Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.

BACKGROUND: Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS: A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS: The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS: A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

External Quality Assessment 2.0: The Importance of a Standardized Implementation of TILs for Daily and Trial Practices.

Increasing data suggests that an intact immune system is required for improvedoutcomes in patients with Human Epidermal Growth Factor Receptor 2 (HER2+) and Triple Negative Breast Cancer (TNBC) [...].

Using DNA sequencing data to quantify T cell fraction and therapy response.

The immune microenvironment influences tumour evolution and can be both prognostic and predict response to immunotherapy1,2. However, measurements of tumour infiltrating lymphocytes (TILs) are limited by a shortage of appropriate data. Whole-exome sequencing (WES) of DNA is frequently performed to calculate tumour mutational burden and identify actionable mutations. Here we develop T cell exome TREC tool (T cell ExTRECT), a method for estimation of T cell fraction from WES samples using a signal from T cell receptor excision circle (TREC) loss during V(D)J recombination of the T cell receptor-α gene (TCRA (also known as TRA)). TCRA T cell fraction correlates with orthogonal TIL estimates and is agnostic to sample type. Blood TCRA T cell fraction is higher in females than in males and correlates with both tumour immune infiltrate and presence of bacterial sequencing reads. Tumour TCRA T cell fraction is prognostic in lung adenocarcinoma. Using a meta-analysis of tumours treated with immunotherapy, we show that tumour TCRA T cell fraction predicts immunotherapy response, providing value beyond measuring tumour mutational burden. Applying T cell ExTRECT to a multi-sample pan-cancer cohort reveals a high diversity of the degree of immune infiltration within tumours. Subclonal loss of 12q24.31-32, encompassing SPPL3, is associated with reduced TCRA T cell fraction. T cell ExTRECT provides a cost-effective technique to characterize immune infiltrate alongside somatic changes.

National Maintenance Cost for Precision Diagnostics Under the Verifying Accurate Leading-Edge In Vitro Clinical Test Development (VALID) Act of 2020.

PURPOSE: The proposed legislation Verifying Accurate and Leading-edge In vitro clinical test Development (VALID) clarifies the US Food and Drug Administration's authority to regulate laboratory-developed tests. Many stakeholders have pointed out that the lack of direct US Food and Drug Administration oversight has led to erroneous results that have serious patient consequences-in particular for patients with cancer. Technology Certification is a key provision proposed in VALID to navigate the balance between safety, patient access, and innovation; however, the maintenance cost of the proposed framework after implementation is unclear. METHODS: On the basis of 2019 retrospective data from a laboratory-developed test-based cancer diagnostics laboratory, we expressed laboratory complexity by the number and complexity of assays and in vitro diagnostic technologies. We estimated the national health care cost increase by modeling three stringencies of complying with the Act. We performed sensitivity analysis of our regulatory stringency model taking into account number of patients tested, materials, submission cost, and labor using extra cost per patient as the output. RESULTS: We estimate the national health care cost increase to range from $33M US dollars (USD) to $1,110M USD or $0.21 USD to $0.70 USD per employed person in the United States. Sensitivity analysis demonstrates that regulatory stringency is the primary driver of extra cost per patient. Cancer testing does not reflect all areas of in vitro diagnostics affected by VALID; nonetheless, concrete cost models are paramount in informing the ongoing legislative negotiations. CONCLUSION: Our findings show the critical importance of clarity in the legislative language to ensure balance between VALID's goals of assuring high-quality test performance and the burden to laboratories and overall health care cost.

Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group.

