Supplementary Figure S3 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling

<p>Calculation of hierarchical positive predictive value (hPPV) and hierarchical sensitivity (hSens). Hierarchical metrics were calculated per sample with values ranging from 0 to 1.0, and then averaged across all samples. The schematic shows level predictions where the “root node” is the broadest category, and not counted toward the calculation. (a) Illustrates 100% hPPV and hSens; (b) illustrates 100% hPPV and 67% hSens; (c) illustrates 67% hPPV and 67% hSens; and (d) illustrates 100% hPPV and 100% hSens, where the model made a more granular prediction than “truth” diagnosis. For example, we commonly receive specimens with a diagnosis of “metastatic carcinoma, consistent with known breast primary” without specifying the subtype. Most likely the cancer was subtyped, but the information wasn’t supplied to us.</p>

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Publication Details

Published
2025-09-01
DOI
https://doi.org/10.1158/2767-9764.30021998
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Supplementary Figure S3 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling

Jennifer R. Ribeiro, Patricia Pittman, Anthony Helmstetter, Hassan Ghani et al.
Radiomics and Machine Learning in Medical Imaging
article

Supplementary Figure S3 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling

Jennifer R. Ribeiro, Patricia Pittman, Anthony Helmstetter, Hassan Ghani, Jaclyn F. Hechtman, Rebecca Feldman, Ari M. Vanderwalde, Elena Florento, Milan M. Radovich, Jeffrey Swensen, Matthew James Oberley, Jim Abraham, Casey Bales, George W. Sledge, Todd Maney, Farah Abdulla, David B. Spetzler, Myra M. George, David Bryant, Stephanie Rock
article en

Abstract

<p>Calculation of hierarchical positive predictive value (hPPV) and hierarchical sensitivity (hSens). Hierarchical metrics were calculated per sample with values ranging from 0 to 1.0, and then averaged across all samples. The schematic shows level predictions where the “root node” is the broadest category, and not counted toward the calculation. (a) Illustrates 100% hPPV and hSens; (b) illustrates 100% hPPV and 67% hSens; (c) illustrates 67% hPPV and 67% hSens; and (d) illustrates 100% hPPV and 100% hSens, where the model made a more granular prediction than “truth” diagnosis. For example, we commonly receive specimens with a diagnosis of “metastatic carcinoma, consistent with known breast primary” without specifying the subtype. Most likely the cancer was subtyped, but the information wasn’t supplied to us.</p>

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Supplementary Figure S3 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling — Jennifer R. Ribeiro, Patricia Pittman, et al. · (2025) | TGRS Research Map | TGRS