Background: The aim of this retrospective study was to evaluate the stability of spinal metastases in gynecologic cancer patients (pts) on the basis of a validated scoring system after radiotherapy (RT), to define prognostic factors for stability and to calculate survival.
Methods: Fourty-four women with gynecologic malignancies and spinal bone metastases were treated at our department between January 2000 and January 2012. Out of those 34 were assessed regarding stability using the Taneichi score before, 3 and 6 months after RT. Additionally prognostic factors for stability, overall survival, and bone survival (time between first day of RT of bone metastases and death from any cause) were calculated.
Results: Before RT 47% of pts were unstable and 6 months after RT 85% of pts were stable. Karnofsky performance status (KPS) >70% (p = 0.037) and no chemotherapy (ChT) (p = 0.046) prior to RT were significantly predictive for response. 5-year overall survival was 69% and 1-year bone survival was 73%.
Conclusions: RT is capable of improving stability of osteolytic spinal metastases from gynecologic cancer by facilitating re-ossification in survivors. KPS may be a predictor for response. Pts who received ChT prior to RT may require additional bone supportive treatment to overcome bone remodeling imbalance. Survival in women with bone metastases from gynecologic cancer remains poor.
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http://dx.doi.org/10.1186/1748-717X-9-194 | DOI Listing |
Breast Cancer (Auckl)
January 2025
Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
Background: Texture analysis has the potential to deliver quantitative imaging markers. Patients receiving computed tomography (CT)-guided percutaneous bone biopsies could be characterized using texture analysis derived from CT. Especially for breast cancer (BC) patients, it could be crucial to better predict the outcome of the biopsy to better reflect the immunohistochemistry status of the tumor.
View Article and Find Full Text PDFNucl Med Mol Imaging
February 2025
National Cyclotron and PET Centre, Chulabhorn Hospital, 906 Kamphaeng Phet 6 Rd., Talat Bang Khen, Lak Si, Bangkok, 10210 Thailand.
Purpose: Prostate-specific membrane antigen (PSMA) Positron emission tomography/magnetic resonance imaging (PET/MRI) surpasses conventional MRI (cMRI) in prostate cancer (PCa) evaluation. Our objective is to evaluate correlation of quantitative parameters in PCa using Fluorine-18 (F-18) PSMA-1007 PET/MRI and their potential for predicting metastases.
Methods: This retrospective study included 51 PCa patients.
Sci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
View Article and Find Full Text PDFClin Cancer Res
January 2025
Stanford University, Palo Alto, CA, United States.
Purpose: After failing primary and secondary hormonal therapy, castration-resistant and neuroendocrine prostate cancer metastatic to the bone is invariably lethal, although treatment with docetaxel and carboplatin can modestly improve survival. Therefore, agents targeting biologically relevant pathways in PCa and potentially synergizing with docetaxel and carboplatin in inhibiting bone metastasis growth are urgently needed.
Experimental Design: Phosphorylated (activated) AXL expression in human prostate cancer bone metastases was assessed by immunohistochemical staining.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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