Oncology/Hematology

BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.

Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-line origin is lymphoid or myeloid, respectively. Deep learning (DL) and artificial intelligence (AI) are revolutionizing medical sciences by assisting c...

Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given that abnormal reprogramming of asparagine metabolism (AM) has emerged as an emerging therapeutic target for anti-tumor therapy. However, the clinical...

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study.

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Rou...

Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice.

BACKGROUND: Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect eso...

Effectiveness of artificial intelligence assisted colonoscopy on adenoma and polyp miss rate: A meta-analysis of tandem RCTs.

BACKGROUND AND AIMS: One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal canc...

Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

BACKGROUND AND PURPOSE: During the ESTRO 2023 physics workshop on "AI for the fully automated radiotherapy treatment chain", the topic of deep learnin...

Artificial intelligence (AI) applications in improvement of IMRT and VMAT radiotherapy treatment planning processes: A systematic review.

INTRODUCTION: Radiotherapy is a common option in the treatment of many types of cancer. Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Mo...

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prog...

Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.

INTRODUCTION: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Unde...

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.

This study developed and validated a deep learning-based diagnostic model with uncertainty estimation to aid radiologists in the preoperative differen...

Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural Networks.

INTRODUCTION: Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' dec...

Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method.

BACKGROUND: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival.

Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images.

OBJECTIVE: Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clea...

Impact of Mitophagy-Related Genes on the Diagnosis and Development of Esophageal Squamous Cell Carcinoma via Single-Cell RNA-seq Analysis and Machine Learning Algorithms.

As a treatment for esophageal squamous cell carcinoma (ESCC), which is common and fatal, mitophagy is a conserved cellular mechanism that selectively ...

Application of machine learning for predicting lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: We review and analyze research on the application of machine learning (ML) and deep learning (DL) models to lymph node metastasis (LNM) pr...

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer ...

From Biosensors to Robotics: Pioneering Advances in Breast Cancer Management.

Breast cancer stands as the most prevalent form of cancer amongst females, constituting more than one-third of all cancer cases affecting women. It ca...

Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.

PURPOSE: Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in mac...

Comparative evaluation of machine learning models in predicting overall survival for nasopharyngeal carcinoma using F-FDG PET-CT parameters.

PURPOSE: The objective of this study is to assess the prognostic efficacy of F-fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomogr...

Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study.

RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it th...