AI Summary
This study focuses on using pretreatment multimodal CT scans to create deep learning-based image biomarkers for predicting the pathological complete response of non-small cell lung cancer (NSCLC) following neoadjuvant immunochemotherapy. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct predictive models, with a fused model showing the highest performance. The LUNAI-fCT model achieved an AUC of 0.866 in the test dataset, demonstrating its potential as a non-invasive imaging biomarker for predicting treatment response in NSCLC patients undergoing neoadjuvant immunochemotherapy.
Objectives
Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.
Methods
This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps.
Results
The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively.
Conclusions
By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.