Longitudinal interpretability of deep learning based breast cancer risk prediction.

Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, and the underlying mechanisms of BCR prediction are not fully understood. Key questions include whether these models can detect breast morphologic changes that lead to cancer.

Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study.

Background: To evaluate the role of the novel quantitative imaging biomarker (QIB) SUV of F-FDG uptake extracted from early F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patients with metastatic melanoma (mM) patients receiving immune-checkpoint inhibitors (ICI).

Patients And Methods: In this prospective non-interventional, one-centre clinical study, patients with mM, receiving ICI treatment, were regularly followed by F-FDG PET/CT. Patients were scanned at baseline, early point at week four (W4), week sixteen (W16) and week thirty-two (W32) after ICI initiation.

DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy.

. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs.