Oncology/Hematology

CNN based method for classifying cervical cancer cells in pap smear images.

The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer early is that it is asymptomatic until it develops into invasive carcinoma. In medical applications, the use of machine learning and deep learning is successful as a classifier in the preliminary ident...

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex molecular and immune landscape of HCC makes accurate patient stratification and personalized treatment essential. In this study, we utilized large-scale gene expression data from TCGA and GSE54236, alongside eQTL GWAS data, to identify key genes that i...

Machine learning-based prediction of G-CSF-induced hematopoietic stem cell mobilization outcomes in healthy volunteers.

Haematopoietic stem cell transplantation (HSC-T) has been established as a fundamental therapeutic intervention for a wide range of hematological mali...

Responsible Artificial Intelligence governance in oncology.

The use of Artificial Intelligence (AI) in healthcare is expanding rapidly, including in oncology. Although generic AI development and implementation ...

A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images.

Early and accurate detection of oral cancer plays a pivotal role in improving patient outcomes. This research introduces a custom-designed, 19-layer c...

Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.

This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning ...

Identification and single-cell analysis of prognostic genes related to mitochondrial and neutrophil extracellular traps in bladder cancer.

The development of bladder cancer (BLCA) is associated with mitochondrial dysfunction and neutrophil extracellular traps (NETs); however, the relation...

Accuracy and Reliability of ChatGPT in Answering Patient Questions About Lung Cancer and Its Surgery: An Expert Panel Evaluation by Thoracic Surgeons.

This study aimed to evaluate the accuracy, clarity, and scientific adequacy of ChatGPT's responses to frequently asked patient questions concerning lu...

Turkish medical oncologists' perspectives on integrating artificial intelligence: knowledge, attitudes, and ethical considerations.

BACKGROUND: Integrating artificial intelligence (AI), especially large language models (LLM) into oncology has potential benefits, yet medical oncolog...

Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition.

BACKGROUND: Pancreatic cancer (PC) represents a highly heterogeneous malignancy with poor prognosis, where precise molecular subtyping facilitates com...

Beam orientation optimization in IMRT using sparse mixed integer programming and non-convex fluence map optimization.

Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, non-convex problem traditionally addressed with heur...

Precise metabolic dependencies of cancer through deep learning and validations.

Cancer cells exhibit metabolic reprogramming to sustain proliferation, creating metabolic vulnerabilities absent in normal cells. While prior studies ...

Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.

Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-int...

A novel hybrid vision UNet architecture for brain tumor segmentation and classification.

This paper focuses on designing and developing novel architectures termed Hybrid Vision UNet-Encoder Decoder (HVU-ED) segmenter and Hybrid Vision UNet...

Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition.

BACKGROUND: Polyploidy is frequently observed in cancer cells and is closely associated with chromosomal instability, which can lead to cancer progres...

Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study.

OBJECTIVES: This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepa...

Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning.

BACKGROUND: The pathogenesis of inflammatory bowel disease (IBD) involves complex molecular mechanisms, and achieving clinical remission remains chall...

Advancing breast cancer prediction using blockchain-secured hybrid genetic algorithm.

Feature selection using evolutionary algorithms-a well-liked technique for choosing pertinent characteristics in huge datasets is explored. In machine...

EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction.

Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to...

Deep learning-based quantification of tumor-infiltrating lymphocytes as a prognostic indicator in nasopharyngeal carcinoma: multicohort findings.

BACKGROUND: Nasopharyngeal carcinoma (NPC) features a tumor-immune microenvironment rich in tumor-infiltrating lymphocytes (TILs), important for progn...