Data-Driven Predictive Models of Diffuse Low-Grade Gliomas Under Chemotherapy

Abstract

Diffuse Low-Grade Gliomas (DLGG) are brain tumors of young adults. They affect the quality of life of the inflicted patients and, if untreated, they evolve into higher grade tumors where the patient's life is at risk. Therapeutic management of DLGGs includes chemotherapy, and tumor diameter is particularly important for the follow-up of DLGG evolution. In fact, the main clinical basis for deciding whether to continue chemotherapy is tumor diameter growth rate. In order to reliably assist the doctors in selecting the most appropriate time to stop treatment, we propose a novel clinical decision support system. Based on two mathematical models, one linear and one exponential, we are able to predict the evolution of tumor diameter under Temozolomide chemotherapy as a first treatment and thus offer a prognosis on when to end it. We present the results of an implementation of these models on a database of 42 patients from Nancy and Montpellier University Hospitals. In this database, 38 patients followed the linear model and four patients followed the exponential model. From a training dataset of a minimal size of five, we are able to predict the next tumor diameter with high accuracy. Thanks to the corresponding prediction interval, it is possible to check if the new observation corresponds to the predicted diameter. If the observed diameter is within the prediction interval, the clinician is notified that the trend is within a normal range. Otherwise, the practitioner is alerted of a significant change in tumor diameter.

Inter-rater agreement in glioma segmentations on longitudinal MRI

“…During radiological follow-up the tumor volume is monitored, and timing of second line treatment is based on tumor growth. Quantitation of MRI tumor volumes has proven to be valuable for studying autonomous growth ( Gui et al, 2018 ; Mandonnet et al, 2013 ; Mandonnet et al, 2008 ), quantification of the effects of (pharmacological) interventions ( Ben Abdallah et al, 2018b ; Mandonnet et al, 2010 ; Pallud et al, 2012a ; Pallud et al, 2012b ), and statistical maps of care ( De Witt Hamer et al, 2013 ; Mandonnet et al, 2007 ) and disease mechanisms ( Amelot et al, 2017 ; Ellingson et al, 2013 ; Wang et al, 2014 ).…”

A Personalized Longitudinal Strategy in Low-Grade Glioma Patients: Predicting Oncological and Neural Interindividual Variability and Its Changes over Years to Think One Step Ahead

“…In case of non-(re)operable LGG, it was even proposed to administrate neoadjuvant chemotherapy to induce a shrinkage of the glioma and then to (re)open the door for subsequent surgery with an optimized EOR [ 97 , 98 ]. Importantly, because responses to Temozolomide or PCV remain highly variable across patients, and in the same patient when receiving a second line of chemotherapy, in addition to the use of multimodal imaging [ 99 ], data-driven models have been developed to predict the evolution of LGG under chemotherapy [ 100 ]. Biomathematical modeling might also be helpful to simulate and compare the activity of different chemo-radiotherapy strategies in silico [ 101 ].…”

Virtual and Real Bidirectional Driving System for the Synchronization of Manipulations in Robotic Joint Surgeries

“…It can be used in surgeries so as to facilitate the surgeons in preoperative planning or intraoperative surgery [19]. For instance, digital twin-based systems were developed to accurately predict the diameter of the tumor [20], the adverse side effects caused by the deep brain stimulation in the subthalamic nucleus [21], and the length of stay in patients undergoing appendectomy [22]. Virtual reality (VR) and augmented reality (AR) are popular technologies that facilitate preoperative planning and surgical training [23].…”