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NCT Number: NCT06451393

Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

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.

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Key information

Conditions

Gastric Cancer Chemotherapy Effect Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Neoplasms Neoplasms by Site Stomach Diseases Stomach Neoplasms

Age range

20 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The Sixth Affiliated Hospital, Sun Yat-sen University

Guangzhou, Guangdong, 510655, China

Location status: Recruiting

Location contact

Xiangen Lu, Master

CONTACT

[email protected]

+86 20 3837 9764

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received NAC and radical gastrectomy;
patients who underwent abdominal multidetector computed tomography (CT) inspection, gastroscope, and tumor tissue biopsy before any intervention started;
Lesions that are assessable according to The Response Evaluation Criteria in Solid Tumors Version 1.1

Exclusion criteria

Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection;
patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling;
patients with insufficient data.

Treatment and study plan

Neoadjuvant chemotherapy with radical tumor resection surgery

Drug

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).

Primary outcomes

Pathological Complete Response

Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Junsheng Peng, MD

CONTACT

[email protected]

+86 13802963578

Yonghe Chen, MD

CONTACT

[email protected]

+86 135 6038 6150

Sponsors and collaborators

Lead sponsor

Sixth Affiliated Hospital, Sun Yat-sen University

Other

Registry information

Official study title

A Radio-Pathomic Multimodal Machine Learning Model for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer: A Retrospective Observational Study

Important dates

Study start: 2013
Primary completion: 2022
Study completion: 2026
First posted: Jun 11, 2024
Registry last updated: Jun 11, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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