Thierauf, Julia

Abstract BACKGROUND Craniopharyngiomas, primary brain tumors of the pituitary-hypothalamic axis, can cause significant clinical sequelae. Treatment using surgery, radiation, or both often entails significant morbidity. In prior work, we demonstrated that ninety-five percent of papillary craniopharyngiomas (PCP) harbor BRAF V600E mutations (Brastianos et al. Nature Genetics 2014). In this multicenter National Cancer Institute cooperative group trial (Alliance A071601), we evaluated the safety and efficacy of BRAF/MEK inhibition in patients with PCP without prior irradiation. METHODS Eligible patients, with measurable disease and whose PCP screened positively for BRAF mutations, without prior radiation, received the BRAF/MEK inhibitor combination vemurafenib/cobimetinib in 28-day cycles. The primary endpoint of objective response rate at 4 months based on centrally determined volumetric data was evaluated in 16 patients on this single arm phase 2 trial. RESULTS Based on volumetric response criteria by central radiology review, 15 of 16 patients (94%; 95% CI: 70% to 100%) had a durable objective partial response to therapy. Thus, this study met primary endpoint for overall response rate. The median tumor reduction was 91% (range of 68% to 99%). Median follow-up was 22 months (95% CI: 19 to 30 months) and median number of treatment cycles was 8. Median progression-free survival and duration of response were not yet reached. Three patients progressed during follow-up after therapy was discontinued; none have died. The sole non-responder stopped treatment after eight days due to toxicity. Grade 3 toxicities at least possibly related to treatment occurred in 12 patients (rash in 6 patients). Grade 4 toxicities were observed in two patients: hyperglycemia (n=1) and increased creatine phosphokinase (n=1). Three patients discontinued treatment for adverse events. CONCLUSIONS Our study indicates that BRAF/MEK inhibitors are a safe and effective treatment for PCP without prior irradiation. Support: U10CA180821, U10CA180882; U24CA196171, U10CA180868 (NRG); Genentech; . ClinicalTrials.gov Identifier: NCT03224767.

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.