(298) Assessing tumor volume: a scoping review of measurement methods and clinical utility in head and neck and lung cancers

Background: : Complete tumor staging, including tumor volume, is crucial to classify severity, guide treatment, and predict prognosis. This scoping review explores established and emerging tumor measurement methods to identify gaps in existing research.

Objective: : To identify and categorize current tumor volume measurement techniques and assess gaps in existing research across head and neck, lung, and other solid tumors.

Methods: : Search terms included tumor volume measurement, head and neck cancer (HNC), and lung cancer (primarily non-small cell lung cancer, NSCLC), on publications referenced in PubMed and PubMed Central databases (January 2020 to April 2025). Although HNC and lung cancer were prioritized; all solid tumors were eligible for inclusion. The measurement methods were categorized by method type (imaging-based, algorithm-based, and innovative methods), cancer type (HNC, lung, breast, and bladder), and clinical utility (prognosis, treatment planning, responsiveness). Innovative methods such as fluorescence-guided imaging or advanced reconstruction algorithms were also included.

Results: : A total of 12 studies on tumor volume measurement were identified: HNC (n=4), lung cancer (n=4), and other cancers (n=4). Imaging-based methods (n=6) included CT (n=5), PET-CT (n=1), MRI (n=2), and ultrasound (n=1). Algorithm-based methods (n=6) included automated segmentation (n=3), spherical/ellipsoid models (n=2), and mathematical models (n=2. Innovations (n=2), including fluorescence imaging (9% deviation) and iterative reconstruction (92.1% sensitivity) improved lung cancer screening. In HNC, tumor volume demonstrated strong prognostic value, especially in patients with smaller primary tumor volumes (GTV-P < 30 cc), nodal volumes (GTV-N < 4 cc), and combined volumes (GTV-P+N < 50 cc). While TNM staging remains standard, some emerging evidence in HNC and NSCLC suggests that volumetric measurements techniques—particularly those using automated segmentation or high-resolution 3D imaging—predicted outcomes such as survival and treatment response more accurately than traditional measurements.

Conclusions: : Tumor volume measurement, particularly 3D volumetric methods with dynamic modeling, enhances treatment precision in solid tumors such as HNC and lung cancer. Automated segmentation and iterative CT reconstruction contribute to more consistent and reproducible measurements. Real-world data typically lacks tumor imaging data or may have limited data on tumor size, further motivating estimation algorithms. There is a need to standardize and validate tumor volume methods, thereby improving clinical outcomes in oncology.

Sponsorship:: None