Oncology/Hematology

Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.

This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 RCC patients who underwent contrast-enhanced CT scans were included in the analysis. Radiomic features were extracted, and a two-step feature selection...

High-Precision Intelligent Diagnosis of Pancreatic Cancer: Flowing Diffuseness from Single to Whole.

Raman spectroscopy, as a label-free optical technique, provides a unique solution for tissue diagnosis. However, due to the limitation of point-by-point acquisition mode and multivariate statistical analysis methods, conventional methods pose a major bottleneck toward achieving highly time-efficient, accurate, and holistic diagnosis. Here, the authors establish line-scan Raman spectrochemical holi...

B lymphocyte subset-based stratification in primary Sjögren's syndrome: implications for lymphoma risk and personalized treatment.

OBJECTIVE: This study aimed to perform a detailed stratification analysis of B lymphocyte subsets in patients with primary Sjögren's syndrome (pSS) an...

Enhancing detection and monitoring of circulating tumor cells: Integrative approaches in liquid biopsy advances.

Liquid biopsy offers a minimally invasive method for detecting and monitoring cancer, with key biomarkers including circulating tumor cells (CTCs), ce...

Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study.

Colorectal cancer (CRC) is the third most common cancer, and it can be prevented by performing early screening. As a hallmark of cancer, the human mic...

From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.

Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transform...

Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.

Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate...

Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis.

BACKGROUND AND AIM: Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancer...

Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.

PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the...

Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.

BACKGROUND: The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with meta...

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-m...

Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

M2 macrophages play a crucial role in the initiation and progression of various tumors, including diffuse large B-cell lymphoma (DLBCL). However, the ...

Assessing ChatGPT for clinical decision-making in radiation oncology, with open-ended questions and images.

PURPOSE: This study assesses the practicality and correctness of ChatGPT-4 and 4O's answers to clinical inquiries in radiation oncology, and evaluates...

Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We...

Mexican dataset of digital mammograms (MEXBreast) with suspicious clusters of microcalcifications.

Breast cancer is one of the most prevalent cancers affecting women worldwide. Early detection and treatment are crucial in significantly reducing mort...

Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.

The use of machine learning to integrate and analyse multimodal information has broad prospects for enhancing the precision of tumour diagnosis. Our s...

Automatic smart brain tumor classification and prediction system using deep learning.

A brain tumor is a serious medical condition characterized by the abnormal growth of cells within the brain. It can cause a range of symptoms, includi...

SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems.

Skin cancer represents a significant global public health issue, and prompt and precise detection is essential for effective treatment. This study int...

Impact of fine-tuning parameters of convolutional neural network for skin cancer detection.

Melanoma skin cancer is a deadly disease with a high mortality rate. A prompt diagnosis can aid in the treatment of the disease and potentially save t...

F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.

BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasivel...