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Koh JH, Lim CYJ, Tan LTP, Makmur A, Gao EY, Ho JSY, Tan JA, See A, Tan BKJ, Tan LF, Tan BYQ. Prevalence and Association of Sarcopenia with Mortality in Patients with Head and Neck Cancer: A Systematic Review and Meta-Analysis. Ann Surg Oncol 2024; 31:6049-6064. [PMID: 38847986 DOI: 10.1245/s10434-024-15510-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/09/2024] [Indexed: 08/09/2024]
Abstract
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.
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Affiliation(s)
- Jin Hean Koh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Claire Yi Jia Lim
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Lucas Tze Peng Tan
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Esther Yanxin Gao
- Department of Otorhinolaryngology - Head and Neck Surgery, Singapore General Hospital, Singapore, Singapore
| | - Jamie Sin Ying Ho
- Department of Medicine, Alexandra Hospital, National University Health System, Singapore, Singapore
| | - Justina Angel Tan
- Division of Geriatric Medicine, Department of Medicine, Alexandra Hospital, National University Health System, Singapore, Singapore
| | - Anna See
- Department of Otorhinolaryngology - Head and Neck Surgery, Singapore General Hospital, Singapore, Singapore
| | - Benjamin Kye Jyn Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| | - Li Feng Tan
- Division of Geriatric Medicine, Department of Medicine, Alexandra Hospital, National University Health System, Singapore, Singapore
| | - Benjamin Yong Qiang Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Division of Neurology, Department of Medicine, National University Hospital, National University Health System, Singapore, Singapore
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2
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Colback AA, Arkfeld DV, Paydar A, Raslan O, Cates DJ, Abouyared M. Sarcopenia may predict recurrence in patients with head and neck cancer. Head Neck 2024. [PMID: 39138607 DOI: 10.1002/hed.27903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 06/14/2024] [Accepted: 07/20/2024] [Indexed: 08/15/2024] Open
Abstract
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.
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Affiliation(s)
- Angela A Colback
- Department of Otolaryngology - Head and Neck Surgery, University of California, Davis, Sacramento, California, USA
| | - Daniel V Arkfeld
- Department of Otolaryngology - Head and Neck Surgery, University of California, Davis, Sacramento, California, USA
| | - Alireza Paydar
- Division of Neuroradiology, Department of Radiology, University of California Davis Medical Center, Sacramento, California, USA
| | - Osama Raslan
- Division of Neuroradiology, Department of Radiology, University of California Davis Medical Center, Sacramento, California, USA
| | - Daniel J Cates
- Department of Otolaryngology - Head and Neck Surgery, University of California, Davis, Sacramento, California, USA
| | - Marianne Abouyared
- Department of Otolaryngology - Head and Neck Surgery, University of California, Davis, Sacramento, California, USA
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3
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Syziu A, Schache A. The prognostic value of pre-treatment sarcopenia in overall survival in head and neck cancer patients: a systematic review. Int J Oral Maxillofac Surg 2024:S0901-5027(24)00221-2. [PMID: 39068047 DOI: 10.1016/j.ijom.2024.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 06/05/2024] [Accepted: 07/12/2024] [Indexed: 07/30/2024]
Abstract
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.
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Affiliation(s)
- A Syziu
- University Hospital Aintree, Fazakerley, Liverpool, UK.
| | - A Schache
- University Hospital Aintree, Fazakerley, Liverpool, UK
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4
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Ye Z, Saraf A, Ravipati Y, Hoebers F, Catalano PJ, Zha Y, Zapaishchykova A, Likitlersuang J, Guthier C, Tishler RB, Schoenfeld JD, Margalit DN, Haddad RI, Mak RH, Naser M, Wahid KA, Sahlsten J, Jaskari J, Kaski K, Mäkitie AA, Fuller CD, Aerts HJWL, Kann BH. Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer. JAMA Netw Open 2023; 6:e2328280. [PMID: 37561460 PMCID: PMC10415962 DOI: 10.1001/jamanetworkopen.2023.28280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/27/2023] [Indexed: 08/11/2023] Open
Abstract
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.
