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A multipronged artificial intelligence (AI)–assisted approach integrated into routine molecular profiling identified 3.1% of cases submitted as lung squamous cell carcinoma as metastases from other origins, revealing a meaningful rate of misdiagnosis in this patient population, according to a cross-sectional study published by Evans et al in JAMA Network Open. 

“Distinguishing primary lung squamous cell carcinoma from squamous metastases to the lung is a clinical challenge due to histopathologic similarities,” the investigators wrote. “Accurate diagnosis is essential to guide treatment decisions.”

They also commented, “These findings suggest the importance of an AI-assisted approach to distinguishing tissues of origin in patients with presumed primary lung squamous cell carcinoma, thus avoiding misdiagnosis and associated impacts on prognosis and therapy selection.”

Study Details

This study leveraged GPSai, a tissue-of-origin AI model automatically applied to each sample submitted for molecular profiling, to identify potential misdiagnoses among research-eligible cases initially classified as lung squamous cell carcinoma. Molecularly profiled cases from the Caris Life Sciences clinicogenomic database, spanning January 2024 to January 2025, were analyzed, and all cases underwent review by board-certified pathologists.

The primary outcome was the misdiagnosis rate among presumed lung squamous cell carcinomas, confirmed through pathologist review and orthogonal evidence. This included clinical history and findings, GATA3 and uroplakin II immunohistochemistry for urothelial carcinoma, ultraviolet variant signature for cutaneous squamous cell carcinoma, CD5 and CD117 (c-KIT) immunohistochemistry for thymic carcinoma, and human papillomavirus positivity for orogenital squamous cell carcinoma (eg, head and neck, cervical).

Key Findings

Using a combination of AI and orthogonal evidence, 123 of 3,958 cases (3.1%) initially diagnosed as presumed lung squamous cell carcinoma were confirmed as misdiagnoses, with affected patients having a median age of 71 years and 76.4% being male.

The cohort comprised 50 cutaneous squamous cell carcinomas (40.7%), 33 orogenital squamous cell carcinomas (26.8%)—including 25 in the head and neck (75.8%)—20 urothelial carcinomas (16.3%), 15 thymic carcinomas (12.2%), 4 nuclear protein in testis carcinomas (3.3%), and 1 prostate squamous cell carcinoma (0.8%).

Clinical history or findings consistent with the revised diagnosis were present in 92 of 123 patients (74.8%). In 88 cases (71.5%), first-line systemic therapy recommendations per guidelines changed following reclassification.

“This cross-sectional study of patients diagnosed with lung squamous cell carcinoma found that an AI-assisted approach integrated into the routine molecular profiling workflow identified a meaningful number of misdiagnoses,” the investigators concluded. “Comprehensive evaluation of orthogonal evidence supported these diagnosis changes, which had important implications for prognosis and therapy selection.”

Hassan Ghani, MD, of Caris Life Sciences, Phoenix, is the corresponding author of the article in JAMA Network Open.

Disclosure: The study was funded by Caris Life Sciences. For full disclosures of the study authors, visit jamanetwork.com.