Condition: Gastric Cancer · Chemotherapy Effect · Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
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.
This description comes directly from the study's public registry record.
Yonghe Chen, MD · +86 135 6038 6150 · chenyhe@mail2.sysu.edu.cn
Junsheng Peng, MD · +86 13802963578 · pengjsh@mail.sysu.edu.cn
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| The Sixth Affiliated Hospital, Sun Yat-sen University | Guangzhou, Guangdong, China | Recruiting |
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Source record: clinicaltrials.gov/study/NCT06451393