Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review

Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.

Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma

To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.

Diagnostic utility of magnetic resonance imaging texture analysis in suppurative osteomyelitis of the mandible

PURPOSE

This study aimed to determine the diagnostic utility of magnetic resonance imaging (MRI) texture analysis for evaluating mandibular suppurative osteomyelitis (OM).

MATERIALS AND METHODS

In this retrospective cohort study, we analyzed the records of 50 patients with and without OM who underwent MRI between April 2019 and March 2021. The presence or absence of OM served as a predictor variable. The outcome variables were the texture features of the region of interest, which were analyzed. Quantitative parameters based on histogram features (90th percentile) and gray-level co-occurrence matrix (GLCM) features (Sum Averg) were calculated using short-tau inversion-recovery data with a region of interest. These six features out of 279 parameters were selected using Fisher, probability of error, and average correlation coefficient methods in MaZda. For the analysis of trivariate statistics, the post-Mann-Whitney test of the Kruskal-Wallis test with Bonferroni adjustment was used, and the p value was set to 0.05. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic effect of texture function to distinguish between acute and chronic diseases.

RESULTS

One histogram feature and five GLCM features showed differences among the non-OM patients, acute OM patients, and chronic OM patients (p < 0.05). The ROC analysis revealed a high area under the curve ranging from 0.91 to 0.96 for six texture features.

CONCLUSION

The six texture features of the mandibular bone marrow demonstrated differences among patients without and with acute and chronic OM. MRI texture analysis may facilitate accurate assessment of the mandibular OM stage.

Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

PURPOSE

This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC).

METHODS

Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation.

RESULTS

The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment.

CONCLUSION

QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

BACKGROUND AND PURPOSE

Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY).

METHODS

Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient.

RESULTS

The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively.

CONCLUSIONS

Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures

OBJECTIVES

Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer.

MATERIALS AND METHODS

Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC).

RESULTS

In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001).

CONCLUSIONS

MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters.

KEY POINTS

• MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

Imaging in oral cancers: A comprehensive review

This review aims at simplifying the relevant imaging anatomy, guiding the optimal imaging method and highlighting the key imaging findings that influence prognosis and management of oral cavity squamous cell carcinoma (OSCC). Early OSCC can be treated with either surgery alone while advanced cancers are treated with a combination of surgery, radiotherapy and/or chemotherapy. Considering the complex anatomy of the oral cavity and its surrounding structures, imaging plays an indispensable role not only in locoregional staging but also in the distant metastatic work-up and post treatment follow-up. Knowledge of the anatomy with understanding of common routes of spread of cancer, allows the radiologist to accurately determine disease extent and augment clinical findings to plan appropriate therapy. This review aims at simplifying the relevant imaging anatomy, guiding the optimal imaging method and highlighting the key imaging findings that influence prognosis and management.

Radiomic analysis for response assessment in advanced head and neck cancers, a distant dream or an inevitable reality? A systematic review of the current level of evidence

OBJECTIVE

The recent increase in publications on radiomic analysis as means to produce diagnostic and predictive biomarkers in head and neck cancers (HNCC) reveal complicated and often conflicting results. The objective of this paper is to systematically review the published data, and evaluate the current level of evidence accumulated that would determine clinical application.

METHODS

Data sources: Articles in the English language available on the Ovid-MEDLINE and Embase databases were used for the literature search. Study selection:Studies which evaluated the role of radiomics as a predictive or prognostic tool for response assessment in HNCC were included in this review.Study appraisal and synthesis methods: The authors set-out to perform a meta-analysis, however given the small number of studies retrieved that presented adequate data, combined with excessive methodological heterogeneity, we could only perform a structured descriptive systematic review summarizing the key findings. Independent extraction of articles was performed by two authors using predefined data fields and any disagreement was resolved by consensus.

