scholarly journals Prostate cancer risk stratification via non-destructive 3D pathology with annotation-free gland segmentation and analysis

2021 ◽  
Author(s):  
W. Xie ◽  
N.P. Reder ◽  
C. Koyuncu ◽  
P. Leo ◽  
S. Hawley ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Stratification ◽  
Ex Vivo ◽  
Prostate Cancer Risk ◽  
Prostate Biopsies ◽  
Image Translation ◽  
Assessment And Treatment ◽  
Prognostic Stratification ◽  
Glandular Structures ◽  
Non Destructive

AbstractProstate cancer treatment planning is largely dependent upon examination of core-needle biopsies. In current clinical practice, the microscopic architecture of the prostate glands is what forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a limited number of 2D histology sections is often unreliable, which contributes to the under- and over-treatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for non-destructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analog of standard H&E staining. Our analysis is based on interpretable glandular features, and is facilitated by the development of image-translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep-learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring real immunolabeling. To provide evidence of the translational value of a computational 3D pathology approach, we analyzed ex vivo biopsies (n = 300) extracted from archived radical-prostatectomy specimens (N = 50), and found that 3D glandular features are superior to corresponding 2D features for risk stratification of low-to intermediate-risk PCa patients based on their clinical biochemical recurrence (BCR) outcomes.SignificanceWe present an end-to-end pipeline for computational 3D pathology of whole prostate biopsies, showing that non-destructive pathology has the potential to enable superior prognostic stratification for guiding critical oncology decisions.

scholarly journals Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis

2021 ◽  
pp. canres.2843.2021
Author(s):  
Weisi Xie ◽  
Nicholas P Reder ◽  
Can F Koyuncu ◽  
Patrick Leo ◽  
Sarah Hawley ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Prostate Cancer Risk ◽  
Non Destructive

scholarly journals Entering an era of radiogenomics in prostate cancer risk stratification

2018 ◽  
Vol 7 (S4) ◽  
pp. S443-S452 ◽  
Author(s):  
Nachiketh Soodana-Prakash ◽  
Radka Stoyanova ◽  
Abhishek Bhat ◽  
Maria C. Velasquez ◽  
Omer E. Kineish ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Prostate Cancer Risk

scholarly journals Pre-treatment risk stratification of prostate cancer patients:A critical review

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
George Rodrigues ◽  
Padraig Warde ◽  
Tom Pickles ◽  
Juanita Crook ◽  
Michael Brundage ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Gleason Score ◽  
Critical Review ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Original Research ◽  
Pre Treatment

Introduction:  The use of accepted prostate cancer risk stratification groups based on prostate-specific antigen, T stage and Gleason score assists in therapeutic treatment decision-making, clinical trial design and outcome reporting. The utility of integrating novel prognostic factors into an updated risk stratification schema is an area of current debate. The purpose of this work is to critically review the available literature on novel pre-treatment prognostic factors and alternative prostate cancer risk stratification schema to assess the feasibility and need for changes to existing risk stratification systems. Methods:  A systematic literature search was conducted to identify original research publications and review articles on prognostic factors and risk stratification in prostate cancer. Search terms included risk stratification, risk assessment, prostate cancer or neoplasms, and prognostic factors. Abstracted information was assessed to draw conclusions regarding the potential utility of changes to existing risk stratification schema. Results:  The critical review identified three specific clinically relevant potential changes to the most commonly used three-group risk stratification system: (1) the creation of a very-low risk category; (2) the splitting of intermediate-risk into a low- and highintermediate risk groups; and (3) the clarification of the interface between intermediate- and high-risk disease. Novel pathological factors regarding high-grade cancer, subtypes of Gleason score 7 and percentage biopsy cores positive were also identified as potentially important risk-stratification factors. Conclusions:  Multiple studies of prognostic factors have been performed to create currently utilized prostate cancer risk stratification systems. We propose potential changes to existing systems.

scholarly journals Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2200 ◽  
Author(s):  
Ahmad Algohary ◽  
Rakesh Shiradkar ◽  
Shivani Pahwa ◽  
Andrei Purysko ◽  
Sadhna Verma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Operating Characteristic ◽  
Prostate Cancer Risk ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
And Diffusion ◽  
Risk Categories

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

108 TEMPLATE TRANSPERINEAL PROSTATE MAPPING BIOPSIES FOR PROSTATE CANCER RISK STRATIFICATION AND CANCER LOCALIZATION

2010 ◽  
Vol 9 (2) ◽  
pp. 67
Author(s):  
P.B. Singh ◽  
H.U. Ahmed ◽  
D. Stevens ◽  
P. Gurung ◽  
A. Freeman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Prostate Cancer Risk

Is clinical stage T2c prostate cancer intermediate- or high-risk disease? Results from the SEARCH database.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 123-123
Author(s):  
Abhay A Singh ◽  
Leah Gerber ◽  
Stephen J. Freedland ◽  
William J Aronson ◽  
Martha K. Terris ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Predictive Accuracy ◽  
Cox Model ◽  
Clinical Stage ◽  
Prostate Cancer Risk ◽  
Clinical Staging ◽  
Intermediate Risk ◽  
Concordance Index

123 Background: Clinical stage T2c is a nebulous factor in the algorithm for prostate cancer risk stratification. According to D’Amico risk stratification cT2c is high-risk category where NCCN guidelines place this stage in intermediate-risk. As diagnostic work up with the use of MRI continues to escalate clinical staging may become more important. As cT2c represents a possible decision fork in treatment decisions we sought to investigate which risk group the clinical behavior of cT2c tumors more closely resembles. Methods: We retrospectively analyzed data from 1089 men who underwent radical prostatectomy (RP) from 1988 to 2009 who did not have low-risk CaP from the SEARCH database. We compared time to BCR between men with cT2c disease, those with intermediate-risk (PSA 10-20 ng/ml or Gleason sum (GS) =7), and those with high-risk (PSA>20 ng/ml, GS 8-10, cT3) using Cox regression models adjusting for age, race, year of RP, center, and percent cores positive. We also compared predictive accuracy of two Cox models wherein cT2c was considered either intermediate- or high-risk by calculating concordance index c. Results: A total of 68 men (3.4%) had cT2c tumors. After a median follow-up of 47.5 months, there was no difference in BCR risk between men with intermediate-risk CaP and those with cT2c tumors (HR=0.90; p=0.60). In contrast, there was a trend for men with high-risk CaP to have nearly 50% increased BCR risk compared to men with cT2c tumors (HR=1.50; 95% CI=0.97-2.30; p=0.07) which did not reach statistical significance. Concordance index c was higher in the Cox model wherein cT2c tumors were considered intermediate-risk (c=0.6147) as opposed to high-risk (c=0.6106). Conclusions: BCR risk for patients with clinical stage T2c was more comparable to men who had intermediate-risk CaP than men with high-risk. In addition, a model which incorporates cT2c disease as intermediate-risk has better predictive accuracy. These findings suggest men with cT2c disease should be offered treatment options for men with intermediate-risk CaP. As clinical staging more routinely incorporates MRI there is the potential to better identify bilateral organ-confined CaP and further establish risk classification.

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