An overview and a roadmap for artificial intelligence in hematology and oncology

BACKGROUND Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future

The EffecTs of Amlodipine and other Blood PREssure Lowering Agents on Microvascular FuncTion in Small Vessel Diseases (TREAT-SVDs) trial: Study protocol for a randomised crossover trial

Background: Hypertension is the leading modifiable risk factor for cerebral small vessel diseases (SVDs). Yet, it is unknown whether antihypertensive drug classes differentially affect microvascular function in SVDs. Aims: To test whether amlodipine has a beneficial effect on microvascular function when compared to either losartan or atenolol, and whether losartan has a beneficial effect when compared to atenolol in patients with symptomatic SVDs. Design: TREAT-SVDs is an investigator-led, prospective, open-label, randomised crossover trial with blinded endpoint assessment (PROBE design) conducted at five study sites across Europe. Patients aged 18 years or older with symptomatic SVD who have an indication for antihypertensive treatment and are suffering from either sporadic SVD and a history of lacunar stroke or vascular cognitive impairment (group A) or CADASIL (group B) are randomly allocated 1:1:1 to one of three sequences of antihypertensive treatment. Patients stop their regular antihypertensive medication for a 2-week run-in period followed by 4-week periods of monotherapy with amlodipine, losartan and atenolol in random order as open-label medication in standard dose. Outcomes: The primary outcome measure is cerebrovascular reactivity (CVR) as determined by blood oxygen level dependent brain MRI signal response to hypercapnic challenge with change in CVR in normal appearing white matter as primary endpoint. Secondary outcome measures are mean systolic blood pressure (BP) and BP variability (BPv). Discussion: TREAT-SVDs will provide insights into the effects of different antihypertensive drugs on CVR, BP, and BPv in patients with symptomatic sporadic and hereditary SVDs. Funding: European Union's Horizon 2020 programme

An overview and a roadmap for artificial intelligence in hematology and oncology.

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals

The prevalence of extramedullary acute myeloid leukemia detected by 18FDG-PET/CT: final results from the prospective PETAML trial

Extramedullary (EM) disease in patients with acute myeloid leukemia (AML) is a known phenomenon. Since the prevalence of EM AML has so far only been clinically determined on examination, we performed a prospective study in patients with AML. The aim of the study was to determine the prevalence of metabolically active EM AML using total body 18Fluorodesoxy-glucose positron emission tomography/computed tomography (18FDG-PET/CT) imaging at diagnosis prior to initiation of therapy. In order to define the dynamics of EM AML throughout treatment, PET-positive patients underwent a second 18FDG-PET/CT imaging series during follow up by the time of remission assessment. A total of 93 patients with AML underwent 18FDG-PET/CT scans at diagnosis. The prevalence of PET-positive EM AML was 19% with a total of 65 EM AML manifestations and a median number of two EM manifestations per patient (range, 1-12), with a median maximum standardized uptake value of 6.1 (range, 2-51.4). When adding those three patients with histologically confirmed EM AML who were 18FDG-PET/CT negative in the 18FDG-PET/CT at diagnosis, the combined prevalence for EM AML was 22%, resulting in 77% sensitivity and 97% specificity. Importantly, 60% (6 of 10) patients with histologically confirmed EM AML still had active EM disease in their follow up 18FDG-PET/CT. 18FDG-PET/CT reveals a high prevalence of metabolically active EM disease in AML patients. Metabolic activity in EM AML may persist even beyond the time point of hematologic remission, a finding that merits further prospective investigation to explore its prognostic relevance. (Trial registered at clinicaltrials.gov identifier: 01278069.

Point Mutations in the FLT3-ITD Region Are Rare but Recurrent Alterations in Adult AML and Associated With Concomitant KMT2A-PTD

