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Deep Learning-Based Multicenter Study on Intelligent Diagnosis and Treatment Response Assessment of Solid Tumors

Deep Learning-Based Multicenter Study on Intelligent Diagnosis and Treatment Response Assessment of Solid Tumors

Status
Recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600120951
Enrollment
Unknown
Registered
2026-03-23
Start date
2026-03-23
Completion date
Unknown
Last updated
2026-03-30

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

Conditions

Solid tumors

Interventions

Responder:Risk factors and radiomic features associated with response to immunotherapy combined with chemotherapy in non-small cell lung cancer
Non-Responder:Risk factors and radiomic features associated with non-response to immunotherapy combined with chemotherapy in non-small cell lung cancer

Sponsors

Wuxi people’s Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.This study will enroll patients diagnosed with non-small cell lung cancer (NSCLC) by histopathology or cytology, with a clinical stage of III or IV, representing advanced cases that are not eligible for surgical resection or radical radiotherapy. 2.Regarding treatment regimens, patients must have received combination chemotherapy based on immune checkpoint inhibitors (PD-1/PD-L1 antibodies) as first-line or subsequent therapy. 3.The integrity of imaging data is a core inclusion criterion. Patients must have complete serial CT images, including baseline before treatment (T0), after 3 treatment cycles (T1), and after 5 treatment cycles (T2). The slice thickness of CT images must be <=1 mm to meet the precision requirements for radiomic feature extraction. 4.In addition, according to RECIST 1.1 criteria, patients must have at least one measurable target lesion, complete clinical baseline data (including age, gender, smoking history, ECOG performance status), and key molecular pathological data (such as EGFR/ALK gene mutation status, PD-L1 expression level;

Exclusion criteria

Exclusion criteria: 1.Patients with poor CT image quality characterized by severe respiratory motion artifacts, cardiac motion artifacts, or metallic implant artifacts that preclude accurate segmentation of the tumor and peritumoral regions or fail to meet data analysis requirements will be excluded from the study. 2.Patients with a history of other malignancies will be excluded. 3.Patients with severe cardiac, pulmonary, hepatic, or renal insufficiency, or other serious complications that result in intolerance to immunotherapy or an extremely short life expectancy (which would affect survival evaluation), will also be excluded. 4.Patients who received radiotherapy to the target lesion area before baseline imaging acquisition will also be excluded to avoid interference with imaging features.

Design outcomes

Primary

MeasureTime frame
Model prediction accuracy (Area Under the Curve,AUC / Sensitivity);

Secondary

MeasureTime frame
Progression-Free Survival (PFS);Objective Response Rate ORR;Dynamic Heterogeneity Index;

Countries

China

Contacts

Public ContactXiaoyun Hu

Wuxi people’s Hospital

drxyh@foxmail.com+86 510 85350345

Outcome results

None listed

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