Publications by authors named "J Ellbrant"

Background: Health-related quality of life and patient-related outcome measures for patients with cancer have gained increased interest over the last decade. However, few prospective studies with longitudinal data evaluated health-related quality of life in patients with breast cancer. This study aimed to investigate how health-related quality of life changed from the time of diagnosis to 1 year after breast cancer surgery for the main surgical techniques.

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Background: Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned.

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Background: Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup.

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Introduction: Neoadjuvant chemotherapy (NAC) is an established treatment option for early breast cancer, potentially downstaging the tumor and increasing the eligibility for breast-conserving surgery (BCS). The primary aim of this study was to assess the rate of BCS after NAC, and the secondary aim was to identify predictors of application of BCS after NAC.

Materials And Methods: This was an observational prospective cohort study of 226 patients in the SCAN-B (Clinical Trials NCT02306096) neoadjuvant cohort during 2014-2019.

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Newly diagnosed breast cancer (BC) patients with clinical T1-T2 N0 disease undergo sentinel-lymph-node (SLN) biopsy, although most of them have a benign SLN. The pilot noninvasive lymph node staging (NILS) artificial neural network (ANN) model to predict nodal status was published in 2019, showing the potential to identify patients with a low risk of SLN metastasis. The aim of this study is to assess the performance measures of the model after a web-based implementation for the prediction of a healthy SLN in clinically N0 BC patients.

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