Histological Patterns and Mammographic Presentation of Invasive Lobular Carcinoma Show No Obvious Associations.

Invasive lobular carcinoma of the breast has different mammographic appearances, including spiculated or lobulated masses, architectural distortion, increased breast density, and the possibility of also being occult. Histologically, the morphology is also variable, as several patterns have been described beside the classical one, including the solid, the alveolar, the trabecular, the one with tubular elements, and others. Of 146 ILC cases, 141 were reviewed for mammographic appearance and 136 for histological patterns by two radiologist and two pathologists, respectively; 132 common cases were analyzed for possible associations between mammographic presentation and the histological patterns.

Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload.

Objective: To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice.

Methods: Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader.

Automatic correction of performance drift under acquisition shift in medical image classification.

Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution.

Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.

Background: Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking.