Oncology/Hematology

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

We proposed a deep learning approach to classify various error types in daily VMAT treatment of head...

Integrated Bioinformatics and Machine Learning Analysis Identify ACADL as a Potent Biomarker of Reactive Mesothelial Cells.

Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma...

Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics.

OBJECTIVES: Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessi...

Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles.

Distant metastasis of cancer is a significant contributor to cancer-related complications, and early...

A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma.

PURPOSE: To establish a deep learning-based model to predict radiotherapy-induced temporal lobe inju...

Physics-Informed Transfer Learning to Enhance Sleep Staging.

OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hos...

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns a...

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics ...

Re: David Musheyev, Alexander Pan, Stacy Loeb, Abdo E. Kabarriti. How Well Do Artificial Intelligence Chatbots Respond to the Top Search Queries About Urological Malignancies? Eur Urol 2023;85:13-6.

Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives.

Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Can...

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging ...

Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review.

This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologi...

Advancing Precision Oncology With Artificial Intelligence: Ushering in the ArteraAI Prostate Test.

From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

PURPOSE: Preventing positive margins is essential for ensuring favorable patient outcomes following ...

Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready.

The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy task...

Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort.

Prostate and breast cancer incidence rates have been on the rise in Japan, emphasising the need for ...

Machine learning for predicting colon cancer recurrence.

INTRODUCTION: Colorectal cancer (CRC) is a global public health concern, ranking among the most comm...

Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review.

MRI has firmly established itself as a mainstay for the detection, staging and surveillance of prost...

Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine...

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectra...

Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models.

RATIONALE AND OBJECTIVES: To investigate the effectiveness of machine learning-based clinical, radio...