Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation

This publication has 48 references indexed in Scilit:

• Dose–response analysis of parotid gland function: What is the best measure of xerostomia?
  Radiotherapy and Oncology, 2013
• Parotid-sparing intensity modulated versus conventional radiotherapy in head and neck cancer (PARSPORT): a phase 3 multicentre randomised controlled trial
  The Lancet Oncology, 2011
• Parotid Gland Function After Radiotherapy: The Combined Michigan and Utrecht Experience
  International Journal of Radiation Oncology - Biology - Physics (IJROBP), 2010
• Pre-trial quality assurance processes for an intensity-modulated radiation therapy (IMRT) trial: PARSPORT, a UK multicentre Phase III trial comparing conventional radiotherapy and parotid-sparing IMRT for locally advanced head and neck cancer
  British Journal of Radiology, 2009
• Parotid gland sparing IMRT for head and neck cancer improves xerostomia related quality of life
  Radiation Oncology, 2008
• Incidence of late rectal bleeding in high-dose conformal radiotherapy of prostate cancer using equivalent uniform dose–based and dose–volume–based normal tissue complication probability models
  International Journal of Radiation Oncology - Biology - Physics (IJROBP), 2007
• Radiation‐induced xerostomia in patients with head and neck cancer
  Cancer, 2006
• Adjusting batch effects in microarray expression data using empirical Bayes methods
  Biostatistics, 2006
• A TCP‐NTCP estimation module using DVHs and known radiobiological models and parameter sets
  Journal of Applied Clinical Medical Physics, 2004
• Complication Probability as Assessed from Dose-Volume Histograms
  Radiation Research, 1985