A clinical study using AI based imaging analysis technology to continuously monitor sarcopenia and clinical prognosis in patients with esophageal cancer.

e16020 Background: In recent years, impact of sarcopenia on the prognosis of esophageal cancer has drawn attention to the esophageal cancer patients. This article aims to combine AI imaging analysis technology to study the incidence of sarcopenia in multimodal treatment of esophageal cancer and evaluate its impact on the long-term prognosis of esophageal cancer patients. Methods: 109 patients were included in the study. Skeletal muscle index (SMI) of patients were determined before treatment, at 3 months, 6 months, and 1 year of treatment (including surgery, chemotherapy, PD-1, etc.). The computed tomography (CT) scan at L3 defines sarcopenia as male SMI < 40.8 cm2/m2 and female SMI < 34.9 cm2/m2. All patients were followed up for 18 months, with clinical death as the endpoint of observation. Results: The 18-months mortality rate of patients with sarcopenia (71.4%) is higher than that of patients without sarcopenia; Among patients without sarcopenia, those with reduced skeletal muscle area had a higher 18-months mortality rate (58.8%) than those with increased skeletal muscle area (40.9%). P < 0.05. Conclusions: Patients with reduced skeletal muscle area but not meeting the diagnostic criteria for sarcopenia may have poor long-term prognosis and require further attention. Comparison of differences in 18-months mortality rate among patients with sarcopenia. Comparison of differences in 18-months mortality rate among patients with sarcopenia 18 months c 2 P death survival sarcopenia NO 19(48.7%) 20(51.3%) 5.561 0.018 YES 50(71.4%) 20(28.6%) Comparison of 18-months mortality differences between patients with sarcopenia or reduced skeletal muscle area 18 months death survival A 50(71.4%)a 20(28.6%)a 6.886 0.032 B 9(40.9%)b 13(59.1%)b C 10(58.8%)ab 7(41.2%)ab annotation:A:sarcopenia;B:Undiagnosed sarcopenia with increased skeletal muscle area;C:Undiagnosed sarcopenia but with reduced skeletal muscle area.Cells containing different lowercase letters represent comparisons between items with P<0.05 Demographic and clinical characteristics of the study population Variables Sarcopenia Non-Sarcopenia t/f P Male,N(%) 68(97.14%) 38(97.44%) 0.008 0.928 Age(years) 60.83 ± 9.21 59.64 ± 6.31 5.573 0.475 Weight(Kg) 63.41± 9.93 70.68± 9.42 0.436 <0.001 height(meter) 1.71(1.67,1.75) 1.70(1.65,1.75) -1.089 0.276 BMI(kg/m 2 ) 21.55(20.06,23.62) 24.57(23.62,25.95) -5.076 <0.001

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Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e16020
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

A clinical study using AI based imaging analysis technology to continuously monitor sarcopenia and clinical prognosis in patients with esophageal cancer.

Chunyi Jia, Haifeng Liu, Xueying Zhang, Guoxing Zhang
Journal of Clinical Oncology
Radiomics and Machine Learning in Medical Imaging
article

A clinical study using AI based imaging analysis technology to continuously monitor sarcopenia and clinical prognosis in patients with esophageal cancer.

Chunyi Jia, Haifeng Liu, Xueying Zhang, Guoxing Zhang
article en

Abstract

e16020 Background: In recent years, impact of sarcopenia on the prognosis of esophageal cancer has drawn attention to the esophageal cancer patients. This article aims to combine AI imaging analysis technology to study the incidence of sarcopenia in multimodal treatment of esophageal cancer and evaluate its impact on the long-term prognosis of esophageal cancer patients. Methods: 109 patients were included in the study. Skeletal muscle index (SMI) of patients were determined before treatment, at 3 months, 6 months, and 1 year of treatment (including surgery, chemotherapy, PD-1, etc.). The computed tomography (CT) scan at L3 defines sarcopenia as male SMI < 40.8 cm2/m2 and female SMI < 34.9 cm2/m2. All patients were followed up for 18 months, with clinical death as the endpoint of observation. Results: The 18-months mortality rate of patients with sarcopenia (71.4%) is higher than that of patients without sarcopenia; Among patients without sarcopenia, those with reduced skeletal muscle area had a higher 18-months mortality rate (58.8%) than those with increased skeletal muscle area (40.9%). P < 0.05. Conclusions: Patients with reduced skeletal muscle area but not meeting the diagnostic criteria for sarcopenia may have poor long-term prognosis and require further attention. Comparison of differences in 18-months mortality rate among patients with sarcopenia. Comparison of differences in 18-months mortality rate among patients with sarcopenia 18 months c 2 P death survival sarcopenia NO 19(48.7%) 20(51.3%) 5.561 0.018 YES 50(71.4%) 20(28.6%) Comparison of 18-months mortality differences between patients with sarcopenia or reduced skeletal muscle area 18 months death survival A 50(71.4%)a 20(28.6%)a 6.886 0.032 B 9(40.9%)b 13(59.1%)b C 10(58.8%)ab 7(41.2%)ab annotation:A:sarcopenia;B:Undiagnosed sarcopenia with increased skeletal muscle area;C:Undiagnosed sarcopenia but with reduced skeletal muscle area.Cells containing different lowercase letters represent comparisons between items with P<0.05 Demographic and clinical characteristics of the study population Variables Sarcopenia Non-Sarcopenia t/f P Male,N(%) 68(97.14%) 38(97.44%) 0.008 0.928 Age(years) 60.83 ± 9.21 59.64 ± 6.31 5.573 0.475 Weight(Kg) 63.41± 9.93 70.68± 9.42 0.436 <0.001 height(meter) 1.71(1.67,1.75) 1.70(1.65,1.75) -1.089 0.276 BMI(kg/m 2 ) 21.55(20.06,23.62) 24.57(23.62,25.95) -5.076 <0.001

Journal of Clinical OncologyVol. 43(16_suppl)
Jilin Province Tumor Hospital (CN)
Openalex Percentile: Top 82%
Radiomics and Machine Learning in Medical Imaging
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