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

NCT ID: NCT06737367

Last Updated: 2024-12-19

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

800 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-09-01

Study Completion Date

2024-11-11

Brief Summary

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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.

Detailed Description

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Conditions

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Lung Cancer - Non Small Cell

Keywords

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NSCLC, ML, DFS

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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training set

CT radiomic analysis

Intervention Type OTHER

Radiomic features of tumor and peritumor tissue

external test set

CT radiomic analysis

Intervention Type OTHER

Radiomic features of tumor and peritumor tissue

Interventions

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CT radiomic analysis

Radiomic features of tumor and peritumor tissue

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

patients with stage I NSCLC (ninth AJCC edition) who underwent curative R0 resections between January 2010 and December 2020 -

Exclusion Criteria

1. absence of enhanced CT
2. history of lung cancer or synchronous lung cancers
3. follow-up records ≤3 Months
4. carcinoma in situ (CIS) or minimally invasive NSCLC
5. death within 30 days of surgery
6. no pathological slides or reports
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Jinling Hospital, China

OTHER

Sponsor Role lead

Responsible Party

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Guangming Lu

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Jinling Hospital, China

Nanjing, , China

Site Status

Countries

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China

Other Identifiers

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2023DZKY-089-01

Identifier Type: -

Identifier Source: org_study_id