RESULTS

Though most papers concluded that radiomics is an effective predictive and prognostic biomarker in the management of HNCC, significant heterogeneity exists in the study methodology and statistical modelling; thus precluding accurate mathematical comparison or the ability to make clear recommendations going forwards. Moreover, most studies have not been validated and the reproducibility of their results will be a challenge.

CONCLUSION

Until robust external validation studies on the reproducibility and accuracy of radiomic analysis methods on HNCC are carried out, the current level of evidence remains low, with the authors advising caution against hasty implementation of these tools in the multidisciplinary clinic.

ADVANCES IN KNOWLEDGE

This review is the first attempt to critically analyze the merits and demerits of currently published literature on tumour heterogeneity studies in HNCC, and identifies specific loop holes that need to be addressed by research groups, for a meaningful clinical translation of this potential biomarker.

Ultrasomics for Early Evaluation of the Tumor Response to MicroRNA-122 in a Nude Mouse Hepatocellular Carcinoma Model

OBJECTIVES

To explore the value of ultrasomics in temporal monitoring of tumor changes in response to gene therapy in hepatocellular carcinoma compared with methods according to the Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST (mRECIST).

METHODS

Hepatocellular carcinoma-bearing mice were injected intratumorally with microRNA-122 (miR-122) mimics and an miR-122 negative control in the treatment and control groups, respectively. The injections were performed every 3 days for 5 times (on days 0, 3, 6, 9, and 12). Before each injection and at the experiment ending, 2-dimensional ultrasound imaging was performed for tumor size measurement with RECIST and computing a quantitative imaging analysis with ultrasomics. To analyze the tumor perfusion by mRECIST, perfusion parameters were analyzed offline based on dynamic contrast-enhanced ultrasound image videos using SonoLiver software (TomTec, Unterschleissheim, Germany) on day 13. Tumor miR-122 expression was then analyzed by real-time reverse transcription-polymerase chain reaction experiments.

RESULTS

Tumors in mice treated with miR-122 mimics demonstrated a mean ± SD 763- ± 60-fold increase in miR-122 levels compared with tumors in the control group. With RECIST, a significant therapeutic response evaluated by tumor size changes was detected after day 9 (days 9, 12, and 13; P < .001). With mRECIST, no parameters showed significant differences (P > .05). Significant different features of the 2-dimensional ultrasound images between the groups were detected by the ultrasomics analysis, and the model could be successfully built. The ultrasomics score values between the groups were statistically significant after day 6 (days 6, 9, 12, and 13; P < .05).

CONCLUSIONS

Ultrasomics revealed significant changes after the second injection of miR-122, showing the potential as an important imaging biomarker for gene therapy.

Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors

OBJECTIVE

To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI).

METHODS

A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets.

RESULTS

Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively.

CONCLUSIONS

Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer

Purpose: FDG-PET adds to clinical factors, such tumor stage and p16 status, in predicting local (LF), regional (RF), and distant failure (DF) in poor prognosis locally advanced head and neck cancer (HNC) treated with chemoradiation. We hypothesized that MRI-based quantitative imaging (QI) metrics could add to clinical predictors of treatment failure more significantly than FDG-PET metrics. Materials and methods: Fifty four patients with poor prognosis HNCs who were enrolled in an IRB approved prospective adaptive chemoradiotherapy trial were analyzed. MRI-derived gross tumor volume (GTV), blood volume (BV), and apparent diffusion coefficient (ADC) pre-treatment and mid-treatment (fraction 10), as well as pre-treatment FDG PET metrics, were analyzed in primary and individual nodal tumors. Cox proportional hazards models for prediction of LRF and DF free survival were used to test the additional value of QI metrics over dominant clinical predictors. Results: The mean ADC pre-RT and its change rate mid-treatment were significantly higher and lower in p16- than p16+ primary tumors, respectively. A Cox model identified that high mean ADC pre-RT had a high hazard for LF and RF in p16- but not p16+ tumors (p = 0.015). Most interesting, persisting subvolumes of low BV (TV_bv) in primary and nodal tumors mid-treatment had high-risk for DF (p < 0.05). Also, total nodal GTV mid-treatment, mean/max SUV of FDG in all nodal tumors, and total nodal TLG were predictive for DF (p < 0.05). When including clinical stage (T4/N3) and total nodal GTV in the model, all nodal PET parameters had a p-value of >0.3, and only TV_bv of primary tumors had a p-value of 0.06. Conclusion: MRI-defined biomarkers, especially persisting subvolumes of low BV, add predictive value to clinical variables and compare favorably with FDG-PET imaging markers. MRI could be well-integrated into the radiation therapy workflow for treatment planning, response assessment, and adaptive therapy.

Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis

Radiomics and texture analysis represent a new option in our biomarkers arsenal. These techniques extract a large number of quantitative features, analyzing their properties to incorporate them in clinical decision-making. Laryngeal cancer represents one of the most frequent cancers in the head and neck area. We hypothesized that radiomics features can be included as a laryngeal cancer precision medicine tool, as it is able to non-invasively characterize the overall tumor accounting for heterogeneity, being a prognostic and/or predictive biomarker derived from routine, standard of care, imaging data, and providing support during the follow up of the patient, in some cases avoiding the need for biopsies. The larynx represents a unique diagnostic and therapeutic challenge for clinicians due to its complex tridimensional anatomical structure. Its complex regional and functional anatomy makes it necessary to enhance our diagnostic tools in order to improve decision-making protocols, aimed at better survival and functional results. For this reason, this technique can be an option for monitoring the evolution of the disease, especially in surgical and non-surgical organ preservation treatments. This concise review article will explain basic concepts about radiomics and discuss recent progress and results related to laryngeal cancer.

How to differentiate uterine leiomyosarcoma from leiomyoma with imaging

Uterine leiomyomas, the most frequent benign myomatous tumors of the uterus, often cannot be distinguished from malignant uterine leiomyosarcomas using clinical criteria. Furthermore, imaging differentiation between both entities is frequently challenging due to their potential overlapping features. Because a suspected leiomyoma is often managed conservatively or with minimally invasive treatments, the misdiagnosis of leiomyosarcoma for a benign leiomyoma could potentially result in significant treatment delays, therefore increasing morbidity and mortality. In this review, we provide an overview of the differences between leiomyoma and leiomyosarcoma, mainly focusing on imaging characteristics, but also briefly touching upon their demographic, histopathological and clinical differences. The main indications and limitations of available cross-sectional imaging techniques are discussed, including ultrasound, computed tomography, magnetic resonance imaging (MRI) and positron emission tomography/computed tomography. A particular emphasis is placed on the review of specific MRI features that may allow distinction between leiomyomas and leiomyosarcomas according to the most recent evidence in the literature. The potential contribution of texture analysis is also discussed. In order to help guide-imaging diagnosis, we provide an MRI-based diagnostic algorithm which takes into account morphological and functional features, both individually and in combination, in an attempt to optimize radiologic differentiation of leiomyomas from leiomyosarcomas.

CT Texture Analysis-Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas

Introduction: Texture analysis is an emergent imaging technique to quantify heterogeneity in radiological images. It is still unclear whether this technique is capable to reflect tumor microstructure. The present study sought to correlate histopathology parameters with texture features derived from contrast-enhanced CT images in head and neck squamous cell carcinomas (HNSCC).

Materials and Methods: Twenty-eight patients with histopathological proven HNSCC were retrospectively analyzed. In every case EGFR, VEGF, Hif1-alpha, Ki67, p53 expression derived from immunhistochemical specimen were semiautomatically calculated. Furthermore, mean cell count was estimated. Texture analysis was performed on contrast-enhanced CT images as a whole lesion measurement. Spearman's correlation analysis was performed, adjusted with Benjamini-Hochberg correction for multiple tests.

Results: Several texture features correlated with histopathological parameters. After correction only CT texture joint entropy and CT entropy correlation with Hif1-alpha expression remained statistically significant (ρ = −0.60 and ρ = −0.50, respectively).

