ArticleQuantitative imaging in medicine and surgery2026
Cascaded deep learning for automated lung cancer tumour burden quantification on [
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: 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). Methods: In this retrospective single-centre study, we collected 593 fluorine-18 fluorodeoxyglucose ([ Results: 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. Conclusions: The proposed cascaded segmentation-classification architecture significantly reduces FPs and yields reliable tumour burden quantification on PET/CT, enhancing accuracy and clinical utility.
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