Establishment and Validation of Pre-Therapy Cervical Vertebrae Muscle Quantification as a Prognostic Marker of Sarcopenia in Patients With Head and Neck Cancer

Prevalence and Association of Sarcopenia with Mortality in Patients with Head and Neck Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

The objective of this meta-analysis was to assess the association of sarcopenia defined on computed tomography (CT) head and neck with survival in head and neck cancer patients.

METHODS

Following a PROSPERO-registered protocol, two blinded reviewers extracted data and evaluated the quality of the included studies using the Quality In Prognostic Studies (QUIPS) tool, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework. A meta-analysis was conducted using maximally adjusted hazard ratios (HRs) with the random-effects model. Heterogeneity was measured using the I2 statistic and was investigated using meta-regression and subgroup analyses where appropriate.

RESULTS

From 37 studies (11,181 participants), sarcopenia was associated with poorer overall survival (HR 2.11, 95% confidence interval [CI] 1.81-2.45; p < 0.01), disease-free survival (HR 1.76, 95% CI 1.38-2.24; p < 0.01), disease-specific survival (HR 2.65, 95% CI 1.80-3.90; p < 0.01), progression-free survival (HR 2.24, 95% CI 1.21-4.13; p < 0.01) and increased chemotherapy or radiotherapy toxicity (risk ratio 2.28, 95% CI 1.31-3.95; p < 0.01). The observed association between sarcopenia and overall survival remained significant across different locations of cancer, treatment modality, tumor stages and geographical region, and did not differ between univariate and multivariate HRs. Statistically significant correlations were observed between the C3 and L3 cross-sectional area, skeletal muscle mass, and skeletal muscle index.

CONCLUSIONS

Among patients with head and neck cancers, CT-defined sarcopenia was consistently associated with poorer survival and greater toxicity.

Sarcopenia may predict recurrence in patients with head and neck cancer

BACKGROUND

Malnutrition is a major consequence of head and neck cancer (HNC), often leading to decreased skeletal muscle mass and impacting survival. The goal of this study is to determine the effect of sarcopenia, as defined by skeletal muscle index (SMI), on survival in patients with HNC.

METHODS

This is a retrospective review of patients with HNC treated with surgery and/or radiation at a single tertiary care institute. All had pre-treatment imaging available for skeletal muscle index (SMI) measurements, and SMI was calculated at the level of the 3rd cervical vertebra. Sarcopenia was defined as an SMI < 41 cm2/m2 in females and as <43 cm2/m2 in underweight or healthy weight males. Sarcopenia was defined as <53 cm2/m2 in overweight or obese males. Chi-square analysis was performed to compare recurrence and survival rates, and survival analysis was performed via Kaplan-Meir curve.

RESULTS

Hundred and twelve patients with HNC were evaluated, 84 men and 28 women with an average age of 60.9 years. Tumors were primarily located in the oral cavity (24.1%) and oropharynx (42%). The majority (69.6%) underwent surgery. Mean body mass index prior to treatment was 28. Sixty-nine patients (61.6%) in our cohort had low SMI. Mean follow-up was 3.9 ± 2.2 years. Recurrence rate was 26% in those with low SMI versus 2% in those without. Patients with low SMI were more likely to have a recurrence (p = 0.02). Overall survival was 72.5% in those with low SMI and 81% in those with normal SMI (p = 0.09).

CONCLUSIONS

Defining sarcopenia as a low skeletal muscle index at the third cervical vertebra is clinically relevant. This study demonstrates that low SMI at this level, and thus sarcopenia, was strongly associated with higher rates of recurrence.

The prognostic value of pre-treatment sarcopenia in overall survival in head and neck cancer patients: a systematic review

The aim of this study was to determine the prognostic value of pre-treatment sarcopenia, defined radiologically (cervical (C3) or lumbar (L3) region), in adult head and neck cancer (HNC) patients undergoing treatment with curative intent. A systematic search of the PubMed and Scopus databases was performed up to March 2024. Inclusion criteria were adult patients with locally advanced HNC, sarcopenia defined radiologically at the C3 and/or L3 level, and patients receiving primary treatment with curative intent. Risk of bias was assessed using the ROBINS-I tool non-randomised studies. Thirty studies involving a total of 6924 adult patients with HNC were included in this review. Pre-treatment sarcopenia was significantly associated with worse overall survival outcomes in 26 of the 30 studies (87%), across all treatment modalities with curative intent. The most frequent sex-specific SMI cut-off values were <52.4 cm2/m2 for males and <38.5 cm2/m2 for females. The findings of this review suggest that sarcopenia is a strong prognostic factor of overall survival in HNC patients undergoing primary curative treatment. Sarcopenia evaluation appears to be a good prognostic marker in the HNC population. Future nutritional interventional studies might focus on reversing the muscle loss and improving overall outcomes in identified sarcopenic individuals.

