ESTRO 2020 Abstract Book

S301 ESTRO 2020

PD-0542 External validation of individual nodal failure prediction models including radiomics in HNC T. Zhai 1 , F. Wesseling 2 , J. Langendijk 3 , Z. Shi 2 , P. Kalendralis 2 , L. Van Dijk 3 , F. Hoebers 2 , R. Steenbakkers 3 , A. Dekker 2 , L. Wee 2 , N. Sijtsema 3 1 Cancer Hospital of Shantou University Medical College, Radiation Oncology, Shantou, China ; 2 GROW School for Oncology and Development Biology Maastricht University Medical Centre+, Radiation Oncology, Maastricht, The Netherlands ; 3 University of Groningen University Medical Center Groningen, Radiation Oncology, Groningen, The Netherlands Purpose or Objective Three pre-treatment prediction models (clinical, radiomic and combined) were developed to identify pathological lymph nodes (pLNs) that are at risk to persist or recur after definitive radiotherapy with or without systemic treatment in head and neck squamous cell carcinoma (HNSCC) patients. These models can be used to select high-risk lymph nodes for radiotherapy treatment intensification or direct surgical dissection, or in case of recurrence for super-selective neck dissection, to limit morbidity of salvage surgery. The main goal of the current study was to validate the three models in a large and independent external cohort to make these models available for clinical application. Material and Methods The external validation cohort consisted of 374 pLNs from 113 HNSCC patients treated between July 2007 and June 2016 by curative radiotherapy with or without systemic treatment in another hospital. Imaging and pathology reports during follow-up were analyzed to indicate persisting or recurring nodes. The prognostic scores of pLNs were calculated using the three models that were trained previously on an European patient cohort : the clinical, radiomic and combined model (Table 1). Lymph nodes were stratified into low-, intermediate- and high-risk groups by using the lower and upper quantile of the prognostic scores from the training cohort. Kaplan- Meier curves were used to show the differences between different risk groups and were compared using log-rank tests. The models’ discriminative power was assessed by Harrell’s concordance index (c-index). Results There were 20 (5.3%) nodal failures from 15 patients after a median follow-up of 36.1 months. Both the radiomic and the combined model showed good agreement between predicted and observed nodal control probabilities (Figure 1). The radiomic (c-index: 0.71; 95% confidence interval (CI): 0.59-0.84) and combined (c-index: 0.71; 95% CI: 0.59- 0.82) models performed better than the clinical model (c- index: 0.57; 95% CI: 0.47-0.68) in this cohort, with a significant difference between the combined and clinical models (z-score test: p=0.005). Kaplan-Meier curves are shown for the three models in Figure 1. They showed that the radiomic and combined model were able to stratify the lymph nodes into high- and low-risk groups with significant differences between the nodal control probability prediction between the groups with p-values of 0.003 and < 0.001, respectively.

Conclusion The clinical, radiomic and the combined (including clinical and radiomic features) models were externally validated. The radiomic and combined models performed better in the external cohort than the clinical model. These models could be used to stratify lymph nodes into high- and low- risk groups for nodal failure. PD-0543 An externally validated prognostic CT radiomics model for head and neck cancer patients S. Keek 1 , S. Sanduleanu 1 , H.C. Woodruff 1,2 , F. Wesseling 3 , D. Mattavelli 4 , M. Ravanelli 5 , T.K. Hoffman 6 , K. Scheckenbach 7 , C.R. Leemans 8 , P. De Graaf 9 , C. Terhaard 10 , J. Van de Kamer 11 , M. Van der Heijden 12 , G. Calareso 13 , E.S. Gazzani 14 , L. Licitra 15,16 , F. Hoebers 3 , T. Poli 17 , P. Lambin 1,2 1 University of Maastricht GROW Research Institute, Department of Precision Medicine, Maastricht, The Netherlands ; 2 Maastricht University Medical Centre+, Department of Radiology and Nuclear medicine, Maastricht, The Netherlands ; 3 Maastricht University Medical Centre+, Department of Radiation Oncology MAASTRO, Maastricht, The Netherlands ; 4 University of Brescia, Unit of Otorhinolaryngology-Head and Neck Surgery, Brescia, Italy ; 5 University of Brescia, Department of Medicine and Surgery, Brescia, Italy ; 6 University Hospital Ulm, Dept. of Otorhinolaryngology- Head and Neck Surgery, Ulm, Germany ; 7 University Hospital Düsseldorf, Dept. of Otorhinolaryngology- Head and Neck Surgery, Düsseldorf, Germany ; 8 Amsterdam UMC, KNO-heelkunde/Hoofd-halschirurgie, Amsterdam, The Netherlands ; 9 Amsterdam UMC, Department of Radiology, Amsterdam, The Netherlands ; 10 University Medical Center Utrecht, Department of Radiotherapy, Utrecht, The Netherlands ; 11 Antoni van Leeuwenhoek- Netherlands Cancer Institute, Department of Radiotherapy, Amsterdam, The Netherlands ; 12 Antoni van Leeuwenhoek- Netherlands Cancer Institute, Department of Head and Neck Oncology and Surgery, Amsterdam, The Netherlands ; 13 Fondazione IRCCS Istituto Nazionale dei Tumori, Department of Radiology, Milan, Italy ; 14 University of Parma, Department of Radiology, Parma, Italy ; 15 Fondazione IRCCS Istituto Nazionale dei Tumori, Head and Neck Medical Oncology Department, Milan, Italy ; 16 University of Milan, Department of Oncology and Hematology-Oncology, Milan, Italy ; 17 University of Parma, Department of Surgical Sciences, Parma, Italy Purpose or Objective Patients with locoregionally advanced head-and-neck squamous cell carcinoma (HNSCC) have high relapse and mortality rates[WH(1] . Imaging-based decision support could help improve prognosis by preventing unnecessary treatment or escalating treatment for suitably stratified patients. We investigate whether a CT radiomics-based prognostic model for TNM-7 stage-III and –IV HNSCC can stratify patients into low- and high-risk groups, validated on a prospective cohort and external validation dataset.