Tian-Tian Zhai1, Johannes A Langendijk2, Lisanne V van Dijk2, Gyorgy B Halmos3, Max J H Witjes4, Sjoukje F Oosting5, Walter Noordzij6, Nanna M Sijtsema2, Roel J H M Steenbakkers2. 1. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China. Electronic address: t.zhai@umcg.nl. 2. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 3. Department of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 4. Department of Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 5. Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 6. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Abstract
OBJECTIVES: The aim of this study was to investigate whether quantitative CT image-biomarkers (IBMs) can improve the prediction models with only classical prognostic factors for local-control (LC), regional-control (RC), distant metastasis-free survival (DMFS) and disease-free survival (DFS) for head and neck cancer (HNC) patients. MATERIALS AND METHODS: The cohort included 240 and 204 HNC patients in the training and validation analysis, respectively. Clinical variables were scored prospectively and IBMs of the primary tumor and lymph nodes were extracted from planning CT-images. Clinical, IBM and combined models were created from multivariable Cox proportional-hazard analyses based on clinical features, IBMs, and both for LC, RC, DMFS and DFS. RESULTS: Clinical variables identified in the multivariable analysis included tumor-site, WHO performance-score, tumor-stage and age. Bounding-box-volume describing the tumor volume and irregular shape, IBM correlation representing radiological heterogeneity, and LN_major-axis-length showing the distance between lymph nodes were included in the IBM models. The performance of IBM LC, RC, DMFS and DFS models (c-index(validated):0.62, 0.80, 0.68 and 0.65) were comparable to that of the clinical models (0.62, 0.76, 0.70 and 0.66). The combined DFS model (0.70) including clinical features and IBMs performed significantly better than the clinical model. Patients stratified with the combined models revealed larger differences between risk groups in the validation cohort than with clinical models for LC, RC and DFS. For DMFS, the differences were similar to the clinical model. CONCLUSION: For prediction of HNC treatment outcomes, image-biomarkers performed as good as or slightly better than clinical variables.
OBJECTIVES: The aim of this study was to investigate whether quantitative CT image-biomarkers (IBMs) can improve the prediction models with only classical prognostic factors for local-control (LC), regional-control (RC), distant metastasis-free survival (DMFS) and disease-free survival (DFS) for head and neck cancer (HNC) patients. MATERIALS AND METHODS: The cohort included 240 and 204 HNC patients in the training and validation analysis, respectively. Clinical variables were scored prospectively and IBMs of the primary tumor and lymph nodes were extracted from planning CT-images. Clinical, IBM and combined models were created from multivariable Cox proportional-hazard analyses based on clinical features, IBMs, and both for LC, RC, DMFS and DFS. RESULTS: Clinical variables identified in the multivariable analysis included tumor-site, WHO performance-score, tumor-stage and age. Bounding-box-volume describing the tumor volume and irregular shape, IBM correlation representing radiological heterogeneity, and LN_major-axis-length showing the distance between lymph nodes were included in the IBM models. The performance of IBM LC, RC, DMFS and DFS models (c-index(validated):0.62, 0.80, 0.68 and 0.65) were comparable to that of the clinical models (0.62, 0.76, 0.70 and 0.66). The combined DFS model (0.70) including clinical features and IBMs performed significantly better than the clinical model. Patients stratified with the combined models revealed larger differences between risk groups in the validation cohort than with clinical models for LC, RC and DFS. For DMFS, the differences were similar to the clinical model. CONCLUSION: For prediction of HNC treatment outcomes, image-biomarkers performed as good as or slightly better than clinical variables.
Authors: Alfredo Páez-Carpio; Santiago Medrano-Martorell; Joan Berenguer; Africa Muxí; Isabel Vilaseca; Izaskun Valduvieco; Paola Castillo; Neus Baste; F Xavier Avilés-Jurado; Juan José Grau; Laura Oleaga Journal: Eur Arch Otorhinolaryngol Date: 2022-10-01 Impact factor: 3.236
Authors: Asier Rabasco Meneghetti; Alex Zwanenburg; Annett Linge; Fabian Lohaus; Marianne Grosser; Gustavo B Baretton; Goda Kalinauskaite; Ingeborg Tinhofer; Maja Guberina; Martin Stuschke; Panagiotis Balermpas; Jens von der Grün; Ute Ganswindt; Claus Belka; Jan C Peeken; Stephanie E Combs; Simon Böke; Daniel Zips; Esther G C Troost; Mechthild Krause; Michael Baumann; Steffen Löck Journal: Sci Rep Date: 2022-10-06 Impact factor: 4.996