In most diagnostic laboratories, targeted next-generation sequencing (NGS) is currently the default assay for the detection of somatic variants in solid as well as haematological tumours. Independent of the method, the final outcome is a list of variants that differ from the human genome reference sequence of which some may relate to the establishment of the tumour in the patient. A critical point towards a uniform patient management is the assignment of the biological contribution of each variant to the malignancy and its subsequent clinical impact in a specific malignancy. These so-called biological and clinical classifications of somatic variants are currently not standardized and are vastly dependent on the subjective analysis of each laboratory. This subjectivity can thus result in a different classification and subsequent clinical interpretation of the same variant. Therefore, the ComPerMed panel of Belgian experts in cancer diagnostics set up a working group with the goal to harmonize the biological classification and clinical interpretation of somatic variants detected by NGS. This effort resulted in the establishment of a uniform, two-level classification workflow system that should enable high consistency in diagnosis, prognosis, treatment and follow-up of cancer patients. Variants are first classified into a tumour-independent biological five class system and subsequently in a four tier ACMG clinical classification. Here, we describe the ComPerMed workflow in detail including examples for each step of the pipeline. Moreover, this workflow can be implemented in variant classification software tools enabling automatic reporting of NGS data, independent of panel, method or analysis software.
We have demonstrated by thorough statistical validation that this approach performs well in converting BCR-ABL1 measurements to improve IS estimation. In expectation of a true golden standard method for BCR-ABL1 IS quantification, the proposed method is a valuable alternative.
Purpose:Successful radiation therapy requires multi‐step processes susceptible to unnecessary delays that can negatively impact clinic workflow, patient satisfaction, and safety. This project applied process improvement tools to assess workflow bottlenecks and identify solutions to barriers for effective implementation.Methods:We utilized the DMAIC (define, measure, analyze, improve, control) methodology, limiting our scope to the treatment planning process. From May through December of 2014, times and dates of each step from simulation to treatment were recorded for 507 cases. A value‐stream map created from this dataset directed our selection of outcome measures (Y metrics). Critical goals (X metrics) that would accomplish the Y metrics were identified. Barriers to actions were binned into control‐impact matrices, in order to stratify them into four groups: in/out of control and high/low impact. Solutions to each barrier were then categorized into benefit‐effort matries to identify those of high benefit and low effort.Results:For 507 cases, the mean time from simulation to treatment was 235 total hours. The mean process and wait time were 60 and 132 hours, respectively. The Y metric was to increase the ratio of all non‐emergent plans completed the business day prior to treatment from 47% to 75%. Project X metrics included increasing the number of IMRT QAs completed at least 24 hours prior to treatment from 19% to 80% and the number of non‐IMRT plans approved at least 24 hours prior to treatment from 33% to 80%. Intervals from simulation to target contour and from initial plan completion to plan approval were identified as periods that could benefit from intervention. Barriers to actions were binned into control‐impact matrices and solutions by benefit‐effort matrices.Conclusion:The DMAIC method can be successfully applied in radiation therapy clinics to identify inefficiencies and prioritize solutions for the highest impact.
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