Predicting Invasiveness of Lung Adenocarcinoma from Chest CT with Few-shot Vision-Language Ternary Classification Model

These scripts demonstrate the step-by-step transformation from the original DICOM-format CT images to anonymized NIfTI (.nii.gz) files and subsequently to MP4-format videos used as model inputs. This workflow provides researchers with a clear reference for ensuring patient privacy protection when applying online vision–language models to medical imaging data. Due to Mendeley Data’s maximum storage capacity of 10 GB, we uploaded all video data used as inputs for the vision–language models (GPT-4o, Google Gemini 2.5 Pro, and Molmo), which together occupy 9.94 GB of space.

Accordingly, this dataset contains only the anonymized video data used for model analysis. The CT images of all patients (totaling 81.1GB) will be disclosed in other databases that can provide the corresponding capacity. Users of this dataset please cite the following publication: “Predicting Invasiveness of Lung Adenocarcinoma from Chest CT with Few-shot Vision–Language Ternary Classification Model.”