Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.

To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists' sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with a panel of seven radiologists using a cancer-enriched data set from 122 patients that included 90 false-negative mammograms obtained up to 5.8 years prior to diagnosis and 32 BIRADS 1 and 2 patients with a 2-year follow-up of negative diagnosis.

Evaluation of fluorodeoxyglucose positron emission tomography imaging in metastatic transitional cell carcinoma with and without prior chemotherapy.

Introduction: This study was designed to determine the value of fluorodeoxyglucose (FDG) positron emission tomography (PET) in the evaluation of metastatic transitional cell carcinoma (TCC).

Methods: Fifty-eight FDG PET scans were performed on 46 consecutive patients with TCC. Results were correlated with radiologic, pathologic, and histologic findings in these patients and the sensitivity of PET for detecting malignancy in untreated TCC patients (n = 48) was compared to the sensitivity in patients who had undergone prior chemotherapy (n = 10).