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A deep learning multi-omics-based non-beneficiary population prediction system for neoadjuvant immunotherapy in lung cancer developed Development and exploration of mechanisms

A deep learning multi-omics-based non-beneficiary population prediction system for neoadjuvant immunotherapy in lung cancer developed Development and exploration of mechanisms

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500098112
Enrollment
Unknown
Registered
2025-03-03
Start date
2025-04-01
Completion date
Unknown
Last updated
2025-03-10

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

Conditions

lung cancer

Interventions

Non-beneficiary group:no
Beneficiary group:no

Sponsors

The Second Xiangya Hospital of Central South University, Changsha,
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1. 18 years old and above; 2. Pathologically confirmed NSCLC; 3. Staging: Ib-III; 4. Received neoadjuvant chemotherapy combined with immunotherapy, regardless of whether radical surgery was performed.

Exclusion criteria

Exclusion criteria: 1. Previously received immunotherapy; 2. No CT from our hospital within 30 days before neoadjuvant chemotherapy combined with immunotherapy; 3. Tumor lesions are not measurable; 4. Poor quality of CT images that cannot be used to extract features; 5. Tumor puncture performed before CT (which will affect the characteristics of CT images); 6. Driver gene mutations in EGFR and ALK genes.

Design outcomes

Primary

MeasureTime frame
Postoperative pathological response (pathology mostly relieved);

Secondary

MeasureTime frame
Response Evaluation Criteria in Solid Tumours;

Countries

China

Contacts

Public ContactChaoyuan Liu

The Second Xiangya Hospital of Central South University

lcyyyxx@csu.edu.cn+86 153 8749 1011

Outcome results

None listed

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