Oncology/Hematology

Lung Cancer

Latest AI and machine learning research in lung cancer for healthcare professionals.

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Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure...

Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and p...

Radiation Dose Reduction and Image Quality Improvement of UHR CT of the Neck by Novel Deep-learning Image Reconstruction.

PURPOSE: We evaluated a dedicated dose-reduced UHR-CT for head and neck imaging, combined with a nov...

Advances in intraoperative imaging technologies for complex biliary disease.

Intraoperative imaging has enhanced the precision of biliary surgery in pediatric patients by improv...

Advances in Breast Cancer Detection: Biomarkers, Mechanisms, and Biosensor Technologies.

Breast cancer (BC), the most common cancer in women, remains a global health concern. According to t...

Aptamer-directed siRNA delivery systems for triple-negative breast cancer therapy.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by the ...

A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.

OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining ...

Artificial Intelligence-Powered Spatial Analysis of Immune Phenotypes in Resected Pancreatic Cancer.

IMPORTANCE: Although tumor-infiltrating lymphocytes (TILs) have been implicated as prognostic biomar...

Differentiating adenocarcinoma and squamous cell carcinoma in lung cancer using semi automated segmentation and radiomics.

INTRODUCTION: Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are frequently observed forms of...

Deep learning dosiomics in grade 4 radiation-induced lymphopenia prediction in radiotherapy for esophageal cancer: a multi-center study.

PURPOSE: To investigate the feasibility and accuracy of using deep learning and dosiomics features, ...

Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.

With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction ...

Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.

Liver cancer remains a significant global health concern, ranking as the sixth most common malignanc...

Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compare...

Innovative technologies and their clinical prospects for early lung cancer screening.

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, due to lack...

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning.

Radiotherapy resistance in nasopharyngeal carcinoma (NPC) is a major cause of recurrence and metasta...

Machine learning-based forest fire susceptibility mapping of Southern Mizoram, a part of Indo-Burma Biodiversity Hotspot.

Forest fires are a significant global environmental hazard, causing widespread economic losses and e...

HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet the response rate rema...

Quasi-supervised MR-CT image conversion based on unpaired data.

. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) imag...

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