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).
PURPOSE: Multiple models have been developed to predict pathologic complete response (pCR) in locally advanced rectal cancerpatients. 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).
Authors: Janna E van Timmeren; Sara Carvalho; Ralph T H Leijenaar; Esther G C Troost; Wouter van Elmpt; Dirk de Ruysscher; Jean-Pierre Muratet; Fabrice Denis; Tanja Schimek-Jasch; Ursula Nestle; Arthur Jochems; Henry C Woodruff; Cary Oberije; Philippe Lambin Journal: PLoS One Date: 2019-06-03 Impact factor: 3.240
Authors: Zhenwei Shi; Kieran G Foley; Juan Pablo de Mey; Emiliano Spezi; Philip Whybra; Tom Crosby; Johan van Soest; Andre Dekker; Leonard Wee Journal: Front Oncol Date: 2019-12-16 Impact factor: 6.244