostate cancer patients

“…This fact unfortunately restricts CSM rate calculations to maximal follow‐up of 6 years. Moreover, important information regarding lesions and number of biopsy cores obtained from magnet resonance imaging targeted biopsy are unavailable in the SEER database and could not be assessed in the current study 23–25 . Moreover, no central review of prostate biopsies was performed.…”

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

“…In the literature, studies that classify different levels of aggressiveness implement various AI techniques, both the traditional Machine Learning (ML) algorithm and the more advanced methods of Deep Learning (DL), in particular based on Convolutional Neural Networks (CNN), particularly tailored for the processing of imaging data. Discarding all research that just included the classification between malign and benign lesions, we selected a total of 18 studies [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], including one study from 2015 [22], three from 2017 [17,18,27], two from 2018 [24,28], nine from 2019 [19,25,26,[29][30][31][32][33][34], and three from 2020 [20,21,23].…”

“…We checked how the studies obtained their reference standard, that is the GS or GG from the histological findings. Although prostatectomy is more precise as the whole mount of the gland is inspected, it is used just in three studies [27,32,33] and partially in one paper [21]. The rest of the studies adopted systematic transrectal ultrasound-guided, MR-guided, or MRI/US-fusion biopsy for acquiring their gold standards.…”

“…Noteworthy, some studies also included PI-RADS scores as part of the AI implementation, for example using the scores as one of the features in ML algorithms [21] or combining AI and PI-RADS outcomes to better discriminate clinically and not clinically significant PCa [22]. In the first case, using some hand-crafted radiomic features together with PI-RADS scores could improve the predictive performance when properly combined.…”

Associations between Statin/Omega3 Usage and MRI-Based Radiomics Signatures in Prostate Cancer

“…In prostate cancer, like in many other cancer sites, radiomics has found success in detecting and diagnosing tumors, characterizing index lesions, predicting tumor aggressiveness, evaluating treatment response and prognosis, and associating with tumor genomics [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. However, to the best of our knowledge, radiomics has never been explored as a potential tool to investigate the relationship between medication exposure and prostate cancer.…”