RESUMEN
CONTEXT.: Title 45, section 164.524 of the Code of Federal Regulations states that health care systems must provide patient health records upon that patient's request. For complex testing, such as next-generation sequencing (NGS), this raises questions related to what data should be released and the laboratory considerations regarding the release of this data. OBJECTIVE.: To describe the laboratory implications of releasing different NGS data files and the limitations for the clinical use of different NGS data files. DESIGN.: The College of American Pathologists workgroup, composed of laboratorians with expertise regarding NGS testing, reviewed pertinent literature, including title 45, section 164.524, and the Health and Human Services "Guidance on Individuals' Right to Access Health Information." RESULT.: From an accreditation standpoint, validation of NGS includes both the wet bench and data processing (bioinformatics) portions, and appropriately validated laboratory testing is required to ensure quality patient results. NGS testing generates intermediate data files that have not completed the fully validated process but are often kept by the laboratory. These files may be requested by patients, but most patients will not be aware of the test validation process and the limitations of data that have not gone through a fully validated process. CONCLUSIONS.: Laboratories should encourage patients to receive their health data and to help individuals understand the content, uses, and limitations of laboratory data they have requested or received. NGS data used in a nonvalidated manner should not be used for clinical purposes without confirmation by a clinically validated method.
RESUMEN
CONTEXT.: The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based models share some features of conventional laboratory testing methods. Accreditation requirements that specifically address clinical laboratories' use of ML remain in the early stages of development. OBJECTIVE.: To identify relevant CAP accreditation requirements that may be applied to the clinical adoption of ML-based molecular oncology assays, and to provide examples of current and emerging ML applications in molecular oncology testing. DESIGN.: CAP accreditation checklists related to molecular pathology and general laboratory practices (Molecular Pathology, All Common and Laboratory General) were reviewed. Examples of checklist requirements that are generally applicable to validation, revalidation, quality management, infrastructure, and analytical procedures of ML-based molecular oncology assays were summarized. Instances of ML use in molecular oncology testing were assessed from literature review. RESULTS.: Components of the general CAP accreditation framework that exist for traditional molecular oncology assay validation and maintenance are also relevant for implementing ML-based tests in a clinical laboratory. Current and emerging applications of ML in molecular oncology testing include DNA methylation profiling for central nervous system tumor classification, variant calling, microsatellite instability testing, mutational signature analysis, and variant prediction from histopathology images. CONCLUSIONS.: Currently, much of the ML activity in molecular oncology is within early clinical implementation. Despite specific considerations that apply to the adoption of ML-based methods, existing CAP requirements can serve as general guidelines for the clinical implementation of ML-based assays in molecular oncology testing.