Assessing the discordance rate between local and central HER2 testing in women with locally determined HER2-negative breast cancer.

BackgroundThe importance of human epidermal growth factor receptor 2 (HER2) as a prognostic and predictive marker in invasive breast cancer is well established. Accurate assessment of HER2 status is essential to determine optimal treatment options.MethodsBreast cancer tumor tissue samples from the VIRGO observational cohort tissue substudy that were locally HER2-negative were retested centrally with both US Food and Drug Administration (FDA)-approved immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) assays, using FDA-approved assay cutoffs; results were compared.ResultsOf the 552 unique patient samples centrally retested with local HER2-negative results recorded, tumor samples from 22 (4.0%) patients were determined to be HER2-positive (95% confidence interval [CI] = 2.5%-5.7%). Of these, 18 had been tested locally by only one testing methodology; 15 of 18 were HER2-positive after the central retesting, based on the testing methodology not performed locally. Compared with the 530 patients with centrally confirmed HER2-negative tumors, the 22 patients with centrally determined HER2-positive tumors were younger (median age 56.5 versus 60.0 years) and more likely to have ER/PR-negative tumors (27.3% versus 22.3%). These patients also had shorter median progression-free survival (6.4 months [95% CI = 3.8-15.9 months] versus 9.1 months [95% CI = 8.3-10.3 months]) and overall survival (25.9 months [95% CI = 13.8-not estimable] versus 27.9 months [95% CI = 25.0-32.9 months]).ConclusionsThis study highlights the limitations of employing just one HER2 testing methodology in current clinical practice. It identifies a cohort of patients who did not receive potentially efficacious therapy because their tumor HER2-positivity was not determined by the test initially used. Because of inherent limitations in testing methodologies, it is inadvisable to rely on a single test to rule out potential benefit from HER2-targeted therapy

Exploring the interpersonal-, organization-, and system-level factors that influence the implementation and use of an innovation-synoptic reporting-in cancer care

<p>Abstract</p> <p>Background</p> <p>The dominant method of reporting findings from diagnostic and surgical procedures is the narrative report. In cancer care, this report inconsistently provides the information required to understand the cancer and make informed patient care decisions. Another method of reporting, the synoptic report, captures specific data items in a structured manner and contains only items critical for patient care. Research demonstrates that synoptic reports vastly improve the quality of reporting. However, synoptic reporting represents a complex innovation in cancer care, with implementation and use requiring fundamental shifts in physician behaviour and practice, and support from the organization and larger system. The objective of this study is to examine the key interpersonal, organizational, and system-level factors that influence the implementation and use of synoptic reporting in cancer care.</p> <p>Methods</p> <p>This study involves three initiatives in Nova Scotia, Canada, that have implemented synoptic reporting within their departments/programs. Case study methodology will be used to study these initiatives (the cases) in-depth, explore which factors were barriers or facilitators of implementation and use, examine relationships amongst factors, and uncover which factors appear to be similar and distinct across cases. The cases were selected as they converge and differ with respect to factors that are likely to influence the implementation and use of an innovation in practice. Data will be collected through in-depth interviews, document analysis, observation of training sessions, and examination/use of the synoptic reporting tools. An audit will be performed to determine/quantify use. Analysis will involve production of a case record/history for each case, in-depth analysis of each case, and cross-case analysis, where findings will be compared and contrasted across cases to develop theoretically informed, generalisable knowledge that can be applied to other settings/contexts. Ethical approval was granted for this study.</p> <p>Discussion</p> <p>This study will contribute to our knowledge base on the multi-level factors, and the relationships amongst factors in specific contexts, that influence implementation and use of innovations such as synoptic reporting in healthcare. Such knowledge is critical to improving our understanding of implementation processes in clinical settings, and to helping researchers, clinicians, and managers/administrators develop and implement ways to more effectively integrate innovations into routine clinical care.</p

Scientific issues related to the cytology proficiency testing regulations

The member organizations of the Cytology Education and Technology Consortium believe there are significant flaws in current cytology proficiency testing regulations. The most immediate needed modifications include lengthening the required testing interval, utilizing stringently validated and continuously monitored slides, changing the grading scheme, and changing the focus of the test from the individual to laboratory level testing. Integration of new computer-assisted and located-guided screening technologies into the testing protocols is necessary for the testing protocol to be compliant with the law

Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system

Medical Errors and the Laboratory: How Healthcare Organizations are Improving Rates and Improving Patient Care

Errors in medicine have been a common occurrence since the birth of medicine, but have been brought to light more recently as more patients are becoming more active in their own care. Errors which involve the laboratory can be catastrophic for a patient, as a wrong test result can alter the entire treatment plan a physician implements. Healthcare organizations and accrediting organizations have become diligent about tracking errors and the most common sources of error, so that new policies, procedures, and technology can be implemented in order to reduce errors. The laboratory itself has made many strides in error prevention, but has encountered hurdles due to the difficulty of tracking errors that occur within the walls of the laboratory. Different steps which occur in the total testing process, or TTP, have been identified to better track sources of errors, and to better focus methods of which to prevent errors. The different steps of the total testing, the pre-analytical, analytical, and post-analytical phases of testing have drastic differences in the amount of errors that occur in each step. The step where the majority of errors occur is the pre-analytical phase, which consists of patient identification, specimen collection, and transport of specimens to the laboratory. Due to the large number of errors that occur in this phase, particularly with patient identification, healthcare organizations have begun to implement barcode technology for patient identification, medication distribution, blood transfusions, and labeling of specimens collected from the patient. Errors have been reduced greatly over the past several years, but there is still a long way to go to prevent all errors from occurring in patient care

Scott & White Healthcare: Opening Up and Embracing Change to Improve Performance

Offers a case study of a multispeciality system with the attributes of an ideal healthcare delivery system as defined by the Fund. Describes a culture of continuous improvement, collaboration and peer accountability, and a comprehensive approach to care

Communicating the Value Contributions of Pathology and Laboratory Medicine (PaLM) to Healthcare Administrators, Evidence of Value from a Multiple Cases Study

Hospital administrators were interviewed to explore their perceptions of the strategic alignment of PaLM value-based activities (VBAs). Hospital based PaLM leaders were interviewed to explore their communication of the VBAs. This study identified a misalignment between the assessments utilized by healthcare administrators for PaLM services and the value contributions of laboratorians. PaLM leaders offered insight into the laboratory’s value chain. Three themes emerged from the data: PaLM VBAs, PaLM communication efforts, and PaLM VBA strategic alignment. Together these findings suggest that hospital laboratorians offer untapped value in healthcare, and hospital administrators failing to recognize this value miss opportunities to improve value and capture cost savings. Suggestions to improve the communication of PaLM VBAs and the perceptions of hospital administrators are made

Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer

BACKGROUND: In prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importa