Background Current strategies are insufficient to predict pathologically complete response
(pCR) for esophageal squamous cell carcinomas (ESCCs) before treatment. Here, we aim to …

No clinically available biomarkers can predict pathological complete response (pCR) for
esophageal squamous cell carcinomas (ESCCs) with neoadjuvant chemoradiotherapy …

Purpose To evaluate and validate CT-based models using pre-and posttreatment deep
learning radiomics features and hematological biomarkers for assessing esophageal …

Background Neoadjuvant chemoradiotherapy (neo-CRT) followed by surgery has been
shown to improve esophageal squamous cell carcinoma (ESCC) patients' survival …

Background Currently, there are insufficient indicators for the reliable assessment of
treatment response following neoadjuvant chemoradiotherapy (nCRT) in patients with …

Objective This study aimed to build radiomic feature-based machine learning models to
predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) …

Background The primary objective of this research is to devise a model to predict the
pathologic complete response in esophageal squamous cell carcinoma (ESCC) patients …

After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous
cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response …

Objective We aimed to develop and validate a radiomics nomogram and determine the
value of radiomic features from lymph nodes (LNs) for predicting pathological complete …

Background Deep learning is promising to predict treatment response. We aimed to
evaluate and validate the predictive performance of the CT-based model using deep …