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Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using diagnostic abdominal computed tomography images.
Querido, Nadira R; Bours, Martijn J L; Brecheisen, Ralph; Valkenburg-van Iersel, Liselot; Breukink, Stephanie O; Janssen-Heijnen, Maryska L G; Keulen, Eric T P; Konsten, Joop L M; de Vos-Geelen, Judith; Weijenberg, Matty P; Simons, Colinda C J M.
Afiliación
  • Querido NR; Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands. Electronic address: nadira.ribeiroquerido@maastrichtuniversity.nl.
  • Bours MJL; Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands.
  • Brecheisen R; Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, the Netherlands.
  • Valkenburg-van Iersel L; Department of Internal Medicine, Division of Medical Oncology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.
  • Breukink SO; Department of Surgery, GROW Research Institute for Oncology and Reproduction, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, the Netherlands.
  • Janssen-Heijnen MLG; Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands; Department of Clinical Epidemiology, VieCuri Medical Centre, Venlo, the Netherlands.
  • Keulen ETP; Department of Internal Medicine and Gastroenterology, Zuyderland Medical Centre Sittard- Geleen, Geleen, the Netherlands.
  • Konsten JLM; Department of Surgery, VieCuri Medical Centre, Venlo, the Netherlands.
  • de Vos-Geelen J; Department of Internal Medicine, Division of Medical Oncology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.
  • Weijenberg MP; Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands.
  • Simons CCJM; Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands.
Clin Nutr ESPEN ; 63: 659-667, 2024 Aug 02.
Article en En | MEDLINE | ID: mdl-39098602
ABSTRACT
BACKGROUND &

AIMS:

Several automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation.

METHODS:

Diagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement.

RESULTS:

A total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900 to 0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement.

CONCLUSIONS:

AutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Nutr ESPEN Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Nutr ESPEN Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido