Objective: To evaluate the accuracy of fine needle aspiration cytology (FNAC) in BI-RADS3 breast lesions.
Methods: Between January 2004 and December 2007, 337 cases from BI-RADS3 lesions underwent FNAC. Three to six needle passes were made on each patient. In 67 cases (20%) a histological biopsy was performed. Cytological and histological interpretations were performed by the same pathologist.
Results: The histological diagnosis showed that 88% (59/67) of BI-RADS3 breast lesions were benign. Only 6% (4/67) were malignant, consisting of ductal carcinoma in situ and infiltrating ductal carcinoma.
Conclusion: BI-RADS3 lesions remain disruptive in their management. However, the correlation between cytology and histology showed that most of these lesions were benign and that finally FNAC remains a useful and accurate test in the management of these lesions.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/j.1365-2303.2009.00652.x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?
Eur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Posttraining Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
Radiol Artif Intell
January 2025
From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15,81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).
Purpose To investigate whether the computational effort of 3D CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality.
View Article and Find Full Text PDFEfficient and accurate commissioning and quality assurance of radiosurgery beam via prior-embedded implicit neural representation learning.
Med Phys
January 2025
Department of Radiation Oncology, Stanford University, Palo Alto, California, USA.
Background: Dosimetric commissioning and quality assurance (QA) for linear accelerators (LINACs) present a significant challenge for clinical physicists due to the high measurement workload and stringent precision standards. This challenge is exacerbated for radiosurgery LINACs because of increased measurement uncertainty and more demanding setup accuracy for small-field beams. Optimizing physicists' effort during beam measurements while ensuring the quality of the measured data is crucial for clinical efficiency and patient safety.
View Article and Find Full Text PDFEnhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2.
MethodsX
June 2025
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India.
The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.
View Article and Find Full Text PDFWant 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!