18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization

The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.

Answer questions and earn CME/CNE The recently released eighth edition of the American Joint Committee on Cancer (AJCC) Staging Manual, Head and Neck Section, introduces significant modifications from the prior seventh edition. This article details several of the most significant modifications, and the rationale for the revisions, to alert the reader to evolution of the field. The most significant update creates a separate staging algorithm for high-risk human papillomavirus-associated cancer of the oropharynx, distinguishing it from oropharyngeal cancer with other causes. Other modifications include: the reorganizing of skin cancer (other than melanoma and Merkel cell carcinoma) from a general chapter for the entire body to a head and neck-specific cutaneous malignancies chapter; division of cancer of the pharynx into 3 separate chapters; changes to the tumor (T) categories for oral cavity, skin, and nasopharynx; and the addition of extranodal cancer extension to lymph node category (N) in all but the viral-related cancers and mucosal melanoma. The Head and Neck Task Force worked with colleagues around the world to derive a staging system that reflects ongoing changes in head and neck oncology; it remains user friendly and consistent with the traditional tumor, lymph node, metastasis (TNM) staging paradigm. CA Cancer J Clin 2017;67:122-137. © 2017 American Cancer Society.

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.