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NCT05617469

DLCS for Predicting Neoadjuvant Chemotherapy Response

Status unknown Last updated 15 November 2022
What this trial tests

trial testing develop and visualized a radio-clinical signatures from pretreatment oversampled CT images in Gastric Cancer in 1,100 participants. Status unknown.

Timeline
1 July 2022
Primary endpoint
30 January 2023
31 July 2023

Quick facts

Lead sponsorZhejiang Cancer Hospital
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment1,100
Start date1 July 2022
Primary completion30 January 2023
Estimated completion31 July 2023
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

Zhejiang Cancer Hospital

Who can join

Adults 18 to 80, any sex, with Gastric Cancer or CT Images. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The early noninvasive screening of patients suitable for neoadjuvant chemotherapy (NCT) is essential for personalized treatment in locally advanced gastric cancer (LAGC). The aim of this study was to develop and visualized a radio-clinical biomarker from pretreatment oversampled CT images to predict the response and prognosis to NCT in LAGC patients.

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

  1. Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients.
    Hu C, Chen W, Li F, Zhang Y, et al · · 2023 · cited 33× · PMID 37132183 · DOI 10.1097/js9.0000000000000432
  2. Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study.
    Cao M, Hu C, Li F, He J, et al · · 2024 · cited 7× · PMID 38896865 · DOI 10.1097/js9.0000000000001627

Verify or expand the search:

Other recruiting trials for Gastric Cancer

Currently open trials in the same condition.

Other Zhejiang Cancer Hospital trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

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