Variational autoencoder-based deep learning and radiomics for predicting pathologic complete response to neoadjuvant
Qing Gu1, Shenlun Chen2,3, Andre Dekker3
1Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
Objectives:
Neoadjuvant chemoimmunotherapy (nCIT) is gradually becoming an important treatment strategy for patients with locally advanced oesophageal squamous cell carcinoma (LA-OSCC). This study aimed to predict the pathological complete response (pCR) of these patients using variational autoencoder (VAE)-based deep learning and radiomics technology.
Methods:
A total of 253 LA-ESCC patients who were treated with nCIT and underwent enhanced CT at our hospital between July 2019 and July 2023 were included in the training cohort. VAE-based deep learning and radiomics were utilized to construct deep learning (DL) models and deep learning radiomics (DLR) models. The models were trained and validated via fivefold cross-validation among 253 patients. Forty patients were recruited from our institution between August 2023 and August 2024 as the test cohort.
Results:
The AUCs of the DL and DLR models were 0.935 (95% CI: 0.786-0.992) and 0.949 (95% CI: 0.910-0.986) in the validation cohort and 0.839 (95% CI: 0.726-0.853) and 0.926 (95% CI: 0.886-0.934) in the test cohort. The performance gap between Precision and Recall of the DLR model was smaller than that of the DL model. The F1 scores of the DL and DLR models were 0.726 (95% confidence interval [CI]: 0.476-0.842) and 0.766 (95% CI: 0.625-0.842) in the validation cohort and 0.727 (95% CI: 0.645-0.811) and 0.836 (95% CI: 0.820-0.850) in the test cohort.
Conclusions:
We constructed a DLR model to predict pCR in nCIT-treated LA-ESCC patients, which demonstrated superior performance compared to the DL model.
Advances In Knowledge:
We innovatively used VAE-based deep learning and radiomics to construct the DLR model for predicting pCR of LA-ESCC after nCIT.
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