AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Официальное название

Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Обзор

This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

Подробное описание

This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy pathology images, and clinical profiles, this model aims to forecast the effectiveness of post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized treatment choices for these participants.

Data Collection: The investigators will gather data from 1,800 retrospective cases and 200 prospective cases from multiple hospitals. The retrospective data will be divided into training and testing sets to train and validate the model, respectively. The model's performance will subsequently be evaluated using the prospective dataset.

Clinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc.

Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included.

Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment.

TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system.

Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the prognosis analysis.

Вмешательства

• Препарат Neoadjuvant Chemotherapy
  Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.

Первичные конечные точки

• Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model [Срок оценки: two months]
• Accuracy of TRG prediction by the AI model [Срок оценки: two months]

Вторичные конечные точки (2)

• Progression-Free Survival (PFS) at 3 years [Срок оценки: Three years]
• Overall Survival (OS) at 5 years [Срок оценки: Five years]

Критерии участия

Критерии включения

• Age 18 years or older;
• Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;
• Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;
• Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;
• CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;
• Patients possess comprehensive preoperative clinical information and post-operative TRG grading.

Критерии исключения

• Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
• Patients diagnosed with other concurrent tumors.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 21 центр

• Cancer Institute and Hospital, Chinese Academy of Medical Sciences — Пекин
• Peking Union Medical College Hospital — Пекин
• Peking University Cancer Hospital & Institute — Пекин
• Peking University People's Hospital — Пекин
• Xiangya Hospital of Central South University — Чанша
• Fujian Cancer Hospital — Фучжоу
• Fujian Medical University Union Hospital — Фучжоу
• Affiliated Cancer Hospital & Institute of Guangzhou Medical University — Гуанчжоу
• … и ещё 13 центров

Италия · 1 центр

• San Raffaele University Hospital, Italy — Milan

Идентификаторы

NCT: NCT06035250 · CASMI004

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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