Oncology/Hematology

A Pilot Study on Deep Learning With Simplified Intravoxel Incoherent Motion Diffusion-Weighted MRI Parameters for Differentiating Hepatocellular Carcinoma From Other Common Liver Masses.

OBJECTIVES: To develop and evaluate a deep learning technique for the differentiation of hepatocellular carcinoma (HCC) using "simplified intravoxel incoherent motion (IVIM) parameters" derived from only 3 b-value images.

Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis.

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a rob...

Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especial...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patien...

Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks.

BACKGROUND AND OBJECTIVE: Patients with metastatic bone disease are at risk of pathological femoral fractures and may require prophylactic surgical fi...

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

PURPOSE: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negativ...

Deciphering the mechanism of baicalein in cervical cancer via bioinformatics, machine learning and computational simulations: PIM1 and CDK2 are key target proteins.

Cervical cancer is one of the leading causes of death among women worldwide. Current treatments are limited by chemoresistance and chemotherapeutic ag...

Integrative PPI network and random forest analysis identifies KIF4A, TOP2A, and ASPM protein macromolecules as novel protein biomarkers in renal carcinoma pathogenesis.

The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. T...

MRI-based multimodal AI model enables prediction of recurrence risk and adjuvant therapy in breast cancer.

Timely intervention and improved prognosis for breast cancer patients rely on early metastasis risk detection and accurate treatment predictions. This...

Design and molecular mechanism investigation of ALK inhibitors based on virtual screening and structural descriptor modeling.

To address the challenges of target specificity and drug resistance in Anaplastic lymphoma kinase (ALK) inhibition, this study conducted a virtual scr...

Driving Knowledge to Action: Building a Better Future With Artificial Intelligence-Enabled Multidisciplinary Oncology.

Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and trea...

Broadening the Net: Overcoming Challenges and Embracing Novel Technologies in Lung Cancer Screening.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages where curative treatment ...

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targeted therapy for lung cancer, making their accurate d...

The present and future of lung cancer screening: latest evidence.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early lung cancer detection improves lung cancer-related mortality and surviva...

A European Multi-Center Breast Cancer MRI Dataset

Detecting breast cancer early is of the utmost importance to effectively treat the millions of women afflicted by breast cancer worldwide every year...

Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding

In this paper, we address the crucial task of brain tumor segmentation in medical imaging and propose innovative approaches to enhance its performan...

Integration of Bulk RNA and Single-Cell Analyses Reveal Distinct Expression Patterns of Anoikis-Related Genes and the Immunosuppressive Role of NQO1 Macrophages in Hepatocellular Carcinoma.

Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes invol...

An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks.

Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the f...

Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades,...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to c...