Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Department of Computer Science, Memorial University of Newfoundland, St. John's, Canada.
Nutrition. 2025 Jan;129:112592. doi: 10.1016/j.nut.2024.112592. Epub 2024 Oct 5.
Body composition evaluation can be used to assess patients' nutritional status to predict clinical outcomes. To facilitate reliable and time-efficient body composition measurements eligible for clinical practice, fully automated computed tomography segmentation methods were developed. The aim of this study was to evaluate automated segmentation by Data Analysis Facilitation Suite in an independent dataset.
Preoperative computed tomography images were used of 165 patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy from 2014 to 2019. Manual and automated measurements of skeletal muscle mass (SMM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) were performed at the third lumbar vertebra. Segmentation accuracy of automated measurements was assessed using the Jaccard index and intra-class correlation coefficients.
Automatic segmentation provided accurate measurements compared to manual analysis, resulting in Jaccard score coefficients of 94.9 for SMM, 98.4 for VAT, 99.1 for SAT, and 79.4 for IMAT. Intra-class correlation coefficients ranged from 0.98 to 1.00. Automated measurements on average overestimated SMM and SAT areas compared to manual analysis, with mean differences (±2 standard deviations) of 1.10 (-1.91 to 4.11) and 1.61 (-2.26 to 5.48) respectively. For VAT and IMAT, automated measurements on average underestimated the areas with mean differences of -1.24 (-3.35 to 0.87) and -0.93 (-5.20 to 3.35), respectively.
Commercially available Data Analysis Facilitation Suite provides similar results compared to manual measurements of body composition at the level of third lumbar vertebra. This software provides accurate and time-efficient body composition measurements, which is necessary for implementation in clinical practice.
身体成分评估可用于评估患者的营养状况，以预测临床结果。为了实现可靠且高效的身体成分测量，适用于临床实践，开发了全自动计算机断层扫描分割方法。本研究的目的是在独立数据集上评估 Data Analysis Facilitation Suite 的自动分割。
使用了 2014 年至 2019 年间接受细胞减灭术和腹腔热灌注化疗的 165 例患者的术前计算机断层扫描图像。在第三腰椎水平进行骨骼肌质量（SMM）、内脏脂肪组织（VAT）、皮下脂肪组织（SAT）和肌内脂肪组织（IMAT）的手动和自动测量。使用 Jaccard 指数和组内相关系数评估自动测量的分割准确性。
与手动分析相比，自动分割提供了准确的测量值，导致 SMM 的 Jaccard 评分系数为 94.9，VAT 为 98.4，SAT 为 99.1，IMAT 为 79.4。组内相关系数范围为 0.98 至 1.00。与手动分析相比，自动测量平均高估了 SMM 和 SAT 区域，平均差异（±2 个标准差）分别为 1.10（-1.91 至 4.11）和 1.61（-2.26 至 5.48）。对于 VAT 和 IMAT，自动测量平均低估了面积，平均差异分别为-1.24（-3.35 至 0.87）和-0.93（-5.20 至 3.35）。
商业上可用的 Data Analysis Facilitation Suite 与第三腰椎水平的手动身体成分测量相比提供了类似的结果。该软件提供了准确和高效的身体成分测量，这对于在临床实践中的实施是必要的。
