Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)
This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer.
• Confirmed history of previous radically or curative-intent treated solid organ cancer within 10 years of new index CT thoracic scan demonstrating a new pulmonary nodule and either of the following:
‣ Biopsy confirming previous malignancy with MDT consensus and successful cancer resolution/remission following anti-cancer treatment on interval imaging or blood assay analysis
⁃ Where biopsy was not possible/confirmed for previous malignancy, MDT consensus outcome confirming cancer (+/- calculated Herder score \>80% if applicable) and decision to treat as malignancy with subsequent resolution/remission following anti-cancer treatment on interval imaging or blood assay analysis
• Radical treatment for previous cancer defined as either of the following:
‣ Surgical resection
⁃ Radical radiotherapy or stereotactic beam radiotherapy
⁃ Radical chemotherapy
⁃ Radical chemo-radiotherapy
⁃ Multi-modality treatment with any of the above
• New pulmonary nodule ground truth known
‣ Scan data showing 2-year stability (based on diameter or volumetry) or resolution in cases of benign disease
⁃ Scan data showing progressive nodule enlargement or increase in nodule number on interval imaging with MDT consensus (+/- PET with Herder score \>80% if applicable) determining metastatic disease or new primary malignancy
⁃ Biopsy sampling confirming benign disease or malignancy and in cases of malignancy, metastasis or new primary lung cancer
• CT scan slice thickness ≤ 2.5mm
• Nodule size ≥ 5mm