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Multimodal radiomics machine learning model for predicting the prognosis of lung adenocarcinoma bone metastasis and analysis of traditional Chinese medicine usage patterns

Multimodal radiomics machine learning model for predicting the prognosis of lung adenocarcinoma bone metastasis and analysis of traditional Chinese medicine usage patterns

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500110817
Enrollment
Unknown
Registered
2025-10-21
Start date
2025-09-19
Completion date
Unknown
Last updated
2025-10-27

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

Conditions

lung adenocarcinoma bone metastasis

Interventions

Case series:None

Sponsors

Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: (1) Patients were diagnosed with lung adenocarcinoma by histopathology; (2) Bone metastasis was confirmed by imaging examination (CT, MRI, ECT or PET/CT); (3) Chest CT and hematological examination were performed when lung adenocarcinoma was diagnosed; (4) Patient medical records were complete, including basic information, clinical data, treatment process and follow-up records.

Exclusion criteria

Exclusion criteria: (1) The pathological type of lung cancer is non-adenocarcinoma or the pathological type is undetermined; (2) The lung malignancy is caused by metastasis of other tumors; (3) It is combined with other primary malignant tumors; (4) It is not determined whether bone metastasis occurs; (5) Chest CT and hematological examinations are incomplete; (6) The medical records are seriously missing, there is no follow-up record, and it cannot meet the research needs.

Design outcomes

Primary

MeasureTime frame
Overall Survival;Living conditions;Machine learning model evaluation index - Area under the receiver operating characteristic curve , AUC;Prediction model evaluation index-Delong test;Prediction model evaluation index - Calibration curves;Prediction model evaluation index - Decision curve analysis, DCA;

Secondary

MeasureTime frame
Gender;Age(years);Hypertension;Diabetes;Antigen identified by monoclonal antibody Ki-67, Ki-67;Epidermal Growth Factor Receptor, EGFR;White Blood Cell Count, WBC;Absolute Neutrophil Count, ANC;Absolute Lymphocyte Count, ALC;Red Blood Cell Count, RBC;Hemoglobin, HGB;Platelet Count, PLT;C-Reactive Protein, CRP;Albumin, ALB;Alkaline Phosphatase, ALP;Lactate Dehydrogenase, LDH;Calcium, Ca;Inorganic Phosphorus, IP;D-Dimer;Neuron-specific enolase, NSE;Cytokeratin 19 fragment antigen211, CYFRA211;Carcinoma embryonic antigen, CEA;Carbohydrate Antigen 19-9, CA19-9;Carbohydrate Antigen 125, CA125;Serum Ferritin, SF;Interleukin-6, IL-6;Cluster of Differentiation 19 positive, CD19+;Absolute CD19-positive T-cell count, CD19+ Count;Absolute CD3-positive T-cell count, CD3+ Count;Absolute CD8-positive T-cell count, CD8+ Count;Maximum diameter;Location;Lesion distribution;Lung lobe distribution;Number of lesions;Ground glass;Lobulation;Spiculation;Vacuole sign;Pleural traction;Rad-score;

Countries

China

Contacts

Public ContactYanping Yang

Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine

yanpingyangks@163.com+86 133 9114 2018

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026