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Takayama H, Yoshimura T, Suzuki H, Hirano Y, Tezuka M, Ishida T, Ishihata K, Amitani M, Amitani H, Nakamura Y, Imamura Y, Inui A, Nakamura N. Comparison between single-muscle evaluation and cross-sectional area muscle evaluation for predicting the prognosis in patients with oral squamous cell carcinoma: a retrospective cohort study. Front Oncol 2024; 14:1336284. [PMID: 38751815 PMCID: PMC11094248 DOI: 10.3389/fonc.2024.1336284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/22/2024] [Indexed: 05/18/2024] Open
Abstract
Introduction The most effective method of assessing sarcopenia has yet to be determined, whether by single muscle or by whole muscle segmentation. The purpose of this study was to compare the prognostic value of these two methods using computed tomography (CT) images in patients with oral squamous cell carcinoma (OSCC). Materials and methods Sex- and age-adjusted Cox proportional hazards models were employed for each parameter of sarcopenia related to overall survival, disease-free survival, and disease-specific survival. Harrell's concordance index was calculated for each model to assess discriminatory power. Results In this study including 165 patients, a significant correlation was found between the CT-based assessment of individual muscles and their cross-sectional area. Single muscle assessments showed slightly higher discriminatory power in survival outcomes compared to whole muscle assessments, but the difference was not statistically significant, as indicated by overlapping confidence intervals for the C-index between assessments. To further validate our measurements, we classified patients into two groups based on intramuscular adipose tissue content (P-IMAC) of the spinous process muscle. Analysis showed that the higher the P-IMAC value, the poorer the survival outcome. Conclusion Our findings indicate a slight advantage of single-muscle over whole-muscle assessment in prognostic evaluation, but the difference between the two methods is not conclusive. Both assessment methods provide valuable prognostic information for patients with OSCC, and further studies involving larger, independent cohorts are needed to clarify the potential advantage of one method over the other in the prognostic assessment of sarcopenia in OSCC.
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Affiliation(s)
- Hirotaka Takayama
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Takuya Yoshimura
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Hajime Suzuki
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Yuka Hirano
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Masahiro Tezuka
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Takayuki Ishida
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Kiyohide Ishihata
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Marie Amitani
- Department of Community-Based Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Haruka Amitani
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Yasunori Nakamura
- Department of Oral Surgery, Kagoshima Medical Center, National Hospital Organization, Kagoshima, Japan
| | - Yasushi Imamura
- Department of Internal Medicine, Kagoshima Kouseiren Hospital, Kagoshima, Japan
| | - Akio Inui
- Pharmacological Department of Herbal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Norifumi Nakamura
- Department of Oral and Maxillofacial Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
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Marschner SN, Lombardo E, Minibek L, Holzgreve A, Kaiser L, Albert NL, Kurz C, Riboldi M, Späth R, Baumeister P, Niyazi M, Belka C, Corradini S, Landry G, Walter F. Risk Stratification Using 18F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy. Diagnostics (Basel) 2021; 11:diagnostics11091581. [PMID: 34573924 PMCID: PMC8468242 DOI: 10.3390/diagnostics11091581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 12/24/2022] Open
Abstract
This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[18F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[18F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.
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Affiliation(s)
- Sebastian N. Marschner
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Correspondence:
| | - Elia Lombardo
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Lena Minibek
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; (A.H.); (L.K.); (N.L.A.)
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; (A.H.); (L.K.); (N.L.A.)
| | - Nathalie L. Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; (A.H.); (L.K.); (N.L.A.)
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Richard Späth
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Philipp Baumeister
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital, LMU Munich, 81377 Munich, Germany;
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- German Cancer Consortium (DKTK), Partner Site Munich, 81377 Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany; (E.L.); (C.K.); (M.R.); (G.L.)
| | - Franziska Walter
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany; (L.M.); (R.S.); (M.N.); (C.B.); (S.C.); (F.W.)
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