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Updated: Feb 7, 2026

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
Published on: April 29, 2014
Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an
Marco Fornili1, Patrizia Boracchi2, Federico Ambrogi2
1Department of Clinical Sciences and Community Health, University of Milan, via Venezian 1, 20133, Milan, Italy. marco.fornili@unimi.it.
Background:
In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of multiple peaks. In this paper we present the use of a neural network extension of the piecewise exponential model to study the shape of the hazard function in time in dependence of covariates. The technique is applied to a dataset of 247 renal cell carcinoma patients from a randomized clinical trial.
Results:
An interaction effect of treatment with number of metastatic lymph nodes but not with pathologic T-stage is highlighted.
Conclusions:
Piecewise Exponential Artificial Neural Networks demonstrate a clinically useful and flexible tool in assessing interaction or time-dependent effects of the prognostic factors on the hazard function.
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