COMPONENS: COMPutational ONcology ENgineered Solutions

Introduction

COMPONENS focuses on the research and development of tools, models and infrastructure needed to interpret large amounts of clinical data and enhance cancer treatments and our understanding of the disease. To this end, COMPONENS serves as a bridge between the data, the engineer, and the clinician in oncological practice.

Thus, knowledge-based predictive mathematical modelling is used to fill gaps in sparse data; assist and train machine learning algorithms; provide measurable interpretations of complex and heterogeneous clinical data sets, and make patient-tailored predictions of cancer progression and response.

GLUECK: Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

Neoplastic processes are described by complex and heterogeneous dynamics. The interaction of neoplastic cells with their environment describes tumor growth and is critical for the initiation of cancer invasion. Despite the large spectrum of tumor growth models, there is no clear guidance on how to choose the most appropriate model for a particular cancer and how this will impact its subsequent use in therapy planning. Such models need parametrization that is dependent on tumor biology and hardly generalize to other tumor types and their variability. Moreover, the datasets are small in size due to the limited or expensive measurement methods. Alleviating the limitations that incomplete biological descriptions, the diversity of tumor types, and the small size of the data bring to mechanistic models, we introduce Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics (GLUECK) a novel, data-driven model based on a neural network capable of unsupervised learning of cancer growth curves. Employing mechanisms of competition, cooperation, and correlation in neural networks, GLUECK learns the temporal evolution of the input data along with the underlying distribution of the input space. We demonstrate the superior accuracy of GLUECK, against four typically used tumor growth models, in extracting growth curves from a set of four clinical tumor datasets. Our experiments show that, without any modification, GLUECK can learn the underlying growth curves being versatile between and within tumor types.

Preprint

https://www.biorxiv.org/content/10.1101/2020.06.13.140715v1

Code

https://gitlab.com/akii-microlab/ecml-2020-glueck-codebase

PRINCESS: Prediction of Individual Breast Cancer Evolution to Surgical Size

Modelling surgical size is not inherently meant to replicate the tumor’s exact form and proportions, but instead to elucidate the degree of the tissue volume that may be surgically removed in terms of improving patient survival and minimize the risk that a second or third operation will be needed to eliminate all malignant cells entirely. Given the broad range of models of tumor growth, there is no specific rule of thumb about how to select the most suitable model for a particular breast cancer type and whether that would influence its subsequent application in surgery planning. Typically, these models require tumor biology-dependent parametrization, which hardly generalizes to cope with tumor heterogeneity. In addition, the datasets are limited in size owing to the restricted or expensive methods of measurement. We address the shortcomings that incomplete biological specifications, the variety of tumor types and the limited size of the data bring to existing mechanistic tumor growth models and introduce a Machine Learning model for the PRediction of INdividual breast Cancer Evolution to Surgical Size (PRINCESS). This is a data-driven model based on neural networks capable of unsupervised learning of cancer growth curves. PRINCESS learns the temporal evolution of the tumor along with the underlying distribution of the measurement space. We demonstrate the superior accuracy of PRINCESS, against four typically used tumor growth models, in extracting tumor growth curves from a set of nine clinical breast cancer datasets. Our experiments show that, without any modification, PRINCESS can learn the underlying growth curves being versatile between breast cancer types.

Preprint

https://www.biorxiv.org/content/10.1101/2020.06.13.150136v1

Code

https://gitlab.com/akii-microlab/cbms2020 

CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

Mathematical and computational oncology has increased the pace of cancer research towards the advancement of personalized therapy. Serving the pressing need to exploit the large amounts of currently underutilized data, such approaches bring a significant clinical advantage in tailoring the therapy. CHIMERA is a novel system that combines mechanistic modelling and machine learning for personalized chemotherapy and surgery sequencing in breast cancer. It optimizes decision-making in personalized breast cancer therapy by connecting tumor growth behaviour and chemotherapy effects through predictive modelling and learning. We demonstrate the capabilities of CHIMERA in learning simultaneously the tumor growth patterns, across several types of breast cancer, and the pharmacokinetics of a typical breast cancer chemotoxic drug. The learnt functions are subsequently used to predict how to sequence the intervention. We demonstrate the versatility of CHIMERA in learning from tumor growth and pharmacokinetics data to provide robust predictions under two, typically used, chemotherapy protocol hypotheses.

Preprint

https://www.biorxiv.org/content/10.1101/2020.06.08.140756v1 

Code

https://gitlab.com/akii-microlab/bibe2020 

PERSEUS: Platform for Enhanced virtual Reality in Sport Exercise Understanding and Simulation

The PERSEUS (Platform for Enhanced virtual Reality in Sport Exercise Understanding and Simulation) is funded by the Central Innovation Programme for small and medium-sized enterprises (SMEs) (ZIM – Zentrales Innovationsprogramm Mittelstand) from the Federal Ministry for Economic Affairs and Energy.

Consortium

Goal 

To support performance, elite athletes, such as goalkeepers, require a combination of general visual skills (e.g. visual acuity, contrast sensitivity, depth perception) and performance-relevant perceptual-cognitive skills (e.g. anticipation, decision-making). While these skills are typically developed as a consequence of regular, on-field practice, training techniques are available that can enhance those skills outside of, or in conjunction with, regular training. Perceptual training has commonly included sports vision training (SVT) that uses generic stimuli (e.g. shapes, patterns) optometry-based tasks with the aim of developing visual skills, or perceptual-cognitive training (PCT), that traditionally uses sport-specific film or images to develop perceptual-cognitive skills. Improvements in technology have also led to the development of additional tools (e.g. reaction time trainers, computer-based vision training, and VR systems) which have the potential to enhance perceptual skill using a variety of different equipment in on- and off-field settings that don’t necessarily fit into these existing categories.

In this context we hypothesize that using high-fidelity VR systems to display realistic 3D sport environments could provide a mean to control anxiety, allowing resilience-training systems to prepare athletes for real-world, high-pressure situations and hence to offer a tool for sport psychology training. Moreover, a VE should provide a realistic rendering of the sports scene to achieve good perceptual fidelity. More important for a sport-themed VE is high functional fidelity, which requires an accurate physics model of a complex environment, real time response, and a natural user interface. This is of course complemented by precise body motion tracking and kinematic model extraction. The goal is to provide multiple scenarios to players at different levels of difficulty, providing them with improved skills that can be applied directly to the real sports arena, contributing to a full biomechanical training in VR.

The project proposes the development of an AI powered VR system for sport psychological (cognitive) and biomechanical training. By exploiting neuroscientific knowledge in sensorimotor processing, Artificial Intelligence algorithms and VR avatar reconstruction, our lab along with the other consortium partners target the development of an adaptive, affordable, and flexible novel solution for goalkeeper training in VR.

Overview

The generic system architecture is depicted in the following diagram.

Using time sequenced data one can extract the motion components from goalkeeper’s motion and generate a VR avatar.

An initial view of the avatar compatibility with the real-world motion of the athlete is described in the next diagram.

The validation is done against ground truth data from a camera, whereas the data from the gloves are used to train the avatar kinematics and reconstruction. The gloves system is a lightweight embedded sensing system.

In the current study, we focus on extracting such goalkeeper analytics from kinematics using machine learning. We demonstrate that information from a single motion sensor can be successfully used for accurate and explainable goalkeeper kinematics assessment.

In order to exploit the richness and unique parameters of the goalkeeper’s motion, we employ a robust machine learning algorithm that is able to discriminate dives from other types of specific motions directly from raw sensory data. Each prediction is accompanied by an explanation of how each sensed motion component contributes to describing a specific goalkeeper’s action.