Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

Bornhäuser, Martin; Wendt, Karsten; Middeke, Jan Moritz

doi:10.1182/bloodadvances.2020002997

Cited by 61 publications

(42 citation statements)

References 85 publications

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“…As their development is complex, collaborations between physicians and software engineers is needed in an iterative approach to increase model performance. Since the majority of recently proposed machine-learning models—along with our model—are built on retrospective data, future studies will have to implement such models in a prospective setting to confirm their diagnostic value [ 42 ]. Due to the heterogeneity of cell morphology as well as close proximity of cells, disease classification from bone marrow is much more complex than in peripheral blood.…”

Section: Discussionmentioning

confidence: 99%

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

Middeke

Riechert²,

Schmittmann³

et al. 2021

Leukemia

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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.

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Section: Discussionmentioning

confidence: 99%

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

Middeke

Riechert²,

Schmittmann³

et al. 2021

Leukemia

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“…DL models consist of massive parallel computing systems consisting of large numbers of interconnected processing units called artificial neurons, [25,26] which can be run efficiently on high performance computing systems. CNNs contain multiple neural layers to provide functionality for image recognition [24] Thus, these capabilities can be utilized for cell segmentation, cell recognition and disease classification in hematological malignancies [27][28][29]. We here present a CNN-based scalable approach that can detect APL among healthy bone marrow donor and non-APL AML samples from bone marrow smear (BMS) images.…”

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confidence: 99%

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

et al. 2022

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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.

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“…At present, there are some bone marrow cell image analysis systems that can be applied to morphometric analysis, bone marrow cytology inspection reports, and chromosome analysis reports, which greatly reduces work intensity and error probability, and improves work efficiency (6,7). However, advanced work is still needed an automatic classification, recognition, and position of bone marrow cell images and the classification and recognition of bone marrow diseased cells through the comprehensive application of image analysis and pattern recognition technology (8)(9)(10). Thus, constructing blood disease diagnosis equipment that integrates artificial intelligence (AI) and big data analysis functions can provide support for the accurate diagnosis of leukemia and improve the effectiveness of the medical service system.…”

Section: Original Articlementioning

confidence: 99%

A deep learning method and device for bone marrow imaging cell detection

Liu¹,

Yuan²,

Li³

et al. 2022

Ann Transl Med

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Background: Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency. Methods: In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.Results: We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.Conclusions: Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.

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Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

Cited by 61 publications

References 85 publications

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

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

Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears

A deep learning method and device for bone marrow imaging cell detection

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