Pre-operative lung ablation prediction using deep learning.

Objective: Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments.

Association of breast cancer with quantitative mammographic density measures for women receiving contrast-enhanced mammography.

Women with high mammographic density have an increased risk of breast cancer. They may be offered contrast-enhanced mammography to improve breast cancer screening performance. Using a cohort of women receiving contrast-enhanced mammography, we evaluated whether conventional and modified mammographic density measures were associated with breast cancer.

Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM.

Evaluation of an Integrated Spectroscopy and Classification Platform for Point-of-Care Core Needle Biopsy Assessment: Performance Characteristics from Ex Vivo Renal Mass Biopsies.

Purpose: To evaluate a transmission optical spectroscopy instrument for rapid ex vivo assessment of core needle cancer biopsies (CNBs) at the point of care.

Materials And Methods: CNBs from surgically resected renal tumors and nontumor regions were scanned on their sampling trays with a custom spectroscopy instrument. After extracting principal spectral components, machine learning was used to train logistic regression, support vector machines, and random decision forest (RF) classifiers on 80% of randomized and stratified data.

Weakly supervised pneumonia localization in chest X-rays using generative adversarial networks.

Purpose: Automatic localization of pneumonia on chest X-rays (CXRs) is highly desirable both as an interpretive aid to the radiologist and for timely diagnosis of the disease. However, pneumonia's amorphous appearance on CXRs and complexity of normal anatomy in the chest present key challenges that hinder accurate localization. Existing studies in this area are either not optimized to preserve spatial information of abnormality or depend on expensive expert-annotated bounding boxes.

Application of Deep Learning Techniques for Characterization of 3D Radiological Datasets - A Pilot Study for Detection of Intravenous Contrast in Breast MRI.

Categorization of radiological images according to characteristics such as modality, scanner parameters, body part etc, is important for quality control, clinical efficiency and research. The metadata associated with images stored in the DICOM format reliably captures scanner settings such as tube current in CT or echo time (TE) in MRI. Other parameters such as image orientation, body part examined and presence of intravenous contrast, however, are not inherent to the scanner settings, and therefore require user input which is prone to human error.

Comparison of Radiographic Approaches to Assess Treatment Response in Pituitary Adenomas: Is RECIST or RANO Good Enough?

Context: Pituitary adenomas (PA) are often irregularly shaped, particularly posttreatment. There are no standardized radiographic criteria for assessing treatment response, substantially complicating interpretation of prospective outcome data. Existing imaging frameworks for intracranial tumors assume perfectly spherical targets and may be suboptimal.