A comparative study between open-face and closed-face masks for head and neck cancer (HNC) in radiation therapy.

AIM: To determine the setup reproducibility in the radiation treatment of Head and Neck (HN) patients using open face head and shoulder masks (OHSM) with customized headrest (CHR) versus standard closed head and shoulder masks (CHSM) and to determine the patient's level of comfort and satisfaction for both masks. METHODS: Forty patients were prospectively randomized into two groups using simple random sampling. Group 1 was assigned with CHSMs, immobilized with a standard HR (SHR) while Group 2 was assigned with OHSMs, and immobilized with CHR. Cone beam computed tomography (CBCT) was taken the first 3 days, followed by weekly CBCT (prior treatment) with results registered to the planning CT to determine translational and rotational inter-fraction shifts and to verify accuracy. Mean (M) and standard deviation (SD) of the systematic and random setup errors of the 2 arms in the translational and rotational directions were analyzed, using Independent t-test and Mann-Whitney U test. Patient comfort was measured using a Likert questionnaire. RESULTS: The vertical, lateral, longitudinal and Z/roll rotational shifts were not significantly different between the two masks. X/yaw and Y/pitch rotational shifts were significantly greater in Group 2 versus Group 1, for both systematic (p = 0.009 and 0.046, respectively) and random settings (p = 0.016 and 0.020) but still within three degrees. Patients reported higher neck and shoulder comfort (p = 0.020) and overall satisfaction (p = 0.026) using the OHSM with the CHR versus the CHSM with the SHR during CT simulation. CONCLUSION: Open masks provide comparable yet comfortable immobilization to closed masks for HN radiotherapy.

Ontology-based prediction of cancer driver genes.

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.