A Standard Set of Value-Based Patient-Centered Outcomes for Breast Cancer: The International Consortium for Health Outcomes Measurement (ICHOM) Initiative.

A major challenge in value-based health care is the lack of standardized health outcomes measurements, hindering optimal monitoring and comparison of the quality of health care across different settings globally. The International Consortium for Health Outcomes Measurement (ICHOM) assembled a multidisciplinary international working group, comprised of 26 health care providers and patient advocates, to develop a standard set of value-based patient-centered outcomes for breast cancer (BC). The working group convened via 8 teleconferences and completed a follow-up survey after each meeting. A modified 2-round Delphi method was used to achieve consensus on the outcomes and case-mix variables to be included. Patient focus group meetings (8 early or metastatic BC patients) and online anonymized surveys of 1225 multinational BC patients and survivors were also conducted to obtain patients' input. The standard set encompasses survival and cancer control, and disutility of care (eg, acute treatment complications) outcomes, to be collected through administrative data and/or clinical records. A combination of multiple patient-reported outcomes measurement (PROM) tools is recommended to capture long-term degree of health outcomes. Selected case-mix factors were recommended to be collected at baseline. The ICHOM will endeavor to achieve wide buy-in of this set and facilitate its implementation in routine clinical practice in various settings and institutions worldwide.

Computer aided analysis of breast MRI enhancement kinetics using mean shift clustering and multifeature iterative region of interest selection.

PURPOSE: To evaluate automatic characterization of a breast MR lesion by its spatially coherent region of interest (ROI). MATERIALS AND METHODS: The method delineated 247 enhancing lesions using Otsu thresholding after manually placing a sphere. Mean Shift Clustering subdivided each volume, based on features including pharmacokinetic parameters. An iteratively trained classifier to predict the most suspicious ROI (IsR) was used, to predict the malignancy likelihood of each lesion. Performance was evaluated using receiver operator characteristic (ROC) analysis, and compared with a previous prototype. IsR was compared with noniterative training. The effect of adding BI-RADS™ morphology (from a radiologist) to the classifier was investigated. RESULTS: The area under the ROC curve (AUC) was 0.83 (95% confidence interval [CI] of 0.77-0.88), and was 0.75 (95%CI = 0.68-0.81; P = 0.029) without pharmacokinetic features. IsR performed better than conventional selection, based on one feature (AUC 0.75, 95%CI = 0.68-0.81; P = 0.035). With morphology, the AUC was 0.84 (95%CI = 0.78-0.88) versus 0.82 without (P = 0.40). CONCLUSION: Breast lesions can be characterized by their most suspicious, contiguous ROI using multi-feature clustering and iterative training. Characterization was improved by including pharmacokinetic modeling, while in our experiments, including morphology did not improve characterization.

Automated analysis of contrast enhancement in breast MRI lesions using mean shift clustering for ROI selection.

PURPOSE: To evaluate a new method for automated determination of a region of interest (ROI) for the analysis of contrast enhancement in breast MRI. MATERIALS AND METHODS: Mean shift multidimensional clustering (MS-MDC) was employed to divide 92 lesions into several spatially contiguous clusters each, based on multiple enhancement parameters. The ROIs were defined as the clusters with the highest probability of malignancy. The performance of enhancement analysis within these ROIs was estimated using the area under the receiver operator characteristic curve (AUC), and compared against a radiologist's final assessment and a classifier using histogram analysis (HA). For HA, the first, second, and third quartiles were evaluated. RESULTS: MS-MDC resulted in AUC = 0.88 with a 95% confidence interval (CI) of 0.81-0.95. The AUC for the radiologist's assessment was 0.93 (95%CI = 0.87-0.97). Best HA performance was found using the first quartile, with AUC = 0.79 (95%CI = 0.69-0.88). There was no significant difference between MS-MDC and the radiologist (P = 0.40). The improvement of MS-MDC over HA was significant (P = 0.018). CONCLUSION: Mean shift clustering followed by automated selection of the most suspicious cluster resulted in accurate ROIs in breast MRI lesions.

Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging.

RATIONALE AND OBJECTIVE: The objective of this study was to evaluate the interobserver variability in reporting descriptive kinetic and morphologic enhancement features at breast magnetic resonance imaging. MATERIALS AND METHODS: Four observers evaluated 103 lesions, 49 malignant and 54 benign, proven by histopathology. They used standardized terminology with the following characteristics: "early enhancement kinetics" and "late enhancement kinetics" in curves from both reader-determined and preset regions of interest (ROIs), "enhancement pattern," "shape," "margin," "internal enhancement," and a final assessment score. Agreement was calculated using the kappa statistic. Differences in agreement were calculated using Fisher exact test. RESULTS: kappa was 0.27 for both early and late enhancement; preset ROIs improved kappa to 0.47 and 0.67, respectively (odds ratios, 1.7 and 4.5). kappa was 0.45 for pattern, 0.42 for shape, 0.26 for margin, 0.25 for internal enhancement, and 0.28 for final assessment. CONCLUSIONS: There was considerable variability in the use of most generally accepted terms. The preparation of ROIs was a major source of variability in the interpretation of enhancement curves.