AIMC Topic: Breast Neoplasms

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Multi-site validation of an interpretable model to analyze breast masses.

PloS one
An external validation of IAIA-BL-a deep-learning based, inherently interpretable breast lesion malignancy prediction model-was performed on two patient populations: 207 women ages 31 to 96, (425 mammograms) from iCAD, and 58 women (104 mammograms) f...

Multi-parameter MRI deep learning model for lymphovascular invasion assessment in invasive breast ductal carcinoma: A multicenter, retrospective study.

Clinical radiology
AIMS: To investigate the value of multi-parametric magnetic resonance imaging (MRI)-based deep learning (DL) in predicting the Lymphovascular Invasion (LVI) status of invasive breast ductal cancer (IBDC).

Predicting complications in breast reconstruction: External validation of a machine learning model.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: Nipple-sparing mastectomy (NSM) with immediate implant-based breast reconstruction provides aesthetic and psychosocial benefits, but nipple-areolar complex (NAC) necrosis remains a significant risk. This study externally validated a previ...

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

Biology direct
BACKGROUND: Pathological complete response (pCR) to neoadjuvant systemic therapy (NAST) is an established prognostic marker in breast cancer (BC). Multimodal deep learning (DL), integrating diverse data sources (radiology, pathology, omics, clinical)...

Detection of breast cancer using fractional discrete sinc transform based on empirical Fourier decomposition.

Bio-medical materials and engineering
Breast cancer is the most common cause of death among women worldwide. Early detection of breast cancer is important; for saving patients' lives. Ultrasound and mammography are the most common noninvasive methods for detecting breast cancer. Computer...

Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets.

PloS one
Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN,...

Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.

Breast cancer research : BCR
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...

Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast ...

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

British journal of hospital medicine (London, England : 2005)
Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result in high false-positive rates, leading to unnecessary biopsies. Artificial intelligence (AI) has dem...