CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer. (January 2019)

• Record Type: Journal Article
• Authors: Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Zschaeck, Sebastian; Zöphel, Klaus; Kotzerke, Jörg; Schreiber, Andreas; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G.C.; Richter, Christian; Löck, Steffen
• Is Part Of: Radiotherapy and oncology. Volume 130(2019)
• Page Start: 10
• Page End: 17
• Publication Date: 2019-01
• Subjects: Radiomic risk modelling -- Computed tomography -- Imaging during treatment -- Patient stratification; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2018.07.020
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: Background and purpose: The development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy. Material and methods: Treatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests. Results: The performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan–Meier analyses, in which risk stratification based on the second week CT could be improved for LRC ( p = 0.002) compared to pre-treatment CT ( p = 0.063). Conclusion: Incorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.