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Differentiation of histological calcification classifications in breast cancer using ultrashort echo time and chemical shift-encoded imaging MRI.
Front Oncol
December 2024
Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom.
Introduction: Ductal carcinoma (DCIS) accounts for 25% of newly diagnosed breast cancer cases with only 14%-53% developing into invasive ductal carcinoma (IDC), but currently overtreated due to inadequate accuracy of mammography. Subtypes of calcification, discernible from histology, has been suggested to have prognostic value in DCIS, while the lipid composition of saturated and unsaturated fatty acids may be altered in synthesis with potential sensitivity to the difference between DCIS and IDC. We therefore set out to examine calcification using ultra short echo time (UTE) MRI and lipid composition using chemical shift-encoded imaging (CSEI), as markers for histological calcification classification, in the initial step towards application.
View Article and Find Full Text PDFUpgrade Rate of Exclusively MRI-Detected Papillomas in Asymptomatic Patients Undergoing Screening or Extent of Disease Examinations.
J Breast Imaging
December 2024
Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.
Objective: To determine the upgrade rate of exclusively MRI-detected benign papillomas in asymptomatic high-risk patients, patients with a history of cancer, or patients with known malignancy.
Methods: This IRB-approved retrospective study reviewed all breast MRI-guided biopsies yielding papilloma on pathology for all asymptomatic patients undergoing breast MRI for high-risk screening, newly diagnosed breast cancer, or a personal history of breast cancer. All cases were followed by excision or 2-year imaging follow-up.
Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care.
Life (Basel)
November 2024
Clinic of Endocrinology and Metabolic Diseases, St George University Hospital, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S.
View Article and Find Full Text PDFIncreasing Rates but Persistent Variability of Immediate Breast Reconstruction: Real-Time Data from a Population-Based Study (2012-2022).
Ann Surg Oncol
November 2024
Department of Surgical Oncology, Netherlands Cancer Institute Antoni van Leeuwenhoek, Amsterdam, The Netherlands.
Article Synopsis
- The study examines the impact of immediate breast reconstruction (IBR) on the quality of life for breast cancer patients in the Netherlands over the past decade.
- Findings show a significant increase in IBR usage for both ductal carcinoma in situ (DCIS) and invasive breast cancer, with positive associations linked to patient age and treatment factors.
- Despite the overall rise in IBR, there are notable differences in its application among various hospitals, prompting further research to address these disparities.
Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.
Comput Biol Med
January 2025
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Pharmaceutical Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, UMC-Location AMC, University of Amsterdam, Amsterdam, the Netherlands. Electronic address:
Article Synopsis
- Axillary lymph node dissection (ALND) is the standard treatment for breast cancer patients with positive sentinel lymph nodes, but many might have no additional cancerous nodes, recently prompting research into better prediction tools for non-sentinel lymph node metastasis.
- The study compares the effectiveness of the MSKCC nomogram and various machine learning (ML) models, finding that the Random Forest model outperforms the nomogram in predicting NSLN metastasis among Iranian breast cancer patients.
- This research highlights the potential of AI and ML to enhance prognosis accuracy while recognizing the complications associated with unnecessary ALND procedures.
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