A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.This research has been supported by grants awarded to VMPG by James S. Mc. Donnell Foundation, United States of America, 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (collaborative award 220020560) and Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/17/180501/000154). VMPG and GFC thank the funding from Ministerio de Ciencia e Innovacion, Spain (grant number PID2019-110895RB-I00). This research has also been supported by a grant awarded to GFC and JBB by the Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/19/180501/000211). AMR received support from Asociacion Pablo Ugarte (http://www.asociacionpablougarte.es). JJS received support from Universidad de Castilla-La Mancha (grant number 2020-PREDUCLM-15634). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Overcoming chemotherapy resistance in low-grade gliomas: A computational approach.

Low-grade gliomas are primary brain tumors that arise from glial cells and are usually treated with temozolomide (TMZ) as a chemotherapeutic option. They are often incurable, but patients have a prolonged survival. One of the shortcomings of the treatment is that patients eventually develop drug resistance. Recent findings show that persisters, cells that enter a dormancy state to resist treatment, play an important role in the development of resistance to TMZ. In this study we constructed a mathematical model of low-grade glioma response to TMZ incorporating a persister population. The model was able to describe the volumetric longitudinal dynamics, observed in routine FLAIR 3D sequences, of low-grade glioma patients acquiring TMZ resistance. We used the model to explore different TMZ administration protocols, first on virtual clones of real patients and afterwards on virtual patients preserving the relationships between parameters of real patients. In silico clinical trials showed that resistance development was deferred by protocols in which individual doses are administered after rest periods, rather than the 28-days cycle standard protocol. This led to median survival gains in virtual patients of more than 15 months when using resting periods between two and three weeks and agreed with recent experimental observations in animal models. Additionally, we tested adaptive variations of these new protocols, what showed a potential reduction in toxicity, but no survival gain. Our computational results highlight the need of further clinical trials that could obtain better results from treatment with TMZ in low grade gliomas

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from F-18-fluorodeoxyglucose (F-18-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using F-18-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of F-18-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan?Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman?s correlation coefficient. Results: Kaplan?Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour

A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.

Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Purpose Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. Materials and methods Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size. Results No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases). Conclusion Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images

Pronóstico y predicción en glioblastoma con biomarcadores de imagen basados en modelos matemáticos

El glioblastoma es el tumor cerebral primario más maligno y letal conocido. Actualmente, la supervivencia media de los pacientes diagnosticados con esta enfermedad es de algo más de un año si se aplica el tratamiento estándar, que consiste en la máxima resección posible del tumor y posteriores sesiones de radioterapia y quimioterapia. La respuesta es variable, y en el caso de GBM múltiples, las cifras de supervivencia se reducen a 8-10 meses. Entender la malignidad de esta enfermedad tan letal es de gran interés. La publicación de trabajos radiómicos ha crecido notablemente en los últimos años. La hipótesis principal de estos trabajos es que a partir de las imágenes médicas, se pueden extraer medidas de diferentes características del tumor que podrían estar relacionadas con la enfermedad del paciente. Paralelamente, también ha experimentado un auge la publicación de otros estudios cuya base es la modelización matemática de estas enfermedades. Mediante ecuaciones matemáticas, compuestas por variables y parámetros relacionados con características de la enfermedad, se simula en el ordenador el crecimiento de la enfermedad o la respuesta a determinados tratamientos. Es una herramienta muy potente como fuente para lanzar hipótesis. En esta tesis, nuestro objetivo principal fue analizar si la morfología del glioblastoma, visualizada en imágenes médicas de resonancia magnética utilizadas de rutina para el diagnóstico, podría estar relacionada con la agresividad del tumor y la respuesta al tratamiento. Para llevar a cabo este objetivo abordamos los siguientes problemas: 1. Diseñamos nuevos biomarcadores morfológicos, con alto sentido físico para un futuro uso en clínica, basados en algoritmos matemáticos de crecimiento de glioblastoma. 2. Para que los resultados fueran robustos, recopilamos la mayor cantidad posible de exploraciones de alta resolución, facilitadas por los hospitales colaboradores. También recopilamos exploraciones de un repositorio público internacional de cáncer, y concretamente de glioblastoma, para contrastar y validar nuestros resultados. 3. Todos los biomarcadores se cuantificaron de manera precisa, previa recopilación de exploraciones reales de resonancia magnética de alta resolución espacial, y el desarrollo de una nueva metodología de extracción y cuantificación en 3D de características morfológicas del tumor a partir de estas imágenes médicas. 4. Analizamos el valor pronóstico de cada uno de estos biomarcadores, relacionando su magnitud con el tiempo de supervivencia de los pacientes y sus tratamientos. 5. Elaboramos modelos matemáticos pronóstico para cuantificar si la combinación de estos biomarcadores incrementaba la clasificación pronóstica. Para evitar efectos de "overfitting", en estos modelos debíamos incorporar un número reducido de variables en los modelos. 6. Finalmente, analizamos el valor predictivo de los resultados obtenidos con las simulaciones del modelo de crecimiento de glioblastoma. Para ello comparamos estos resultados con los obtenidos de los pacientes reales

Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model

We construct a minimal macroscopic model of glioblastoma growth including necrosis to explain the recently observed correlation between MRI-observed features and tumor growth speed. A theoretical study of the modified model was carried out. In particular, we obtained an expression for the minimal wave speed of the traveling wave solutions. We also solved numerically the model using a set of realistic parameter values and used these numerical solutions to compare the model dynamics against patient’s imaging and clinical data. The mathematical model provides theoretical support to the observation that tumors with broad contrast enhancing areas as observed in T1-weighted pretreatment postcontrast magnetic resonance images have worse survival than those with thinner areas