Background: Sentinel lymph node biopsy (SLNB) has largely replaced axillary dissection (ALND) for axillary staging in early breast cancer. However, intense pathologic evaluation is not routinely available intraoperatively; therefore, patients with SLN metastasis may require a second surgery for completion ALND. We hypothesized that a single-section approach (by either frozen section [FS] or touch preparation analysis [TPA]) could be accurate for intraoperative SLN evaluation.
Methods: We performed a prospective, blinded study of patients undergoing SLNB for breast cancer from September 2004 to July 2006. SLNs were bivalved along the long axis, underwent FS and TPA of the facing halves, followed by routine sentinel node processing (serial sectioning with hematoxylin/eosin staining). A single pathologist reviewed all study slides and was blinded to the permanent section interpretation.
Results: We analyzed 233 nodes from 118 patients. Overall, 21% of patients (N = 25) had SLN metastasis by serial-section histopathology. Single-section FS and TPA had similar sensitivities (0.67 and 0.66, P = .82) and specificities (0.995 and 0.995, P = 1.0) for detection of SLN metastasis, yielding equivalent accuracies (95%). All micrometastases (<2 mm; N = 4) were missed by both techniques. False positives were rare-only one in each group (2% overall).
Conclusion: Single-section TPA and FS have similar accuracies and can be safely used to identify the majority of patients with SLN metastasis, sparing these patients a delayed ALND. False-negative results from TPA or FS occur in patients with micrometastatic disease, for which the role of completion ALND remains controversial.
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http://dx.doi.org/10.1245/s10434-008-9944-8 | DOI Listing |
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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