Vol 30, No 1 (2025)
Technical note
Published online: 2025-02-04
Performance evaluation of MVision AI Contour+ in gastric MALT lymphoma segmentation
DOI: 10.5603/rpor.104144
Rep Pract Oncol Radiother 2025;30(1):122-125.
Abstract
Not required
Keywords: deep learningautomated segmentationgastric MALT lymphoma
References
- Lim HW, Kim TH, Choi IlJu, et al. Radiation therapy for gastric mucosa-associated lymphoid tissue lymphoma: dose-volumetric analysis and its clinical implications. Radiat Oncol J. 2016; 34(3): 193–201.
- Jansen EPM, Nijkamp J, Gubanski M, et al. Interobserver variation of clinical target volume delineation in gastric cancer. Int J Radiat Oncol Biol Phys. 2010; 77(4): 1166–1170.
- Mir R, Kelly SM, Xiao Y, et al. Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines. Radiother Oncol. 2020; 150: 30–39.
- Jabbour SK, Hashem SA, Bosch W, et al. Upper abdominal normal organ contouring guidelines and atlas: a Radiation Therapy Oncology Group consensus. Pract Radiat Oncol. 2014; 4(2): 82–89.
- Sherer MV, Lin D, Elguindi S, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol. 2021; 160: 185–191.
- Ahn SH, Yeo AU, Kim KH, et al. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer. Radiat Oncol. 2019; 14(1): 213.
- Strolin S, Santoro M, Paolani G, et al. How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images. Front Oncol. 2023; 13: 1089807.
- Flegal KM, Shepherd JA, Looker AC, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr. 2009; 89(2): 500–508.