Other Cancers

WaveSleepNet: An Interpretable Network for Expert-Like Sleep Staging.

Although deep learning algorithms have proven their efficiency in automatic sleep staging, their "black-box" nature has limited their clinical adoption. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that reasons in a similar way to sleep clinical experts. In this network, we utilize the latent space representations generated during training to identify c...

A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a computed tomography-based radiomics nomogram for cancer-specific survival (CSS) prediction in curatively resected colorectal cancer (CRC), and its performance was compared with the American Joint Committee on Cancer (AJCC) staging and clinical-pathological models.

Integration of radiomic and deep features to reliably differentiate benign renal lesions from renal cell carcinoma.

PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial for optimized management, particularly for small ...

Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses.

Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and disti...

Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC.

BACKGROUND: Immunotherapy has introduced new breakthroughs in improving the survival of head and neck squamous cell carcinoma (HNSCC) patients, yet dr...

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early cl...

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using gen...

Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients.

BACKGROUND: A second primary malignant tumor is one of the most important factors affecting the long-term survival of young women with breast cancer (...

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existi...

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are ma...

Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microen...

Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.

BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), wher...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging ou...

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal...

Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.

RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(...

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.