title: Changes of lung tumour volume on CT - prediction of the reliability of assessments.
abstract: BACKGROUND
  For oncological evaluations, quantitative radiology gives clinicians significant insight into patients' response to therapy. In regard to the Response Evaluation Criteria in Solid Tumours (RECIST), the classification of disease evolution partly consists in applying thresholds to the measurement of the relative change of tumour. In the case of tumour volumetry, response thresholds have not yet been established. This study proposes and validates a model for calculating thresholds for the detection of minimal tumour change when using the volume of pulmonary lesions on CT as imaging biomarker.
  
  
  METHODS
  Our work is based on the reliability analysis of tumour volume measurements documented by the Quantitative Imaging Biomarker Alliance. Statistics of measurements were entered into a multi-parametric mathematical model of the relative changes derived from the Geary-Hinkley transformation. The consistency of the model was tested by comparing modelled thresholds against Monte Carlo simulations of tumour volume measurements with additive random error. The model has been validated by repeating measurements on real patient follow ups.
  
  
  RESULTS
  For unchanged tumour volume, relying on a normal distribution of error, the agreement between model and simulations featured a type I error of 5.25%. Thus, we established that a threshold of 35% of volume reduction corresponds to a partial response (PR) and a 55% volume increase corresponds to progressive disease (PD). Changes between -35 and +55% are categorized as stable disease (SD). Tested on real clinical data, 97.1% [95.7; 98.0] of assessments fall into the range of variability predicted by our model of confidence interval.
  
  
  CONCLUSIONS
  Our study indicates that the Geary Hinkley model, using published statistics, is appropriate to predict response thresholds for the volume of pulmonary lesions on CT.
authors.affiliation: MEDIAN Technologies - Sciences, 1800 route des crêtes Les Deux Arcs Bat B, Valbonne, 06560, France. Hubert.beaumont@mediantechnologies.com.
authors.forename: Hubert
authors.initials: H
authors.lastname: Beaumont
authors.affiliation: Université d'Angers - Mathematics, Angers, 49000, France. souchet@math.univ-angers.fr.
authors.forename: Simon
authors.initials: S
authors.lastname: Souchet
authors.affiliation: Université d'Angers - Mathematics, Angers, 49000, France. Jean-Marc.Labatte@univ-angers.fr.
authors.forename: Jean Marc
authors.initials: JM
authors.lastname: Labatte
authors.affiliation: Centre Anticancer Antoine Lacassagne - Radiology, Nice, 06100, France. antoine.iannessi@nice.unicancer.fr.
authors.forename: Antoine
authors.initials: A
authors.lastname: Iannessi
authors.affiliation: START - South Texas Accelerated Research Therapeutics, LLC - Clinical Research, San Antonio, TX, USA. atolcher@start.stoh.com.
authors.forename: Anthony William
authors.initials: AW
authors.lastname: Tolcher
mesh_terms: D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D008954:Models, Biological; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed; D047368:Tumor Burden