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Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC by CT Images and Pathological Factors

NCT06737367 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 800

Last updated 2024-12-19

No results posted yet for this study

Summary

The investigators retrospectively collected the participants with stage I non-small cell lung cancer (NSCLC) patients resected between January 2010 to December 2020 for training and internal validation. The Clinical data, preoperative clinical information, laboratory results and CT images were collected. The investigators also collected the disease-free survival time. On the Deepwise multi-modal research platform, the images were semi-automatically segmented and expanded outward by 3mm to obtain the peritumor tissue. PyRadiomics was used to extract the radiomic features. LASSOcox and rsf were used to select the features. we developed a machine learning-based integrative prognostic model that utilizes radiomic and pathological variables as input using LOOCV framework. And it was further tested on the internal and external cohorts. Discrimination was assessed by using the C-index and area under the receiver operating characteristic curve (AUC), IBS, DCA.

Conditions

  • Lung Cancer - Non Small Cell

Interventions

OTHER

CT radiomic analysis

Radiomic features of tumor and peritumor tissue

Sponsors & Collaborators

  • Jinling Hospital, China

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-09-01
Primary Completion
2024-09-20
Completion
2024-11-11

Countries

  • China

Study Locations

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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 NCT06737367 on ClinicalTrials.gov