Objective: The aim of this retrospective study was to analyze the predictive capabilities of preoperative mammography, dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI), and diffusion-weighted imaging (DWI) in determining hormone receptor (HRc) status for pure ductal carcinoma (DCIS) lesions.
Materials And Methods: The study included a total of 79 patients who underwent preoperative mammography (MG) and MRI between December 2018 and December 2023 and were subsequently diagnosed with pure DCIS after surgery. The correlation between MG, DCE-MRI, and DWI features and estrogen receptor (ER) and progesterone receptor (PR) status was examined.
Results: Among the lesions, 44 were double HRc-positive (ER and PR-positive), 13 were single HRc-positive (ER-positive and PR-negative or ER-negative and PR-positive) and 22 were double HRc-negative (ER and PR-negative). The presence of symptom ( = 0.029), the presence of comedo necrosis ( = 0.005) and high histological grade (<0.001) were found to be associated with ER and PR negativity. Amorphous microcalcifications were more commonly observed in the double HRc-negative group, while linear calcifications were more prevalent in both double and single HRc-positive groups ( = 0.020). Non-mass enhancement (NME) with a linear distribution was significantly more common in double HRc-negative lesions (38%), and NME with a segmental distribution in both double (43%) and single (50%) receptor-positive lesions ( = 0.042). Evaluation of DWI findings revealed that a higher lesion-to-normal breast parenchyma apparent diffusion coefficient (ADC) ratio statistically increased the probability of HRc positivity ( = 0.033).
Conclusion: Certain clinicopathological, mammography, and MRI features, along with the lesion-to-normal breast parenchyma ADC ratio, can serve as predictors for HRc status in DCIS lesions.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11589184 | PMC |
| http://dx.doi.org/10.4274/ejbh.galenos.2024.2024-5-1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Construction and evaluation of a multifactorial clinical model for discriminating benign and malignant breast tumors using LASSO algorithm based on retrospective cohort study.
Am J Cancer Res
December 2024
Department of Breast Surgery, Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine Shanghai 200011, China.
Breast cancer is one of the malignant tumors that seriously threaten women's health, and early diagnosis and detection of breast cancer are crucial for effective treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important diagnostic tool that allows for the dynamic observation of blood flow characteristics of breast tumors, including small lesions within the affected tissue. Currently, it is widely used in clinical practice and has been shown promising prospects.
View Article and Find Full Text PDFImaging Characteristics of Primary Mucinous Cystadenocarcinoma of the Breast: A Case Report and Literature Review.
Curr Med Imaging
January 2025
Department of Pathology, Affiliated Jinhua Hospital Zhejiang University School of Medicine, Jinhua, Zhejiang, 32100, P. R. China.
Introduction: Mucinous Cystadenocarcinoma (MCA) of the breast remains a relatively rare condition, and to date, there is no systematic summary of its imaging manifestations. Therefore, this report presents a detailed account of the diagnosis and treatment of mucinous cystadenocarcinoma in a 40-year-old woman, with a particular focus on imaging findings. Additionally, we conducted a comprehensive literature review on this disease and summarized its key imaging features.
View Article and Find Full Text PDFDevelopment and Validation of Dynamic 5-Year Breast Cancer Risk Model Using Repeated Mammograms.
JCO Clin Cancer Inform
December 2024
Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St Louis, St Louis, MO.
Article Synopsis
- Current long-term risk prediction models for breast cancer are not fully utilizing past mammogram images, and dynamic models have not been explored for routine medical use.
- A study examined a large group of women, analyzing their mammogram data over time to create a dynamic model that predicts the 5-year risk of developing breast cancer.
- The results showed that incorporating previous mammogram images significantly improved risk prediction, identifying high-risk women who could benefit from further screening or preventive measures.
Enhanced breast mass segmentation in mammograms using a hybrid transformer UNet model.
Comput Biol Med
January 2025
Information Technology Engineering Group, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran. Electronic address:
Breast mass segmentation plays a crucial role in early breast cancer detection and diagnosis, and while Convolutional Neural Networks (CNN) have been widely used for this task, their reliance on local receptive fields limits ability to capture long-range dependencies. Vision Transformers (ViTs), on the other hand, excel in this area by leveraging multi-head self-attention mechanisms to generate attention maps that dynamically gather global spatial information, significantly outperforming CNN-based architectures in various tasks. However, traditional transformer-based models come with challenges, including high computational complexity due to the self-attention mechanism and inefficiency in the static MLP fusion process.
View Article and Find Full Text PDFPerformance of high-resolution diffusion-weighted magnetic resonance imaging for detecting clinically occult early breast cancers: a multi-reader study.
Breast Cancer Res Treat
November 2024
Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Purpose: To compare mammography, breast ultrasound (US), high-resolution diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced breast MRI (DCE-MRI), and their combinations for detecting clinically occult early breast cancers (EBCs), including ductal carcinoma in situ (DCIS).
Methods: Three hundred and three consecutive women with screening imaging-detected early breast cancers (60 pure DCIS, 36 DCIS with microinvasion, and 207 invasive carcinoma less than 20 mm) who underwent breast MRI at 3 T including DW-MRI (b-values of 0, 800 and 1200 s/mm; in-plane resolution, 1.1 × 1.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!