Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer

Nicolò, Chiara; Périer, C.; Prague, Mélanie; Bellera, C.; MacGrogan, Gaëtan; Saut, Olivier; Benzekry, Sébastien

doi:10.1200/cci.19.00133

Cited by 56 publications

(67 citation statements)

References 48 publications

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“…Indeed, an older tumor has a greater probability of having already spread than a younger one. Altogether, the present findings could contribute to the development of personalized computational models of metastasis [24,64,65].…”

Section: DV Dtmentioning

confidence: 58%

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

et al. 2020

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Tumor growth curves are classically modeled by means of ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model, which could be used to reduce the dimensionality and improve predictive power. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 833 measurements in 94 animals. Candidate models of tumor growth included the exponential, logistic and Gompertz models. The exponential and-more notably-logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The previously reported populationlevel correlation between the Gompertz parameters was further confirmed in our analysis (R 2 > 0.92 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a reduced Gompertz function consisting of a single individual parameter (and one population parameter). Leveraging the population approach using Bayesian inference, we estimated times of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precision (width of the 95% prediction interval) was 15.6 days versus 210 days, for the breast cancer cell line. These results demonstrate the superior predictive power of the reduced Gompertz model, especially when combined with Bayesian estimation. They offer possible clinical perspectives for personalized prediction of

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Section: DV Dtmentioning

confidence: 58%

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

et al. 2020

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“…There is a growing number of ML models for survival analysis 2 , 27 . Similarly to the CPH case, we predicted patient survival ranking using three of them: Random Survival Forests (due to their frequent use in literature relevant to this study 11 , 13 – 15 ), Survival Support Vector Machines (given that their use of kernels to map survival in high-dimensional spaces makes them an attractive option 16 , 28 ), and Extreme Gradient Boosting (since in addition to its execution speed and performance 29 , its use for survival analysis remains largely unexplored). These are described as follows.…”

Section: Methodsmentioning

confidence: 99%

Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

Moncada‐Torres

Maaren

Hendriks

et al. 2021

Sci Rep

195

145

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Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

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“…An example of such mechanistic learning study was recently published for the prediction of metastatic relapse in early stage breast cancer. 146 Instead of using a biologically agnostic model for survival Figure 4 Mechanistic learning. To account for the increasing dimension of the quantitative data able to feed mechanistic models, we propose to combine methods from machine learning (ML) and mechanistic modeling.…”

Section: Perspectives For Combining Ai and Mathematical Modeling: Mecmentioning

confidence: 99%

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

Benzekry

2020

Clin Pharma and Therapeutics

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The amount of “big” data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: “mechanistic learning.”

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Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer

Cited by 56 publications

References 48 publications

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

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