AIMC Topic: Hematologic Neoplasms

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Advances in next-generation sequencing and emerging technologies for hematologic malignancies.

Haematologica
Innovations in molecular diagnostics have often evolved through the study of hematologic malignancies. Examples include the pioneering characterization of the Philadelphia chromosome by cytogenetics in the 1970s, the implementation of polymerase chai...

Broadening the horizon: potential applications of CAR-T cells beyond current indications.

Frontiers in immunology
Engineering immune cells to treat hematological malignancies has been a major focus of research since the first resounding successes of CAR-T-cell therapies in B-ALL. Several diseases can now be treated in highly therapy-refractory or relapsed condit...

AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears.

Journal of hematology & oncology
Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French-American-British (FAB) classification system remains an essential criterion in the diagnosis of hematological m...

An AI-Aided Diagnostic Framework for Hematologic Neoplasms Based on Morphologic Features and Medical Expertise.

Laboratory investigation; a journal of technical methods and pathology
A morphologic examination is essential for the diagnosis of hematological diseases. However, its conventional manual operation is time-consuming and laborious. Herein, we attempt to establish an artificial intelligence (AI)-aided diagnostic framework...

Therapy resistance mechanisms in hematological malignancies.

International journal of cancer
Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as prototypes for developing effective therapies. Indeed, the concept of systemic cancer therapy originated in hematologic malignancies and has guided t...

Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guide specialized treatment, but must be balanced against the escalating costs of healthcare to identify...

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.

Scientific reports
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system...

Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning.

International journal of hematology
This study aimed to establish a predictive model to identify children with hematologic malignancy at high risk for delayed clearance of high-dose methotrexate (HD-MTX) based on machine learning. A total of 205 patients were recruited. Five variables ...

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence.

Acta cytologica
INTRODUCTION: The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a...

Stable gene selection by self-representation method in fuzzy sample classification.

Medical & biological engineering & computing
In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challen...