Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

Abstract

Introduction: An increasing number of parameters can be considered when making decisions in oncology. Tumor characteristics can also be extracted from imaging through the use of radiomics and add to this wealth of clinical data. Machine learning can encompass these parameters and thus enhance clinical decision as well as radiotherapy workflow. Methods: We performed a description of machine learning applications at each step of treatment by radiotherapy in head and neck cancers. We then performed a systematic review on radiomics and machine learning outcome prediction models in head and neck cancers. Results: Machine Learning has several promising applications in treatment planning with automatic organ at risk delineation improvements and adaptative radiotherapy workflow automation. It may also provide new approaches for Normal Tissue Complication Probability models. Radiomics may provide additional data on tumors for improved machine learning powered predictive models, not only on survival, but also on risk of distant metastasis, in field recurrence, HPV status and extra nodal spread. However, most studies provide preliminary data requiring further validation. Conclusion: Promising perspectives arise from machine learning applications and radiomics based models, yet further data are necessary for their implementation in daily care.

Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy

“…The format of the extracted data is a set of features, including first-order intensity histogram statistics, shape-and size statistics, and (filtered) texture features. Complex models that combine radiomics with clinical parameters may be better in detecting HNSCC patients that have a higher likelihood to relapse early after CRT [10].…”

Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

“…Advancements in high-throughput computing and machine-learning led to emergence of the "-omics" concept, referring to collective characterization and quantification of pools of biologic information, such as genomics, proteomics, or metabolomics. Radiomics refers to automated extraction of high-dimensional, quantitative descriptor ("feature") sets from medical images for various applications, including survival modelling, treatment guidance, and biomarker design [13][14][15][16][17]. Such features correlate with clinical outcome and convey medically meaningful information describing tumor heterogeneity, microenvironment, pathophysiology, and mutational burden [13,18,19].…”

“…Such features correlate with clinical outcome and convey medically meaningful information describing tumor heterogeneity, microenvironment, pathophysiology, and mutational burden [13,18,19]. While prior studies demonstrated prognostic value of radiomics biomarkers in head and neck cancers [15,16,[20][21][22][23][24][25][26][27][28], none have incorporated or compared the AJCC 8th edition staging scheme in OPSCC survival modelling and stratification. In this study, we explored the potential added value of radiomics biomarkers in prognostication of PFS and OS-beyond the AJCC staging scheme-in a multi-institutional cohort.…”

Radiation induced temporal lobe necrosis in nasopharyngeal cancer patients after radical external beam radiotherapy

“…The resulting features can be used to inform imaging diagnosis, prognosis, therapy response and radiationrelated toxicity in oncology [41]. Its applications on head and cancers have been increasing [42,43]. For instance, Zhou et al used radiomics to predict treatment outcome and toxicities of brain tumours [44].…”