Supplementary Figure S4 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling

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

doi:10.1158/2767-9764.30021995

Key Points

GPSai effectively assigns scores for tumor types, improving prediction of tissue of origin during profiling.
The model's scoring for lung adenocarcinoma and lung squamous cell carcinoma ensures accuracy in categorizing tumors.
Scoring in GPSai maintains a total unity score for major categories like non-small cell lung carcinoma, enhancing reliability.
Clinical validation of AI tools like GPSai supports more accurate cancer diagnosis and tailored treatment approaches.

Implication

Observational analysis of GPSai predicts tissue of origin in tumor profiling, highlighting its clinical validity.

Abstract

GPSai model scoring. A representative example of scoring assigned by GPSai is shown. Subcategories (e.g., lung adenocarcinoma, lung squamous cell carcinoma) must sum to unity of the major category score (e.g., non-small cell lung carcinoma).

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Ghani et al. (2025) studied this question.

synapsesocial.com/papers/68bb49cc6d6d5674bccffab8 https://doi.org/10.1158/2767-9764.30021995

View Full Paper

Ask AI

Bookmark

Share

Supplementary Figure S4 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling

Key Points

Implication

Abstract

Discussion

Cite This Study

Supplementary Figure S4 from GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading