Value of MRI texture analysis for predicting new Gleason grade group

MRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies

RATIONALE AND OBJECTIVES

Prostate cancer (PCa) is the second most common cancer among men and a leading cause of cancer-related mortalities. Radiomics has shown promising performances in the classification of PCa grade group (GG) in several studies. Here, we aimed to systematically review and meta-analyze the performance of radiomics in predicting GG in PCa.

MATERIALS AND METHODS

Adhering to PRISMA-DTA guidelines, we included studies employing magnetic resonance imaging-derived radiomics for predicting GG, with histopathologic evaluations as the reference standard. Databases searched included Web of Sciences, PubMed, Scopus, and Embase. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and METhodological RadiomICs Score (METRICS) tools were used for quality assessment. Pooled estimates for sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the curve (AUC) were calculated. Cochran's Q and I-squared tests assessed heterogeneity, while meta-regression, subgroup analysis, and sensitivity analysis addressed potential sources. Publication bias was evaluated using Deek's funnel plot, while clinical applicability was assessed with Fagan nomograms and likelihood ratio scattergrams.

RESULTS

Data were extracted from 43 studies involving 9983 patients. Radiomics models demonstrated high accuracy in predicting GG. Patient-based analyses yielded AUCs of 0.93 for GG≥2, 0.91 for GG≥3, and 0.93 for GG≥4. Lesion-based analyses showed AUCs of 0.84 for GG≥2 and 0.89 for GG≥3. Significant heterogeneity was observed, and meta-regression identified sources of heterogeneity. Radiomics model showed moderate power to exclude and confirm the GG.

CONCLUSION

Radiomics appears to be an accurate noninvasive tool for predicting PCa GG. It improves the performance of standard diagnostic methods, enhancing clinical decision-making.

Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels

BACKGROUND

Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk.

METHODS

Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy.

RESULTS

Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy.

CONCLUSIONS

There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.