Evaluation of sarcopenia in small-cell lung cancer patients by routine chest CT

Kim, Eun Young; Kim, Young Saing; Park, Inkeun; Ahn, Hee Kyung; Cho, Eun Kyung; Jeong, Yu Mi; Kim, Jeong-Ho

doi:10.1007/s00520-016-3321-0

Cited by 63 publications

(67 citation statements)

References 15 publications

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“…However, L3 vertebrae is not included in several CT examination protocols, such as chest, pelvis or head and neck CT examinations. Some investigators have assessed alternative measurements for the detection of sarcopenia in patients with chest-only CT or pelvis-only CT examinations [13][14][15][16][17]. Swartz et al [18] compared SMA at L3 level (L3MA) and paravertebral muscle area at the third cervical vertebrae level (C3MA) in 103 subjects (51 trauma patients and 52 patients with malignancy) and they have reported that C3MA may be an alternative to L3MA in head and neck CT examinations.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Sarcopenia in Head and Neck Computed Tomography: Cervical Muscle Mass as a Strong Indicator of Sarcopenia

Ufuk

Herek

Yüksel³

2019

Clin Exp Otorhinolaryngol

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Objectives. Patients with head and neck cancer (HNC) have a high risk of sarcopenia, which is associated with poor prognosis. Skeletal-muscle area and index at the third lumbar (L3) vertebra level (L3MA and L3MI) are recommended for the detection of sarcopenia. However, L3 level is not included in many imaging protocols and there are no data for optimal levels and cutoffs for the diagnosis of sarcopenia in head and neck computed tomography (HNCT) scans. Our aim was to assess the relationship between cervical paravertebral muscle values and L3MI and to investigate optimal level to diagnose sarcopenia on HNCTs. Methods. Patients with HNC (n=159) who underwent positron emission tomography-CT for tumor staging were retrospectively analyzed. On CT images, paravertebral and sternocleidomastoid muscle areas at second (C2), third (C3), and fourth (C4) cervical vertebrae levels (C2MA, C3MA, C4MA, SCMA) and L3MA were measured. Cross-sectional areas were normalized for stature (muscle area/height square) and muscle index (C2MI, C3MI, C4MI, SCMI, L3MI) values were obtained. Spearman correlation and linear regression analyses were used for assessing correlations. To calculate the diagnostic performance of SCMI, C2MI, C3MI, and C4MI for the diagnosis of sarcopenia with respect to the cutoffs of L3MI, receiver operating characteristic (ROC) analysis was used. Results. Males had significantly higher muscle areas than females. Although C2MI, C3MI, C4MI, and SCMI values all showed very strong and significant correlation with L3MI (P<0.001). According to the ROC analysis, the best discriminative for sarcopenia was C3MI in males (area under curve [AUC], 0.967) and SCMI in females (AUC, 0.898). Conclusion. C2MI, C3MI, C4MI, and SCMI values can be used as alternatives for the diagnosis of sarcopenia in routine HNCT examinations.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Sarcopenia in Head and Neck Computed Tomography: Cervical Muscle Mass as a Strong Indicator of Sarcopenia

Ufuk

Herek

Yüksel³

2019

Clin Exp Otorhinolaryngol

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“…2 Therefore, various studies have attempted to solve this dilemma by conducting CT scans at different vertebra levels (T4, L1) for SMM estimates in NSCLC patients. 5,6,21,22 Although good results have been achieved, the applications were not so extensive. Herein, an accurate and reproducible classifier was constructed through the integration of a large panel of skeletal muscle CT radiomic features and high efficiency lightGBM model, to differentiate NSCLC patients with sarcopenia from those without sarcopenia, achieving high accuracy and AUC were recorded as 0.900 and 0.889 in optimal lightGBM mode.…”

Section: Discussionmentioning

confidence: 99%

Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Dong

et al. 2020

Thoracic Cancer

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Background Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ability of chest CT radiomics combined with machine learning classifiers to identify sarcopenia in advanced non‐small cell lung cancer (NSCLC) patients. Methods This study retrospectively analyzed CT images of 99 patients with NSCLC. Skeletal muscle radiomics were extracted from a single axial slice of the chest CT scan at the 12th thoracic vertebrae level. In total, 854 radiomic and clinical features were obtained from each patient. Feature selection was conducted with FeatureSelector module, optimal key features were fed into the lightGBM classifier for model construction, and Bayesian optimization was adopted to tune hyperparameters. The model's performance was evaluated by specificity, sensitivity, accuracy, precision, F1‐score, Matthew's correlation coefficient (MCC), Cohen's kappa coefficient (Kappa), and AUC. Results A total of 40 patients were found to have sarcopenia. Five optimal features were selected. In the base lightGBM model, the specificity, sensitivity, accuracy, precision, F1‐score, AUC, MCC, Kappa of validation set were 0.889, 0.750, 0.833, 0.818, 0.783, 0.819, 0.649, 0.648, respectively. After Bayesian hyperparameter tuning, the optimized lightGBM model achieved better prediction performance, and the corresponding values were 0.944, 0.833, 0.900, 0.909, 0.870, 0.889, 0.791, 0.789, respectively. Conclusions Chest CT‐based radiomics has the potential to identify sarcopenia in NSCLC patients with the lightGBM classifier, and the optimal lightGBM model via Bayesian hyperparameter tuning demonstrated better performance. Key points Significant findings of the study Our study demonstrates that chest CT‐based radiomics combined with lightGBM classifier has the ability to identify sarcopenia in NSCLC patients. What this study adds Skeletal muscle radiomics would be a potential biomarker for sarcopenia identity in NSCLC patients.

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“…66 Several smaller studies have used chest, spine, or neck CTs. [67][68][69][70] Depending on the CT chosen, various muscles and muscle groups have been measured. On abdominal CTs, the region of interest (ROI) used to measure sarcopenia may include the psoas muscles, the paraspinous muscles, and all visualized abdominal muscles (►Figs.…”

Section: Technical Considerationsmentioning

confidence: 99%

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning

Lenchik

Boutin

2018

Semin Musculoskelet Radiol

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As populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunistic imaging, precision medicine, and machine learning. This review focuses on the state-of-the-art imaging of sarcopenia and provides context for such imaging by discussing the epidemiology, pathophysiology, consequences, and future directions in the field of sarcopenia.

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Evaluation of sarcopenia in small-cell lung cancer patients by routine chest CT

Abstract: Chest CT-determined L1MI is highly correlated with L3MI in SCLC patients. L1MI, as determined by chest CT, could be used to determine the presence of sarcopenia with suggested cutoffs of 46 cm(2)/m(2) for men and 29 cm(2)/m(2) for women.

Cited by 63 publications

References 15 publications

Diagnosis of Sarcopenia in Head and Neck Computed Tomography: Cervical Muscle Mass as a Strong Indicator of Sarcopenia

Diagnosis of Sarcopenia in Head and Neck Computed Tomography: Cervical Muscle Mass as a Strong Indicator of Sarcopenia

Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning

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