AIMC Topic: Breast Neoplasms

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A systematic review of machine learning algorithms for breast cancer detection.

Tissue & cell
Breast cancer is one of the leading causes of death and morbidity among women worldwide. Identifying cancerous cells remains a complex and time-consuming task, particularly when performed manually by radiologists or pathologists, contributing to high...

Effect of AI-based pre-hospital health education via QR code on APAIS scores in patients with breast nodules: A retrospective study.

Breast (Edinburgh, Scotland)
PURPOSE: To explore the effect of AI-based pre-hospital health education via QR code on preoperative anxiety and information needs in patients with breast nodules and provide a decision-making reference for ongoing optimizing clinical workflows.

Breast tumour classification in DCE-MRI via cross-attention and discriminant correlation analysis enhanced feature fusion.

Clinical radiology
AIM: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be highly sensitive in diagnosing breast tumours, due to the kinetic and volumetric features inherent in it. To utilise the kinetics-related and volume-related informat...

Breast Pathology Through the Digital Lens.

Seminars in diagnostic pathology
Digital pathology (DP) has significantly transformed breast pathology at Mount Sinai Hospital by enhancing diagnostic accuracy, collaboration, and education. The institution has integrated the Philips IntelliSite Pathology Solution (PIPS) for primary...

Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.

Scientific reports
Breast cancer is the most prevalent cancer and the second cause of cancer related death among women in the United States. Accurate and early detection of breast cancer can reduce the number of mortalities. Recent works explore deep learning technique...

Options for postoperative radiation therapy in patients with de novo metastatic breast cancer.

Breast (Edinburgh, Scotland)
BACKGROUND: Although meta-analyses have demonstrated survival benefits associated with primary tumor resection in MBC, guidelines lack consensus on the survival benefit of postoperative radiation therapy (RT).

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

Computer methods and programs in biomedicine
BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critical prognostic marker for patients with breast cancer, potentially allowing surgery omission. However, noninvasive and accurate pCR diagnosis remains a...

EfficientNet-Based Attention Residual U-Net With Guided Loss for Breast Tumor Segmentation in Ultrasound Images.

Ultrasound in medicine & biology
OBJECTIVE: Breast cancer poses a major health concern for women globally. Effective segmentation of breast tumors for ultrasound images is crucial for early diagnosis and treatment. Conventional convolutional neural networks have shown promising resu...

Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator.

World journal of surgical oncology
BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk predi...

A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.

Scientific reports
The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important factor in deciding whether to have axillary surgery or pursue alternative treatments. Based on axillary ultrasound (US) and histopathologic data, three gr...