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NCT Number: NCT05465512

Using Radiomics to Predict Neoadjuvant Chemotherapy Efficacy

Neoadjuvant chemotherapy (NC) is an important treatment for advanced gastric cancer (AGC). However, tools that effectively predict the efficacy of NC before treatment are lacking. Computed tomography images before and after NC were used to construct a deep learning-based radiomics signature to predict the efficacy of NC, prognoses and postoperative adjuvant chemotherapy benefit.

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Key information

Conditions

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

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Gastric Surgery

Fuzhou, Fujian, China

About this study

Background: Neoadjuvant chemotherapy (NC) is an important treatment for advanced gastric cancer (AGC). However, tools that effectively predict the efficacy of NC before treatment are lacking.

Methods: Computed tomography images before and after NC were used to construct a deep learning-based radiomics signature to predict the efficacy of NC, prognoses and postoperative adjuvant chemotherapy benefit. Tumor regression grade (TRG) =0 or 1 was defined as a good response to neoadjuvant chemotherapy (GRNC), and TRG=2 or 3 was defined as a poor response to neoadjuvant chemotherapy (PRNC). 193 patients with AGC from January 2010 to December 2018 in two different China university hospitals were included in this study. The before neoadjuvant chemotherapy imaging scoring system (BNCISS), imaging change scoring system before and after neoadjuvant chemotherapy (ICSS), which were constructed based on computed tomography images before after treatment.

Who can participate

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

Inclusion criteria

  • Prospective collection and a retrospective analysis of patients seen from January 2010 to December 2018 at Fujian Medical University Union Hospital (FMUUH) and the First Hospital of Lanzhou University (FHLU) who were diagnosed with gastric adenocarcinoma and underwent radical resection

Exclusion criteria

  • Patients with concurrent malignant disease, distant metastases, palliative resection, or gastric stump cancer were excluded.

Treatment and study plan

response to neoadjuvant chemotherapy

Other

Tumor regression grade (TRG) =0 or 1 was defined as a good response to neoadjuvant chemotherapy (GRNC), and TRG=2 or 3 was defined as a poor response to neoadjuvant chemotherapy (PRNC).

Primary outcomes

  1. AUC for ICSS

    Time frame: 2022-01-01 to 2022-07-31

    The predictive performance of imaging change scoring system before and after neoadjuvant chemotherapy (ICSS)

  2. AUC for BNCISS

    Time frame: 2022-01-01 to 2022-07-31

    The predictive performance of before neoadjuvant chemotherapy imaging scoring system (BNCISS)

Secondary outcomes

  1. overall survival

    Time frame: 2010-01-01 to 2022-07-31

    3 year overall survival

  2. disease free survival

    Time frame: 2010-01-01 to 2022-07-31

    3 year disease free survival

Sponsors and collaborators

Lead sponsor

Fujian Medical University

Other

Registry information

Official study title

Using Radiomics to Predict Neoadjuvant Chemotherapy Efficacy and Postoperative Adjuvant Chemotherapy Benefit in Advanced Gastric Cancer: a Two-center Study

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Jul 19, 2022
Registry last updated
Jul 19, 2022

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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