Axillary lymph node status is the most important prognostic factor in breast cancer patients and is currently determined by surgical dissection. This study was performed to assess whether dynamic gadopentetate dimeglumine (Gd) enhanced MRI is an accurate method for non-invasive staging of the axilla. 47 women with a new primary breast cancer underwent pre-operative dynamic Gd enhanced MRI of the ipsilateral axilla. Lymph node enhancement was quantitatively analysed using a region of interest method. Enhancement indices and nodal area were compared with histopathology of excised nodes using a receiver operating characteristic (ROC) curve approach. 10 patients had axillary metastases pathologically and all had > or =1 lymph node with an enhancement index of >21% and a nodal area of >0.4 cm(2). 37 patients had negative axillary nodes pathologically. 20 of these had enhancement indices <21% and nodal areas <0.4 cm(2). Using this method, a sensitivity of 100%, a specificity of 56%, a positive predictive value of 38% and a negative predictive value of 100% could be achieved. Using this method of quantitative assessment, dynamic Gd enhanced MRI may be a reliable method of predicting absence of axillary nodal metastases in women with breast cancer, thereby avoiding axillary surgery in women with a negative MRI study.
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http://dx.doi.org/10.1259/bjr.75.891.750220 | DOI Listing |
BMC Med Imaging
January 2025
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).
Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%).
BMC Med Imaging
January 2025
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Cognitive Sciences, University of California, 2201 Social & Behavioral Sciences Gateway, Irvine, CA, 92697, USA.
In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior rather than noise or other irrelevant factors.
View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
Center for Interdisciplinary Digital Sciences (CIDS), Department Information Services and High-Performance Computing (ZIH), Dresden University of Technology, 01062, Dresden, Germany.
Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades.
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