Clinical trial · Observational
Research on Early Screening and Diagnosis of Pulmonary Nodules Based on Novel Non-invasive Technologies.
Research on Precise Early Screening and Diagnosis of Pulmonary Nodules Based on a Novel Multidimensional Non-invasive Approach
- 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)
This is a prospective observational study designed to address the clinical challenge posed by the high false-positive rate associated with CT imaging in early lung cancer screening. The primary objective is to develop a multi-omics technology for early lung cancer screening, leveraging \*\*exhaled breath metabolomics, plasma metabolomics, radiomics, and liquid biopsy. Based on large-sample detection data, the study aims to construct a \*\*multi-dimensional, sequential decision-making system\*\*. This system utilises the high accessibility of metabolomics for primary screening, combined with radiomics and ctDNA technologies for subsequent \*\*differentiation and definitive diagnosis. The research plans to prospectively enrol 300 patients with non-small cell lung cancer, along with corresponding subjects with benign nodules and healthy controls. By optimising the model using machine learning and deep learning algorithms (such as SVM, HRNet, and PAResNet), the ultimate goal is to establish a novel lung cancer early screening system characterised by \*\*high sensitivity, high accuracy, and high accessibility\*\*, enabling the precise differentiation and screening of healthy individuals, benign pulmonary nodules, and early-stage lung cancer.
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 | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Employing multi-omics diagnostic approaches to enhance diagnostic efficacy | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Healthy, Benign, Malignant
- description
- Incorporating healthy individuals, benign nodules, and malignant nodules into the study population to reflect real-world screening scenarios.
- interventionNames
- Diagnostic Test: Employing multi-omics diagnostic approaches to enhance diagnostic efficacy
Primary outcomes (1)
- measure
- diagnostic sensitivity
- timeFrame
- From enrollment to the end of treatment at 6-8 weeks
- description
- The primary research indicators in this study focus on evaluating the diagnostic efficacy of multi-omics models for early-stage lung cancer. Firstly, diagnostic sensitivity serves as the core metric to assess the model's ability to correctly identify lung cancer patients, with a target value set at no less than 85%. Diagnostic specificity measures the model's capacity to correctly exclude non-lung cancer individuals, with a target value set at no less than 90%. The area under the receiver operating characteristic curve serves as a comprehensive indicator of the model's discriminative capability, with a target value exceeding 0.90 to ensure robust overall diagnostic performance. Regarding early detection capability, the detection rate for stage I lung cancer represents a key primary indicator in this study, specifically encompassing the detection of stage IA and IB lung cancer. This is because patients at this stage typically present with the optimal surgical resection opportunities an
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria 1. Age \>18 years old. 2. Availability of both exhaled breath and peripheral blood samples, and raw CT image data; the collection time is within one month before biopsy or surgical resection, and the subject has not received any treatment in between. 3. Pulmonary nodular lesions identified by chest CT with a diameter \< 3 cm. 4. Pulmonary nodular lesions must be surgically resected and have complete, definitive pathological information regarding their benign or malignant nature. 5. No prior history of malignant tumors. 6. Has not received anti-tumor treatments such as radiotherapy, chemotherapy, or targeted therapy. 7. Signed informed consent. Exclusion Criteria 1. Missing clinical data or incomplete sample collection. 2. Presence or suspicion of active infection or other severe co-morbidities. 3. Abnormal liver or kidney function. 4. Indefinite or inconclusive postoperative pathological results.
References
Publications (0)
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