Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas

Abstract

Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the "-omics" concept for the broader field of head and neck cancer-"Radiomics". This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.

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].…”

Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics

“…While pre-treatment PET/CT imaging is a mainstay of disease work-up and cancer staging, human visual interpretation cannot seize the full prognostic utility encoded in metabolic and structural bioimaging patterns [11] , [12] , [13] , [14] . By capturing such bioimaging features, radiomic biomarkers may help identify patients who are at increased risk for LRP, and may potentially improve patient selection in future trials of treatment de-intensification for HPV-associated OPSCC, and guide personalized clinical treatment planning.…”

“…Radiomics analysis has expanded the scope of “-omics” to quantitative characterization of medical images by extracting high-dimensional sets of “features” from volumes of interest (VOI) such as primary tumor lesions, which capture lesion shape, image intensity and texture patterns. The resulting imaging biomarkers may be correlated with treatment outcome, tumor microenvironment, tissue heterogeneity and pathophysiology; and may enable development of prognostic tools substituting or supplementing traditional outcome predictors such as cancer staging [11] , [12] , [13] , [14] , [15] . Depending on the imaging modality used, radiomic features can represent a variety of tumor characteristics; [ 18 F]fluorodeoxyglucose positron emission tomography (PET) radiomics may provide wholistic quantification of tumor metabolic activity and activity distribution; whereas computed tomography (CT) radiomics can describe structural properties and tissue density.…”

Technical feasibility of radiomics signature analyses for improving detection of occult tonsillar cancer

“…More recently, research based on medical imaging informatics has greatly improved. Radiomics, a data mining approach that extracts high-dimensional data in the form of a multitude of features from clinical images to build machine-learning or statistical models, has been applied to various imaging modalities to answer relevant clinical questions 10 . In the field of head and neck cancer radiomics, classification and survival regression models have been applied to predict molecular markers and identify genomic signatures for the diagnostic differentiation of suspected tissues, survival prognostication, and to predict treatment responses [11][12][13] .…”

“…Recently, several papers have reported that radiomics features derived from the volumetric analyses of a whole tumor in CT or MRI scans have shown potential for tumor detection, grading, and predicting the recurrence of head and neck cancer 10 – 13 . In oropharyngeal cancer, a few papers have reported the potential of radiomic features analysis (RFA) for discrimination of HPV status, tumor grading, and detection of local recurrence 11 – 13 .…”