scholarly journals Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1213
Author(s):  
Joshua S. Jue ◽  
David Mikhail ◽  
Javier González ◽  
Mahmoud Alameddine
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Specific Antigen ◽  
Learning Model ◽  
Molecular Forms ◽  
Model Diagnostics ◽  
High Grade ◽  
Resonance Imaging ◽  
Psa Density ◽  
Deep Learning Model

Prostate-specific antigen (PSA) has been criticized for its low specificity for prostate cancer, which has led to the increased adoption of additional biomarkers, PSA density (PSAD), and multiparametric magnetic resonance imaging (mpMRI) to increase the localization, risk stratification, and diagnosis of prostate cancer [...]

scholarly journals Reply to Jue et al. Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on “Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335”

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1214
Author(s):  
Francesco Gentile ◽  
Matteo Ferro ◽  
Bartolomeo Della Ventura ◽  
Evelina La Civita ◽  
Antonietta Liotti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Molecular Forms ◽  
Model Diagnostics ◽  
High Grade ◽  
Psa Density ◽  
Deep Learning Model

In their comment “Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer [...]

scholarly journals Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 335
Author(s):  
Francesco Gentile ◽  
Matteo Ferro ◽  
Bartolomeo Della Ventura ◽  
Evelina La Civita ◽  
Antonietta Liotti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Specific Antigen ◽  
Molecular Forms ◽  
Psa Density ◽  
Prostate Specific Antigen Test ◽  
Free Psa ◽  
Artificial Neuronal Networks ◽  
Total Psa ◽  
Deep Learning Model

After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

scholarly journals Usefulness of Prostate-Specific Antigen Density as an Indicator for Recommending Prebiopsy Magnetic Resonance Imaging to Prevent Missed Prostate Cancer Diagnoses

2021 ◽  
Vol 19 (3) ◽  
pp. 155-163
Author(s):  
Jin Hyung Jeon ◽  
Kyo Chul Koo ◽  
Byung Ha Chung ◽  
Kwang Suk Lee
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Resonance Imaging ◽  
Detection Rates ◽  
Psa Density ◽  
Systematic Biopsy ◽  
Target Biopsy

Purpose: To identify the indication for recommending prebiopsy magnetic resonance imaging (MRI) to prevent prostate cancer missed diagnoses in cases without prebiopsy MRI.Materials and Methods: Between January 2017 and September 2020, 585 patients suspected with prostate cancer underwent prostate biopsy after MRI. For patients with visible lesions, MRI-targeted biopsy using an image-based fusion program was performed in addition to the 12- core systematic biopsy. Patients for whom MRI was performed in other institutions (n=4) and patients who underwent target biopsy alone (n=7) were excluded.Results: Of 574 patients (median prostate-specific antigen [PSA] level, 6.88 ng/mL; mean age, 68.2 years), 342 (59.6%) were diagnosed with prostate cancer (visible lesions=312/449 [69.5%]; nonvisible lesions=30/123 [24.0%]). The detection rates of visible lesions stratified using the Prostate Imaging Reporting and Data System score (3 vs. 4 vs. 5) were 30.9% (54 of 175), 61.2% (150 of 245), and 90.1% (127 of 141), respectively. Multivariate analysis showed that PSA density was a significant factor for presence of visible lesions, prostate cancer, and significant prostate cancer diagnosis. Among patients with positive lesions, 27 (8.2%) were diagnosed with prostate cancer concomitant with negative systematic biopsy results. A PSA density of 0.15 ng/mL/cm<sup>3</sup> was identified as the significant cutoff value for predicting positive target biopsy in groups with negative systematic biopsy. Sixty of the negative target lesions (26.1%) were diagnosed using systematic biopsy.Conclusions: To maximize cancer detection rates, both targeted and systematic biopsies should be implemented. PSA density was identified as a useful factor for recommending prebiopsy MRI to patients suspected with prostate cancer.

scholarly journals Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer

2019 ◽  
Vol 26 (2) ◽  
pp. 945-962 ◽  
Author(s):  
Okyaz Eminaga ◽  
Omran Al-Hamad ◽  
Martin Boegemann ◽  
Bernhard Breil ◽  
Axel Semjonow
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Gleason Score ◽  
Clinical Decision ◽  
Learning Model ◽  
Classification Performance ◽  
Study Data ◽  
Learning Approaches ◽  
Tumor Stages ◽  
Deep Learning Model

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient’s subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.

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