Complexity in Tumour Growth Patterns

The development of brain tumours, after diagnosis, is routinely recorded by different medical imaging techniques like computerised tomography (CT) or magnetic resonance imaging (MRI). However, it is only through the formulation of mathematical models that an analysis of the spatio-temporal tumour growth revealed on each patient serial scans can lead to a quantification of parameters characterising the proliferative and expensive dynamic of the brain tumour. This paper reviews some of the results and limitations encountered in modelling the different stages of a brain tumour growth, namely before and after diagnosis and therapy. It extends an original two-dimensional approach by considering three-dimensional growth of brain tumours submitted to the spatial constraints exerted by the skull and ventricles boundaries. Considering the dynamic of both the pre- and post-diagnosis stages, the tumour growth patterns obtained with various combinations of nonlinear growth rates and cellular diffusion laws are considered and compared to real MRI scans taken in a patient with a glioblastoma and having undergone radiotherapy. From these simulations, we characterise the effects of different therapies on survival durations, with special attention to the effect of cell diffusion inside the resected brain region when surgical resection of the tumour is carried out.

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Should we resist to including tumour growth patterns in Response Evaluation Criteria in Solid Tumours evaluation? (Response to Litière et al.)

European Journal of Cancer ◽

10.1016/j.ejca.2014.07.026 ◽

2014 ◽

Vol 50 (16) ◽

pp. 2887-2888 ◽

Cited By ~ 1

Author(s):

Loic Verlingue ◽

Serge Koscielny ◽

Charles Ferté

Keyword(s):

Tumour Growth ◽

Evaluation Criteria ◽

Growth Patterns ◽

Solid Tumours ◽

Response Evaluation ◽

Response Evaluation Criteria

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Variation of prostate-specific antigen expression in different tumour growth patterns present in prostatectomy specimens

Urological Research ◽

10.1007/bf00295844 ◽

1990 ◽

Vol 18 (3) ◽

pp. 181-187 ◽

Cited By ~ 34

Author(s):

M. P. W. Gallee ◽

E. Visser-de Jong ◽

J. A. G. M. van der Korput ◽

Th. H. van der Kwast ◽

F. J. W. ten Kate ◽

...

Keyword(s):

Prostate Specific Antigen ◽

Tumour Growth ◽

Specific Antigen ◽

Antigen Expression ◽

Growth Patterns

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Longitudinal evaluation of five nasopharyngeal carcinoma animal models on the microPET/MR platform

10.21203/rs.3.rs-934896/v1 ◽

2021 ◽

Author(s):

Jingjing Shi ◽

Zhichao Xue ◽

Kel-Vin Tan ◽

Hui Yuan ◽

Anna Chi Man Tsang ◽

...

Keyword(s):

Growth Rate ◽

Drug Discovery ◽

Nasopharyngeal Carcinoma ◽

Animal Models ◽

Metabolic Activity ◽

Tumour Growth ◽

Tumour Size ◽

Standardized Uptake Value ◽

Growth Patterns ◽

Novel Drug

Abstract Purpose We longitudinally evaluated the tumour growth and metabolic activity of two well-established nasopharyngeal carcinoma (NPC) animal models (C666-1, C17) and three novel models (Xeno76, Xeno23 and NPC43) using a microPET/MR system. With a better understanding of the interplay between tumour growth and metabolic characteristics of these NPC models, we aim to provide insights for the selection of appropriate NPC cell line/xenograft models to assist novel drug discovery and evaluation. Methods Mice were imaged by [18F]FDG microPET/MR twice a week for consecutive 3–7 weeks. [18F]FDG uptake was quantified by standardized uptake value (SUV) and presented as SUVmean tumour-to-liver ratio (SUVRmean). Longitudinal tumour growth patterns and metabolic patterns were recorded. SUVRmean and histological characteristics were compared across the five NPC models. Cisplatin was administrated to one selected optimal tumour model, C17 to evaluate our imaging platform. Results We found variable tumour growth and metabolic patterns across different NPC tumour types. C17 has an optimal growth rate and higher tumour metabolic activity compared with C666-1. C666-1 has a fast growth rate but is low in SUVRmean at endpoint due to necrosis as confirmed by H&E. NPC43 and Xeno76 have relatively slow growth rates and are low in SUVRmean, due to severe necrosis. Xeno23 has the slowest growth rate, and a relative high SUVRmean. Cisplatin showed the expected therapeutic effect in the C17 model in marked reduction of tumour size and metabolism. Conclusion Our study establishes an imaging platform that characterizes the growth and metabolic patterns of different NPC models, and the platform is well able to demonstrate drug treatment outcome supporting its use in novel drug discovery and evaluation for NPC.

