Oncology/Hematology

CT-Based radiomics and deep learning for the preoperative prediction of peritoneal metastasis in ovarian cancers.

OBJECTIVES: To develop a CT-based deep learning radiomics nomogram (DLRN) for the preoperative prediction of peritoneal metastasis (PM) in patients with ovarian cancer (OC). METHODS: A total of 296 patients with OCs were randomly divided into training dataset (N = 207) and test dataset (N = 89). The radiomics features and DL features were extracted from CT images of each patient. Specifically, rad...

Determining the Survival Impact and Cost-Effectiveness of Multi-Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches.

OBJECTIVE: To determine the effectiveness and cost-effectiveness of multi-gene panel sequencing compared to single-gene KRAS testing for metastatic colorectal cancer (mCRC). STUDY SETTING AND DESIGN: British Columbia, Canada (BC) is a provincial single-payer public healthcare system, and it was the first province to publicly reimburse multi-gene sequencing for mCRC. Panels expand treatment de-esca...

Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in pa...

QCNN-Swin-UNet: Quantum Convolutional Neural Network Integrated with Optimized Swin-UNet for Efficient Liver Tumor Segmentation and Classification on Edge Devices.

Accurate segmentation and classification of liver tumors are crucial for early diagnosis and effective treatment planning. However, conventional deep ...

Real-time Use of Computer-aided Diagnosis in the Optical Diagnosis of Gastric Neoplasia: A Multicenter Randomized Controlled Trial.

BACKGROUND & AIMS: The multicenter, randomized, control trial was conducted to evaluate whether computer-aided diagnosis (CADx) improves the optical d...

Predicting and Optimizing Synergistic Drug Combinations for Breast Cancer Treatment Using Machine Learning.

OBJECTIVES: The study aims to identify highly synergistic drug combinations for breast cancer treatment using machine learning models. The primary obj...

Efficacy of artificial intelligence for adenoma detection in water exchange colonoscopy: a two-center randomized controlled trial.

Water exchange and artificial intelligence-based computer-aided detection (CADe) separately improve the adenoma detection rate (ADR) and number of ade...

PD-L1 expression scoring using artificial intelligence algorithms in non-small cell lung cancer harbouring ROS1 fusions: potential clinical-biological significance.

BACKGROUND: PD-L1 expression in ROS1-positive non-small cell lung carcinoma (NSCLC) patients remains unclear regarding its possible clinical-biologica...

Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using 18FDG PET/CT images.

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision t...

Portrayal of Cancer Patients in the Era of AI: A Content Analysis of Images Produced by Generative AI Tools.

This study sought to characterize images of cancer patients generated by Artificial Intelligence (AI) text-to-image tools, and assess whether images d...

Cancer vaccines: Discovery, development, and challenges for clinical translation.

Cancer vaccines stimulate antitumor immunity by delivering tumor antigens and, in recent years, have emerged as a promising therapeutic strategy again...

Liquid biopsy in breast cancer management: From circulating biomarkers to clinical breakthroughs.

Breast cancer continues to be a major global health concern, particularly for women, despite improvements in early detection and treatment strategies....

Repeatability and Reproducibility of Artificial Imaging-based Digital Pathology for the Evaluation of Liver Fibrosis.

BACKGROUND & AIMS: Fibrosis stage is a key determinant of outcomes in metabolic dysfunction-associated steatohepatitis (MASH). Assessment of fibrosis ...

Intraductal Papillary Mucinous Neoplasm and Pancreatic Cancer: Opportunity Knocks Twice.

Pancreatic cystic lesions are widely recognized as harbingers of pancreatic cancer. Intraductal papillary mucinous neoplasm (IPMN) is the most common ...

Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.

OBJECTIVE: This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate can...

MILK10k: A Hierarchical Multimodal Imaging-Learning Toolkit for Diagnosing Pigmented and Nonpigmented Skin Cancer and its Simulators.

In this study, we introduce MILK10k, a multimodal image dataset designed to enhance machine learning and artificial intelligence-driven applications f...

Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs.

INTRODUCTION: Chest X-rays (CXR) rank among the most conducted X-ray examinations. They often require repeat imaging due to inadequate quality, leadin...

AI Prognostication in Nonsmall Cell Lung Cancer: A Systematic Review.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognosticat...

Intestinal therapeutic agent delivery microrobot with magnetic targeting and pH-triggered degradation abilities.

Oral drug delivery remains a clinically preferred route for colorectal cancer therapy due to its noninvasive nature and patient compliance. However, c...

Machine Learning Risk Stratification for Older Breast Cancer Survivors: Clinical Care Implications.

OBJECTIVE: To develop and validate a clinical risk prediction algorithm to identify breast cancer survivors at high risk for adverse outcomes. STUDY S...