Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

Ram, Malihe; Afrash, Mohammad Reza; Moulaei, Khadijeh; Parvin, Mohammad; Esmaeeli, Erfan; Karbasi, Zahra; Heydari, Soroush; Sabahi, Azam

doi:10.1186/s12885-024-12764-y

BMC Cancer

2024

DOI: 10.1186/s12885-024-12764-y

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Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

Malihe Ram,

Mohammad Reza Afrash,

Khadijeh Moulaei

et al.

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Cited by 3 publications

References 34 publications

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Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Khan,

Asif,

Zhao

et al. 2025

Neurocomputing

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Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Khan,

Asif,

Zhao

et al. 2025

Neurocomputing

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Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection

Taylor,

Alle,

Wilson

et al. 2024

Nanomaterials

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Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.

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Nanotechnology based approaches for leukemia therapy

2024

Biomed. Lett.

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Leukemia, a leading cause of cancer-related morbidity and mortality, primarily affect blood-forming tissues. It is classified into four main types: acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL). These subtypes vary in characteristics and prevalence, affecting different age groups, from children to adults, with prognosis heavily influenced by the type and severity of the disease. Conventional treatments for leukemia, including chemotherapy, radiation, and stem cell therapy, have notable limitations, such as non-specific targeting, high costs, drug resistance, and issues related to donor compatibility. These limitations underscore the urgent need for innovative solutions. One of the major challenges in treating leukemia with tyrosine kinase inhibitors (TKIs) is the frequent resistance due to factors like lack of specific targeting, underdosing, limited bioavailability, and severe adverse effects. Nanotechnology presents a promising solution to these challenges by utilizing nanoscale materials such as liposomes, metallic nanoparticles, polymeric nanoparticles, and biomimetic nanoparticles for targeted drug delivery. Nanoparticle-based drug delivery systems offer enhanced drug targeting, reduced systemic toxicity, and improved therapeutic efficacy. This review highlights recent advancements in nanotechnology to improve leukemia treatment.

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Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

Cited by 3 publications

References 34 publications

Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection

Nanotechnology based approaches for leukemia therapy

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