Prognostic Factors and Outcomes Associated With Neck Lymphedema in Head and Neck Cancer Survivors.

Objectives: The purpose of this study is to determine the predictors of neck lymphedema and to explore its association with symptoms and patient-reported outcomes (PROs) in Head and Neck Cancer (HNC) patients who underwent non-operative treatment.

Methods: This study involved a cross-sectional secondary analysis of data from patients diagnosed with head and neck squamous cell carcinoma who underwent radiation therapy (±chemotherapy). Patients with visits <6 weeks or >2 years following completion of radiation and those with recurrent or metastatic cancer were excluded.

Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.

Objective: Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC).

Materials & Methods: 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified.

Changes in Apparent Diffusion Coefficient (ADC) in Serial Weekly MRI during Radiotherapy in Patients with Head and Neck Cancer: Results from the PREDICT-HN Study.

Background: The PREDICT-HN study aimed to systematically assess the kinetics of imaging MR biomarkers during head and neck radiotherapy. Methods: Patients with intact squamous cell carcinoma of the head and neck were enrolled. Pre-, during, and post-treatment MRI were obtained.

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).