Cascaded deep learning for automated lung cancer tumour burden quantification on [¹⁸F]FDG PET/CT

Accurate assessment of tumour burden in lung cancer is critical for diagnosis, prognosis, and treatment planning. To enhance segmentation accuracy and reduce false positives (FPs), we developed a cascaded deep learning framework that combines lesion segmentation and subsequent classification, aiming to enable reliable automated tumour burden estimation on positron emission tomography/computed tomography (PET/CT).

In this retrospective single-centre study, we collected 593 fluorine-18 fluorodeoxyglucose ([¹⁸F]FDG) PET/CT scans from lung cancer patients with two scanners. Scanner 1 data (N=496) were split for five-fold cross-validation (training/validation) and internal testing; Scanner 2 data (N=97) served for external testing. The proposed cascaded framework integrated a segmentation stage with a subsequent classification stage to suppress FP findings and generate lesion-level tumour segmentation for tumour burden quantification. We compared the cascaded model to standalone segmentation using Dice similarity coefficient (DSC), lesion-level precision/recall, and assessed metabolic tumour volume (MTV) and total lesion glycolysis (TLG) against manual annotations.

On internal and external test sets, the cascaded model achieved consistent segmentation accuracy (DSC =0.82) with improved precision compared to segmentation alone (P<0.05). Tumour burden estimation showed strong correlations with manual measurements (r=0.984 for MTV, r=0.998 for TLG; both P<0.05) and moderate agreement.

The proposed cascaded segmentation-classification architecture significantly reduces FPs and yields reliable tumour burden quantification on PET/CT, enhancing accuracy and clinical utility.