Explainable AI identifies diagnostic cells of genetic AML subtypes

Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient’s blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms. Author summary The analysis of blood and bone marrow smear microscopy by trained human experts remains an essential cornerstone of the diagnostic workup for severe blood diseases, like acute myeloid leukemia. While this step yields insight into a patient’s blood system composition, it is also tedious, time consuming and not standardized. Here, we present SCEMILA, an algorithm trained to distinguish blood smears from healthy stem cell donors and four different types of acute myeloid leukemia. Our algorithm is able to classify a patient’s blood sample based on roughly 400 single cell images, and can highlight cells most relevant to the algorithm. This allows us to cross-check the algorithm’s decision making with human expertise. We show that SCEMILA is able to identify relevant cells for acute myeloid leukemia, and therefore believe that it will contribute towards a future, where machine learning algorithms and human experts collaborate to form a synergy for high-performance blood cancer diagnosis

Regulation of Foxo-1 and the angiopoietin-2/Tie2 system by shear stress

Transcription factor Foxo-1 can be inactivated via Akt-mediated phosphorylation. Since shear stress activates Akt, we determined whether Foxo-1 and the Foxo-1-dependent, angiogenesis-related Ang-2/Tie2-system are influenced by shear stress in endothelial cells. Expression of Foxo-1 and its target genes p27Kip1 and Ang-2 was decreased under shear stress (6dyn/cm(2), 24h), nuclear exclusion of Foxo-1 by phosphorylation increased. eNOS and Tie2 were upregulated. No effects on Ang-1 expression were detected. In conclusion, Foxo-1 and Ang-2/Tie2 are part of the molecular response to shear stress, which may regulate angiogenesis

Improved survival in metastatic breast cancer: results from a 20-year study involving 1033 women treated at a single comprehensive cancer center

Purpose!#!Diagnosis and treatment of breast cancer have changed profoundly over the past 25 years. The outcome improved dramatically and was well quantified for early stage breast cancer (EBC). However, progress in the treatment of metastatic disease has been less convincingly demonstrated. We have studied survival data of patients with metastatic breast cancer (MBC) from a large academic cancer center over a period of 20 years.!##!Methods!#!Data from 1033 consecutive MBC patients who were treated at the Department of Medical Oncology of the West German Cancer Center from January 1990 to December 2009 were retrospectively analyzed for overall survival (OS) and risk factors. Patients were grouped in 5-year cohorts, and survival parameters of each cohort were compared before and after adjustment for risk factors.!##!Results!#!Overall survival of patients with MBC treated at specialized center has significantly improved from 1990 to 2010 (hazard ratio 0.7, 95%CI 0.58-0.84). The increments in OS have become less profound over time (median OS 1990-1994: 24.2 months, 1995-1999: 29.6 months, 2000-2004: 36.5 months, 2005-2009: 37.8 months).!##!Conclusion!#!Survival of patients with MBC has improved between 1990 and 2004, but less from 2005 to 2009. Either this suggests an unnoticed shift in the patient population, or a lesser impact of therapeutic innovations introduced in the most recent period

nestor-materialien 17

Das von nestor entwickelte und angebotene Verfahren der erweiterten Selbstevaluierung für digitale Langzeitarchive auf der Grundlage der DIN-Norm 31644 bietet digitalen Langzeitarchiven eine abgestimmte und praxisgerechte Möglichkeit zu prüfen, ob sie gemäß der „Kriterien für vertrauenswürdige digitale Langzeitarchive“ vertrauenswürdig sind. Bei einem positiven Ergebnis des Prüfverfahrens können sie dies durch das nestor-Siegel für vertrauenswürdige digitale Langzeitarchive auch darstellen.Version 2.