Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

Abstract

To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). Materials and methods: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. Results: Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823−0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669−0.976), 0.841, and 0.727, respectively, in the test cohort. Conclusion: Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.

Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer

“…This result suggested that radiomic models offered a high diagnostic accuracy and outperformed the corresponding PI-RADS v2 scores. Min et al investigated an mp-MRI-based radiomic signature for identifying csPCa with an AUC of 0.823 in the validation group (18). The AUC of the radiomic signature for predicting csPCa was 0.86 (internal validation group) and 0.84 (external validation group) in our study, which differed from the result provided by Min et al The difference may be illustrated by differences in research populations and patient selection criteria.…”

“…Radiomics has been mainly used in oncology, for instance, lung cancer, brain astrocytoma, and breast carcinoma, wherein radiomics is utilized to identify tumor stage, curative effect, prognosis assessment, and genetic analysis (12)(13)(14). Radiomics has also been extended to PCa, mainly focusing on PCa diagnosis and differentiation (15)(16)(17)(18). Min et al investigated an mp-MRI-based radiomic signature for predicting patients with csPCa (18).…”

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

“…The performance of the created model was significantly enhanced by combining T2-W and ADC MRI-based texture features, whether the tumor was in the PZ or in the TZ. Similarly, Min et al [37] found consistent results by combining T2-W, ADC, and DWI MRI-based texture features. The mean sensitivity and specificity were 65% (0-100%) and 81% (50-100%), respectively.…”

“…PCa does not have the same characteristics and severity in an Afro-American or Asian population [103]. Yet, most of the previously reviewed studies are based on Asian populations [20,26,28,37,42,50,59] and are probably not applicable to an African American population. Heintzelman [63] emphasizes this difference by showing that Afro-American patients in their study were clustered at the upper end of the pain index spectrum, even if these results were not significant.…”

Radiomics in prostate cancer: basic concepts and current state-of-the-art

“…Another study by Wibmer et al [55] using MRI in 147 patients with PCa confirmed by biopsy showed that Haralick texture features derived from T2-weighted images and apparent diffusion coefficient (ADC) maps had the potential to differentiate PCa and non-cancerous prostate tissue. In the discrimination between clinically significant PCa (csPCa) and clinically insignificant PCa (ciPCa), Min et al [56] demonstrated that mpMRI-based radiomics signature had the potential to noninvasively work it done using a cross-validation of a machine learning method, which may help clinicians to facilitate prebiopsy and pre-treatment risk stratification (AUC, sensitivity, and specificity are 0.823, 0.841, and 0.727, respectively). Furthermore, more useful parameters with good performance are being excavated.…”