P. J. Brown
Leeds Teaching Hospitals NHS Trust
Machine learningInternal medicineRadiologyOncologyLogistic regressionArtificial intelligenceTomographyStage (cooking)Standardized uptake valueClinical endpointChemoradiotherapyDiseaseMEDLINEDistributed learningAnal cancerLetter to the editorLarynxHypopharynx squamous cell carcinomaAnal Squamous Cell CarcinomaFdg pet ctPre treatmentIncidence (epidemiology)Proportional hazards modelRadiation therapyReceiver operating characteristicMedicineCohortArea under the curve
4Publications
1H-index
12Citations
Publications 4
Newest
#1Ananya Choudhury (Maastricht University Medical Centre)H-Index: 2
#2Stelios Theophanous (University of Leeds)
Last. Ane L Appelt (University of Leeds)H-Index: 15
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Abstract Background and purpose Predicting outcomes is challenging in rare cancers. Single-institutional datasets are often small and multi-institutional data sharing is complex. Distributed learning allows machine learning models to use data from multiple institutions without exchanging individual patient-level data. We demonstrate this technique in a proof-of-concept study of anal cancer patients treated with chemoradiotherapy across multiple European countries. Materials and methods atomCAT i...
1 CitationsSource
#1Jim Zhong (Leeds Teaching Hospitals NHS Trust)H-Index: 5
#2R. Frood (Leeds Teaching Hospitals NHS Trust)H-Index: 6
Last. Andrew Scarsbrook (University of Leeds)H-Index: 8
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AIM To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[18F]-fluoro-2-deoxy- d- glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally advanced larynx and hypopharynx squamous cell carcinoma (SCC) receiving (chemo)radiotherapy. MATERIALS AND METHODS Patients with larynx and hypopharynx SCC treated with definitive (chemo)radiotherapy at a specialist cancer centre undergoing pre-tre...
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#1Peter Brown (Leeds Teaching Hospitals NHS Trust)
#1P. J. Brown (Leeds Teaching Hospitals NHS Trust)H-Index: 1
Last. Andrew Scarsbrook (Leeds Teaching Hospitals NHS Trust)H-Index: 29
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#1P. J. Brown (Leeds Teaching Hospitals NHS Trust)H-Index: 1
#2Jim Zhong (Leeds Teaching Hospitals NHS Trust)H-Index: 5
Last. Andrew Scarsbrook (University of Leeds)H-Index: 29
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Purpose Incidence of anal squamous cell carcinoma (ASCC) is increasing, with curative chemoradiotherapy (CRT) as the primary treatment of non-metastatic disease. A significant proportion of patients have locoregional treatment failure (LRF), but distant relapse is uncommon. Accurate prognostication of progression-free survival (PFS) would help personalisation of CRT regimens. The study aim was to evaluate novel imaging pre-treatment features, to prognosticate for PFS in ASCC.
13 CitationsSource