Tian-Tian Zhai1, Johannes A Langendijk2, Lisanne V van Dijk2, Arjen van der Schaaf2, Linda Sommers2, Johanna G M Vemer-van den Hoek2, Henk P Bijl2, 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, 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, The Netherlands. 3. Department of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, The Netherlands. 4. Department of Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, The Netherlands. 5. Department of Medical Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. 6. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands.
Abstract
BACKGROUND AND PURPOSE: To develop and validate a pre-treatment radiomics-based prediction model to identify pathological lymph nodes (pLNs) at risk of failures after definitive radiotherapy in head and neck squamous cell carcinoma patients. MATERIALS AND METHODS: Training and validation cohorts consisted of 165 patients with 558 pLNs and 112 patients with 467 pLNs, respectively. All patients were primarily treated with definitive radiotherapy, with or without systemic treatment. The endpoint was the cumulative incidence of nodal failure. For each pLN, 82 pre-treatment CT radiomic features and 7 clinical features were included in the Cox proportional-hazard analysis. RESULTS: There were 68 and 23 nodal failures in the training and validation cohorts, respectively. Multivariable analysis revealed three clinical features (T-stage, gender and WHO Performance-status) and two radiomic features (Least-axis-length representing nodal size and gray level co-occurrence matrix based - Correlation representing nodal heterogeneity) as independent prognostic factors. The model showed good discrimination with a c-index of 0.80 (0.69-0.91) in the validation cohort, significantly better than models based on clinical features (p < 0.001) or radiomics (p = 0.003) alone. High- and low-risk groups were defined by using thresholds of estimated nodal failure risks at 2-year of 60% and 10%, resulting in positive and negative predictive values of 94.4% and 98.7%, respectively. CONCLUSION: A pre-treatment prediction model was developed and validated, integrating the quantitative radiomic features of individual lymph nodes with generally used clinical features. Using this prediction model, lymph nodes with a high failure risk can be identified prior to treatment, which might be used to select patients for intensified treatment strategies targeted on individual lymph nodes.
BACKGROUND AND PURPOSE: To develop and validate a pre-treatment radiomics-based prediction model to identify pathological lymph nodes (pLNs) at risk of failures after definitive radiotherapy in head and neck squamous cell carcinomapatients. MATERIALS AND METHODS: Training and validation cohorts consisted of 165 patients with 558 pLNs and 112 patients with 467 pLNs, respectively. All patients were primarily treated with definitive radiotherapy, with or without systemic treatment. The endpoint was the cumulative incidence of nodal failure. For each pLN, 82 pre-treatment CT radiomic features and 7 clinical features were included in the Cox proportional-hazard analysis. RESULTS: There were 68 and 23 nodal failures in the training and validation cohorts, respectively. Multivariable analysis revealed three clinical features (T-stage, gender and WHO Performance-status) and two radiomic features (Least-axis-length representing nodal size and gray level co-occurrence matrix based - Correlation representing nodal heterogeneity) as independent prognostic factors. The model showed good discrimination with a c-index of 0.80 (0.69-0.91) in the validation cohort, significantly better than models based on clinical features (p < 0.001) or radiomics (p = 0.003) alone. High- and low-risk groups were defined by using thresholds of estimated nodal failure risks at 2-year of 60% and 10%, resulting in positive and negative predictive values of 94.4% and 98.7%, respectively. CONCLUSION: A pre-treatment prediction model was developed and validated, integrating the quantitative radiomic features of individual lymph nodes with generally used clinical features. Using this prediction model, lymph nodes with a high failure risk can be identified prior to treatment, which might be used to select patients for intensified treatment strategies targeted on individual lymph nodes.
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: Joanna Kaźmierska; Michał R Kaźmierski; Tomasz Bajon; Tomasz Winiecki; Anna Bandurska-Luque; Adam Ryczkowski; Tomasz Piotrowski; Bartosz Bąk; Małgorzata Żmijewska-Tomczak Journal: J Pers Med Date: 2022-06-30