Objective: This study was performed to investigate the proportion as well as the predictive factors of pathologic complete response in HER2-positive and axillary lymph node positive breast cancer after neoadjuvant paclitaxel, carboplatin plus with trastuzumab (PCH).
Results: The pCR rate in the breast, axilla and both was 44.3% (39/88), 47.7% (42/88) and 34.1% (30/88), respectively. Patients with and without pCR were similar in term of age, BMI, menstrual status, family history, treatment cycles and tumor characteristics (laterality and size of tumor). Multivariate logistic regression demonstrated that pCR was significantly associated with HR negativity (HR = 5.587, 95% CI 2.25-3.889, < 0.001), high Ki67 index (HR = 4.130, 95% CI 1.607-10.610, = 0.003). Further investigation found that patients with HR-negative/high Ki67 index had higher pCR rate, compared to other patients (HR = 7.583, 95% CI 2.503-22.974, < 0.001).
Materials And Methods: 88 consecutive Chinese HER2-positive/axillary lymph node-positive breast cancer patients with neodjuvant therapy regimen containing paclitaxel, carboplatin and trastuzumab were divided into two groups: pathological complete response (pCR) or non-pCR group. Clinico-pathological characteristics were compared and analyzed, and univariate and multivariate analyses were performed to detect the predictive factors of pCR.
Conclusions: Preoperative PCH regimen was an effective neoadjuvant therapy in HER2 positive and axillary lymph node positive patients, and patients coexisting with HR-negative and high Ki67 index may benefit more from this regimen.
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http://dx.doi.org/10.18632/oncotarget.17993 | DOI Listing |
Sci Rep
December 2024
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
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December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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