Conclusions: CT texture joint entropy and CT entropy were associated with Hif1-alpha expression in HNSCC and might be able to reflect hypoxic areas in this entity.

MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer

BACKGROUND

The aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome. The ability to predict disease recurrence following CRT has the potential to inform personalized radiotherapy approaches currently being explored in novel clinical trials.

METHODS

An IRB waiver was obtained for retrospective analysis of standard care MRIs from ASCC patients presenting between 2010 and 2014. Whole tumor 3D volume-of-interest (VOI) was outlined on T2-weighted (T2w) and diffusion weighted imaging (DWI) of the pre- and post-treatment scans. Independent imaging features most predictive of disease recurrence were added to the baseline clinico-pathological model and the predictive value of respective extended models was calculated using net reclassification improvement (NRI) algorithm. Cross-validation analysis was carried out to determine percentage error reduction with inclusion of imaging features to the baseline model for both endpoints.

RESULTS

Forty patients who underwent 1.5 T pelvic MRI at baseline and following completion of CRT were included. A combination of two baseline MR heterogeneity features (baseline T2w energy and DWI coefficient of variation) was most predictive of disease recurrence resulting in significant NRI (p = 0 < 0.001). This was confirmed in cross-validation analysis with 34.8% percentage error reduction for the primary endpoint and 18.1% reduction for the secondary endpoint with addition of imaging variables to baseline model.

CONCLUSION

MRI heterogeneity analysis offers complementary information, in addition to clinical staging, in predicting outcome of CRT in anal SCC, warranting validation in larger datasets.

The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies

BACKGROUND

To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML).

METHODS

The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI).

RESULTS

The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10^- 1 vs. 2.53 ± 0.12 × 10^- 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10^- 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10^- 1).

CONCLUSIONS

Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Functional imaging early during (chemo)radiotherapy for response prediction in head and neck squamous cell carcinoma; a systematic review

This systematic review gives an extensive overview of the current state of functional imaging during (chemo)radiotherapy to predict locoregional control (LRC) and overall survival (OS) for head and neck squamous cell carcinoma. MEDLINE and EMBASE were searched for literature until April 2018 assessing the predictive performance of functional imaging (computed tomography perfusion (CTp), MRI and positron-emission tomography (PET)) within 4 weeks after (chemo)radiotherapy initiation. Fifty-two studies (CTp: n = 4, MRI: n = 19, PET: n = 26, MRI/PET: n = 3) were included involving 1623 patients. Prognostic information was extracted according the PRISMA protocol. Pooled estimation and subgroup analyses were performed for comparable parameters and outcome. However, the heterogeneity of included studies limited the possibility for comparison. Early tumoral changes from (chemo)radiotherapy can be captured by functional MRI and ¹⁸F-FDG-PET and could allow for personalized treatment adaptation. Lesions showed potentially prognostic intratreatment changes in perfusion, diffusion and metabolic activity. Intratreatment ADC_mean increase (decrease of diffusion restriction) and low SUV_max (persistent low or decrease of ¹⁸F-FDG uptake) were most predictive of LRC. Intratreatment persistent high or increase of perfusion on CT/MRI (i.e. blood flow, volume, permeability) also predicted LRC. Low SUV_max and total lesion glycolysis (TLG) predicted favorable OS. The optimal timing to perform functional imaging to predict LRC or OS was 2-3 weeks after treatment initiation.

Dual-energy computed tomography for prediction of loco-regional recurrence after radiotherapy in larynx and hypopharynx squamous cell carcinoma

PURPOSE

To investigate the role of quantitative pre-treatment dual-energy computed tomography (DECT) for prediction of loco-regional recurrence (LRR) in patients with larynx/hypopharynx squamous cell cancer (L/H SCC).

