This systematic review and network meta-analysis aimed to compare the diagnostic accuracy of 2-[F]FDG-PET/CT, F-NaF-PET/CT, MRI, contrast-enhanced CT, and bone scintigraphy for diagnosing bone metastases in patients with breast cancer. Following PRISMA-DTA guidelines, we reviewed studies assessing 2-[F]FDG-PET/CT, F-NaF-PET/CT, MRI, contrast-enhanced CT, and bone scintigraphy for diagnosing bone metastases in high-stage primary breast cancer (stage III or IV) or known primary breast cancer with suspicion of recurrence (staging or re-staging). A comprehensive search of MEDLINE/PubMed, Scopus, and Embase was conducted until February 2024. Inclusion criteria were original studies using these imaging methods, excluding those focused on AI/machine learning, primary breast cancer without metastases, mixed cancer types, preclinical studies, and lesion-based accuracy. Preference was given to studies using biopsy or follow-up as the reference standard. Risk of bias was assessed using QUADAS-2. Screening, bias assessment, and data extraction were independently performed by two researchers, with discrepancies resolved by a third. We applied bivariate random-effects models in meta-analysis and network meta-analyzed differences in sensitivity and specificity between the modalities. Forty studies were included, with 29 contributing to the meta-analyses. Of these, 13 studies investigated one single modality only. Both 2-[F]FDG-PET/CT (sensitivity: 0.94, 95% CI: 0.89-0.97; specificity: 0.98, 95% CI: 0.96-0.99), MRI (0.94, 0.82-0.98; 0.93, 0.87-0.96), and F-NaF-PET/CT (0.95, 0.85-0.98; 1, 0.93-1) outperformed the less sensitive modalities CE-CT (0.70, 0.62-0.77; 0.98, 0.97-0.99) and bone scintigraphy (0.83, 0.75-0.88; 0.96, 0.87-0.99). The network meta-analysis of multi-modality studies supports the comparable performance of 2-[F]FDG-PET/CT and MRI in diagnosing bone metastases (estimated differences in sensitivity and specificity, respectively: 0.01, -0.16 - 0.18; -0.02, -0.15 - 0.12). The results from bivariate random effects modelling and network meta-analysis were consistent for all modalities apart from F-NaF-PET/CT. We concluded that 2-[F]FDG-PET/CT and MRI have high and comparable accuracy for diagnosing bone metastases in breast cancer patients. Both outperformed CE-CT and bone scintigraphy regarding sensitivity. Future multimodality studies based on consented thresholds are warranted for further exploration, especially in terms of the potential role of F-NaF-PET/CT in bone metastasis diagnosis in breast cancer.
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http://dx.doi.org/10.1053/j.semnuclmed.2024.10.008 | DOI Listing |
Cancer Cell Int
December 2024
Department of Ultrasound, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China.
Gas therapy represents a promising strategy for cancer treatment, with nitric oxide (NO) therapy showing particular potential in tumor therapy. However, ensuring sufficient production of NO remains a significant challenge. Leveraging ultrasound-responsive nanoparticles to promote the release of NO is an emerging way to solve this challenge.
View Article and Find Full Text PDFClin Breast Cancer
December 2024
MKA Breast Cancer Clinic, Tepe Prime, Ankara, Turkey. Electronic address:
Trends Mol Med
December 2024
Cancer Signaling and Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA, USA. Electronic address:
Genetic and epigenetic defects of the p53 system have previously been associated with resistance to CDK4/6 inhibitors in women with HR breast cancer. Recent data from Kudo et al. demonstrate that CDK2-targeting agents may offer an effective strategy to circumvent such resistance by enforcing cellular senescence downstream of RBL2 dephosphorylation.
View Article and Find Full Text PDFSci Bull (Beijing)
December 2024
Breast Cancer Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China. Electronic address:
Am J Pathol
December 2024
Department of Computer Science, Faculty of Engineering Sciences, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of AI in computational histopathology to evaluate hypoxia in breast cancer. Weakly Supervised Deep Learning (WSDL) models can accurately detect morphological changes associated with hypoxia in routine Hematoxylin and Eosin (H&E) whole slide images (WSI).
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