Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

Kondylakis, Haridimos; Axenie, Cristian; Bastola, Dhundy; Katehakis, Dimitrios G.; Kouroubali, Angelina; Kurz, Daria; Larburu, Nekane; Macía, Iván; Maguire, Roma; Maramis, Christos; Marias, Kostas; Morrow, Philip; Muro, Naiara; Núñez-Benjumea, Francisco J.; Rampun, Andrik; Rivera-Romero, Octavio; Scotney, Bryan; Signorelli, Gabriel Ruiz; Wang, Hui; Tsiknakis, Manolis; Zwiggelaar, Reyer

doi:10.2196/22034

Cited by 14 publications

(7 citation statements)

References 132 publications

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“…This perspective advocates the combination of mechanistic modelling and machine learning that goes beyond measurement-informed bio-physical models and towards personalised disease evolution profiles learnt from clinical oncology data [85]. Tackling both bottom-up and top-down interactions, such a framework could offer a better understanding of the causes and consequences of (physical) interactions in cancer and their connections to the biological hallmarks of cancer broadly described in Sections 2.1-2.3.…”

Section: Learning Mechanistic Interaction Networkmentioning

confidence: 99%

The Multiple Dimensions of Networks in Cancer: A Perspective

2021

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This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

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Section: Learning Mechanistic Interaction Networkmentioning

confidence: 99%

The Multiple Dimensions of Networks in Cancer: A Perspective

2021

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“…This interest is the result of an intrinsic limitation of measuring tools, for which it is rare that a single acquisition method provides complete understanding about a phenomenon or a system of interest [4], [6], [7]. Indeed, it is true that natural and anthropogenic processes and systems can be very complex, so that different types of instruments, measurement techniques, experimental setups, and other types of sources of information might be necessary to provide a complete characterization of their relevant properties and conditions.…”

Section: Introductionmentioning

confidence: 99%

“…For these reasons, multimodal data analysis has become a crucial factor in a number of physical, biological, environmental, and eventually sociological application scenarios [4]- [8], [10]. In fact, multimodal data analysis can help in extracting relevant information from the data for multiple purposes and end-users, so that knowledge can be built and decision can be safely taken.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, data generated by very complex systems, driven by numerous underlying processes and that depend on a large number of variables are difficult to directly evaluate and compare. It is worth noting that large scale heterogeneous datasets are typically considered when performing multimodal data analysis in order to fully exploit its ability to characterize complex phenomena, hence enabling global scale understanding of the considered problem [4]- [6], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Marinoni,

Chlaily,

Khachatrian

et al. 2021

Preprint

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Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations. In fact, in multimodal data analysis, not all observations would show the same level of reliability or information quality, nor an homogeneous distribution of errors and uncertainties. This condition might undermine the classic assumptions ensemble learning and transfer learning methods rely on. In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue. By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified. This information is then used to set-up ensemble learning and transfer learning architectures. We test our approach on multimodal datasets acquired in diverse research fields (remote sensing, brain-computer interfaces, photovoltaic energy). Experimental results show the validity and the robustness of our approach, able to outperform state-of-the-art techniques.

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“…Our study advocates the improvement of mechanistic modeling with the help of machine learning. Our thesis goes beyond measurements-informed biophysical processes models, as described by Cristini et al (2017) , and toward human-understandable personalized disease evolution and therapy profiles learned from data, as foreseen by Kondylakis et al (2020) .…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning

Kurz¹,

Sánchez²,

Axenie³

2021

Front. Artif. Intell.

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For decades, researchers have used the concepts of rate of change and differential equations to model and forecast neoplastic processes. This expressive mathematical apparatus brought significant insights in oncology by describing the unregulated proliferation and host interactions of cancer cells, as well as their response to treatments. Now, these theories have been given a new life and found new applications. With the advent of routine cancer genome sequencing and the resulting abundance of data, oncology now builds an “arsenal” of new modeling and analysis tools. Models describing the governing physical laws of tumor–host–drug interactions can be now challenged with biological data to make predictions about cancer progression. Our study joins the efforts of the mathematical and computational oncology community by introducing a novel machine learning system for data-driven discovery of mathematical and physical relations in oncology. The system utilizes computational mechanisms such as competition, cooperation, and adaptation in neural networks to simultaneously learn the statistics and the governing relations between multiple clinical data covariates. Targeting an easy adoption in clinical oncology, the solutions of our system reveal human-understandable properties and features hidden in the data. As our experiments demonstrate, our system can describe nonlinear conservation laws in cancer kinetics and growth curves, symmetries in tumor’s phenotypic staging transitions, the preoperative spatial tumor distribution, and up to the nonlinear intracellular and extracellular pharmacokinetics of neoadjuvant therapies. The primary goal of our work is to enhance or improve the mechanistic understanding of cancer dynamics by exploiting heterogeneous clinical data. We demonstrate through multiple instantiations that our system is extracting an accurate human-understandable representation of the underlying dynamics of physical interactions central to typical oncology problems. Our results and evaluation demonstrate that, using simple—yet powerful—computational mechanisms, such a machine learning system can support clinical decision-making. To this end, our system is a representative tool of the field of mathematical and computational oncology and offers a bridge between the data, the modeler, the data scientist, and the practicing clinician.

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Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

Cited by 14 publications

References 132 publications

The Multiple Dimensions of Networks in Cancer: A Perspective

The Multiple Dimensions of Networks in Cancer: A Perspective

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning

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