Objectives: The primary aim of this prospective study was to evaluate the accuracy of pre-operative ultrasound (US) alone and associated with a fine needle aspiration cytology (FNAC) or a core needle biopsy (CNB) in the diagnosis of axillary node involvement in patient with breast cancer. The secondary study objective was to determine if this US±FNAC or CNB can lead to the adequate axillary surgery in cN0 and cN1 patient.
Methods: A total of 121 consecutive women with stage cT1 to cT2, cN0/cN1, invasive breast cancer were prospectively identified at our institution between February 2, 2013 and August 30, 2013. The sensitivity, specificity, VPP, NPV were calculated, with confidence intervals, using the definitive histological result of the sentinel node biopsy (SLNB) or axillary lymph node dissection (ALND) as the baseline.
Results: Twenty-seven CNB and 2 FNAC were performed. For the whole series, the sensitivity and the specificity of US alone were 48.7% [36-59%] and 89% [83-94%]. For US±FNAC or CNB, the sensitivity and the specificity were 35.9% [26-38%] and 98.8% [94-100%]. Seven women with cN1 clinical examination had SLNB, which permit to decrease the number of ALND of 16.3%. It would have avoided unnecessary SLNB, prompting immediate ALND in 9 patients with cN0 axillae, which means a reduction of SLNB of 8.6%. US±FNAC or CNB lead to the adequate surgery in 72.7% of cases.
Conclusion: US±CNB or FNAC is also a relatively efficient and safe test and should be considered routinely. It allowed triaging patients to the well axillary surgery (SLNB or ALND).
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http://dx.doi.org/10.1016/j.gyobfe.2015.04.007 | DOI Listing |
Ann Surg Oncol
January 2025
Department of Radiology, University of Washington, Seattle, WA, USA.
Background: Ductal carcinoma in situ (DCIS) is overtreated, in part because of inability to predict which DCIS cases diagnosed at core needle biopsy (CNB) will be upstaged at excision. This study aimed to determine whether quantitative magnetic resonance imaging (MRI) features can identify DCIS at risk of upstaging to invasive cancer.
Methods: This prospective observational clinical trial analyzed women with a diagnosis of DCIS on CNB.
Unlabelled: Asymmetric cell division is used by stem cells to create diverse cell types while self-renewing the stem cell population. Biased segregation of molecularly distinct centrosomes could provide a mechanism to maintain stem cell fate, induce cell differentiation or both. However, the molecular mechanisms generating molecular and functional asymmetric centrosomes remain incompletely understood.
View Article and Find Full Text PDFPNAS Nexus
January 2025
Logic of Genomic Systems Laboratory (CNB-CSIC), Madrid E-28049, Spain.
While more data are becoming available on gene activity at different levels of biological organization, our understanding of the underlying biology remains incomplete. Here, we introduce a metabolic efficiency framework that considers highly expressed proteins (HEPs), their length, and biosynthetic costs in terms of the amino acids (AAs) they contain to address the observed balance of expression costs in cells, tissues, and cancer transformation. Notably, the combined set of HEPs in either cells or tissues shows an abundance of large and costly proteins, yet tissues compensate this with short HEPs comprised of economical AAs, indicating a stronger tendency toward mitigating costs.
View Article and Find Full Text PDFFront Neurol
January 2025
Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
Background: Spontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mortality and disability rates, imposing a significant economic burden on families and society. This retrospective study aimed to develop and evaluate an interpretable machine learning model to predict functional outcomes 3 months after SICH.
Methods: A retrospective analysis was conducted on clinical data from 380 patients with SICH who were hospitalized at three different centers between June 2020 and June 2023.
Molecules
January 2025
Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models.
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