FLT3-ITD mutations are common druggable alterations in patients with acute myeloid leukemia (AML) and associated with poor prognosis. Beside typical ITD mutations, point mutations and deletions in the juxtamembrane domain (JMD) have been observed. However, due to the low frequency of these alterations, there is only limited information on molecular and clinical associations. To evaluate the prognostic impact of non-ITD mutations in the FLT3 JMD region, we analyzed a large cohort of 1,539 adult AML patients treated in different protocols of the Study Alliance Leukemia, using next-generation sequencing. Non-ITD point mutations and deletions within the FLT3 JMD were identified with a prevalence of ~1.23% (n = 19). Both FLT3-ITD and non-ITD mutations were associated with a higher rate of NPM1 (42%–61%; p < 0.001) and DNMT3A mutations (37%–43%; p < 0.001), as well as an increased percentage of peripheral blood (54%–65%) and bone marrow blast cells (74%; p < 0.001), compared to FLT3-wild-type patients. Most significantly, AML patients with FLT3 non-ITD mutations had a higher rate of concomitant KMT2A-PTD mutations (37.5%; p < 0.001) as compared to FLT3-ITD (7%) or FLT3-wild-type cases (4.5%). In a multivariable analysis, FLT3 non-ITD mutations were not an independent prognostic factor. However, patients with dual FLT3 non-ITD and KMT2A-PTD mutations showed a trend for inferior outcome, which points at a functional interaction in this subset of AML

Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning

Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77–0.86 and between 0.63–0.74, respectively in our test set, and between 0.71–0.80 and 0.65–0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology

Impact of IDH1 and IDH2 mutational subgroups in AML patients after allogeneic stem cell transplantation

Background The role of allogeneic hematopoietic cell transplantation (alloHCT) in acute myeloid leukemia (AML) with mutated IDH1/2 has not been defined. Therefore, we analyzed a large cohort of 3234 AML patients in first complete remission (CR1) undergoing alloHCT or conventional chemo-consolidation and investigated outcome in respect to IDH1/2 mutational subgroups (IDH1 R132C, R132H and IDH2 R140Q, R172K). Methods Genomic DNA was extracted from bone marrow or peripheral blood samples at diagnosis and analyzed for IDH mutations with denaturing high-performance liquid chromatography, Sanger sequencing and targeted myeloid panel next-generation sequencing, respectively. Statistical as-treated analyses were performed using R and standard statistical methods (Kruskal–Wallis test for continuous variables, Chi-square test for categorical variables, Cox regression for univariate and multivariable models), incorporating alloHCT as a time-dependent covariate. Results Among 3234 patients achieving CR1, 7.8% harbored IDH1 mutations (36% R132C and 47% R132H) and 10.9% carried IDH2 mutations (77% R140Q and 19% R172K). 852 patients underwent alloHCT in CR1. Within the alloHCT group, 6.2% had an IDH1 mutation (43.4% R132C and 41.4% R132H) and 10% were characterized by an IDH2 mutation (71.8% R140Q and 24.7% R172K). Variants IDH1 R132C and IDH2 R172K showed a significant benefit from alloHCT for OS (p = .017 and p = .049) and RFS (HR = 0.42, p = .048 and p = .009) compared with chemotherapy only. AlloHCT in IDH2 R140Q mutated AML resulted in longer RFS (HR = 0.4, p = .002). Conclusion In this large as-treated analysis, we showed that alloHCT is able to overcome the negative prognostic impact of certain IDH mutational subclasses in first-line consolidation treatment and could pending prognostic validation, provide prognostic value for AML risk stratification and therapeutic decision making

Pilot Study on Mass Spectrometry–Based Analysis of the Proteome of CD34+CD123+ Progenitor Cells for the Identification of Potential Targets for Immunotherapy in Acute Myeloid Leukemia

Targeting of leukemic stem cells with specific immunotherapy would be an ideal approach for the treatment of myeloid malignancies, but suitable epitopes are unknown. The comparative proteome-level characterization of hematopoietic stem and progenitor cells from healthy stem cell donors and patients with acute myeloid leukemia has the potential to reveal differentially expressed proteins which can be used as surface-markers or as proxies for affected molecular pathways. We employed mass spectrometry methods to analyze the proteome of the cytosolic and the membrane fraction of CD34 and CD123 co-expressing FACS-sorted leukemic progenitors from five patients with acute myeloid leukemia. As a reference, CD34+CD123+ normal hematopoietic progenitor cells from five healthy, granulocyte-colony stimulating factor (G-CSF) mobilized stem cell donors were analyzed. In this Tandem Mass Tag (TMT) 10-plex labelling–based approach, 2070 proteins were identified with 171 proteins differentially abundant in one or both cellular compartments. This proof-of-principle-study demonstrates the potential of mass spectrometry to detect differentially expressed proteins in two compartment fractions of the entire proteome of leukemic stem cells, compared to their non-malignant counterparts. This may contribute to future immunotherapeutic target discoveries and individualized AML patient characterization