METHODS

Patients with L/H SCC treated with curative intent loco-regional radiotherapy and that underwent treatment planning with contrast-enhanced DECT of the neck were included. Primary and nodal gross tumor volumes (GTVp and GTVn) were contoured and transferred into a Matlab® workspace. Using a two-material decomposition, GTV iodine concentration (IC) maps were obtained. Quantitative histogram statistics (maximum, mean, standard deviation, kurtosis and skewness) were retrieved from the IC maps. Cox regression analysis was conducted to determine potential predictive factors of LRR.

RESULTS

Twenty-five patients, including 20 supraglottic and 5 pyriform sinus tumors were analysed. Stage I, II, III, IVa and IVb constituted 4% (1 patient), 24%, 36%, 28% and 8% of patients, respectively; 44% had concurrent chemo-radiotherapy and 28% had neodjuvant chemotherapy. Median follow-up was 21 months. Locoregional control at 1 and 2 years were 75% and 69%, respectively. For the entire cohort, GTVn volume (HR 1.177 [1.001-1.392], p = 0.05), voxel-based maximum IC of GTVp (HR 1.099 [95% CI: 1.001-1.209], p = 0.05) and IC standard deviation of GTVn (HR 9.300 [95% CI: 1.113-77.725] p = 0.04) were predictive of LRR. On subgroup analysis of patients treated with upfront radiotherapy +/- chemotherapy, both voxel-based maximum IC of GTVp (HR 1.127 [95% CI: 1.010-1.258], p = 0.05) and IC kurtosis of GTVp (HR 1.088 [95% CI: 1.014-1.166], p = 0.02) were predictive of LRR.

CONCLUSION

This exploratory study suggests that pre-radiotherapy DECT-derived IC quantitative analysis of tumoral volume may help predict LRR in L/H SCC.

Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging

The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC).Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case-control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D, and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response.The nonresidue group had lower tumor volume, ADC, D, CorrelatADC, CorrelatD, InvDfMomADC, InvDfMomD and InvDfMomD values, together with higher ContrastD, Contrastf, SumAvergADC, SumAvergD, and SumAvergD values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMomADC (P = .033) and SumAvergD (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%.Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.

Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review

Background

Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.

Methods

Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.

Results

Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].

Conclusion

Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

Objective

To develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI).

Methods

Eighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation. Radiomic features based on the ADC map were generated automatically by Artificial Intelligence Kit software (A.K. software; GE Healthcare, China). Feature reduction was conducted using the Mann-Whitney test and Spearman correlation after pre-treatment. A prediction model of ADC radiomics was established by logistic linear regression and cross-validation. A nomogram was established based on ADC radiomic features, pharmacokinetics and clinical features, including the morphology and ADC value for breast BI-RADS 4 lesions on MRI.

Results

A total of 396 radiomic features were extracted automatically by the A.K. software. Five features were selected after pre-processing, Mann-Whitney tests and Spearman correlation analysis. The area under the ROC curve of the prediction model comprising ADC radiomic features was 0.79 when the cutoff value was 0.45, and the accuracy, sensitivity and specificity were 80.0%, 0.813 and 0.778, respectively. A visualized differential nomogram based on the radiomic score, pharmacokinetics and clinical features was established. The decision curve showed good consistency.

Conclusions

ADC radiomic features could provide an important reference for differential diagnosis between benign and malignant lesions in suspicious BI-RADS 4 lesions. The visualized nomogram based on ADC radiomic features, pharmacokinetics and clinical features may have good prospects for clinical application.

Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

PURPOSE

An association between radiomic features extracted from CT and local tumor control in the head and neck squamous cell carcinoma (HNSCC) has been shown. This study investigated the value of pretreatment functional imaging (18F-FDG PET) radiomics for modeling of local tumor control.

MATERIAL AND METHODS

Data from HNSCC patients (n = 121) treated with definitive radiochemotherapy were used for model training. In total, 569 radiomic features were extracted from both contrast-enhanced CT and 18F-FDG PET images in the primary tumor region. CT, PET and combined PET/CT radiomic models to assess local tumor control were trained separately. Five feature selection and three classification methods were implemented. The performance of the models was quantified using concordance index (CI) in 5-fold cross validation in the training cohort. The best models, per image modality, were compared and verified in the independent validation cohort (n = 51). The difference in CI was investigated using bootstrapping. Additionally, the observed and radiomics-based estimated probabilities of local tumor control were compared between two risk groups.

