Background And Purpose: The ability to determine the risk and predictors of lymphedema is vital in improving the quality of life for head and neck (HN) cancer patients. However, selecting robust features is challenging due to the multicollinearity and high dimensionality of radiotherapy (RT) data. This study aims to overcome these challenges using an ensemble feature selection technique with machine learning (ML).
View Article and Find Full Text PDFStudies to date have not resolved how diverse transcriptional programs contribute to the intratumoral heterogeneity of small cell lung carcinoma (SCLC), an aggressive tumor associated with a dismal prognosis. Here, we identify distinct and commutable transcriptional states that confer discrete functional attributes in individual SCLC tumors. We combine an integrative approach comprising the transcriptomes of 52,975 single cells, high-resolution measurement of cell state dynamics at the single-cell level, and functional and correlative studies using treatment naïve xenografts with associated clinical outcomes.
View Article and Find Full Text PDFPurpose/objectivess: We sought to determine the rate of brachial plexopathy (BPX) in patients exceeding RTOG dose constraints for treatment of apical lung tumors.
Materials/methods: Patients with apical lung tumors treated with four- or five-fraction SBRT were identified from a prospective registry. Dosimetric data were obtained for ipsilateral subclavian vein (SCV) and anatomic BP (ABP) contours.