Evaluation of an AI Model to Assess Future Breast Cancer Risk.

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66

Breast lesion analysis of shape technique: semiautomated vs. manual morphological description.

PURPOSE: To investigate the efficacy of an automated method of shape measurement for improving the discrimination of benign and malignant breast lesions. MATERIALS AND METHODS: A total of 47 breast lesions (32 malignant and 15 benign) were examined using a 1.5 Tesla system. Regions of interest (ROIs) were manually drawn and extracted from high-resolution, fat-suppressed, postcontrast images, or were extracted with the use of a semiautomated computer algorithm. Shape parameters (i.e., complexity, convexity, circularity, and degree of elongation) were determined to assess whether they could be used to discriminate breast lesions. RESULTS: Convexity differed significantly between the benign and malignant groups for both ROI methods. In addition, the semiautomated method demonstrated significantly different values of complexity. CONCLUSION: This work demonstrates the usefulness of several shape descriptors for characterizing breast lesions, and shows that the automated method of analysis improves the discrimination and standardization of data.

Diffusion-weighted imaging of normal and malignant prostate tissue at 3.0T.

PURPOSE: To measure the apparent diffusion coefficient (ADC) of normal and malignant prostate tissue at 3.0T using a phased-array coil and parallel imaging, and determine the utility of ADC values in differentiating tumor from normal peripheral zone (PZ). MATERIALS AND METHODS: ADC values were calculated for 49 patients (tumor and PZ) with evidence of prostate cancer. Additionally, for nine asymptomatic volunteers, ADC values were determined for apparently normal central gland and PZ. A single-shot EPI diffusion-weighted imaging (DWI) technique with b = 0 and 500 seconds/mm2 was employed. RESULTS: ADC values were significantly lower for tumor (1.38 +/- 0.32 x 10(-3) mm2/second) than for patient PZ (1.95 +/- 0.50 x 10(-3) mm2/second, P < 0.001) and volunteer PZ (1.60 +/- 0.25 x 10(-3) mm2/second, P = 0.031). A considerable overlap of ADC values was noted between patient tissue types. CONCLUSION: DWI of the prostate at 3.0T in conjunction with a phased-array coil and parallel imaging allows ADC calculation of the prostate. ADC values were lower for tumors compared to normal-appearing PZ; however, there was considerable intersubject variability.

Timing of antibiotic prophylaxis in surgery for adult hip fracture.

BACKGROUND: Antibiotic prophylaxis is widely used in surgery for hip fractures. METHODS AND RESULTS: In a retrospective study of case notes of 100 patients, frequent inaccuracies in dose administration were observed. This was applicable to both the pre-operative and the postoperative doses. Longer time intervals between the doses, failure to administer the prescribed doses, and failure of proper documentation were observed. CONCLUSIONS: Improvement in the awareness of staff and timely administration of prophylactic antibiotic has resulted from this study.