@article{TEXTUAL,
      recid = {6314},
      author = {Ghaffari Laleh, Narmin and Lavinia Loeffler, Chiara Maria  and Grajek, Julia and Staňková, Kateřina and Pearson,  Alexander T. and Muti, Hannah Sophie and Trautwein,  Christian and Enderling, Heiko and Poleszczuk, Jan and  Kather, Jakob Nikolas},
      title = {Classical mathematical models for prediction of response  to chemotherapy and immunotherapy},
      journal = {PLOS Computational Biology},
      address = {2022-02-04},
      number = {TEXTUAL},
      abstract = {Classical mathematical models of tumor growth have shaped  our understanding of cancer and have broad practical  implications for treatment scheduling and dosage. However,  even the simplest textbook models have been barely  validated in real world-data of human patients. In this  study, we fitted a range of differential equation models to  tumor volume measurements of patients undergoing  chemotherapy or cancer immunotherapy for solid tumors. We  used a large dataset of 1472 patients with three or more  measurements per target lesion, of which 652 patients had  six or more data points. We show that the early treatment  response shows only moderate correlation with the final  treatment response, demonstrating the need for nuanced  models. We then perform a head-to-head comparison of six  classical models which are widely used in the field: the  Exponential, Logistic, Classic Bertalanffy, General  Bertalanffy, Classic Gompertz and General Gompertz model.  Several models provide a good fit to tumor volume  measurements, with the Gompertz model providing the best  balance between goodness of fit and number of parameters.  Similarly, when fitting to early treatment data, the  general Bertalanffy and Gompertz models yield the lowest  mean absolute error to forecasted data, indicating that  these models could potentially be effective at predicting  treatment outcome. In summary, we provide a quantitative  benchmark for classical textbook models and state-of-the  art models of human tumor growth. We publicly release an  anonymized version of our original data, providing the  first benchmark set of human tumor growth data for  evaluation of mathematical models.},
      url = {http://knowledge.uchicago.edu/record/6314},
}