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CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC

NCT04452058 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2020-06-30

No results posted yet for this study

Summary

The purpose of this study was to investigate whether the combined radiomic model based on radiomic features extracted from focus and perifocal area (5mm) can effectively improve prediction performance of distinguishing precancerous lesions from early-stage lung adenocarcinoma, which could assist clinical decision making for surgery indication. Besides, response and long term clinical benefit of immunotherapy of advanced NSCLC lung cancer patients could also be predicted by this strategy.

Conditions

  • Predictive Cancer Model
  • Lung Cancer
  • Preinvasive Adenocarcinoma

Interventions

OTHER

Radiomic Algorithm

Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Sponsors & Collaborators

  • Guangdong Provincial People's Hospital

    collaborator OTHER

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

    lead OTHER

Principal Investigators

  • Haiyu Zhou, PhD · Guangdong Provincial People's Hospital

  • Luyu Huang · Guangdong Provincial People's Hospital

  • Herui Yao, PhD · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  • Yunfang Yu · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  • Hanbo Cao, PhD · Zhoushan Lung Cancer Institution

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-08-01
Primary Completion
2021-12-01
Completion
2022-12-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04452058 on ClinicalTrials.gov