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Affiliation(s)
- Zezhong Ye
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anurag Saraf
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Frank Hoebers
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Paul J. Catalano
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Data Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Yining Zha
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anna Zapaishchykova
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christian Guthier
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Roy B. Tishler
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jonathan D. Schoenfeld
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Danielle N. Margalit
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Robert I. Haddad
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Raymond H. Mak
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mohamed Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Antti A. Mäkitie
- Department Otorhinolaryngology–Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hugo J. W. L. Aerts
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Benjamin H. Kann
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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5
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Erul E, Guven DC, Onur MR, Yazici G, Aksoy S. Role of sarcopenia on survival and treatment-related toxicity in head and neck cancer: a narrative review of current evidence and future perspectives. Eur Arch Otorhinolaryngol 2023:10.1007/s00405-023-08014-9. [PMID: 37188907 DOI: 10.1007/s00405-023-08014-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/08/2023] [Indexed: 05/17/2023]
Abstract
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.
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Affiliation(s)
- Enes Erul
- Department of Internal Medicine, Hacettepe University, 06100, Sihhiye, Ankara, Turkey.
| | - Deniz Can Guven
- Department of Medical Oncology, Hacettepe University, Cancer Institute, Ankara, Turkey
| | | | - Gozde Yazici
- Department of Radiation Oncology, Hacettepe University, Ankara, Turkey
| | - Sercan Aksoy
- Department of Medical Oncology, Hacettepe University, Cancer Institute, Ankara, Turkey
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6
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Ye Z, Saraf A, Ravipati Y, Hoebers F, Zha Y, Zapaishchykova A, Likitlersuang J, Tishler RB, Schoenfeld JD, Margalit DN, Haddad RI, Mak RH, Naser M, Wahid KA, Sahlsten J, Jaskari J, Kaski K, Mäkitie AA, Fuller CD, Aerts HJ, Kann BH. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.01.23286638. [PMID: 36945519 PMCID: PMC10029039 DOI: 10.1101/2023.03.01.23286638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Anurag Saraf
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Frank Hoebers
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Yining Zha
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Anna Zapaishchykova
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Roy B. Tishler
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Jonathan D. Schoenfeld
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Danielle N. Margalit
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Robert I. Haddad
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Raymond H. Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Mohamed Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Antti A. Mäkitie
- Department Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hugo J.W.L. Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Benjamin H. Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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7
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Bentahila R, Giraud P, Decazes P, Kreps S, Nay P, Chatain A, Fabiano E, Durdux C. The impact of sarcopenia on survival and treatment tolerance in patients with head and neck cancer treated with chemoradiotherapy. Cancer Med 2023; 12:4170-4183. [PMID: 36263581 PMCID: PMC9972161 DOI: 10.1002/cam4.5278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/17/2022] [Accepted: 06/24/2022] [Indexed: 11/07/2022] Open
Abstract
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.
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Affiliation(s)
- Rita Bentahila
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
| | - Philippe Giraud
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
| | - Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France
| | - Sarah Kreps
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
| | - Paula Nay
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
| | - Augustin Chatain
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
| | - Emmanuelle Fabiano
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
| | - Catherine Durdux
- Department of Radiation Oncology, European Georges Pompidou Hospital, Paris, France
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8
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Wahid KA, Olson B, Jain R, Grossberg AJ, El-Habashy D, Dede C, Salama V, Abobakr M, Mohamed ASR, He R, Jaskari J, Sahlsten J, Kaski K, Fuller CD, Naser MA. Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer. Sci Data 2022; 9:470. [PMID: 35918336 PMCID: PMC9346108 DOI: 10.1038/s41597-022-01587-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 07/22/2022] [Indexed: 11/09/2022] Open
Abstract
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.
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Affiliation(s)
- Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brennan Olson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon, USA.,Medical Scientist Training Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Rishab Jain
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Aaron J Grossberg
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Dina El-Habashy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Clinical Oncology, Menoufia University, Shibin Al Kawm, Egypt
| | - Cem Dede
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Vivian Salama
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Moamen Abobakr
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Mohamed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
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9
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Naser MA, Wahid KA, Grossberg AJ, Olson B, Jain R, El-Habashy D, Dede C, Salama V, Abobakr M, Mohamed ASR, He R, Jaskari J, Sahlsten J, Kaski K, Fuller CD. Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer. Front Oncol 2022; 12:930432. [PMID: 35965493 PMCID: PMC9366009 DOI: 10.3389/fonc.2022.930432] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/29/2022] [Indexed: 12/22/2022] Open
Abstract
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.
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Affiliation(s)
- Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Aaron J. Grossberg
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Brennan Olson
- Medical Scientist Training Program, Oregon Health & Science University, Portland, OR, United States
| | - Rishab Jain
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Dina El-Habashy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Clinical Oncology, Menoufia University Shibin El Kom, Shibin El Kom, Egypt
| | - Cem Dede
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vivian Salama
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Moamen Abobakr
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Abdallah S. R. Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Clifton D. Fuller,
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