Oncology/Hematology

RepSE-CBAMNet: A Hybrid Attention-Enhanced CNN for Brain Tumor Detection.

The effective detection of brain tumors is closely linked to their timely diagnosis and treatment which can help in the prevention of deaths and in improving the quality of life. The objective of this paper is to present an enhanced YOLO (You Look Only Once) architecture designed specifically for brain tumor detection, significantly improving detection accuracy using Convolutional Block Attention ...

Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.

Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncology research. This study aims to evaluate the relationship between WB radiomic features, derived from various neuroimaging modalities in patients with gliomas, and some key baseline characteristics of patients and tumors such as sex, histological tumo...

Sociodemographic Profile of People with Diagnosed Pancreatic Cancer in the UK: Retrospective Sentinel Network Cohort Study.

Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim of this study was to characterise, using descriptiv...

Machine Learning-Based Multimodal Radiomics and Transcriptomics Models for Predicting Radiotherapy Sensitivity and Prognosis in Esophageal Cancer.

Radiotherapy plays a critical role in treating esophageal cancer, but individual responses vary significantly, impacting patient outcomes. This study ...

A machine learning-based glycolysis and fatty acid metabolism-related prognostic signature is constructed and identified ACSL5 as a novel marker inhibiting the proliferation of breast cancer.

INTRODUCTION: A new perspective on cancer metabolism suggests that it varies by context and is diverse. Cancer metabolism reprogramming can create a h...

Investigating the Genetic Links Between Immune Cell Profiles and Bladder Cancer: A Multidisciplinary Bioinformatics Approach.

: Bladder cancer (BC) is a common malignancy in the urinary system, with an increasing incidence rate. Immune cell infiltration within the tumor micro...

Short Tandem Repeat (STR) Somatic Mutation in Non-Melanoma Skin Cancer (NMSC): Association with Transcriptomic Profile and Potential Implications for Therapy.

BACKGROUND: Studies on somatic mutations in cancer typically report single-nucleotide variants in coding regions, while mutations in short tandem repe...

Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma.

The transcription factor SOX9 plays a critical role in various diseases, including hepatocellular carcinoma (HCC), and has been implicated in resista...

Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials.

Understanding the toxic behavior of metal and metal oxide nanoparticles (M/MOx NPs) is essential for effective tumor diagnosis and treatment, yet gene...

Automated grading and staging of ovarian cancer using deep learning on the transmission optical microscopy bright-field images of thin biopsy tissue samples.

Ovarian cancer remains a challenging malignancy to diagnose and manage, with prognosis heavily dependent on the stage at detection. Accurate grading a...

Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations.

With gastric cancer ranking among the most prevalent and deadly malignancies worldwide, early detection and individualized prognosis remain essential ...

Integrating ultrasound and serum indicators for evaluating outcomes of targeted immunotherapy in advanced liver cancer.

BACKGROUND: Hepatocellular carcinoma (HCC) is a major global contributor to cancer-related mortality, with advanced stages presenting substantial ther...

Research status and progress of deep learning in automatic esophageal cancer detection.

Esophageal cancer (EC), a common malignant tumor of the digestive tract, requires early diagnosis and timely treatment to improve patient prognosis. A...

Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is c...

Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing pro...

Predicting gastric cancer survival using machine learning: A systematic review.

BACKGROUND: Gastric cancer (GC) has a poor prognosis, and the accurate prediction of patient survival remains a significant challenge in oncology. Mac...

Longitudinal changes in body composition during palliative systemic chemotherapy and survival outcomes in metastatic colorectal cancer.

BACKGROUND: In patients with metastatic colorectal cancer, chemotherapy may lead to changes in body composition, including skeletal muscle quantity an...

Using Machine Learning for the Fusion of Tumor Records on a Real-World Dataset.

Cancer registries collect multiple reports describing the same tumor, potentially leading to duplicate or conflicting values across different records....

Fake It till You Predict It: Data Augmentation Strategies to Detect Initiation and Termination of Oncology Treatment.

At the hospital, the dispersion of information regarding anti-cancer treatment makes it difficult to extract. We proposed a solution capable of identi...

Automating Performance Status Annotation in Oncology Using Llama-3.

This work explores the automated extraction of medical information from Dutch clinical notes using Llama-3 and a limited amount of annotations. We com...