title: Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging.
abstract: OBJECTIVE
  To develop and validate an accurate predictive model and a nomogram for pathologic complete response (pCR) after chemoradiotherapy (CRT) for rectal cancer based on clinical and sequential PET-CT data. Accurate prediction could enable more individualised surgical approaches, including less extensive resection or even a wait-and-see policy.
  
  
  METHODS
  Population based databases from 953 patients were collected from four different institutes and divided into three groups: clinical factors (training: 677 patients, validation: 85 patients), pre-CRT PET-CT (training: 114 patients, validation: 37 patients) and post-CRT PET-CT (training: 107 patients, validation: 55 patients). A pCR was defined as ypT0N0 reported by pathology after surgery. The data were analysed using a linear multivariate classification model (support vector machine), and the model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
  
  
  RESULTS
  The occurrence rate of pCR in the datasets was between 15% and 31%. The model based on clinical variables (AUC(train)=0.61±0.03, AUC(validation)=0.69±0.08) resulted in the following predictors: cT- and cN-stage and tumour length. Addition of pre-CRT PET data did not result in a significantly higher performance (AUC(train)=0.68±0.08, AUC(validation)=0.68±0.10) and revealed maximal radioactive isotope uptake (SUV(max)) and tumour location as extra predictors. The best model achieved was based on the addition of post-CRT PET-data (AUC(train)=0.83±0.05, AUC(validation)=0.86±0.05) and included the following predictors: tumour length, post-CRT SUV(max) and relative change of SUV(max). This model performed significantly better than the clinical model (p(train)<0.001, p(validation)=0.056).
  
  
  CONCLUSIONS
  The model and the nomogram developed based on clinical and sequential PET-CT data can accurately predict pCR, and can be used as a decision support tool for surgery after prospective validation.
authors.affiliation: Department of Radiation Oncology (MAASTRO), Maastricht University Medical Centre, The Netherlands. ruud.vanstiphout@maastro.nl
authors.forename: Ruud G P M
authors.initials: RG
authors.lastname: van Stiphout
authors.forename: Guido
authors.initials: G
authors.lastname: Lammering
authors.forename: Jeroen
authors.initials: J
authors.lastname: Buijsen
authors.forename: Marco H M
authors.initials: MH
authors.lastname: Janssen
authors.forename: Maria Antonietta
authors.initials: MA
authors.lastname: Gambacorta
authors.forename: Pieter
authors.initials: P
authors.lastname: Slagmolen
authors.forename: Maarten
authors.initials: M
authors.lastname: Lambrecht
authors.forename: Domenico
authors.initials: D
authors.lastname: Rubello
authors.forename: Marcello
authors.initials: M
authors.lastname: Gava
authors.forename: Alessandro
authors.initials: A
authors.lastname: Giordano
authors.forename: Eric O
authors.initials: EO
authors.lastname: Postma
authors.forename: Karin
authors.initials: K
authors.lastname: Haustermans
authors.forename: Carlo
authors.initials: C
authors.lastname: Capirci
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Valentini
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D049268:Positron-Emission Tomography; D012004:Rectal Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D014057:Tomography, X-Ray Computed