Ki67 immunohistochemistry (IHC), commonly used as a proliferation marker in breast cancer, has limited value for treatment decisions due to questionable analytical validity. The International Ki67 in Breast Cancer Working Group (IKWG) consensus meeting, held in October 2019, assessed the current evidence for Ki67 IHC analytical validity and clinical utility in breast cancer, including the series of scoring studies the IKWG conducted on centrally stained tissues. Consensus observations and recommendations are: 1) as for estrogen receptor and HER2 testing, preanalytical handling considerations are critical; 2) a standardized visual scoring method has been established and is recommended for adoption; 3) participation in and evaluation of quality assurance and quality control programs is recommended to maintain analytical validity; and 4) the IKWG accepted that Ki67 IHC as a prognostic marker in breast cancer has clinical validity but concluded that clinical utility is evident only for prognosis estimation in anatomically favorable estrogen receptor-positive and HER2-negative patients to identify those who do not need adjuvant chemotherapy. In this T1-2, N0-1 patient group, the IKWG consensus is that Ki67 5% or less, or 30% or more, can be used to estimate prognosis. In conclusion, analytical validity of Ki67 IHC can be reached with careful attention to preanalytical issues and calibrated standardized visual scoring. Currently, clinical utility of Ki67 IHC in breast cancer care remains limited to prognosis assessment in stage I or II breast cancer. Further development of automated scoring might help to overcome some current limitations.

Addressing the dichotomy between individual and societal approaches to personalised medicine in oncology.

Academic, industry, regulatory leaders and patient advocates in cancer clinical research met in November 2018 at the Innovation and Biomarkers in Cancer Drug Development meeting in Brussels to address the existing dichotomy between increasing calls for personalised oncology approaches based on individual molecular profiles and the need to make resource and regulatory decisions at the societal level in differing health-care delivery systems around the globe. Novel clinical trial designs, the utility and limitations of real-world evidence (RWE) and emerging technologies for profiling patient tumours and tumour-derived DNA in plasma were discussed. While randomised clinical trials remain the gold standard approach to defining clinical utility of local and systemic therapeutic interventions, the broader adoption of comprehensive tumour profiling and novel trial designs coupled with RWE may allow patient and physician autonomy to be appropriately balanced with broader assessments of safety and overall societal benefit.

Societal challenges of precision medicine: Bringing order to chaos.

The increasing number of drugs targeting specific proteins implicated in tumourigenesis and the commercial promotion of relatively affordable genome-wide analyses has led to an increasing expectation among patients with cancer that they can now receive effective personalised treatment based on the often complex genomic signature of their tumour. For such approaches to work in routine practice, the development of correspondingly complex biomarker assays through an appropriate and rigorous regulatory framework will be required. It is becoming increasingly evident that a re-engineering of clinical research is necessary so that regulatory considerations and procedures facilitate the efficient translation of these required biomarker assays from the discovery setting through to clinical application. This article discusses the practical requirements and challenges of developing such new precision medicine strategies, based on leveraging complex genomic profiles, as discussed at the Innovation and Biomarkers in Cancer Drug Development meeting (8th-9th September 2016, Brussels, Belgium).

A risk-management approach for effective integration of biomarkers in clinical trials: perspectives of an NCI, NCRI, and EORTC working group.

Clinical cancer research today often includes testing the value of biomarkers to direct treatment and for drug development. However, the practical challenges of integration of molecular information into clinical trial protocols are increasingly appreciated. Inherent difficulties include evidence gaps in available biomarker data, a paucity of robust assay methods, and the design of appropriate studies within the constraints of feasible trial operations, and finite resources. Scalable and proportionate approaches are needed to systematically cope with these challenges. Therefore, we assembled international experts from three clinical trials organisations to identify the common challenges and common solutions. We present a practical risk-assessment framework allowing targeting of scarce resources to crucial issues coupled with a library of useful resources and a simple actionable checklist of recommendations. We hope that these practical methods will be useful for running biomarker-driven trials and ultimately help to develop biomarkers that are ready for integration in routine practice.

Tratamiento del dolor postoperatorio en cirugía torácica / Pain treatment postoperative in thoracic surgery

Con el objeto de abolir o disminuir el dolor durante el postoperatorio inmediato, 58 pacientes con diferentes tipos de toracotomías amplias fueron tratados con inyección de morfina en el espacio peridural. Fue administrada a través de un catéter introducido a nivel lumbar. La disminución del dolor fue significativa en todos los casos. No se observó contaminación del espacio peridural ni de los catéteres. La morfina administrada en el espacio epidural es un método efectivo y seguro para el control del dolor durante el postoperatorio inmediato de la cirugía del tórax.