Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced
Yifan Liu1, Wei Chen2, Ruiwen Ruan3
1Department of Gastrointestinal Surgery, First Affiliated Hospital of Sun Yat-sen University, Zhongshan 2nd Street, No. 58, Guangzhou, 510080, 86, Guangdong, China.
Background:
Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration's (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC.
Methods:
In this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs).
Results:
Analyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001).
Conclusion:
ICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients' responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient's unique response situations.
Insights
Deep learning analyzes pathological images to predict treatment response in advanced gastric cancer (GC) patients receiving PD-1 inhibitor plus chemotherapy. This approach aids in selecting patients for personalized therapy.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- First-line chemotherapy was standard for advanced unresectable gastric cancer (GC).
- Recent FDA approval of PD-1 inhibitor plus chemotherapy offers significant benefits but requires precise patient selection due to cost and side effects.
- Deep learning (DL) shows promise in predicting tumor treatment responses from pathological images.
Purpose of the Study:
- To utilize DL for analyzing pathological images to predict treatment response to first-line PD-1 inhibitor combined with chemotherapy in advanced GC.
- To develop a novel histopathological biomarker for predicting treatment response.
Main Methods:
- A multicenter retrospective analysis of Hematoxylin and Eosin (H&E)-stained slides from advanced GC patients.
- Three DL models were used in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) from Whole Slide Images (WSIs).
- Treatment response was evaluated using iRECIST 1.1 criteria.
Main Results:
- An ensemble DL model, ICIsNet, was developed using 148,181 patches from 313 WSIs of 264 patients.
- The model achieved high predictive accuracy with AUC values of 0.92, 0.95, 0.96, and 1 across testing datasets.
- The ICIsRS demonstrated statistically significant differences between patients with good and poor treatment response (p < 0.001).
Conclusions:
- The ICIsRS, a DL-derived biomarker from WSIs, effectively predicts response to PD-1 combined chemotherapy in advanced GC.
- This biomarker offers a novel approach for personalized treatment planning.
- It enables more individualized and potentially effective treatment strategies based on patient-specific response.
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