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An agent-based model of cancer stem cell initiated avascular tumour growth and metastasis: the effect of seeding frequency and location

Journal of The Royal Society Interface ◽

10.1098/rsif.2014.0640 ◽

2014 ◽

Vol 11 (100) ◽

pp. 20140640 ◽

Cited By ~ 17

Author(s):

Kerri-Ann Norton ◽

Aleksander S. Popel

Keyword(s):

Stem Cells ◽

Stem Cell ◽

Tumour Growth ◽

Temporal Distribution ◽

Growth Patterns ◽

Metastatic Tumour ◽

Cell Seeding ◽

Seeding Rate ◽

Agent Based ◽

Cellular Quiescence

It is very important to understand the onset and growth pattern of breast primary tumours as well as their metastatic dissemination. In most cases, it is the metastatic disease that ultimately kills the patient. There is increasing evidence that cancer stem cells are closely linked to the progression of the metastatic tumour. Here, we investigate stem cell seeding to an avascular tumour site using an agent-based stochastic model of breast cancer metastatic seeding. The model includes several important cellular features such as stem cell symmetric and asymmetric division, migration, cellular quiescence, senescence, apoptosis and cell division cycles. It also includes external features such as stem cell seeding frequency and location. Using this model, we find that cell seeding rate and location are important features for tumour growth. We also define conditions in which the tumour growth exhibits decremented and exponential growth patterns. Overall, we find that seeding, senescence and division limit affect not only the number of stem cells, but also their spatial and temporal distribution.

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Hepatocellular carcinoma tumour volume doubling time: a systematic review and meta-analysis

Gut ◽

10.1136/gutjnl-2020-321040 ◽

2020 ◽

pp. gutjnl-2020-321040 ◽

Cited By ~ 6

Author(s):

Piyush Nathani ◽

Purva Gopal ◽

Nicole Rich ◽

Adam Yopp ◽

Takeshi Yokoo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Hepatitis B ◽

Doubling Time ◽

Tumour Growth ◽

Tumour Volume ◽

Treatment Decisions ◽

Growth Patterns ◽

Volume Doubling Time ◽

Volume Doubling ◽

Tumour Volume Doubling Time

BackgroundTumour growth patterns have important implications for surveillance intervals, prognostication and treatment decisions but have not been well described for hepatocellular carcinoma (HCC). The aim of our study was to characterise HCC doubling time and identify correlates for indolent and rapid growth patterns.MethodsWe performed a systematic literature review of Medline and EMBASE databases from inception to December 2019 and national meeting abstracts from 2010 to 2018. We identified studies reporting HCC tumour growth or tumour volume doubling time (TVDT), without intervening treatment, and abstracted data to calculate TVDT and correlates of growth patterns (rapid defined as TVDT <3 months and indolent as TVDT >9 months). Pooled TVDT was calculated using a random-effects model.ResultsWe identified 20 studies, including 1374 HCC lesions in 1334 patients. The pooled TVDT was 4.6 months (95% CI 3.9 to 5.3 months I2=94%), with 35% classified as rapid, 27.4% intermediate and 37.6% indolent growth. In subgroup analysis, studies from Asia reported shorter TVDT than studies elsewhere (4.1 vs 5.8 months). The most consistent correlates of rapid tumour growth included hepatitis B aetiology, smaller tumour size (continuous), alpha fetoprotein doubling time and poor tumour differentiation. Studies were limited by small sample sizes, measurement bias and selection bias.ConclusionTVDT of HCC is approximately 4–5 months; however, there is heterogeneity in tumour growth patterns, including more aggressive patterns in Asian hepatitis B-predominant populations. Identifying correlates of tumour growth patterns is important to better individualise HCC prognostication and treatment decisions.

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