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Abstract: The purpose of this study was to determine the frequency and outcomes of breast malpractice suits among all enrollees in One Call Medical Inc.'s panel of interpreting radiologists nationally and to evaluate state‐by‐state variations. The 8401 radiologists enrolled had 4764 suits, of which 826 were related to breast disease. In New York and New Jersey, the ratio of breast suits to One Call Radiologists was 0.28 and 0.27, twice as much as in any other state. Breast suits in radiology have a wide variation in frequency across the country with New York and New Jersey far exceeding all others in relative frequency and number of radiologists with multiple breast suits. [ABSTRACT FROM AUTHOR]

We investigated a deep learning strategy to analyze optical coherence tomography image for accurate tissue characterization based on a single fiber OCT probe. We obtained OCT data from human breast tissue specimens. Using OCT data obtained from adipose breast tissue (normal tissue) and diseased tissue as confirmed in histology, we trained and validated a convolutional neural network (CNN) for accurate breast tissue classification. We demonstrated tumor margin identification based CNN classification of tissue at different spatial locations. We further demonstrated CNN tissue classification in OCT imaging based on a manually scanned single fiber probe. Our results demonstrated that OCT imaging capability integrated into a low-cost, disposable single fiber probe, along with sophisticated deep learning algorithms for tissue classification, allows minimally invasive tissue characterization, and can be used for cancer diagnosis or surgical margin assessment.

We develop a spatial coordinate corrected (SCC) motion tracking method for optical coherence elastography. SCC motion tracking refers the instantaneous velocity field extracted from optical coherence tomography (OCT) data to the laboratory coordinate system and accurately reconstructs the displacement field established during the mechanical excitation (compression) process. We acquired image data from compression OCE experiments on human breast tissue specimens, and reconstructed the displacement field through Doppler analysis of OCT data. Our results suggested that SCC tracking enables accurate reconstruction of displacement field, and enables effective identification mechanical heterogeneity that can be used as a biomarker for cancer diagnosis and tumor margin assessment.

Objective: Our aim was to evaluate the histologic characteristics of tissue extracted on the probe immediately after radiofrequency ablation of malignant tumors in the liver., Materials and Methods: From April to December 2001, 20 radiofrequency ablations were performed in 19 patients with primary (n = 17) and metastatic (n = 2) liver masses. Track ablation according to device protocol was performed after each ablation. Tissue was adherent to the probe after all radiofrequency probe passes. All pieces of tissue found on the probe were collected and preserved in formalin., Results: Tissue was examined by the study pathologist. In eight (40%) of 20 specimens, coagulation necrosis was present. In five (25%) of 20 specimens, possibly nonviable tissue was extracted, although some cell characteristics were identified. In seven (35%) of 20 specimens with hepatocellular carcinoma, possibly viable tissue was found. Five specimens were identified as hepatocellular carcinoma, and two, as cirrhotic nodules., Conclusion: Histopathologic evaluation of the tissue extracted on the radiofrequency probe after ablation is feasible. This study showed that coagulation necrosis was clearly present in at least 40% of the patients, which proves that nonviable tissue can be seen immediately after ablation. Whether this pathologic finding has prognostic value is not known.