Oncology/Hematology

Applicability of Artificial Intelligence Analysis in Oral Cytopathology: A Pilot Study.

INTRODUCTION: Oral cancer, especially oral squamous cell carcinoma (OSCC), is a global health challenge due to factors such as late detection and high mortality rates. Early detection is essential through monitoring by healthcare professionals. Cytopathology is a cellular analysis model for evaluating cellular damage preceding the clinical appearance of OSCC, but it requires training and has diagn...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thus allowing a much larger number of patients to undergo imaging. However, the diagnosis of X-ray images relies heavily on the expertise and experience ...

Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection.

Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for dete...

Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer.

With the incorporation of immune checkpoint inhibitors into the treatment of endometrial cancer (EC), a deeper understanding of the tumor immune micro...

Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images.

There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found tha...

Improving Outcomes in Hepatocellular Carcinoma through Integration of Machine Learning: Development of a Tumor-Associated Macrophage Signature.

INTRODUCTION: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally. Macrophages, as essential components of the immune s...

External validation of 12 existing survival prediction models for patients with spinal metastases.

BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making.

Optimizing Skin Cancer Diagnosis: A Modified Ensemble Convolutional Neural Network for Classification.

Skin cancer is recognized as one of the most harmful cancers worldwide. Early detection of this cancer is an effective measure for treating the diseas...

The Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update.

This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PS...

NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review.

This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and cli...

Multiscale deep learning radiomics for predicting recurrence-free survival in pancreatic cancer: A multicenter study.

PURPOSE: This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (R...

Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.

Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. ...

Towards unbiased skin cancer classification using deep feature fusion.

This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially ...

Predicting survival in malignant glioma using artificial intelligence.

Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor progno...

Automated recognition and segmentation of lung cancer cytological images based on deep learning.

Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. H...

BCT-Net: semantic-guided breast cancer segmentation on BUS.

Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely empl...

Enhancing surgical precision in squamous cell carcinoma of the head and neck: Hyperspectral imaging and artificial intelligence for improved margin assessment in an ex vivo setting.

BACKGROUND: Head and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete re...

Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular Testing.

Tumor cellularity (TC) in lung adenocarcinoma slides submitted for molecular testing is important in identifying actionable mutations, but lack of bes...

Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer.

Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the imm...

Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.

OBJECTIVES: To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancr...