Assessment of prostate cancer prognostic Gleason grade group using zonal‐specific features extracted from biparametric MRI using a KNN classifier

Abstract

Purpose: To automatically assess the aggressiveness of prostate cancer (PCa) lesions using zonal-specific image features extracted from diffusion weighted imaging (DWI) and T2W MRI. Methods: Region of interest was extracted from DWI (peripheral zone) and T2W MRI (transitional zone and anterior fibromuscular stroma) around the center of 112 PCa lesions from 99 patients. Image histogram and texture features, 38 in total, were used together with a k-nearest neighbor classifier to classify lesions into their respective prognostic Grade Group (GG) (proposed by the International Society of Urological Pathology 2014 consensus conference). A semi-exhaustive feature search was performed (1-6 features in each feature set) and validated using threefold stratified cross validation in a one-versus-rest classification setup. Results: Classifying PCa lesions into GGs resulted in AUC of 0.87, 0.88, 0.96, 0.98, and 0.91 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5 for the peripheral zone, respectively. The results for transitional zone and anterior fibromuscular stroma were AUC of 0.85, 0.89, 0.83, 0.94, and 0.86 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5, respectively. Conclusion: This study showed promising results with reasonable AUC values for classification of all GG indicating that zonal-specific imaging features from DWI and T2W MRI can be used to differentiate between PCa lesions of various aggressiveness.

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

“…Since almost all clinically-significant lesions (i.e., 71 out of 73) have a Gleason grade group > 1, we can compare this value with the AUC of 88.8% obtained by our method for the G1 vs all task. Our experiments also confirm results of previous works showing the efficacy of radiomics for analyzing PCa images [40], [50]- [55]. In [56], entropy-based texture features extracted from gray-level co-occurrence matrix (GLCMs) were found to be related to GS, more specifically, that a higher GS is associated with a higher ADC entropy and low ADC energy.…”

“…Among these 112 lesions/findings, there were 36, 41, 19, 8, 8 tumors with GS ≤ 6 (G1), GS = 7 (3+4; G2), GS = 7 (4+3; G3), GS = 8 (4+4, 3+5, or 5+3; G4), and GS ≥ 9 (G5), respectively ( Table I). In the cohort of 99 patients (average age 65 years, range 42-78 years), 87 patients had one lesion, 11 patients had two, and a single patient had three [40]. Table II reports the number of layers in each of the 9 pretrained CNN architectures and their corresponding number of unique DEFs.…”

“…It is unknown if peripheral zones (PZ) are biologically different than transitional Zones (TZ). With radomic analysis applied on mpMRI in understanding the mechanism of PCa, the impact of tumor location on biological behavior may have significant implications for optimum treatment modalities [40]. Figure 6A shows the DEFs most correlated with GS, for lesions located in the three anatomical zones of the prostate (i.e., PZ, TZ, and anterior -AZ).…”

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

“…The flow diagram is depicted in Figure 1 . In total, 27 articles were eligible for inclusion in this review [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. From these, 13 studies reported enough information to perform a meta-analysis.…”

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

“…It is an effective model that is widely employed for data mining [ 15 ], and it functions effectively as a nonparametric model for classification and regression [ 16 ]. It is also commonly used in research on PCa [ 15 , 17 – 19 ].…”