Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm
Abstract Objective The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms. Materials and methods We retrospectively analysed data from 147 patie...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-10-01
|
| Series: | BJUI Compass |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/bco2.421 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846110578329780224 |
|---|---|
| author | Alan Priester Sakina Mohammed Mota Kyla P. Grunden Joshua Shubert Shannon Richardson Anthony Sisk Ely R. Felker James Sayre Leonard S. Marks Shyam Natarajan Wayne G. Brisbane |
| author_facet | Alan Priester Sakina Mohammed Mota Kyla P. Grunden Joshua Shubert Shannon Richardson Anthony Sisk Ely R. Felker James Sayre Leonard S. Marks Shyam Natarajan Wayne G. Brisbane |
| author_sort | Alan Priester |
| collection | DOAJ |
| description | Abstract Objective The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms. Materials and methods We retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison. Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test. Results Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%). Conclusions Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients. |
| format | Article |
| id | doaj-art-fafe9e34c2904c0c96cf0817777da766 |
| institution | Kabale University |
| issn | 2688-4526 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | BJUI Compass |
| spelling | doaj-art-fafe9e34c2904c0c96cf0817777da7662024-12-24T02:30:45ZengWileyBJUI Compass2688-45262024-10-015101100111110.1002/bco2.421Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithmAlan Priester0Sakina Mohammed Mota1Kyla P. Grunden2Joshua Shubert3Shannon Richardson4Anthony Sisk5Ely R. Felker6James Sayre7Leonard S. Marks8Shyam Natarajan9Wayne G. Brisbane10Avenda Health, Inc. United StatesAvenda Health, Inc. United StatesDepartment of Urology David Geffen School of Medicine United StatesAvenda Health, Inc. United StatesDepartment of Urology David Geffen School of Medicine United StatesDepartment of Pathology David Geffen School of Medicine United StatesDepartment of Radiology David Geffen School of Medicine United StatesDepartment of Radiological Sciences and Biostatistics University of California, Los Angeles United StatesDepartment of Urology David Geffen School of Medicine United StatesAvenda Health, Inc. United StatesDepartment of Urology David Geffen School of Medicine United StatesAbstract Objective The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms. Materials and methods We retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison. Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test. Results Unfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%). Conclusions Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients.https://doi.org/10.1002/bco2.421artificial intelligencefusion biopsyextracapsular extensionMRIprostate cancer |
| spellingShingle | Alan Priester Sakina Mohammed Mota Kyla P. Grunden Joshua Shubert Shannon Richardson Anthony Sisk Ely R. Felker James Sayre Leonard S. Marks Shyam Natarajan Wayne G. Brisbane Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm BJUI Compass artificial intelligence fusion biopsy extracapsular extension MRI prostate cancer |
| title | Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm |
| title_full | Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm |
| title_fullStr | Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm |
| title_full_unstemmed | Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm |
| title_short | Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm |
| title_sort | extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm |
| topic | artificial intelligence fusion biopsy extracapsular extension MRI prostate cancer |
| url | https://doi.org/10.1002/bco2.421 |
| work_keys_str_mv | AT alanpriester extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT sakinamohammedmota extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT kylapgrunden extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT joshuashubert extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT shannonrichardson extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT anthonysisk extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT elyrfelker extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT jamessayre extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT leonardsmarks extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT shyamnatarajan extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm AT waynegbrisbane extracapsularextensionriskassessmentusinganartificialintelligenceprostatecancermappingalgorithm |