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

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

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06737367
Acronym
Stage I NSCLC
Enrollment
800
Registered
2024-12-17
Start date
2023-09-01
Completion date
2024-11-11
Last updated
2024-12-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Cancer - Non Small Cell

Keywords

NSCLC, ML, DFS

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.

Interventions

Radiomic features of tumor and peritumor tissue

Sponsors

Jinling Hospital, China
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
DFS(Disease-free survival)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.DFS was defined as the duration from the date of primary surgery to the first occurrence of recurrence or death from any cause.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026