Breast Cancer

Segmentation of liver and liver lesions using deep learning.

Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mass image processing studies. Deep leaning created liver and liver lesion segmentation on clinical 3D MRI data has not been fully addressed in previous experiments. To this end, the required data were collected from 128 patients, including their T1w an...

A Review of deep learning methods for denoising of medical low-dose CT images.

To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recen...

Exploring the role of large language models in radiation emergency response.

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, pop...

Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chr...

Artificial Intelligence for Breast Ultrasound: Expert Panel Narrative Review.

Breast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, ho...

A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer.

Comprehensive action patterns of programmed cell death (PCD) in bladder cancer (BLCA) have not yet been thoroughly investigated. Here, we collected 19...

Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.

OBJECTIVES: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk strat...

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BACKGROUND:: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep lear...

Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis.

BACKGROUND: Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imagi...

Deep learning-based algorithms for low-dose CT imaging: A review.

The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is sign...

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized wel...

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized a...

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between...

ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies.

Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary cli...

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinica...

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibit...

Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma.

MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in...

Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.

Breast cancer is the most common type of cancer in women, and early abnormality detection using mammography can significantly improve breast cancer su...

Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults?

PURPOSE: Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adap...

CT image denoising methods for image quality improvement and radiation dose reduction.

With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the nee...