JAMA network open · 2026
An AI Approach to Differentiating Lung Squamous Cell Carcinoma From Metastases of Other Origins.
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Evans Mark G, Ribeiro Jennifer R, Maney Todd, Helmstetter Anthony, Johnson Jennifer, Karnezis Anthony N, Bales Casey, Sledge George W, Spetzler David, Vanderwalde Ari, Oberley Matthew, Halmos Balazs, Borghaei Hossein, Abdulla Farah, Bryant David, Hirsch Fred R, Ghani Hassan
Аннотация
IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (SCC) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities. Accurate diagnosis is essential to guide treatment decisions.
OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life Sciences clinicogenomic database from January 1, 2024, to January 31, 2025, were queried. All cases were reviewed by board-certified pathologists.
MAIN OUTCOMES AND MEASURES: The primary outcome was the rate of misdiagnosis among presumed lung SCCs confirmed by pathologist review and orthogonal evidence, which included clinical history and clinical findings, GATA3 and uroplakin II immunohistochemistry for urothelial carcinoma, UV variant signature for cutaneous SCC, CD5 and CD117 (c-KIT) immunohistochemistry for thymic carcinoma, and human papillomavirus positivity for orogenital SCC (eg, head and neck, cervical).
RESULTS: Through a combination of AI and orthogonal evidence, 123 (3.1%) misdiagnoses were confirmed among 3958 cases submitted as presumed lung SCC (patients misdiagnosed: median [range] age, 71 [39 to >89]; 76.4% male). The cohort included 50 cutaneous SCCs (40.7%), 33 orogenital SCCs (26.8%) (including 25 head and neck [75.8%]), 20 urothelial carcinomas (16.3%), 15 thymic carcinomas (12.2%), 4 NUT carcinomas (3.3%), and 1 prostate SCC (0.8%). Ninety-two of the 123 patients (74.8%) had clinical history or findings consistent with the new diagnosis. Eighty-eight cases (71.5%) had differences in guideline-preferred first-line systemic therapies following the diagnosis change.
CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patients diagnosed with lung SCC, a meaningful number of patients experienced misdiagnosis, which was identified using a multipronged AI-assisted approach. Diagnosis changes prompted by AI and orthogonal evidence may assist clinicians in prognostication and therapy selection.
OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life Sciences clinicogenomic database from January 1, 2024, to January 31, 2025, were queried. All cases were reviewed by board-certified pathologists.
MAIN OUTCOMES AND MEASURES: The primary outcome was the rate of misdiagnosis among presumed lung SCCs confirmed by pathologist review and orthogonal evidence, which included clinical history and clinical findings, GATA3 and uroplakin II immunohistochemistry for urothelial carcinoma, UV variant signature for cutaneous SCC, CD5 and CD117 (c-KIT) immunohistochemistry for thymic carcinoma, and human papillomavirus positivity for orogenital SCC (eg, head and neck, cervical).
RESULTS: Through a combination of AI and orthogonal evidence, 123 (3.1%) misdiagnoses were confirmed among 3958 cases submitted as presumed lung SCC (patients misdiagnosed: median [range] age, 71 [39 to >89]; 76.4% male). The cohort included 50 cutaneous SCCs (40.7%), 33 orogenital SCCs (26.8%) (including 25 head and neck [75.8%]), 20 urothelial carcinomas (16.3%), 15 thymic carcinomas (12.2%), 4 NUT carcinomas (3.3%), and 1 prostate SCC (0.8%). Ninety-two of the 123 patients (74.8%) had clinical history or findings consistent with the new diagnosis. Eighty-eight cases (71.5%) had differences in guideline-preferred first-line systemic therapies following the diagnosis change.
CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patients diagnosed with lung SCC, a meaningful number of patients experienced misdiagnosis, which was identified using a multipronged AI-assisted approach. Diagnosis changes prompted by AI and orthogonal evidence may assist clinicians in prognostication and therapy selection.