An AI Approach to Differentiating Lung Squamous Cell Carcinoma From Metastases of Other Origins.
3.1% of cases diagnosed as lung squamous cell carcinoma were actually misdiagnosed
An AI Approach to Differentiating Lung Squamous Cell Carcinoma From Metastases of Other Origins.
IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (squamous cell carcinoma) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities.
To assess the utility of an artificial intelligence (artificial intelligence) 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 artificial intelligence model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung squamous cell carcinoma.
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 squamous cell carcinoma (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.
In this cross-sectional study of patients diagnosed with lung squamous cell carcinoma, a meaningful number of patients experienced misdiagnosis, which was identified using a multipronged artificial intelligence-assisted approach.
Diagnosis changes prompted by artificial intelligence and orthogonal evidence may assist clinicians in prognostication and therapy selection.