Deep learning-based non-invasive prediction of PD-L1 status and immunotherapy survival stratification in esophageal cancer using [<sup>18</sup>F]FDG PET/CT.
Authors
Affiliations (10)
Affiliations (10)
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Electronic engineering, School of Electronic engineering, Xidian University, Xi'an, Shaanxi Province, China.
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. [email protected].
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- Department of Electronic engineering, School of Electronic engineering, Xidian University, Xi'an, Shaanxi Province, China. [email protected].
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
Abstract
This study aimed to develop and validate deep learning models using [<sup>18</sup>F]FDG PET/CT to predict PD-L1 status in esophageal cancer (EC) patients. Additionally, we assessed the potential of derived deep learning model scores (DLS) for survival stratification in immunotherapy. In this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [<sup>18</sup>F]FDG PET/CT scans. For comparison, we also constructed a logistic model incorporating clinical factors (clinical model). The DLS were evaluated as radiological markers for survival stratification, and nomograms for predicting survival were constructed. The models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy. Deep learning models based on [<sup>18</sup>F]FDG PET/CT can accurately predict PD-L1 status in esophageal cancer patients. The derived DLS can effectively stratify survival outcomes following immunotherapy, particularly when combined with clinical factors.