Sirintrapun, Sahussapont Joseph

Abstract 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.

BACKGROUND: Immune checkpoint inhibitors are an important therapeutic option for urothelial carcinoma, but durable responses are achieved in a minority of patients. Identifying pre-treatment biomarkers that may predict response to these therapies or who exhibit intrinsic resistance, is of paramount importance. OBJECTIVE: To explore the prevalence of PD-L1 copy number alteration in urothelial carcinoma and correlate with response to immune checkpoint inhibitors. METHODS: We analyzed a cohort of 1050 carcinomas of the bladder and upper urinary tract that underwent targeted next generation sequencing, prospectively. We assessed PD-L1 protein expression, copy number status (next generation sequencing/FISH), and detailed treatment response. RESULTS: We identified 9 tumors with PD-L1 amplification and 9 tumors with PD-L1 deletion. PD-L1 protein expression was the highest in PD-L1 amplified tumors. Of the 9 patients whose tumors harbored PD-L1 amplification, 6 received immunotherapy with 4 deriving clinical benefit, and 2 achieving durable response. Of the 9 patients whose tumors had PD-L1 copy number losses, 4 received immunotherapy with 3 experiencing disease progression. CONCLUSIONS: PD-L1 copy number alterations may serve as potential biomarkers of response to immunotherapy in urothelial carcinoma patients, if validated in larger cohorts.