Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy
This study presents the development and validation of an artificial intelligence (AI) prediction system that utilizes pre-neoadjuvant immunotherapy plain scans and enhanced multimodal CT scans to extract deep learning features. The aim is to predict the occurrence of pathological complete response in non-small cell lung cancer patients undergoing neoadjuvant immunochemotherapyy.
- EligibilityCriteria: Inclusion Criteria:  Patients' with non-small cell lung cancer, diagnosed through biopsy pathology and clinically classified as stage IB to III; Patients who receive at least two cycles of neoadjuvant immunotherapy combined with chemotherapy induction therapy; According to the IASLC guidelines, postoperative pathological evaluation was performed on the treatment response of the tumor primary lesion and lymph nodes.  Exclusion Criteria:  Missing or inadequate quality of CT; Time interval between CT and start of treatment is greater than 1 month; Incomplete clinicopathologic data. - HealthyVolunteers: No - Gender: All - MinimumAge: 18 Years - MaximumAge: 80 Years - StdAgeList: Adult, Older Adult - StudyPopulation: patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment - SamplingMethod: Non-Probability Sample