PROMISE: Personalized Rehabilitation in Oncology specific Motor deficits using Intelligent Sensing and Extended reality

Context

Chemotherapy-induced neuropathies (CIPN) have gained clinical significance due to the prevalence of malignant disease and the use of new chemotherapeutic drugs; their prevalence is also reported to be 30%–40%, with high variance depending on the drug(s) used and treatment scheme. With over 600.000 new cases in 2018 and almost 2 million prevalent cases on a 5-year prediction in Germany, cancer is still in the foreground of chronic diseases that require innovative and impactful solutions. CIPN is one of the most frequent adverse effects of many commonly used chemotherapeutic agents and has a strong impact on patients’ quality of life. CIPN is a severe problem in oncology leading to dose reduction, treatment delay, or discontinuation. It causes long-lasting disturbances of daily functioning and quality of life in a considerable proportion of patients, due to its sensory and motor symptoms.

Research partners

Goal 

PROMISE is a technological intervention for polyneuropathies that provides personalized quantification and adaptive compensation of sensorimotor deficits. The initial instantiation tackles CIPN and its rehabilitation in cancer survivors after chemotherapy. It proves the potential that motion capturing technologies, wearables, and machine learning algorithms have in combination in a digital intervention aiming at personalized physical rehabilitation.

PROMISE explores the potential that digital interventions have for sensorimotor CIPN assessment and rehabilitation in cancer patients. Our primary goal is to demonstrate the benefits and impact that Extended Reality (XR) avatars and AI algorithms have in combination in a digital intervention aiming at 1) assessing the complete kinematics of deficits through learning underlying patient sensorimotor parameters, and 2) parametrize a multimodal XR stimulation to drive personalized deficit compensation in rehabilitation 3) quantitatively track the patient’s progress and automatically generate personalized recommendations 4) quantitatively evaluate and benchmark the effectiveness of the administered therapies.

Overview

The generic system architecture is depicted in the following diagram. The rich sensory data is responsible to describe a complete assessment. Through machine learning algorithms each of the modules provides a powerful interface from the patient to the physician.

PROMISE will address the sensorimotor symptoms of CIPN. It will use commodity digital technologies, such as Extended Reality (XR) and Machine Learning (ML), in combination, for personalized CIPN sensorimotor rehabilitation. We expect that PROMISE will reduce the severity of the weakened or absent reflexes or the loss of balance control through personalized quantification of deficits and optimal compensation. All these with an affordable, adaptive, and accessible platform for both clinical and home-based rehabilitation.

The core element is the inference system capable of translating the assessment into actionable insights and recommendations for the patient in both clinical and home-based rehabilitation deployment of PROMISE.

As a technical innovation, PROMISE will offer a platform which can be used in both clinical (laboratory) and home rehabilitation. PROMISE will use a combination of affordable wearables (i.e. IMU, EMG, HR) and recommend the best configuration of number and placement of such sensors that capture patient motion peculiarities.

For instance, the laboratory version will comprise the base platform and additionally all sensors and physician dashboards (i.e. cameras, IMUs, wearables, VR trackers) for a high-accuracy assessment and stimulation. The simulation plays an important role in the clinical version as the physician/therapist can guide the patient. The home-based rehabilitation version of the solution will use a limited set of sensors (i.e. IMUs) and focus continuous monitoring with limited / no stimulation. Such a modular design will allow PROMISE to cover the whole rehabilitation spectrum allowing for continuous patient interaction.

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