Oncology/Hematology

Long-term exposure to PM and liver cancer mortality: Insights into the role of smaller particulate fractions.

Particulate matter (PM) is a recognized carcinogen, but the effects of PM on liver cancer remain underexplored. This study investigates the long-term association between PM and liver cancer mortality, as well as the contribution of smaller particles relative to larger PM effects. Data on 244,558 liver cancer deaths in Shandong were collected. The 10-year weighted moving average of PM was calculate...

MFDSMC: Accurate Identification of Cancer-Driver Synonymous Mutations Using Multiperspective Feature Representation Learning.

Synonymous mutations do not change amino acid sequences, but they can drive cancer by influencing splicing, mRNA structure, translation efficiency, and other molecular mechanisms. Although driver synonymous mutations are significantly outnumbered by functionally neutral passenger mutations in cancer, their accurate discrimination is critical to understanding tumorigenesis. In this study, we develo...

Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.

The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed ...

Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.

Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurren...

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.

In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diag...

Machine learning and multi-omics analysis reveal key regulators of proneural-mesenchymal transition in glioblastoma.

Glioblastoma (GBM) is classified into subtypes according to the molecular expression profile; the proneural subtype has a relatively good prognosis, a...

A Novel Boundary-Aware Transformer Based Fire Hawk Algorithm for Leukemia Classification using Blood Smear Images.

Leukemia is a type of blood cancer affecting people of all ages and is the leading cause of death worldwide. The most common form of bone marrow leuke...

Preoperative Identification of Papillary Thyroid Carcinoma Subtypes and Lymph Node Metastasis via Deep Learning-Assisted Surface-Enhanced Raman Spectroscopy.

Accurate preoperative diagnosis of papillary thyroid carcinoma (PTC) histological subtypes and lymph node metastasis is essential for formulating pers...

Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.

Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent...

Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.

Urethral recurrence (UR) following radical cystectomy for bladder cancer represents an aggressive disease failure with typically poor survival outcome...

UNIK (Urologic Non-Neoplastic Investigation of Kidneys): a machine learning approach to decode benign lesion.

PURPOSE: Predicting the likelihood of benign neoplasia in patients with suspected renal cell carcinoma (RCC) is a cornerstone of presurgical planning....

Advancing blood cell detection and classification: performance evaluation of modern deep learning models.

The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leuke...

AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.

PURPOSE OF REVIEW: This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy d...

FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the dea...

Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study.

Kinesiophobia is particularly common in postoperative lung cancer patients, which causes patients may be reluctant to cough and move due to mispercept...

Deep learning-based electrical impedance spectroscopy analysis for malignant and potentially malignant oral disorder detection.

Electrical impedance spectroscopy (EIS) is a powerful tool used to investigate the properties of materials and biological tissues. This study presents...

Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.

BACKGROUND: Preoperative diagnosis of muscle invasion and American Joint Committee on Cancer (AJCC) stage plays a crucial role in guiding treatment st...

Current AI technologies in cancer diagnostics and treatment.

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, an...

Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.

Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infect...

Intelligent and precise auxiliary diagnosis of breast tumors using deep learning and radiomics.

BACKGROUND: Breast cancer is the most common malignant tumor among women worldwide, and early diagnosis is crucial for reducing mortality rates. Tradi...