Clinical trial · Observational
Classification of Benign and Malignant Lung Nodules Based on CT Raw Data
Comparison and Analysis of Predictive Performance of CT and Raw Data in Benign and Malignant Classification of Pulmonary Nodules
- 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 employ of medical images combined with deep neural networks to assist in clinical diagnosis, therapeutic effect, and prognosis prediction is nowadays a hotspot. However, all the existing methods are designed based on the reconstructed medical images rather than the lossless raw data. Considering that medical images are intended for human eyes rather than the AI, we try to use raw data to predict the malignancy of pulmonary nodules and compared the predictive performance with CT. Experiments will prove the feasibility of diagnosis by CT raw data. We believe that the proposed method is promising to change the current medical diagnosis pipeline since it has the potential to free the radiologists.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Image, Body | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No interventions | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- The First Hospital of Ji Lin University
- description
- CT data and corresponding CT raw data of patients with lung nodule will be collected.
- interventionNames
- Other: No interventions
Primary outcomes (3)
- measure
- Area under the receiver operating characteristic curve (ROC)
- timeFrame
- 8 months
- description
- Area under curve (AUC) of raw data in discriminating malignant nodules from benign nodules.
- measure
- Disease free survival
- timeFrame
- 5 years
- description
- The association between raw data and disease free survival (DFS), which defined as the time from the beginning of diagnosis of lung cancer to the confirmed time of recurrence or metastatic disease, or death occurred.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Patients who are screened out lung nodule. 2. The CT data and corresponding CT raw data are available before the surgery. 3. Final pathology diagnosis of the malignancy of the nodule is available. Exclusion Criteria: 1. Previous history of lung malignancies. 2. Artifacts on CT images seriously deteriorating the observation of the lesion. 3. The time interval between CT scan and pathology diagnosis is more than 4 weeks.
References
Publications (1)
- BACKGROUNDKalra M, Wang G, Orton CG. Radiomics in lung cancer: Its time is here. Med Phys. 2018 Mar;45(3):997-1000. doi: 10.1002/mp.12685. Epub 2017 Dec 12. No abstract available. PMID 29159886