Background: Radiologic assessment of tumor size is an integral part of the work-up for breast carcinoma. With improved radiologic equipment, surgical decision relies profoundly upon radiologic/clinical stage. We wanted to see the concordance between radiologic and pathologic tumor size to infer how accurate radiologic/clinical staging is.
Materials And Methods: The surgical pathology and ultrasonography reports of patients with breast carcinoma were reviewed. Data were collected for 406 cases. Concordance was defined as a size difference within ±2 mm.
Results: The difference between radiologic and pathologic tumor size was within ±2 mm in 40.4% cases. The mean radiologic size was 1.73 ± 1.06 cm. The mean pathologic size was 1.84 ± 1.24 cm. A paired -test showed a significant mean difference between radiologic and pathologic measurements (0.12 ± 1.03 cm, = 0.03). Despite the size difference, stage classification was the same in 59.9% of cases. Radiologic size overestimated stage in 14.5% of cases and underestimated stage in 25.6% of cases. The concordance rate was significantly higher for tumors ≤2 cm (pT1) (51.1%) as compared to those greater than 2 cm (≥pT2) (19.7%) ( < 0.0001). Significantly more lumpectomy specimens (47.5%) had concordance when compared to mastectomy specimens (29.8%) ( < 0.0001). Invasive ductal carcinoma had better concordance compared to other tumors ( = 0.02).
Conclusion: Mean pathologic tumor size was significantly different from mean radiologic tumor size. Concordance was in just over 40% of cases and the stage classification was the same in about 60% of cases only. Therefore, surgical decision of lumpectomy versus mastectomy based on radiologic tumor size may not always be accurate.
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http://dx.doi.org/10.1177/1742271X18804278 | DOI Listing |
Sci Rep
December 2024
Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada.
The goal of this study was to determine how radiologists' rating of image quality when using 0.5T Magnetic Resonance Imaging (MRI) compares to Computed Tomography (CT) for visualization of pathology and evaluation of specific anatomic regions within the paranasal sinuses. 42 patients with clinical CT scans opted to have a 0.
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December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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December 2024
Cancer Center, Department of Neurosurgery, Zhejiang Provincial People's Hospital,Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Approximately 90% of glioblastoma recurrences occur in the peritumoral brain zone (PBZ), while the spatial heterogeneity of the PBZ is not well studied. In this study, two PBZ tissues and one tumor tissue sample are obtained from each patient via preoperative imaging. We assess the microenvironment and the characteristics of infiltrating immune/tumor cells using various techniques.
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December 2024
Molecular Imaging Program at Stanford, Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, USA.
Molecular imaging using positron emission tomography (PET) provides sensitive detection and mapping of molecular targets. While cancer-associated fibroblasts and integrins have been proposed as targets for imaging of pancreatic ductal adenocarcinoma (PDAC), herein, spatial transcriptomics and proteomics of human surgical samples are applied to select PDAC targets. We find that selected cancer cell surface markers are spatially correlated and provide specific cancer localization, whereas the spatial correlation between cancer markers and immune-related or fibroblast markers is low.
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