Artificial Intelligence Holds Promise for Lung Cancer Detection
Artificial intelligence holds promise for detecting lung cancer if research presented last month at the International Association for the Study of Lung Cancer (IASLC) 2024 World Conference on Lung Cancer is any indicator.1
Wenhua Liang, MD, from the China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, China, and colleagues trained a tool called DeepGEM to scan routinely acquired histology slides and evaluate them for gene mutations. The initial cohort included an internal data set of 1717 patients. Then the program was subsequently tested on an external data set from 15 additional centers with 1719 patients and a public data set of 535 patients. To assess generalizability, the model was also tested on a lymph node metastases data set consisting of 331 biopsies.
Liang and colleagues noted that “DeepGEM demonstrated performance with a median area under the curve of 0.938 for excisional biopsies and 0.891 for aspiration biopsies in the internal data set. On the multicenter external data set, the model achieved median AUCs of 0.859 for excisional biopsies and 0.826 for aspiration biopsies.” The researchers added that DeepGEM’s ability to predict mutations from primary biopsies extended to lymph node metastases, indicating a potential for prognostic prediction of targeted therapies.
“Compared to previous studies, DeepGEM achieved robust and superior predictive performance across various genes validating on the largest multicenter datasets to date. The rapid prediction capabilities of DeepGEM allow for quicker decision-making in treatment, enabling patients with severe symptoms to receive targeted therapies sooner. Furthermore, it presents opportunities for multi-gene mutation detection and precision treatment in economically underdeveloped areas where genomic testing is unaffordable. This innovative approach has the potential to transform the clinical management of lung cancer patients, making advanced genomic insights more accessible and actionable,” Liang said in a press release about the findings.2
References
- Xiong S, Zhao Y, Ren Q, et al. Deep multiple instances learning-enabled gene mutation prediction of lung cancer from histopathology images. Presented at: 2024 IASLC World Conference on Lung Cancer; September 7-11, 2024; San Diego, CA. Oral abstract 03.05.
- Artificial intelligence method transforms gene mutation prediction in lung cancer: DeepGEM data releases at IASLC 2024 World Conference on Lung Cancer. Press release. Published September 7, 2024. Accessed September 22, 2024. www.iaslc.org/iaslc-news/press-release/artificial-intelligence-method-transforms-gene-mutation-prediction-lung