Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS

Brück, Oscar; Lallukka-Brück, Susanna E.; Hohtari, Helena; Ianevski, Aleksandr; Ebeling, Freja; Kovanen, Panu E.; Kytölä, Soili; Aittokallio, Tero; Ramos, Pedro Marques; Porkka, Kimmo; Mustjoki, Satu

doi:10.1158/2643-3230.bcd-20-0162

Cited by 34 publications

(31 citation statements)

References 34 publications

(35 reference statements)

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“…This may be helpful to identify patients in need of further and usually more invasive and expensive testing, such as bone marrow aspirates or genome sequencing. Recent applications of computational cytomorphology on bone marrow smears have demonstrated its ability to automatically identify different leukocytes 30,62 and assist diagnostic predictions 27–29 in specialized haemato-oncology. By demonstrating that this can now be extended to blood smears/slides, our work reveals the potential for the large-scale incorporation of automated cytomorphology into routine diagnostic workflows.…”

Section: Discussionmentioning

confidence: 99%

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Computational analysis of peripheral blood smears detects disease-associated cytomorphologies

Almeida

Gudgin

Besser

et al. 2022

Preprint

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Many hematological diseases are characterized by altered abundance and morphology of blood cells and their progenitors. Myelodysplastic syndromes (MDS), for example, are a type of blood cancer manifesting via a range of cytopenias and dysplastic changes of blood and bone marrow cells. While experts analyze cytomorphology to diagnose MDS, similar alterations can be observed in other conditions such as haematinic deficiency anemias, and definitive diagnosis requires complementary information such as blood counts, karyotype and molecular testing. However, recent works demonstrated that computational analysis of bone marrow slides predicts not only MDS or AML but also the presence of specific mutations. Here, we present and make available Haemorasis, a computational method that detects and characterizes white and red blood cells (WBC and RBC, respectively) in peripheral blood slides, and apply it to over 300 individuals with different conditions (SF3B1-mutant and SF3B1-wildtype MDS, megaloblastic anemia and iron deficiency anemia), where Haemorasis detects over half a million WBC and millions of RBC. We then show how these large sets of cell images can be used in diagnosis and prognosis, whilst identifying novel associations between computational morphotypes and disease. We find that hypolobulated neutrophils and large RBC are characteristic of SF3B1-mutant MDS, and, while prevalent in both iron deficiency and megaloblastic anemia, hyperlobulated neutrophils are larger in the latter. Finally, we externally validate these methods, showing they generalize to other centers and scanners.

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Section: Discussionmentioning

confidence: 99%

“…This can create challenges in identifying relevant cytomorphology-disease associations. Computational methods, which have shown promise in the characterization and prediction of MDS and AML using bone marrow slides 27–29 and identification of abnormal leukocytes 30 , can help address these problems.…”

Section: Introductionmentioning

confidence: 99%

Computational analysis of peripheral blood smears detects disease-associated cytomorphologies

Almeida

Gudgin

Besser

et al. 2022

Preprint

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“…Then mutation prediction probability is identified to correlate with variant allele frequency. Thus the results demonstrating that the propounded algorithms had the potential to recognize the various morphologic patterns [20].…”

Section: Related Workmentioning

confidence: 94%

Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection

Kavitha¹,

Viswanathan²

2022

Intelligent Automation &Amp; Soft Computing

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Bone marrow cancer is considered to be the most complex and dangerous disease which results due to an uncontrolled growth of white blood cells called leukocytes. Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) are considered to be important categories of bone cancers, which induces a larger number of cancer cells in the bone marrow, results in preventing the production of healthy blood cells. The advent of Artificial Intelligence, especially machine and deep learning, has expanded humanity's capacity to analyze and detect these increasingly complex diseases. But, accurate detection of cancer cells and reducing the probability of false alarm rates remain on the darker side of the research. This paper proposes novel deep convolutional neural networks in which the hyper parameters are optimized by adaptive Multi-objective CAT algorithms. The proposed model is trained on the preprocessed cell images followed by training with Optimized Convolutional Neural Network (OCNN) and finally detecting the category of cancer cells present in the bone marrow. The extensive experimentations have been carried out using SN-AM datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are calculated and analyzed. The overall accuracy was found to be 99.45% in predicting different categories of cancer and it outperforms the pre-trained deep learning models such as Alexnets, VGG-16 nets, and U-nets. To establish the superiority of the proposed model, we have compared the performance of the proposed model with other machine learning models such as Support Vector Machines, Random Forest, Naïve Bayes, and Artificial Neural Networks. From the above intensive studies, it is clear that the proposed model was able to produce brighter performances than the other learning models in detecting the category of cancer cells.

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“…We found only single studies published in the field of DL‐based mutation prediction in hematological neoplasms and in endometrial, ovarian, and prostate cancers, respectively. Brück et al used bone marrow histopathology images to detect important genetic features in myelodysplastic syndrome and myeloproliferative neoplasm to predict mutations in genes regulating the cell cycle, cell differentiation, DNA chromatin structure, and RAS pathway and mutations in IDH1 , IDH2 , NRAS , KRAS , and spliceosome [50]. In endometrial cancer, polymerase ε ( POLE ) ultra‐mutated, MSI‐high hypermutated, copy number‐low (CNV‐L), copy number‐high (CNV‐H) subtypes, and the mutation status of 18 endometrial carcinoma‐related genes were predictable using histopathology images as input [67].…”

Section: Deep Learning For Prediction Of Genetic Alterationsmentioning

confidence: 99%

Artificial intelligence to identify genetic alterations in conventional histopathology

Cifci

Foersch

Kather

2022

The Journal of Pathology

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Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53, and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows.

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Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS

Cited by 34 publications

References 34 publications

Computational analysis of peripheral blood smears detects disease-associated cytomorphologies

Computational analysis of peripheral blood smears detects disease-associated cytomorphologies

Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection

Artificial intelligence to identify genetic alterations in conventional histopathology

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