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Validated clinico-pathologic nomogram in the prediction of HER2 status in gastro-oesophageal cancer
Lorenzo Fornaro1, Caterina Vivaldi2, Annamaria Parnofiello3
1Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy. lorenzo.fornaro@gmail.com.
Background:
HER2 is the only validated predictive biomarker in gastro-oesophageal carcinoma (GOC). However, several factors, such as heterogeneity in protein expression, shortage of evaluable tumour tissue and need for quick target assessment, underline the usefulness of a pre-screening tool in order to anticipate HER2 status.
Methods:
Data from 723 consecutive GOC analysed for HER2 at four Italian Institutions were collected. HER2 positivity was defined as 3+ by immunohistochemistry (IHC) or 2+ with gene amplification by in situ hybridisation (ISH). A multivariate logistic regression model was built using data from 413 cases, whereas 310 patients served as validation cohort. C-index, visual inspection of the calibration plot, Brier score and Spiegelhalter z-test were used to assess the performance of the nomogram.
Results:
HER2 positive rate was 17.4%. Four variables were retained after adjustment in the final model: grading, Lauren's histotype, pathologic material analysed (surgical specimen/biopsy) and site of tissue collection (primary tumour/metastases). Visual inspection of the calibration plot revealed a very good overlap between predicted and observed probabilities, with a Brier score of 0.101 and a non-significant Spiegelhalter z-test (P = 0.319). C-index resulted in 0.827 (95%CI 0.741-0.913).
Conclusion:
A simple nomogram based on always-available pathologic information accurately predicts the probability of HER2 positivity in GOC.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Key parameters for method validation include:

