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Current clinical follow-up frequency and treatment timing for pulmonary subsolid nodules (SSNs) rely mostly on whether the nodules grow, which may not accurately reflect the pathological status, and may lead to unnecessary follow-ups. This study aims to use multi-omics techniques to dynamically observe the growth and invasiveness evolution process of SSNs and uncover its invasiveness mechanism. Radiological characteristics of SSNs in different invasiveness stages were also analyzed and summarized by analyzing preoperative CT. This can overcome the bottleneck of invasiveness assessment in the growth process of SSN and provide scientific evidence for the scientific management and clinical treatment timing choice of SSN patients, thus facilitating the rational allocation of medical resources and prolonging the expected survival of national health.
This prospective observational cohort study aims to recruit 120 patients with subsolid nodules (SSNs) and 100 healthy volunteers. Enroll 120 patients with SSNs planned for surgery and 100 healthy volunteers. Sequence blood and tissue samples from patients and compare the relevance of biomarkers between the two. Use blood from healthy volunteers as blank controls. Additionally, analyzes radiological characteristics of SSNs at different invasive stages using preoperative CT.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Subsolid nodules group | Patients with pulmonary subsolid nodules were identified by CT examination and intended for surgical treatment. All participants will undergo chest CT, genomic, and proteomic tests to identify related biomarkers and establish a clinical diagnostic model for the invasiveness of subsolid nodules. |
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| Volunteers group | Healthy volunteers. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Mass spectrometry-based proteomics | Diagnostic Test | Detecting genomic and proteomic information |
|
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of subsolid nodule invasive diagnosis | The efficacy of screening features for SSN invasiveness was compared to pathological diagnosis, using metrics such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). | Through study completion, an average of 1 year. |
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Inclusion Criteria:
Patients with SSNs
Healthy Volunteers
Exclusion Criteria:
Patients with SSNs
Healthy Volunteers
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Patients who underwent chest CT examination and found SSN and planned to undergo surgical treatment from November 2023 to September 2024 and Healthy volunteers.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Li Zhang, Doctor | Contact | 15010225989 | zhangli_cams@163.com | |
| Meng Li, Doctor | Contact | 01087787532 | lmcams@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Li Zhang, Doctor | Cancer Institute and Hospital, Chinese Academy of Medical Sciences | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cancer Hospital Chinese Academy of Medical Sciences | Recruiting | Beijing | Beijing Municipality | 100021 | China |
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| ID | Term |
|---|---|
| D000077192 | Adenocarcinoma of Lung |
| ID | Term |
|---|---|
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
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| ID | Term |
|---|---|
| D059472 | Exome |
| ID | Term |
|---|---|
| D016678 | Genome |
| D040342 | Genetic Structures |
| D055614 | Genetic Phenomena |
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| D009369 | Neoplasms |
| D008175 | Lung Neoplasms |
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |