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Radiomics and Molecular Expression Predictive Model for Esophago-Gastric Junction and Gastric Cancer TRG

Active, not recruiting
Conditions
Gastric Cancer
Adenocarcinoma of the Stomach
Registration Number
NCT04878783
Lead Sponsor
University of Roma La Sapienza
Brief Summary

The aim of this study is to develop a CT scan-based radiomics predictive model about tumor regression grade (TRG) in patients with esophago-gastric junction (EGJ) ang gastric cancer undergoing perioperative chemotherapy. The molecular expression of the neoplasms will be evaluated to assess its association with the TRG and the radiomic features.

Detailed Description

• To compare the radiomic features of the CT scans at the time of diagnosis (T0) and at the end of the preoperative chemotherapy (T1) in order to predict the TRG with the texture analysis on the first CT scan.

A non-good response (non-GR) has shown to be predictable with texture analysis on the pre-treatment CT scan.

Therefore, we hypothesize that texture analysis could let to identify the good response patients.

• To find correlation between the molecular expression of the tumor and the radiomics features.

Texture analysis on the pre-chemotherapy CT scan founded that entropy and compactness were higher and uniformity lower in responders. Nonetheless the association between radiomics features and molecular expression has not been investigated yet.

Therefore, we hypothesize to add some others radiomics signatures to the analysis and to find an association with the molecular expression.

• To find correlation between the molecular expression of the tumor and the TRG.

MSI gastric cancer has been shown to be less responsive to preoperative chemotherapy.

Therefore, we hypothesize to confirm this result.

Recruitment & Eligibility

Status
ACTIVE_NOT_RECRUITING
Sex
All
Target Recruitment
138
Inclusion Criteria
  • Patients with histologically proven adenocarcinoma of the EGJ (Siewert II-III) or stomach.
  • Preoperative staging: cT2-T4a, cN0-N3, M0.
  • Patients >18 years old.
  • Patients undergoing perioperative chemotherapy with Docetaxel, oxaliplatin, leucovorin, and 5-fluorouracil (FLOT).
Exclusion Criteria
  • Siewert I EGJ tumor
  • Patients undergoing preoperative radiotherapy.
  • Absence of both pre and post-chemotherapy CT-scan.
  • Patients with tumor progression during preoperative chemotherapy.
  • Patients undergoing other neoadjuvant chemotherapy regimen different from FLOT
  • Exploratory laparoscopy with positive cytology on the peritoneal lavage or evidence of peritoneal carcinosis

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Predictive performance of radiomics analysis on the pre-treatment CT scan.2 months

Comparing the radiomic features of the CT scans at the time of diagnosis (T0) and at the end of the preoperative chemotherapy (T1) in order to predict the TRG with the texture analysis on the first CT scan.

Secondary Outcome Measures
NameTimeMethod
Evaluation of the association between TRG and the molecular expression of the tumor.2 months

Investigate if the molecular expression of the tumor can influence the TRG

Association of the radiomics features with the molecular expression of the tumor.2 months

Texture analysis on the pre-chemotherapy CT scan founded that entropy and compactness were higher and uniformity lower in responders. Nonetheless the association between radiomics features and molecular expression has not been investigated yet.

Therefore, we hypothesize to add some others radiomics signatures to the analysis and to find an association with the molecular expression.

Association between radiomics and molecular expression in regards to long-term outcomes5 years

Analysis of the 3 and 5y DFS and OS of patients with the radiomics and molecular expression profile

Trial Locations

Locations (5)

University of Padova

🇮🇹

Padova, Italy

Link Campus University

🇮🇹

Roma, Italy

University of Verona

🇮🇹

Verona, Italy

Amsterdam UMC location University of Amsterdam

🇳🇱

Amsterdam, Nord-Holland, Netherlands

Giovanni Maria Garbarino

🇮🇹

Roma, Italy

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