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A Study on Predicting the Therapeutic Response to Neoadjuvant Therapy for Non-Small Cell Lung Cancer Based on Artificial Intelligence Models

Predicting the Therapeutic Response to Neoadjuvant Therapy for Non-Small Cell Lung Cancer Based on Artificial Intelligence

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600120900
Enrollment
Unknown
Registered
2026-03-22
Start date
2026-03-25
Completion date
Unknown
Last updated
2026-03-23

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

Conditions

Non-small cell lung cancer

Interventions

Observation Group:None

Sponsors

The First Affiliated Hospital of Nanchang University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age >= 18 years old. 2. Pathological diagnosis of stage II-IIIB non-small cell lung cancer by percutaneous lung puncture or bronchoscopic biopsy. 3. Received standard neoadjuvant therapy before the operation and completed at least 2 cycles. 4. Underwent radical lung cancer resection after neoadjuvant therapy and had a complete postoperative pathological assessment report. 5. Have high-quality chest CT image data before neoadjuvant therapy. 6. Digital pathological sections of tumor tissue biopsies before neoadjuvant therapy are available.

Exclusion criteria

Exclusion criteria: 1. Received other anti-tumor treatments before neoadjuvant therapy. 2. Incomplete clinical, imaging or pathological data cannot meet the requirements for model construction. 3. Suffering from other active malignant tumors.

Design outcomes

Primary

MeasureTime frame
Post-neoadjuvant pathological response;

Secondary

MeasureTime frame
Clinical variables: age, gender, smoking history, clinical TNM stage, pathological type, PD-L1 expression level (TPS/CPS), hematological markers (such as tumor markers, etc.);Radiomics features: Extracted from baseline chest CT images before neoadjuvant therapy. Three-dimensional delineation of the primary tumor was performed on arterial phase images, and thousands of quantitative features (including first-order statistics, shape features, texture features, and wavelet signs) were extracted using dedicated software.;Pathological image features: Digital scanning of HE staining sections of biopsy tissues before neoadjuvant therapy is conducted to generate full section digital images.;

Countries

China

Contacts

Public ContactXia Guojin

The First Affiliated Hospital of Nanchang University

258345164@qq.com+86 180 7910 1159

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Apr 3, 2026