Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for
Yunsong Liu1, Yi Wang2, Xinyang Hu3
1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Objectives:
This study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).
Materials And Methods:
Patients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.
Results:
The study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.
Conclusion:
A multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.
Critical Relevance Statement:
Our research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.
Key Points:
After neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%. The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy. The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.
