Radiomics of Immunotherapeutics Response Evaluation and Prediction

Summary

This study aims to investigate the feasibility and efficiency of CT radiomic analysis which serves as a high through-put analytical strategy applied to image big-data resource in evaluating and predicting the response of immunotherapeutics. A multi-center retrospective diagnostic test has been designed for this aim to compare the predictive performance of clinical model, qualitative model incorporating semantic CT features and image-based quantitative radiomic model. The reference standard of therapeutic effect is determined by the latest evaluation result utilizing iRECIST within 365 days after recruited. This study intends to enroll 400 participates who had been diagnosed with advanced somatic solid tumor confirmed by histo- or cyto-pathological examination and were planning to receive immunotherapy.

Conditions

• Solid Tumor
• Predictive Cancer Model

Interventions

DIAGNOSTIC_TEST

Clinical

Clinical: A diagnostic model incorporating clinical variables (Age, Pathological diagnosis, level of serum tumor biomarkers etc.)

DIAGNOSTIC_TEST

Semantic

Semantic: A diagnostic model incorporating semantic radiological features (shape, location, border, density etc.)

DIAGNOSTIC_TEST

Radiomic

Radiomic: A diagnostic model incorporating quantitative radiomic features (histogram, texture, morphology etc.)

Sponsors & Collaborators

• Tianjin Chest Hospital

  collaborator OTHER

• Shandong Tumor Hospital

  collaborator OTHER

• West China Hospital

  collaborator OTHER

• First Affiliated Hospital of Zhejiang University

  collaborator OTHER

• Sun Yat-sen University

  collaborator OTHER

• Cancer Institute and Hospital, Chinese Academy of Medical Sciences

  collaborator OTHER

• Liaoning Cancer Hospital & Institute

  collaborator OTHER

• Hubei Cancer Hospital

  collaborator OTHER

• First Affiliated Hospital Xi'an Jiaotong University

  collaborator OTHER

• Tianjin Medical University Cancer Institute and Hospital

  lead OTHER

Principal Investigators

• Zhaoxiang Ye, M.D, Ph.D · Tianjin Medical University Cancer Institute and Hospital

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2017-01-01

Primary Completion

2019-12-31

Completion

2020-02-25

Countries

• China

Study Locations