Publications by authors named "Harriet Branford-White"

Purpose: Currently there is no generally accepted standardized approach for the pathological evaluation of soft tissue sarcoma (STS) histology appearance after preoperative radiotherapy (PORT). This study aimed to investigate the prognostic value of pathological appearance after PORT for patients with high-grade limb/trunk STS.

Methods: A cohort of 116 patients with high-grade STS of the limb/trunk treated with PORT followed by resection were evaluated.

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The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the 'molecular tumour board' into 'standard of care' ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting.

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Article Synopsis
  • * The Birmingham Orthopaedic Oncology Meeting in January 2024 gathered 300 experts from over 50 countries to reach global consensus on chondrosarcoma treatment and address challenges related to periprosthetic joint infection in cancer surgeries.
  • * The meeting aimed to not only resolve current controversies but also to encourage collaboration among specialists for future research that could improve patient outcomes worldwide.
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Objective: The aim of this study was to analyze the wound complication (WC) rate and to determine the risk factors for WC in patients with soft tissue sarcoma treated with preoperative radiotherapy followed by surgical resection.

Methods: Using the database of Oxford University Hospital (OUH) we retrospectively studied 126 cases of soft tissue sarcomas treated with preoperative radiotherapy and surgery between 2007 and 2021. WC were defined as minor wound complication (MiWC) not requiring surgical intervention or major wound complication (MaWC) if they received a secondary surgical intervention.

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Article Synopsis
  • Preoperative radiotherapy for soft tissue sarcoma increases the likelihood of major wound complications after surgery, prompting a study to create a predictive nomogram.
  • The analysis was based on 126 patients treated at Oxford University Hospital from 2007 to 2021, identifying age, tumor size, and metastasis as key independent risk factors for complications.
  • The developed nomogram achieved a sensitivity of 0.90 and specificity of 0.76, with an AUC of 0.86, demonstrating its effectiveness in predicting major wound complications post-surgery.
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Background: Establishment of a cancer registry is a complex process that requires substantial resources and careful planning. There are numerous resources available to provide guidance for this, which include guidelines and frameworks of varying quality. It is the authors' goal to identify evidence-based recommendations within the literature to help guide the process of designing a new registry with optimal efficiency, workability and data use.

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A 72-year-old lady underwent a Copeland hemiarthoplasty of the shoulder for rotator cuff arthropathy with a good functional outcome. Her past medical history included previous management of a malignant melanoma. Several years following arthroplasty surgery, she acutely developed signs and symptoms of prosthetic joint infection.

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Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning.

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