Long-Term Survival of a Child with Atypical Teratoid-Rhabdoid Tumor and Acute Lymphoblastic Leukemia: A Case Report

Andreevna, Kolcheva Maria; Vyacheslavovna, Kumirova Ella; Valeryevna, Gorbatykh Svetlana; Gunai Nariman, Makhmudova; Igorevich, Livshits Matvey; Yegorovich, Chmutin Gennadiy; Nikolaevich, Kislyakov Alexey; Nikolaevich, Umerenkov Viktor; Encarnacion Ramirez, Manuel de Jesus; Montemurro, Nicola

doi:10.3390/surgeries5020018

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Leukemia is categorized into four distinct types: AML, ALL, chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL), as depicted in Figure 1. This disease can spread to other organs, including the spleen, brain, liver, and kidney, by traveling through the circulation [6][7][8]. Leukemia is diagnosed through a blood test or a biopsy involving bone marrow sampling.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

Zare,

Rahmani,

Khaleghi

et al. 2024

Bioengineering

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Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.

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Section: Introductionmentioning

confidence: 99%