Artificial Intelligence-Powered Prediction of Gene Rearrangement in Patients With Non-Small-Cell Lung Cancer.

Purpose: Several studies reported the possibility of predicting genetic abnormalities in non-small-cell lung cancer by deep learning (DL). However, there are no data of predicting gene rearrangement () using DL. We evaluated the predictability using the DL platform.

Materials And Methods: We selected 66 -positive cases and 142 -negative cases, which were diagnosed by immunohistochemical staining in our institution from January 2009 to March 2019. We generated virtual slide of 300 slides (150 -positive slides and 150 -negative slides) using NanoZoomer. HALO-AI was used to analyze the whole-slide imaging data, and the DenseNet network was used to build the learning model. Of the 300 slides, we randomly assigned 172 slides to the training cohort and 128 slides to the test cohort to ensure no duplication of cases. In four resolutions (16.0/4.0/1.0/0.25 μm/pix), prediction models were built in the training cohort and prediction performance was evaluated in the test cohort. We evaluated the diagnostic probability of by receiver operating characteristic analysis in each probability threshold (50%, 60%, 70%, 80%, 90%, and 95%). We expected the area under the curve to be 0.64-0.85 in the model of a previous study. Furthermore, in the test cohort data, an expert pathologist also evaluated the presence of by hematoxylin and eosin staining on whole-slide imaging.

Results: The maximum area under the curve was 0.73 (50% threshold: 95% CI, 0.65 to 0.82) in the resolution of 1.0 μm/pix. In this resolution, with an probability of 50% threshold, the sensitivity and specificity were 73% and 73%, respectively. The expert pathologist's sensitivity and specificity in the same test cohort were 13% and 94%.

Conclusion: The prediction by DL was feasible. Further study should be addressed to improve accuracy of prediction.