RESULTS

The feature selection using principal component analysis and the classification based on the multivariabale Cox regression with backward selection of the variables resulted in the best models for all image modalities (CI_CT = 0.72, CI_PET = 0.74, CI_PET/CT = 0.77). Tumors more homogenous in CT density (decreased GLSZM_{size_zone_entropy}) and with a focused region of high FDG uptake (higher GLSZM_SZLGE) indicated better prognosis. No significant difference in the performance of the models in the validation cohort was observed (CI_CT = 0.73, CI_PET = 0.71, CI_PET/CT = 0.73). However, the CT radiomics-based model overestimated the probability of tumor control in the poor prognostic group (predicted  = 68%, observed  = 56%).

CONCLUSIONS

Both CT and PET radiomics showed equally good discriminative power for local tumor control modeling in HNSCC. However, CT-based predictions overestimated the local control rate in the poor prognostic validation cohort, and thus, we recommend to base the local control modeling on the 18F-FDG PET.

CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy

BACKGROUND AND PURPOSE

The accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy.

MATERIALS AND METHODS

This retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status.

RESULTS

Twenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68, P = .026], harmonic mean [hazard ratio = 8.61, P = .004], and fourth moment [hazard ratio = 4.56, P = .048]) and 4 gray-level run-length features (short-run emphasis [hazard ratio = 3.75, P = .044], gray-level nonuniformity [hazard ratio = 5.72, P = .004], run-length nonuniformity [hazard ratio = 4.15, P = .043], and short-run low gray-level emphasis [hazard ratio = 5.94, P = .035]) were significant predictors of outcome after adjusting for clinical variables.

CONCLUSIONS

Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy

OBJECTIVE

Human cancers display intra-tumor phenotypic heterogeneity and recent research has focused on developing image processing methods extracting imaging descriptors to characterize this heterogeneity. This work assesses the role of pretreatment 18F-FDG PET and DWI-MR with respect to the prognosis and prediction of neoadjuvant chemotherapy (NAC) outcomes when image features are used to characterize primitive lesions from breast cancer (BC).

MATERIALS AND METHODS

A retrospective protocol included 38 adult women with biopsy-proven BC. Patients underwent a pre-therapy 18F-FDG PET/CT whole-body study and a pre-therapy breast multi-parametric MR study. Patients were then referred for NAC treatment and then for surgical resection, with an evaluation of the therapy response. Segmentation methods were developed in order to identify functional volumes both on 18F-FDG PET images and ADC maps. Macroscopic and histogram features were extracted from the defined functional volumes.

RESULTS

Our work demonstrates that macroscopic and histogram features from 18F-FDG PET are able to biologically characterize primitive BC, and define the prognosis. In addition, histogram features from ADC maps are able to predict the response to NAC.

CONCLUSION

Our work suggests that pre-treatment 18F-FDG PET and pre-treatment DWI-MR provide useful complementary information for biological characterization and NAC response prediction in BC.

"Radio-oncomics" : The potential of radiomics in radiation oncology

INTRODUCTION

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow.

METHODS

After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created.

RESULTS

Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization.

DISCUSSION

Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches.

CONCLUSION

This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Texture analysis of medical images for radiotherapy applications

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

Characterization of cervical lymph-nodes using a multi-parametric and multi-modal approach for an early prediction of tumor response to chemo-radiotherapy

PURPOSE

In the treatment of Head-and-Neck Squamous Cell Carcinoma (HNSCC), the early prediction of residual malignant lymph nodes (LNs) is currently required. Here, we investigated the potential of a multi-modal characterization (combination of CT, T2w-MRI and DW-MRI) at baseline and at mid-treatment, based on texture analysis (TA), for the early prediction of LNs response to chemo-radiotherapy (CRT).

METHODS

30 patients with pathologically confirmed HNSCC treated with CRT were considered. All patients underwent a planning CT and two serial MR examinations (including T2w and DW images), one before and one at mid-CRT. For each patient the largest malignant LN was selected and within each LN, morphological and textural features were estimated from T2w-MRI and CT, besides a quantification of the apparent diffusion coefficient (ADC) from DW-MRI. After a median follow-up time of 26.6months, 19 LNs showed regional control, while 11 LNs showedregional failure at a median time of 4.6months. Linear discriminant analysis was used to test the accuracy of the image-based features in predicting the final response.

RESULTS

Pre-treatment features showed higher predictive power than mid-CRT features, the ADC having the highest accuracy (80%); CT-based indices were found not predictive. When ADC was combined with TA, the classification performance increased (accuracy=82.8%). If only T2w-MRI features were considered, the best combination of pre-CRT indices and their variation reached an equivalent accuracy (81.8%).

CONCLUSION

Our results may suggest that TA on T2w-MRI and ADC can be combined together to obtain a more accurate prediction of response to CRT.

Early responses assessment of neoadjuvant chemotherapy in nasopharyngeal carcinoma by serial dynamic contrast-enhanced MR imaging

PURPOSE

To evaluate the feasibility of utilizing serial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) prospectively for early prediction of neoadjuvant chemotherapy (NAC) response in nasopharyngeal carcinoma (NPC) patients.

MATERIALS AND METHODS

Sixty-three advanced NPC patients were recruited and received three DCE-MRI exams before treatment (Pre-Tx), 3days (Day3-Tx) and 20days (Day20-Tx) after initiation of chemotherapy (one NAC cycle). Early response to NAC was determined based on the third MRI scan and classified partial response (PR) as responders and stable disease (SD) as non-responders. After intensity-modulated radiotherapy (IMRT), complete response (CR) patients were classified as responders. The kinetic parameters (K^trans, K_ep, v_e, and v_p) derived from extended Tofts' model analysis and their corresponding changes ΔMetrics_(0-X) (X=3 or 20days) were compared between the responders and non-responders using the Student's T-test or Mann-Whitney U test.

RESULTS

Compared to the SD group, the PR group after one NAC cycle presented significantly higher mean K^trans values at baseline (P=0.011) and larger ΔK^trans_(0-3) and ΔK_ep(0-3) values (P=0.003 and 0.031). For the above parameters, we gained acceptable sensitivity (range: 66.8-75.0%) and specificity (range: 60.0-66.7%) to distinguish the non-responders from the responders and their corresponding diagnosis efficacy (range: 0.703-0.767). The PR group patients after one NAC cycle showed persistent inhibition of tumor perfusion by NAC as explored by DCE-MRI parameters comparing to the SD group (P<0.05) and presented a higher cure ratio after IMRT than those who did not (83.3% vs. 73.8%).

CONCLUSIONS

This primarily DCE-MRI based study showed that the early changes of the kinetic parameters during therapy were potential imaging markers to predicting response right after one NAC cycle for NPC patients.

Radiomics in head and neck cancer: from exploration to application

In the context of clinical oncology, a fundamental goal of radiomics is the extraction of large amounts of quantitative features whose subsequent analysis can be used for decision support towards personalized and actionable cancer care. Head and neck cancers present a unique set of diagnostic and therapeutic challenges by nature of its complex anatomy and heterogeneity. Radiomics holds the potential to address these barriers, but only if as a collective field we direct future effort towards investigating specific oncologic function and oncologic outcomes, with external validation and collaborative multi-institutional efforts to begin standardizing and refining radiomic signatures. Here we present an overview of radiomic texture analysis methods as well as the software infrastructure, review the developments of radiomics in head and neck cancer applications, discuss unmet challenges, and propose key recommendations for moving the field forward.