AIMC Topic: Breast Neoplasms

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Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.

Journal of medical economics
BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers i...

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...

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...

Data-driven survival modeling for breast cancer prognostics: A comparative study with machine learning and traditional survival modeling methods.

PloS one
Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML...

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Scientific reports
As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of ...

Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images.

Scientific reports
The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient's survival. Mammography has recently been recommended as diag...