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

Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC: a Multicenter Analysis

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.

Study Overview

• Status: Completed
• Conditions: Lung Cancer - Non Small Cell
• Intervention / Treatment: Other: CT radiomic analysis

• Study Type: Observational
• Enrollment (Actual): 800

Contacts and Locations

Study Locations

• China
  • NanJing, China
    • Jinling Hospital, China

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Jinling Hospital, China

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
training set	Other: CT radiomic analysis; Radiomic features of tumor and peritumor tissue
external test set	Other: CT radiomic analysis; Radiomic features of tumor and peritumor tissue

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
DFS（Disease-free survival）	DFS was defined as the duration from the date of primary surgery to the first occurrence of recurrence or death from any cause.	Record from the date of surgery to the date of recurrence or death from any cause, whichever comes first, and assess up to a maximum of 5 years.

Collaborators and Investigators

• Sponsor: Jinling Hospital, China

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2023
• Primary Completion (Actual): September 20, 2024
• Study Completion (Actual): November 11, 2024

Study Registration Dates

• First Submitted: December 11, 2024
• First Submitted That Met QC Criteria: December 15, 2024
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: December 16, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Keywords: NSCLC, ML, DFS
• Other Study ID Numbers: 2023DZKY-089-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No