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Saraf A, Ye Z, Likitlersuang J, Hoebers F, Tishler RB, Schoenfeld JD, Margalit DN, Haddad RI, Ravipati Y, Zha Y, Naser M, Wahid KA, Mak RH, Mäkitie A, Kaski K, Aerts H, Fuller CD, Kann BH. Automated Sarcopenia Assessment and Outcomes in Head and Neck Cancer with Deep Learning Analysis of Cervical Neck Skeletal Muscle. Int J Radiat Oncol Biol Phys 2023; 117:e623. [PMID: 37785866 DOI: 10.1016/j.ijrobp.2023.06.2009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Sarcopenia is an established prognostic factor in patients diagnosed with head and neck cancers (HNC), typically measured by the skeletal muscle index (SMI) from abdominal muscle mass at L3. While sarcopenia assessment could inform HNC management, it remains impractical, time- and labor-intensive, and operator-dependent. To overcome these challenges, we developed an automated deep learning (DL) platform to calculate SMI at L3 by quantifying cross-sectional cervical skeletal muscle area (SMA) at C3 through auto-segmentation, externally validated it, and evaluated associations with clinical outcomes. MATERIALS/METHODS Eight hundred twenty-one patients diagnosed with HNC from multiple institutes from 1999-2013, treated with definitive chemoradiation with baseline pre-treatment CT scans, were included for model development (335 training, 96 tuning) and for independent testing (48 internal, and 342 external). Ground truth single-slice segmentations of SM at the mid-C3 vertebral level were manually annotated by radiation oncologists using an established protocol. A multi-stage DL pipeline was developed, with a 2D DenseNet to select the middle slice of C3 section and a 2D UNet to segment the SM, from which SMA was calculated. The model was evaluated using the Dice Similarity Coefficient (DC) for the internal test set, and human acceptability testing on the external test set was performed by two radiation oncologists not involved in annotations. SMI was calculated from C3 SMA based on prior literature, and sarcopenia was defined by an established, sex-specific SMI cutoff. Sarcopenia associations with overall survival (OS) and toxicities were assessed on the external dataset with Cox and logistic multivariable regressions, as indicated. RESULTS Model DC on the internal test set as 0.90 [95% CI: 0.90-0.91], with an intra-class coefficient of 0.96 for SMA. Human acceptability testing showed a pass rate of 94.4%. Of the 342 patients in the clinical analysis, 261 (76.3%) patients had sarcopenia. Five-year survival was 84.4% in patients without sarcopenia vs 73.1% in patients with sarcopenia (HR 2.21, p = 0.028) (median f/u: 44 mo (IQR: 25 - 66 mo)). On multivariable regression, sarcopenia (HR 2.06, p = 0.037), ACE-27 score 2+ (HR 2.25, p = 0.001), non-oropharynx diagnosis (HR 3.96, p<0.001), and T3-4 stage (HR 2.37, p<0.001) were associated with worse OS. Sarcopenia was associated with longer PEG tube duration on multivariable analysis (HR 1.59, p = 0.003), along with ACE-27 score (HR 1.20, p = 0.012) and non-oropharynx primary site (HR 1.46, p = 0.034). Sarcopenia was associated with higher risk of having PEG tube at last follow up (OR 2.25, p = 0.046). An observed increase in risk of hospitalization <3 months after RT was non-significant (OR 2.18, p = 0.117). CONCLUSION We developed and externally validated a fully-automated platform for sarcopenia assessment that can be used on routine HNC imaging. This algorithm is positioned for prospective testing to determine if use will inform HNC management.
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Affiliation(s)
- A Saraf
- Brigham and Women's Hospital/Dana Farber Cancer Institute, Boston, MA; Harvard Radiation Oncology Program, Boston, MA
| | - Z Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
| | - J Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
| | - F Hoebers
- Brigham and Women's Hospital, Boston, MA
| | - R B Tishler
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - J D Schoenfeld
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - D N Margalit
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - R I Haddad
- Dana-Farber Cancer Institute, Boston, MA
| | - Y Ravipati
- Brigham and Women's Hospital, Boston, MA
| | - Y Zha
- Brigham and Women's Hospital, Boston, MA
| | - M Naser
- MD Anderson Cancer Center, Houston, TX
| | - K A Wahid
- MD Anderson Cancer Center, Houston, TX
| | - R H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - A Mäkitie
- Department of Otorhinolaryngology - Head and Neck Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - K Kaski
- Aalto University School of Science, Aalto, Finland
| | - H Aerts
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - C D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - B H Kann
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
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