Swallow Preservation Exercises during Chemoradiation Therapy Maintains Swallow Function

OBJECTIVE: To evaluate a swallow preservation protocol (SPP) in which patients received swallow therapy before, during, and after radiation treatment and its efficacy in maintaining swallowing function in head and neck cancer patients. DESIGN: Case series with chart review. SETTING: Tertiary care academic medical center. SUBJECTS AND METHODS: Eighty-five patients who received radiation (RT) or chemoradiation (CRT) participated in the SPP from 2007 to 2012. Subjects were divided into 2 groups: compliant and noncompliant with SPP. At each SPP visit, the diet of each patient was recorded as regular (chewable), puree, liquid, or gastrostomy tube (G-tube) dependent, along with their compliance with the swallow exercises. Patients were stratified by age, gender, tumor stage, type of treatment, radiation dose, diet change, dysguesia, odynophagia, pain, and stenosis. Statistical analysis was performed comparing the 2 compliance groups in regards to swallowing-related outcomes at 1 month after completion of therapy. RESULTS: Fifty-seven patients were compliant and 28 were non-compliant with SPP during treatment. The compliant group had a higher percentage of patients tolerating a regular diet (54.4% vs 21.4%, P = .008), a lower G-tube dependence (22.8% vs 53.6%, P = .008), and a higher rate of maintaining or improving their diet (54.4% vs 25.0%, P = .025) compared to noncompliant patients. CONCLUSION: A swallow preservation protocol appears to help maintain or improve swallow function in head and neck cancer patients undergoing RT or CRT. Patients who are able to comply with swallow exercises are less likely to worsen their diet, receive a G-tube, or develop stenosis

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset