Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine
Abstract Background Cross‐sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) can be used for sarcopenia diagnosis. The measurement of CSMA is time‐consuming and thus restricted to clinical research. We aimed to compare the automatic module ABACS (Automatic Body composition Analyser u...
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| Format: | Article |
| Language: | English |
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Wiley
2021-07-01
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| Series: | JCSM Rapid Communications |
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| Online Access: | https://doi.org/10.1002/rco2.37 |
| _version_ | 1818624380583804928 |
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| author | Louise Caudron Alexandre Bussy Svetlana Artemova Katia Charrière Salma El Lakkiss Alexandre Moreau‐Gaudry Jean‐Luc Bosson Gilbert R. Ferretti Eric Fontaine Cécile Bétry |
| author_facet | Louise Caudron Alexandre Bussy Svetlana Artemova Katia Charrière Salma El Lakkiss Alexandre Moreau‐Gaudry Jean‐Luc Bosson Gilbert R. Ferretti Eric Fontaine Cécile Bétry |
| author_sort | Louise Caudron |
| collection | DOAJ |
| description | Abstract Background Cross‐sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) can be used for sarcopenia diagnosis. The measurement of CSMA is time‐consuming and thus restricted to clinical research. We aimed to compare the automatic module ABACS (Automatic Body composition Analyser using Computed tomography image Segmentation software) with manual segmentation for CSMA assessment into clinical routine. Methods The study population was screened retrospectively from a computed tomography‐scan (CT‐scan) database. All consecutive participants, hospitalized at the Grenoble University Hospital (CHU Grenoble Alpes) between January and May 2018, and with an abdominal CT‐scan including sagittal reconstruction were included. The software SliceOmatic complemented with the module ABACS (ABACS‐SliceOmatic) was compared with the software ImageJ. Their agreement was determined using Lin's concordance correlation coefficient and visualized in Bland–Altman plots for the CSMA measurement or with Cohen's kappa coefficient (κ) for sarcopenia status. Results Data from 680 participants were analysed (mean age 59 ± 19 years, %females: 45.7). The concordance correlation coefficient between both types of software was 0.93 (CI95%: 0.92 to 0.94). Mean CSMA was significantly higher with ABACS‐SliceOmatic (mean difference: 6.51 ± 10.50 cm2; P < 0.001). Kappa agreement for sarcopenia diagnosis was moderate: 0.68 (CI95%: 0.62–0.74) and 0.71 (CI95%: 0.65–0.76) for Prado's and Derstine's definitions, respectively. Conclusions ABACS‐SliceOmatic has moderate agreement with the manual software ImageJ in a routine clinical database. Our work suggests that ABACS‐SliceOmatic should be used with caution in clinical practice. To improve its reliability, we suggest to manually validate the automatic segmentation. |
| first_indexed | 2024-12-16T18:56:02Z |
| format | Article |
| id | doaj.art-ce7d5bea702e4856867724526c804f9b |
| institution | Directory Open Access Journal |
| issn | 2617-1619 |
| language | English |
| last_indexed | 2024-12-16T18:56:02Z |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | JCSM Rapid Communications |
| spelling | doaj.art-ce7d5bea702e4856867724526c804f9b2022-12-21T22:20:32ZengWileyJCSM Rapid Communications2617-16192021-07-014210311010.1002/rco2.37Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routineLouise Caudron0Alexandre Bussy1Svetlana Artemova2Katia Charrière3Salma El Lakkiss4Alexandre Moreau‐Gaudry5Jean‐Luc Bosson6Gilbert R. Ferretti7Eric Fontaine8Cécile Bétry9Service d'Endocrinologie Diabétologie Nutrition, CHU Grenoble Alpes Univ. Grenoble Alpes Grenoble 38000 FranceService d'Endocrinologie Diabétologie Nutrition, CHU Grenoble Alpes Univ. Grenoble Alpes Grenoble 38000 FrancePôle Santé Publique, CHU Grenoble Alpes, Clinical Investigation Center‐Technological Innovation, INSERM CIC1406 Univ. Grenoble Alpes Grenoble FranceClinical Investigation Center‐Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes Univ. Grenoble Alpes Grenoble FranceService d'Endocrinologie Diabétologie Nutrition, CHU Grenoble Alpes Univ. Grenoble Alpes Grenoble 38000 FrancePublic Health Department CHU Grenoble Alpes, Grenoble INP, TIMC‐IMAG, Center‐Technological Innovation, INSERM CIC1406 Univ. Grenoble Alpes, CNRS Grenoble FrancePublic Health Department CHU Grenoble Alpes, Grenoble INP, TIMC‐IMAG Univ. Grenoble Alpes, CNRS Grenoble FranceService de radiologie diagnostique et interventionnelle CS 10217 Grenoble FranceINSERM, LBFA Univ. Grenoble Alpes Grenoble FranceService d'Endocrinologie, Diabétologie, Nutrition, Pole Digidune, CHU Grenoble AlpesAbstract Background Cross‐sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) can be used for sarcopenia diagnosis. The measurement of CSMA is time‐consuming and thus restricted to clinical research. We aimed to compare the automatic module ABACS (Automatic Body composition Analyser using Computed tomography image Segmentation software) with manual segmentation for CSMA assessment into clinical routine. Methods The study population was screened retrospectively from a computed tomography‐scan (CT‐scan) database. All consecutive participants, hospitalized at the Grenoble University Hospital (CHU Grenoble Alpes) between January and May 2018, and with an abdominal CT‐scan including sagittal reconstruction were included. The software SliceOmatic complemented with the module ABACS (ABACS‐SliceOmatic) was compared with the software ImageJ. Their agreement was determined using Lin's concordance correlation coefficient and visualized in Bland–Altman plots for the CSMA measurement or with Cohen's kappa coefficient (κ) for sarcopenia status. Results Data from 680 participants were analysed (mean age 59 ± 19 years, %females: 45.7). The concordance correlation coefficient between both types of software was 0.93 (CI95%: 0.92 to 0.94). Mean CSMA was significantly higher with ABACS‐SliceOmatic (mean difference: 6.51 ± 10.50 cm2; P < 0.001). Kappa agreement for sarcopenia diagnosis was moderate: 0.68 (CI95%: 0.62–0.74) and 0.71 (CI95%: 0.65–0.76) for Prado's and Derstine's definitions, respectively. Conclusions ABACS‐SliceOmatic has moderate agreement with the manual software ImageJ in a routine clinical database. Our work suggests that ABACS‐SliceOmatic should be used with caution in clinical practice. To improve its reliability, we suggest to manually validate the automatic segmentation.https://doi.org/10.1002/rco2.37SarcopeniaTomodensitometryMuscleSkeletalBody compositionSoftware |
| spellingShingle | Louise Caudron Alexandre Bussy Svetlana Artemova Katia Charrière Salma El Lakkiss Alexandre Moreau‐Gaudry Jean‐Luc Bosson Gilbert R. Ferretti Eric Fontaine Cécile Bétry Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine JCSM Rapid Communications Sarcopenia Tomodensitometry Muscle Skeletal Body composition Software |
| title | Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine |
| title_full | Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine |
| title_fullStr | Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine |
| title_full_unstemmed | Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine |
| title_short | Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine |
| title_sort | sarcopenia diagnosis comparison of automated with manual computed tomography segmentation in clinical routine |
| topic | Sarcopenia Tomodensitometry Muscle Skeletal Body composition Software |
| url | https://doi.org/10.1002/rco2.37 |
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