This study aims to construct an early diagnostic biomarker panel and risk stratification model for anti-tumor drug-related ILD through integrative analysis of multi-omics data including genomics, transcriptomics, proteomics, and metabolomics. Using baseline and post-treatment longitudinal samples collected from a multi-center prospective cohort, we will apply machine learning to screen for stable and reproducible feature sets and evaluate their sensitivity, specificity, and clinical applicability in an independent validation cohort. The goal is to achieve early identification and stratified management of ILD, optimize treatment decisions, reduce the incidence of severe adverse events, and improve patient survival and quality of life.

Inclusion Criteria:

• Adults ≥18 years of age with the ability to independently provide written informed consent, fulfilling the criteria for either the oncology or control cohort, capable of undergoing baseline BALF and peripheral blood sampling at treatment commencement or time of diagnosis, and consenting to serial blood sampling at 2-4 weeks and 8-12 weeks following treatment initiation.

Exclusion Criteria:

1. Active infection or established non-drug-related etiology of ILD (including but not limited to connective tissue disease-associated ILD, environmental or occupational exposure-induced ILD, active radiation pneumonitis)
2. Prolonged pre-enrollment treatment with moderate-to-high dose systemic corticosteroids or immunosuppressive agents that would compromise baseline evaluation
3. Absolute contraindications to bronchoscopic procedures or inability to comply with scheduled key follow-up assessments
4. Severe decompensated comorbid conditions
5. Pregnancy or lactation period.

The study population includes four categories of subjects: patients with drug-induced interstitial lung disease (ED group), patients without ILD during treatment (TN group), patients with non-cancer interstitial lung disease (NC-ILD group), and healthy volunteers with benign pulmonary nodules (BPN group).