Classical mathematical models for prediction of response to chemotherapy and immunotherapy
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
PLoS computational biology - 18(2022), 2 vom: 01. Feb., Seite e1009822 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ghaffari Laleh, Narmin [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 12.04.2022 Date Revised 12.04.2022 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pcbi.1009822 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
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| 700 | 1 | |a Grajek, Julia |e verfasserin |4 aut | |
| 700 | 1 | |a Staňková, Kateřina |e verfasserin |4 aut | |
| 700 | 1 | |a Pearson, Alexander T |e verfasserin |4 aut | |
| 700 | 1 | |a Muti, Hannah Sophie |e verfasserin |4 aut | |
| 700 | 1 | |a Trautwein, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Enderling, Heiko |e verfasserin |4 aut | |
| 700 | 1 | |a Poleszczuk, Jan |e verfasserin |4 aut | |
| 700 | 1 | |a Kather, Jakob Nikolas |e verfasserin |4 aut | |
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