AIMC Topic: Precursor Cell Lymphoblastic Leukemia-Lymphoma
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Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study.
BMJ open
Jun 27, 2025
OBJECTIVE: To develop and validate a prediction model of Philadelphia chromosome-positive acute lymphoblastic leukaemia (Ph+ALL).
Error Reduction in Leukemia Machine Learning Classification With Conformal Prediction.
JCO clinical cancer informatics
May 28, 2025
PURPOSE: Recent advances in machine learning have led to the development of classifiers that predict molecular subtypes of acute lymphoblastic leukemia (ALL) using RNA-sequencing (RNA-seq) data. Although these models have shown promising results, the...
ALL diagnosis: can efficiency and transparency coexist? An explainble deep learning approach.
Scientific reports
Apr 14, 2025
Acute Lymphoblastic Leukemia (ALL) is a life-threatening malignancy characterized by its aggressive progression and detrimental effects on the hematopoietic system. Early and accurate diagnosis is paramount to optimizing therapeutic interventions and...
An Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi-Modal Deep Neural Network.
International journal of laboratory hematology
Jan 14, 2025
BACKGROUND: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment pla...
An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia.
ISA transactions
Jan 8, 2025
Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learnin...
Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.
EBioMedicine
Dec 24, 2024
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study ai...
Differential impact of CD34+ cell dose for different age groups in allogeneic hematopoietic cell transplantation for acute leukemia: a machine learning-based discovery.
Experimental hematology
Nov 23, 2024
Allogeneic hematopoietic cell transplantation (allo-HCT) presents a potentially curative treatment for hematologic malignancies yet carries associated risks and complications. Continuous research focuses on predicting outcomes and identifying risk fa...
Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques.
Leukemia research
Nov 20, 2024
BACKGROUND: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), re...
Leukemia
Hematopoietic Stem Cell Transplantation
Transplantation, Homologous
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Treatment Outcome
Follow-Up Studies
Graft vs Host Disease
Female
Middle Aged
Aged
Humans
Adolescent
Machine Learning
Survival Rate
Child, Preschool
Prognosis
Leukemia, Myeloid, Acute
Child
Retrospective Studies
Male
Young Adult
Adult
BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.
Journal of imaging informatics in medicine
Sep 25, 2024
Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-lin...
Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.
BMC medical informatics and decision making
Sep 16, 2024
BACKGROUND: Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this stu...
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