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer

Importance

Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use.

Objective

To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes.

Design, Setting, and Participants

For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023.

Exposure

C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC.

Main Outcomes and Measures

Overall survival and treatment toxicity outcomes of HNSCC.

Results

The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia.

Conclusions and Relevance

This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Role of sarcopenia on survival and treatment-related toxicity in head and neck cancer: a narrative review of current evidence and future perspectives

PURPOSE

The purpose of this article is to provide an up-to-date summary of sarcopenia and its clinical implications for patients with head and neck cancer (HNC).

METHODS

We conducted a literature review of recent studies investigating the prevalence of sarcopenia in HNC patients, its detection using MRI or CT scans, and its association with clinical outcomes such as disease-free and overall survival time, radiotherapy-related side effects, cisplatin toxicity, and surgical complications.

RESULTS

Sarcopenia, characterized by low skeletal muscle mass (SMM), is a prevalent condition in HNC patients and can be effectively detected using routine MRI or CT scans. Low SMM in HNC patients is associated with increased risks of shorter disease-free and overall survival times, as well as radiotherapy-related side effects such as mucositis, dysphagia, and xerostomia. In addition, cisplatin toxicity is more severe in HNC patients with low SMM, leading to higher dose-limiting toxicity and treatment interruptions. Low SMM may also predict higher risks of surgical complications in head and neck surgery. Identifying sarcopenic patients can aid physicians in better riskstratifying HNC patients for therapeutic or nutritional interventions to improve clinical outcomes.

CONCLUSIONS

Sarcopenia is a significant concern for HNC patients and can impact their clinical outcomes. Routine MRI or CT scans can effectively detect low SMM in HNC patients. Identifying sarcopenic patients can aid physicians in better risk-stratifying HNC patients for therapeutic or nutritional interventions to improve clinical outcomes. Further research is needed to explore the potential of interventions to mitigate the negative effects of sarcopenia in HNC patients.

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline

Purpose

Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes.

Materials and Methods

899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression.

Results

DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r β 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis.

Conclusion

We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC.

SUMMARY STATEMENT

In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.

The impact of sarcopenia on survival and treatment tolerance in patients with head and neck cancer treated with chemoradiotherapy

BACKGROUND

Sarcopenia appears to be a negative prognostic factor for poor survival outcomes and worse treatment tolerance in patients with head-and-neck squamous cell carcinoma (HNSCC). We evaluated sarcopenia's impact on overall survival (OS), disease-free survival (DFS) and chemo-radiation tolerance in patients with head-and-neck cancer (HNC) treated with chemoradiotherapy (CRT) from a monocentric observational study.

METHODS

We identified patients with HNC treated by CRT between 2009 and 2018 with pretreatment imaging using positron emission tomography-computed tomography scans (PET/CT). Sarcopenia was measured using the pretreatment PET/CT at the L3 vertebral body using previously published methods. Clinical variables were retrospectively retrieved.

RESULTS

Of 216 patients identified, 54 patients (25.47%) met the criteria for sarcopenia. These patients had a lower mean body mass index before treatment (21.92 vs. 25.65 cm/m² , p < 0.001) and were more likely to have a history of smoking (88.89% vs. 71.52%, p = 0.01), alcohol use (55.56% vs. 38.61%, p = 0.03) and positive human papilloma virus status (67.74% vs. 41.75%, p = 0.011). At 3 years of follow-up, OS and DFS were 75% and 70% versus 82% and 85% for sarcopenic and non-sarcopenic patients, respectively (p = 0.1 and p = 0.00015). On multivariate analysis, sarcopenia appeared as a pejorative factor on DFS (hazard ratio 2.174, p = 0.0001) in the overall cohort. Sarcopenic patients did not require more chemotherapy and radiation-treatment interruptions and did not suffer from more chemo-induced and radiation-induced grade 3-4 toxicities than their non-sarcopenic counterparts.

CONCLUSION

Sarcopenia in HNSCC patients is an independent adverse prognostic factor for DFS after definitive chemoradiotherapy.

Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer

The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, a set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.

Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer

Background/Purpose

Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.

Materials/Methods

390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmented SM cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival.

Results

Mean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI.

Conclusion

We developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC.