scholarly journals An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

2021 ◽  
Vol 3 (7) ◽  
pp. e445-e454
Author(s):  
Amogh Hiremath ◽  
Rakesh Shiradkar ◽  
Pingfu Fu ◽  
Amr Mahran ◽  
Ardeshir R Rastinehad ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Multicentre Study ◽  
Data System ◽  
Clinical Variables ◽  
Prostate Imaging ◽  
Clinically Significant

Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer

2021 ◽  
pp. 20201434
Author(s):  
Yasuyo Urase ◽  
Yoshiko Ueno ◽  
Tsutomu Tamada ◽  
Keitaro Sofue ◽  
Satoru Takahashi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Data System ◽  
Transition Zones ◽  
Prostate Imaging ◽  
Institutional Review ◽  
3.0 T ◽  
Clinically Significant ◽  
Interreader Agreement

Objectives: To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1, in comparison with v2. Methods: Institutional review board approval was obtained for this retrospective study. Seventy-seven consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v2.1 and v2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated. Results 228 regions were pathologically diagnosed as positive for csPC. With a cutoff ≥3, the agreement among all readers was better with v2.1 than v2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cutoff ≥4, the agreement among all readers was also better with v2.1 than v2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v2.1 was higher than with v2 (TZ, 0.826–0.907 vs 0.788–0.856; PZ, 0.857–0.919 vs 0.853–0.902). Conclusions: Our study suggests that the PI-RADS v2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v2. Advances in knowledge: PI-RADS v2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

scholarly journals The Effect of Different Levels of Prostate-Specific Antigen and Prostate Imaging Report and Data System Scores on Logistic Regression Model to Predict Clinically Significant Prostate Cancer—A Single Center Retrospective Study

2020 ◽  
Author(s):  
Loudong Zhang ◽  
Hua Zhu ◽  
Donghua Gu ◽  
Xiaodong Pan ◽  
bing zheng
Keyword(s):  
Prostate Cancer ◽  
Retrospective Study ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Data System ◽  
Diagnostic Efficiency ◽  
Prostate Imaging ◽  
Clinically Significant ◽  
Different Levels

Abstract Background: At present, there are various clinical regression models for predicting prostate cancer. But what about the diagnostic effectiveness of these models in different parameter ranges, and are the models applicable to everyone? This study aimed to study the influence of different levels of prostate-specific antigen (PSA) and Prostate Imaging Report and Data System version 2 (PI-RADS v2) scores on the regression model to predict clinically significant prostate cancer (csPCa).Methods: This retrospective study screened 251 patients from our hospital, who were divided into different groups. The regression model was established for each group to predict csPCa, and the effects of PSA and PI-RADS scores on each model were analyzed through the diagnostic effects of the model.Results: In patients with lower PSA scores, although the model was less sensitive than PSA, the AUC of the model was much greater. With the rise of PSA, the sensitivity of the model surpassed that of PSA, while the specificity became the opposite, and the AUC gap also gradually decreased. In the group with low PI-RADS score, the sensitivity and specificity of PI-RADS were lower than the model, and the gap was larger. Although the gap between the two gradually decreased with the increase of PI-RADS, the diagnostic efficiency of the model was still slightly larger than that of pure PI-RADS.Conclusion: As the PSA and PI-RADS v2 scores increase, the diagnostic advantages of the regression model will gradually decrease. However, for patients with low levels of PSA and PI-RADS scores,the regression model is less affected by PSA and PI-RADS, and can better utilize its clinical diagnostic advantages.

scholarly journals Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

2021 ◽  
Author(s):  
Matin Hosseinzadeh ◽  
Anindo Saha ◽  
Patrick Brand ◽  
Ilse Slootweg ◽  
Maarten de Rooij ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Prior Knowledge ◽  
Training Data ◽  
Expert Performance ◽  
Free Response ◽  
Algorithm Performance ◽  
Prostate Imaging ◽  
Key Points ◽  
Clinically Significant

Abstract Objectives To assess Prostate Imaging Reporting and Data System (PI-RADS)–trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve men with a suspicion of PCa. Methods Multi-institution data included 2734 consecutive biopsy-naïve men with elevated PSA levels (≥ 3 ng/mL) that underwent multi-parametric MRI (mpMRI). mpMRI exams were prospectively reported using PI-RADS v2 by expert radiologists. A DL framework was designed and trained on center 1 data (n = 1952) to predict PI-RADS ≥ 4 (n = 1092) lesions from bi-parametric MRI (bpMRI). Experiments included varying the number of cases and the use of automatic zonal segmentation as a DL prior. Independent center 2 cases (n = 296) that included pathology outcome (systematic and MRI targeted biopsy) were used to compute performance for radiologists and DL. The performance of detecting PI-RADS 4–5 and Gleason > 6 lesions was assessed on 782 unseen cases (486 center 1, 296 center 2) using free-response ROC (FROC) and ROC analysis. Results The DL sensitivity for detecting PI-RADS ≥ 4 lesions was 87% (193/223, 95% CI: 82–91) at an average of 1 false positive (FP) per patient, and an AUC of 0.88 (95% CI: 0.84–0.91). The DL sensitivity for the detection of Gleason > 6 lesions was 85% (79/93, 95% CI: 77–83) @ 1 FP compared to 91% (85/93, 95% CI: 84–96) @ 0.3 FP for a consensus panel of expert radiologists. Data size and prior zonal knowledge significantly affected performance (4%, $$p<0.05$$ p < 0.05 ). Conclusion PI-RADS-trained DL can accurately detect and localize Gleason > 6 lesions. DL could reach expert performance using substantially more than 2000 training cases, and DL zonal segmentation. Key Points • AI for prostate MRI analysis depends strongly on data size and prior zonal knowledge. • AI needs substantially more than 2000 training cases to achieve expert performance.

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