Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
By M. Vallières, E. Kay-Rivest, Léo Perrin, X. Liem, C. Furstoss, Hugo J. W. L. Aerts, N. Khaouam, P. Nguyen-Tan, Chang-Shu Wang, K. Sultanem and 2 more. Scientific Reports, 2017
488 citations recorded by the source corpus.
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.
Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/b80925eccdd729f66423305c1b57904bd11d6ceb
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Citations in the corpus
- A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, methodology): “Specific quantitative imaging modeling studies were performed on a comprehensive list of tumor sites, including head and neck, (4, 8-13) lung, (3, 4, 14-18) breast, (19-29) liver, (30, 31) cervix, (32-36) prostate, (37, 38) extremities (sarcoma), (39, 40) and brain.”
- The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “Vallières et al used radiomics for assessing tumor failure in patients with NPC from 4 centers [116].”
- AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (2018, background): “3) DATASET 3 (177 samples) consists of CT images from four different institutions in Québec, Canada [37], also downloaded from TCIA [36].”