Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility

Johan van Soest*, Elisa Meldolesi, Ruud van Stiphout, Roberto Gatta, Andrea Damiani, Vincenzo Valentini, Philippe Lambin, Andre Dekker

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Purpose: Multiple models have been developed to predict pathologic complete response (pCR) in locally advanced rectal cancer patients. Unfortunately, validation of these models normally omit the implications of cohort differences on prediction model performance. In this work, we will perform a prospective validation of three pCR models, including information whether this validation will target transferability or reproducibility (cohort differences) of the given models. 
Methods: We applied a novel methodology, the cohort differences model, to predict whether a patient belongs to the training or to the validation cohort. If the cohort differences model performs well, it would suggest a large difference in cohort characteristics meaning we would validate the transferability of the model rather than reproducibility. We tested our method in a prospective validation of three existing models for pCR prediction in 154 patients. 
Results: Our results showed a large difference between training and validation cohort for one of the three tested models [Area under the Receiver Operating Curve (AUC) cohort differences model: 0.85], signaling the validation leans towards transferability. Two out of three models had a lower AUC for validation (0.66 and 0.58), one model showed a higher AUC in the validation cohort (0.70).
Discussion: We have successfully applied a new methodology in the validation of three prediction models, which allows us to indicate if a validation targeted transferability (large differences between training/validation cohort) or reproducibility (small cohort differences).
Original languageEnglish
JournalMedical Physics
Issue number9
Publication statusPublished - 2017

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