Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer

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Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

Section: Discussionmentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

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“…81.8% (36/44) TZ tumors were identified in 36 patients and the sample was one of convenience. 22.7% (10/44) of the TZ cancers have been previously investigated by our group with mp‐MRI for diagnosis of extraprostatic extension and by using quantitative shape analysis; however, quantitative ADC, texture analysis, and logistic regression (LR) and support vector machine (SVM) modeling pertaining to the current study have not been previously studied or published by our group.…”

Section: Methodsmentioning

confidence: 99%

Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Krishna

Thornhill

et al. 2019

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Background The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI‐RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions. Purpose To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa. Study Type Retrospective. Population Ninety patients; 44 consecutive TZ PCa were compared with 61 consecutive BPH nodules (26 glandular/35 stromal). Field Strength/Sequence 3 T/T2‐weighted (T2W) fast spin‐echo, diffusion weighted imaging. Assessment A radiologist manually segmented lesions on axial images for quantitative ADC (mean, 10th, 25th‐centile‐ADC), T2W‐shape (circularity, convexity) and T2W‐texture (kurtosis, skewness, entropy, run‐length nonuniformity [RLNU], gray‐level nonuniformity [GLNU]) analysis. A second radiologist segmented one‐fifth of randomly selected lesions to determine the reproducibility of measurements. The reference standard was histopathology for all lesions. Statistical Tests Quantitative features were selected a priori and were compared using univariate and multivariate analysis. LR and SVM models of statistically significant features were constructed and evaluated using receiver operator characteristic (ROC) analysis. Subgroup analysis of TZ PCa vs. only stromal BPH and in lesions measuring <15 mm was performed. Agreement in measurements was assessed using the Dice similarity coefficient (DSC). Results Mean, 25th and 10th‐centile ADC, circularity, and texture (entropy, RLNU, GLNU) features differed between groups (P < 0.0001–0.0058); however, at multivariate analysis only circularity and ADC metrics (P < 0.001) remained significant. LR and SVM models were highly accurate for the diagnosis of TZ PCa (sensitivity/specificity/AUC): 93.2%/98.4%/0.989 and 93.2%/96.7%/0.949, respectively, with no significance difference between the LR and SVM models (P = 0.2271). Reproducibility of segmentation was excellent (DSC 0.84 tumors and 0.87 BPH). Subgroup analyses of TZ PCa vs. stromal BPH (AUC = 0.976) and in <15 mm lesions (AUC = 0.990) remained highly accurate. Data Conclusion LR and SVM models incorporating previously described quantitative ADC, shape and texture analysis features are highly accurate for the diagnosis of TZ PCa and remained accurate when comparing TZ PCa with stromal BPH and in smaller lesions. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:940–950.

“…For instance, lesions graded category 5 by either PI‐RADSv2 or the in‐house system achieved the highest tumor detection rates (>70% for all cancers, >80% for CS cancer). Definite EPE at mpMRI, a key component of both systems' categorization to 5 has been linked to detection of CS prostate cancer, higher Gleason scores, and more extensive disease . However, PI‐RADSv2 does not incorporate varying degrees of confidence in the diagnosis of EPE, whereas the in‐house system assigns 1 extra point for mild EPE and 2 points for definite EPE.…”

Section: Discussionmentioning

confidence: 99%

Prospective comparison of PI‐RADS version 2 and qualitative in‐house categorization system in detection of prostate cancer

Gaur

Harmon

Mehralivand

et al. 2018