AIMC Topic: Breast Neoplasms

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Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

Bioscience reports
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England)
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...

Radiomics, deep learning and early diagnosis in oncology.

Emerging topics in life sciences
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists' task and help with challenging c...

[Not Available].

Bulletin du cancer
HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negativ...

Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases.

Studies in health technology and informatics
Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited ti...

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

Briefings in bioinformatics
MOTIVATION: Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic m...

Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From Mammograms.

JCO precision oncology
PURPOSE: The molecular subtype of breast cancer is an important component of establishing the appropriate treatment strategy. In clinical practice, molecular subtypes are determined by receptor expressions. In this study, we developed a model using d...

Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early detection of breast cancer is a powerful tool towards decreasing its socioeconomic burden. Although, artificial intelligence (AI) methods have shown remarkable results towards this goal, their "black box" nature hinders their wide adoption in c...

Clinical Artificial Intelligence Applications: Breast Imaging.

Radiologic clinics of North America
This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, di...

[The application of artificial intelligence on the classification of benign and malignant breast tumors based on dynamic enhanced MR images].

Zhonghua yi xue za zhi
This retrospective analysis was conducted on clinical obtained DCE-MR images of 198 patients, age from 21 to 79 years(45.5±13.7). The CBAM-ResNet model was developed to perform the classification automatically at the image-level based on deep learnin...