Leukemia

An interactive and explainable AI approach to improve human-machine teaming in cancer subtyping from digital cytopathology.

Algorithmic decision support is rapidly becoming a staple of personalized medicine, particularly for high-stakes recommendations such as cancer subtyping in which access to patient-specific information can drastically alter the course of treatment, and thus, patient outcome. To enhance the utility of decision support systems, it is vital to provide not just recommendations, but also contextual inf...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multiple studies have extensively charted the TME's impact on immunotherapy, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), ...

AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

PURPOSE: To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (11C)...

Recent advances and applications of single-cell sequencing in insects.

Single-cell genomic technologies are transforming insect science, shifting the field from basic description to functional and mechanistic research. Th...

Development of a selectively AURKB targeting peptide degradation drug with artificial intelligence-assisted design for the treatment of acute lymphoblastic leukemia.

INTRODUCTION: Acute lymphoblastic leukemia (ALL) is a highly heterogeneous hematologic malignancy with poor prognosis in refractory and relapsed cases...

Artificial intelligence-driven oocyte scores are associated with early morphokinetics and embryo outcomes after intracytoplasmic sperm injection.

RESEARCH QUESTION: How are the scores obtained using the deep learning-based oocyte assessment system, MAGENTA, associated with outcomes of intracytop...

Development and external validation of interpretable machine learning models for personalized multiple treatment recommendations in non-small cell lung cancer.

OBJECTIVE: Treatment decision-making for non-small cell lung cancer (NSCLC) is complex, necessitating individualized decision-support tools to improve...

AI-assisted age estimation in children based on a combination of bone and tooth maturity.

BACKGROUND: International protocols for age estimation in subadults recommend combining different evidence according to tooth and bone maturity by rad...

Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction.

OBJECTIVE: To develop and rigorously validate radiomics-based predictive models using postoperative intravoxel incoherent motion diffusion-weighted im...

Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi.

Somatic evolution leads to clonal heterogeneity, which fuels cancer progression and therapy resistance. To decipher the consequences of clonal heterog...

Frequency-Domain Object Detection Network for Leukemia Diagnosis in Bone Marrow Microscopy.

Leukemia remains a prevalent hematologic malignancy, and its morphological heterogeneity presents challenges for reliable identification under optical...

Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a prevalent global health issue, and nutritional management of CKD is an integral component through all stages of the ...

Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [18F]FDG PET/CT sub-study of the Phase 3 GMMG-HD7 Trial.

PURPOSE: The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multi...

Multiomics combined with machine learning defines unique molecular subtypes of cholangiocarcinoma and identifies TNK1 as a therapeutic target.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To...

AI Screening Tool Based on X-Rays Improves Early Detection of Decreased Bone Density in a Clinical Setting.

PURPOSE: Osteoporosis is an under-screened musculoskeletal disorder that results in diminished quality of life and significant burden to the healthcar...

Tissue-Engineered Endothelial Keratoplasty with Controlled Cell Density: Toward Super TEEKs.

Over the past 20 years, endothelial keratoplasty procedures have revolutionized the treatment of corneal endothelial disorders. These conditions have ...

Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.

PURPOSE: This study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC)...

Application of Convolutional Neural Network Image Analysis and Machine Learning to Basic Blood Tests for Intelligent Diagnostic Assistance.

BACKGROUND AND OBJECTIVES: We developed an automated morphological image recognition deep learning system (image recognition DLS) of peripheral blood ...

Leveraging Machine Learning for Rapid and Accurate Diagnosis of Acute Leukemia.

CONTEXT: Early detection of acute leukemia (AL) is crucial for timely intervention and improved outcomes. Machine learning (ML) models provide a promi...

Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi-Sequence MRI in Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.

OBJECTIVE: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics ...