Ignite Creation Date:
2025-12-25 @ 12:33 AM
Ignite Modification Date:
2025-12-25 @ 10:41 PM
Study NCT ID:
NCT06737367
Status:
COMPLETED
Last Update Posted:
2024-12-19
First Post:
2024-12-11
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC by CT Images and Pathological Factors
Sponsor:
Jinling Hospital, China
Organization:
Study Overview
Official Title:
Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC: a Multicenter Analysis
Status:
COMPLETED
Status Verified Date:
2024-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
Stage I NSCLC
Brief 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.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: