Texture analysis to assess structural modifications induced by radiotherapy

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.

Emerging Concepts and Novel Strategies in Radiation Therapy for Laryngeal Cancer Management

Laryngeal squamous cell carcinoma is the second most common head and neck cancer. Its pathogenesis is strongly associated with smoking. The management of this disease is challenging and mandates multidisciplinary care. Currently, accepted treatment modalities include surgery, radiation therapy, and chemotherapy—all focused on improving survival while preserving organ function. Despite changes in smoking patterns resulting in a declining incidence of laryngeal cancer, the overall outcomes for this disease have not improved in the recent past, likely due to changes in treatment patterns and treatment-related toxicities. Here, we review emerging concepts and novel strategies in the use of radiation therapy in the management of laryngeal squamous cell carcinoma that could improve the relationship between tumor control and normal tissue damage (therapeutic ratio).

CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms

To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT

PURPOSE

Image texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers.

METHODS

We performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia.

RESULTS

Seventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664).

CONCLUSIONS

Xerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.

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.

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.