Impact of specimen type on digital histopathology-based multimodal artificial intelligence (MMAI) biomarker risk score: Whole slide image vs tissue microarray.

400 Background: The ArteraAI Prostate Test (v1.2), a digital pathology-based multimodal artificial intelligence (MMAI) biomarker, was developed and validated using clinical data (age, T stage, PSA) and whole slide images (WSI) from prostate biopsies to prognosticate risk of 10-year distant metastasis for men with localized prostate cancer (PCa). This study aimed to apply the MMAI biomarker to prostatectomy (RP) tissue microarray (TMA) samples and, for the first time, explore the impact of using RP TMA in place of RP WSI on MMAI scores. Methods: The analysis cohort included men with localized PCa who had undergone RP. Black men were matched in a 4:1 ratio to White men with similar baseline characteristics. MMAI scores were generated using digitized TMA and WSI from each patient’s RP specimen. The normality of score distribution was examined using the Shapiro-Wilk (SW) test. Wilcoxon signed rank (WSR) test and Spearman rank coefficients were used to compare TMA-derived and WSI-derived scores. Analyses were performed in the entire cohort and Black or White subgroups. Results: Paired MMAI scores were generated for 98 men with a median age of 58, PSA 5.4 ng/mL, the majority were Gleason grade groups 1-2 (84%), 82% were Black. The distribution of TMA-derived scores (SW test, P=0.04) was more skewed than that of WSI-derived scores (SW test, P=0.39). The median MMAI score was significantly higher for WSI images than for TMA images in all men and in Black men (WSR test, P<0.01). There was no notable variation in either set of scores by race (Table). The correlation coefficient was 0.496 (0.519 for Black men). Using pre-specified cutoffs in MMAI scores (high, intermediate, low), 30/98 (31%) men were classified into lower risk groups by TMA than WSI scores (23/80 [29%] Black men). Conclusions: In this application of the MMAI biomarker to RP samples within this cohort of primarily Black men, we found WSI-derived and TMA-derived MMAI scores to be significantly different but moderately correlated. Given that TMA represents only a portion of WSI and sampling location may impact the results, TMA may be insufficiently reliable for MMAI score generation. MMAI biomarkers that are robust across different specimen preparation methods have potential for clinical and research utility; these hypothesis-generating results support further research along these lines. Continuous MMAI scores and categorical MMAI risk group distribution using WSI- and TMA-derived images by subgroup. WSI-derived score TMA-derived score Patients Continuous 1 Low 2 Int 2 High 2 Continuous 1 Low 2 Int 2 High 2 All 0.47(0.44-0.53) 0(0%) 69(70%) 29(30%) 0.37(0.33-0.42) 6(6%) 87(89%) 5(5%) Black 0.47(0.45-0.51) 0(0%) 58(73%) 22(27%) 0.37(0.33-0.43) 4(5%) 73(91%) 3(4%) White 0.48(0.43-0.54) 0(0%) 11(61%) 7(39%) 0.37(0.32-0.41) 2(11%) 14(78%) 2(11%) 1 Presented as median (IQR). 2 Presented as n (%).

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Publication Details

Journal
Journal of Clinical Oncology
Published
2025-02-10
DOI
https://doi.org/10.1200/jco.2025.43.5_suppl.400
Primary Topic
AI in cancer detection
Type
article
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article

Impact of specimen type on digital histopathology-based multimodal artificial intelligence (MMAI) biomarker risk score: Whole slide image vs tissue microarray.

G Migneco, Timothy Norman Showalter, Felix Yi-Chung Feng, Siyi Tang et al.
Journal of Clinical Oncology
AI in cancer detection
article

Impact of specimen type on digital histopathology-based multimodal artificial intelligence (MMAI) biomarker risk score: Whole slide image vs tissue microarray.

G Migneco, Timothy Norman Showalter, Felix Yi-Chung Feng, Siyi Tang, Meghan Tierney, Nitin Kumar Mittal, Danielle C. Croucher, Rajanikanth Vadigepalli, Adam P. Dicker, Trevor J. Royce, William K. Kelly, Dibya Mukherjee, Sujata Patil, Andre Esteva, Leonard G. Gomella, Xiao Ma, Stefany Hinojosa
article en

Abstract

400 Background: The ArteraAI Prostate Test (v1.2), a digital pathology-based multimodal artificial intelligence (MMAI) biomarker, was developed and validated using clinical data (age, T stage, PSA) and whole slide images (WSI) from prostate biopsies to prognosticate risk of 10-year distant metastasis for men with localized prostate cancer (PCa). This study aimed to apply the MMAI biomarker to prostatectomy (RP) tissue microarray (TMA) samples and, for the first time, explore the impact of using RP TMA in place of RP WSI on MMAI scores. Methods: The analysis cohort included men with localized PCa who had undergone RP. Black men were matched in a 4:1 ratio to White men with similar baseline characteristics. MMAI scores were generated using digitized TMA and WSI from each patient’s RP specimen. The normality of score distribution was examined using the Shapiro-Wilk (SW) test. Wilcoxon signed rank (WSR) test and Spearman rank coefficients were used to compare TMA-derived and WSI-derived scores. Analyses were performed in the entire cohort and Black or White subgroups. Results: Paired MMAI scores were generated for 98 men with a median age of 58, PSA 5.4 ng/mL, the majority were Gleason grade groups 1-2 (84%), 82% were Black. The distribution of TMA-derived scores (SW test, P=0.04) was more skewed than that of WSI-derived scores (SW test, P=0.39). The median MMAI score was significantly higher for WSI images than for TMA images in all men and in Black men (WSR test, P<0.01). There was no notable variation in either set of scores by race (Table). The correlation coefficient was 0.496 (0.519 for Black men). Using pre-specified cutoffs in MMAI scores (high, intermediate, low), 30/98 (31%) men were classified into lower risk groups by TMA than WSI scores (23/80 [29%] Black men). Conclusions: In this application of the MMAI biomarker to RP samples within this cohort of primarily Black men, we found WSI-derived and TMA-derived MMAI scores to be significantly different but moderately correlated. Given that TMA represents only a portion of WSI and sampling location may impact the results, TMA may be insufficiently reliable for MMAI score generation. MMAI biomarkers that are robust across different specimen preparation methods have potential for clinical and research utility; these hypothesis-generating results support further research along these lines. Continuous MMAI scores and categorical MMAI risk group distribution using WSI- and TMA-derived images by subgroup. WSI-derived score TMA-derived score Patients Continuous 1 Low 2 Int 2 High 2 Continuous 1 Low 2 Int 2 High 2 All 0.47(0.44-0.53) 0(0%) 69(70%) 29(30%) 0.37(0.33-0.42) 6(6%) 87(89%) 5(5%) Black 0.47(0.45-0.51) 0(0%) 58(73%) 22(27%) 0.37(0.33-0.43) 4(5%) 73(91%) 3(4%) White 0.48(0.43-0.54) 0(0%) 11(61%) 7(39%) 0.37(0.32-0.41) 2(11%) 14(78%) 2(11%) 1 Presented as median (IQR). 2 Presented as n (%).

Journal of Clinical OncologyVol. 43(5_suppl)
Thomas Jefferson University (US), University of California, San Francisco (US), UCSF Helen Diller Family Comprehensive Cancer Center (US)
Openalex Percentile: Top 99%
AI in cancer detection
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