Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.

Utilization of primary tumor samples for cancer neoantigen discovery.

Background: The use of tumor-infiltrating T lymphocytes (TIL) that recognize cancer neoantigens has led to lasting remissions in metastatic melanoma and certain cases of metastatic epithelial cancer. For the treatment of the latter, selecting cells for therapy typically involves laborious screening of TIL for recognition of autologous tumor-specific mutations, detected through next-generation sequencing of freshly resected metastatic tumors. Our study explored the feasibility of using archived formalin-fixed, paraffin-embedded (FFPE) primary tumor samples for cancer neoantigen discovery, to potentially expedite this process and reduce the need for resections normally required for tumor sequencing.

Young, Empowered & strong (YES): a study protocol paper for a randomized controlled trial of an mHealth symptom monitoring and self-management intervention for adolescent and young adult (AYA) breast cancer survivors.

Background: Compared to older adults with breast cancer (BC), adolescents and young adults (AYAs) develop more aggressive disease necessitating more intensive therapy with curative intent, which is disruptive to planned life trajectories. The burden of unmet needs among AYA BC survivors exists in two domains: (1) symptoms (e.g.

Graph convolution networks model identifies and quantifies gene and cancer specific transcriptome signatures of cancer driver events.

Background: The identification and drug targeting of cancer causing (driver) genetic alterations has seen immense improvement in recent years, with many new targeted therapies developed. However, identifying, prioritizing, and treating genetic alterations is insufficient for most cancer patients. Current clinical practices rely mainly on DNA level mutational analyses, which in many cases fail to identify treatable driver events.