Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

Condition(s) studied

Investigational drug(s) / intervention(s)

Neoadjuvant chemotherapy with radical tumor resection surgery: All patients were pathologically diagnosed as advanced gastric cancer, all receive neoadjuvant chemotherapy, after the completion of neoadjuvant chemotherapy, all patients receive radical tumor resection surgery (partial gastrectomy or total gastrectomy, as proper).

Study summary

This study aims to develop a multimodal model combining radiomic and pathomic features to predict pathological complete response (pCR) in advanced gastric cancer patients undergoing neoadjuvant chemotherapy (NAC). The researchers intended to collected pre-intervention CT images and pathological slides from patients, extract radiomic and pathomic features, and build a prediction model using machine learning algorithms. The model will be validated using a separate cohort of patients. This research intend to build a radiomic-pathomic model that can outperform models based on either radiomic or pathomic features alone, aiming to improve the prediction of pCR in gastric cancer.

Eligibility

Primary outcome measure(s)

• Pathological Complete Response — Assessed within 30 days after radical resection surgery.
  Pathological complete response (pCR) was defined as no viable cells remained in the primary tumor lesions and the dissected lymph nodes.

Trial sites (1)

More Sixth Affiliated Hospital, Sun Yat-sen University trials in China

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