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

Point-of-care lung ultrasound in COVID-19 patients: inter- and intra-observer agreement in a prospective observational study

With an urgent need for bedside imaging of coronavirus disease 2019 (COVID-19), this study's main goal was to assess inter- and intraobserver agreement in lung ultrasound (LUS) of COVID-19 patients. In this single-center study we prospectively acquired and evaluated 100 recorded ten-second cine-loops in confirmed COVID-19 intensive care unit (ICU) patients. All loops were rated by ten observers with different subspeciality backgrounds for four times by each observer (400 loops overall) in a random sequence using a web-based rating tool. We analyzed inter- and intraobserver variability for specific pathologies and a semiquantitative LUS score. Interobserver agreement for both, identification of specific pathologies and assignment of LUS scores was fair to moderate (e.g., LUS score 1 Fleiss' kappa =0.27; subpleural consolidations Fleiss' kappa =0.59). Intraobserver agreement was mostly moderate to substantial with generally higher agreement for more distinct findings (e.g., lowest LUS score 0 vs. highest LUS score 3 (median Fleiss' kappa =0.71 vs. 0.79) or air bronchograms (median Fleiss' kappa =0.72)). Intraobserver consistency was relatively low for intermediate LUS scores (e.g. LUS Score 1 median Fleiss' kappa =0.52). We therefore conclude that more distinct LUS findings (e.g., air bronchograms, subpleural consolidations) may be more suitable for disease monitoring, especially with more than one investigator and that training material used for LUS in point-of-care ultrasound (POCUS) should pay refined attention to areas such as B-line quantification and differentiation of intermediate LUS scores

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

Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears

The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 (NPM1)-one of the most common mutations in AML-with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1-mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

Background: Acute promyelocytic leukemia (APL) is considered a hematologic emergency due to high risk of bleeding and fatal hemorrhages being a major cause of death. Despite lower death rates reported from clinical trials, patient registry data suggest an early death rate of 20%, especially for elderly and frail patients. Therefore, reliable diagnosis is required as treatment with differentiation-inducing agents leads to cure in the majority of patients. However, diagnosis commonly relies on cytomorphology and genetic confirmation of the pathognomonic t(15;17). Yet, the latter is more time consuming and in some regions unavailable. - Methods: In recent years, deep learning (DL) has been evaluated for medical image recognition showing outstanding capabilities in analyzing large amounts of image data and provides reliable classification results. We developed a multi-stage DL platform that automatically reads images of bone marrow smears, accurately segments cells, and subsequently predicts APL using image data only. We retrospectively identified 51 APL patients from previous multicenter trials and compared them to 1048 non-APL acute myeloid leukemia (AML) patients and 236 healthy bone marrow donor samples, respectively. - Results: Our DL platform segments bone marrow cells with a mean average precision and a mean average recall of both 0.97. Further, it achieves high accuracy in detecting APL by distinguishing between APL and non-APL AML as well as APL and healthy donors with an area under the receiver operating characteristic of 0.8575 and 0.9585, respectively, using visual image data only. - Conclusions: Our study underlines not only the feasibility of DL to detect distinct morphologies that accompany a cytogenetic aberration like t(15;17) in APL, but also shows the capability of DL to abstract information from a small medical data set, i. e. 51 APL patients, and infer correct predictions. This demonstrates the suitability of DL to assist in the diagnosis of rare cancer entities. As our DL platform predicts APL from bone marrow smear images alone, this may be used to diagnose APL in regions were molecular or cytogenetic subtyping is not routinely available and raise attention to suspected cases of APL for expert evaluation

Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence

We used two different methodologies of generative artificial intelligence, CTAB-GAN+ and normalizing flows (NFlow), to synthesize patient data based on 1606 patients with acute myeloid leukemia that were treated within four multicenter clinical trials. The resulting data set consists of 1606 synthetic patients for each of the models. Data Dictionary NAME LABEL TYPE CODELIST AGE age num in years AMLSTAT AML status char de novo, sAML, tAML ASXL1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' ATRX mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' BCOR mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' BCORL1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' BRAF mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' CALR mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' CBL mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' CBLB mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' CDKN2A mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' CEBPA CEBPA mutation char 0 = 'no mutation', 1 = 'mutation' CGCX complex cytogenetic karyotype char 0 'No', 1 'Yes' CGNK cytogenetic normal karyotype char 0 'No', 1 'Yes' CR1 first complete remission char 0 = 'not achieved', 1 = 'achieved' CSF3R mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' CUX1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' DNMT3A mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' EFSSTAT status variable for EFSTM num 0 'censored' 1 'event' EFSTM event free survival time num in months ETV6 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' EXAML extramedullary AML char 0 'No', 1 'Yes' EZH2 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' FBXW7 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' FLT3I FLT3-ITD mutation status char 0 = 'no mutation', 1 = 'mutation' FLT3T FLT3-TKD mutation status char 0 = 'no mutation', 1 = 'mutation' GATA2 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' GNAS mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' HB hemoglobin num in mmol/l HRAS mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' IDH1 IDH1 mutation status char 0 = 'no mutation', 1 = 'mutation' IDH2 IDH2 mutation status char 0 = 'no mutation', 1 = 'mutation' IKZF1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' JAK2 Jak2 Mutation char 0 = 'no mutation', 1 = 'mutation' KDM6A mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' KIT mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' KRAS mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' MPL mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' MYD88 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' NOTCH1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' NPM1 NPM1 mutation status char 0 = 'no mutation', 1 = 'mutation' NRAS mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' OSSTAT status variable for OSTM num 0 'censored' 1 'event' OSTM overall survival time num in months PDGFRA mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' PHF6 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' PLT platelet count num in 10⁶/l PTEN mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' PTPN11 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' RAD21 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' RUNX1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' SETBP1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' SEX sex char f 'female', m 'male' SF3B1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' SMC1A mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' SMC3 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' SRSF2 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' STAG2 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' SUBJID subject identifier char TET2 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' TP53 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' U2AF1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' WBC white blood count num in 10⁶/l WT1 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' ZRSR2 mutation indicator, NGS num 0 = 'no mutation', 1 = 'mutation' inv16_t16.16 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t8.21 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.6.9..p23.q34. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' inv.3..q21.q26.2. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' minus.5 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' del.5q. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.9.22..q34.q11. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' minus.7 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' minus.17 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.v.11..v.q23. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' abn.17p. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.9.11..p21.23.q23. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.3.5. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.6.11. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.10.11. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' t.11.19..q23.p13. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' del.7q. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' del.9q. mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' trisomy 8 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' trisomy 21 mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' minus.Y mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation' minus.X mutation indicator, cytogenetics num 0 = 'no mutation', 1 = 'mutation

Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles

Abstract Background Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. Methods While unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available. Results Unsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients. Conclusions Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology

The multimodality cell segmentation challenge: towards universal solutions

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging