To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.
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http://dx.doi.org/10.1038/s41598-024-67217-0 | DOI Listing |
Breast cancer is a significant health challenge worldwide, and disproportionately affects women of African ancestry (AA) who experience higher mortality rates relative to other racial/ethnic groups. Several studies have pointed to biological factors that affect breast cancer outcomes. A recently discovered stromal cell population that expresses P ROCR, Z EB1 and P DGFRα (PZP cells) was found to be enriched in normal healthy breast tissue from AA donors, and only in tumor adjacent tissues from donors of European ancestry (EA).
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January 2025
Electronics and Communications, Arab Academy for Science, Heliopolis, Cairo, 2033, Egypt.
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 Da Hua Road, Dong Dan, Beijing 100730, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, PR China.
Purpose: This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).
Methods: This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software.
Anticancer Drugs
January 2025
Department of Oncology, Lianyungang Clinical College of Nanjing Medical University/The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, Jiangsu Province, China.
Triple-negative breast cancer (TNBC) is highly prone to early relapse and metastasis following standard treatment. CXCL8 is a key factor in tumor invasion and metastasis, but its role in TNBC prognosis and clinicopathological correlations remains poorly understood. This study investigated CXCL8 expression and its clinical significance in TNBC to develop a prognostic nomogram for guiding intensive treatment and follow-up strategies.
View Article and Find Full Text PDFAndes Pediatr
October 2024
Instituto de Enfermería, Facultad de Medicina, Universidad Austral de Chile, Valdivia, Chile.
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