Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer - Non Small Cell | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| CT radiomic analysis | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- training set
- interventionNames
- Other: CT radiomic analysis
- label
- external test set
- interventionNames
- Other: CT radiomic analysis
Primary outcomes (1)
- measure
- DFS（Disease-free survival）
- timeFrame
- 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.
- description
- DFS was defined as the duration from the date of primary surgery to the first occurrence of recurrence or death from any cause.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (0)
Data not yet available