ESTRO 2022 - Abstract Book

S762

Abstract book

ESTRO 2022

MO-0876 Assessing the generalisability of radiomics features for predicting sticky saliva and xerostomia

T. Berger 1 , D.J. Noble 2,3 , L.E. Shelley 1 , T. McMullan 1 , A. Bates 3 , S. Thomas 4 , L.J. Carruthers 1 , G. Beckett 5 , A. Duffton 6 , C. Paterson 6 , R. Jena 3 , D.B. McLaren 2 , N.G. Burnet 7 , W.H. Nailon 1,8 1 Edinburgh Cancer Centre, Western General Hospital, Department of Oncology Physics, Edinburgh, United Kingdom; 2 Edinburgh Cancer Centre, Western General Hospital, Department of Clinical Oncology, Edinburgh, United Kingdom; 3 The University of Cambridge, Department of Oncology, Cambridge, United Kingdom; 4 Cambridge University Hospitals NHS Foundation Trust, Department of Medical Physics and Clinical Engineering, Cambridge, United Kingdom; 5 Bayes Centre, Edinburgh Parallel Computing Centre, Edinburgh, United Kingdom; 6 Beatson West of Scotland Cancer Centre, Department of Oncology, Glasgow, United Kingdom; 7 The Christie NHS Foundation Trust, ,, Manchester, United Kingdom; 8 the University of Edinburgh, School of Engineering, Edinburgh, United Kingdom Purpose or Objective Few studies reporting radiomics-based models for prediction of clinical outcomes are externally validated and fewer are replicated. While core to the scientific approach, reproducibility of experimental results, is often challenging for such studies because of the complexity of the methods. Recently, on a cohort of patients with head and neck cancer (HNC), van Dijk et al identified radiomics features that improve prediction of moderate-to-severe sticky saliva (SS12m) and xerostomia (Xer12m) at 12 months after radiotherapy, compared to models only based on dose and clinical parameters. In this replication study, we assessed the generalisability of these findings using a different cohort of HNC patients. Materials and Methods The methods described by van Dijk et al were applied to a cohort of 109 HNC patients treated with 50-70Gy in 20-35fx using TomoTherapy. Xerostomia and sticky saliva scores were collected at baseline and 12 months after RT (EORTC QLQ-HN35). For each patient, a planning CT (Toshiba Aquilion/LB) was acquired and parotid and submandibular glands (SMG) contoured. Imaging features identified by van Dijk et al as associated with the clinical outcomes of interest were calculated on each slice of the contoured structure on planning CTs. Specifically, van Dijk et al found Short Run Emphasis (SRE) and maximum CT intensity (maxHU) to improve prediction of Xer12m and SS12m respectively, compared to models solely using baseline toxicity and mean dose to the salivary glands. We evaluated, on our cohort, the predictive performance of the variables identified by van Dijk et al. However, inherent differences were present between the two approaches (Table 1). In an attempt to determine the impact these differences had on the performance of the models, tests were run on subgroups of patients with varying proportions having: 1) intact salivary glands, 2) excluded CT slices with dental implants, and 3) consistent fractionation schedules.