ArticleJournal of cachexia, sarcopenia and muscle2021
Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer.
Article in Journal of cachexia, sarcopenia and muscle, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Machine learning for predicting muscle loss after radiotherapy using clinical and toxicity data in oral cavity cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.European radiology · 2025Article
- Androgen Deprivation Therapy-Induced Muscle Loss and Fat Gain Predict Cardiovascular Events in Prostate Cancer Patients.Journal of cachexia, sarcopenia and muscle · 2025Article
- Thresholds of Body Composition Changes Associated with Survival During Androgen Deprivation Therapy in Prostate Cancer.European urology open science · 2024Article
- Improved prediction of swimming talent through random forest analysis of anthropometric and physiological phenotypes.Phenomics (Cham, Switzerland) · 2024Article
- Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.Scientific reports · 2024Article
- Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
- Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets.Frontiers in public health · 2024Article
- Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer.Journal of cachexia, sarcopenia and muscle · 2023Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- Normative values and integrated score of functional fitness among Chinese community-dwelling older adults in Suzhou.Frontiers in physiology · 2022Article
- Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer.Journal of cachexia, sarcopenia and muscle · 2021Article
- Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas.Cancers · 2021Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExcessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results.
methodsWe randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ
resultsThe five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively.
conclusionsMachine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.
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