Oscillations in growth of multicellular tumour spheroids: a revisited quantitative analysis

Joint fitting reveals hidden interactions in tumor growth

Tumor growth is often the result of the simultaneous development of two or more cancer cell populations. Crucial to the system evolution are the interactions between these populations. To obtain information about these interactions we apply the recently developed vector universality (VUN) formalism to various instances of competition between tumor populations. The formalism allows us (a) to quantify the growth mechanisms of a HeLa cell colony, describing the phenotype switching responsible for its fast expansion, (b) to reliably reconstruct the evolution of the necrotic and viable fractions in both in vitro and in vivo tumors using data for the time dependences of the total masses alone, and (c) to show how the shedding of cells leading to subspheroid formation is beneficial to both the spheroid and subspheroid populations, suggesting that shedding is a strong positive influence on cancer dissemination.

Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging

Dynamic contrast-enhanced study in magnetic resonance imaging (DCE-MRI) is an important tool in oncology to visualize tissues vascularization and to define tumour aggressiveness on the basis of an altered perfusion and permeability. Pharmacokinetic models are generally used to extract hemodynamic parameters, providing a quantitative description of the contrast uptake and wash-out. Empirical functions can also be used to fit experimental data without the need of any assumption about tumour physiology, as in pharmacokinetic models, increasing their diagnostic utility, in particular when automatic diagnosis systems are implemented on the basis of an MRI multi-parametric approach. Phenomenological universalities (PUN) represent a novel tool for experimental research and offer a simple and systematic method to represent a set of data independent of the application field. DCE-MRI acquisitions can thus be advantageously evaluated by the extended PUN class, providing a convenient diagnostic tool to analyse functional studies, adding a new set of features for the classification of malignant and benign lesions in computer aided detection systems. In this work the Tofts pharmacokinetic model and the class EU1 generated by the PUN description were compared in the study of DCE-MRI of the prostate, evaluating complexity of model implementation, goodness of fitting results, classification performances and computational cost. The mean R² obtained with the EU1 and Tofts model were equal to 0.96 and 0.90, respectively, and the classification performances achieved by the EU1 model and the Tofts implementation discriminated malignant from benign tissues with an area under the receiver operating characteristic curve equal to 0.92 and 0.91, respectively. Furthermore, the EU1 model has a simpler functional form which reduces implementation complexity and computational time, requiring 6 min to complete a patient elaboration process, instead of 8 min needed for the Tofts model analysis.

A novel approach to the analysis of human growth

Objectives

Several formulations have been proposed in order to model human growth from birth to maturity. They are usually based on “ad hoc” heuristic assumptions. In the present contribution we adopt, as an alternative, a completely general (interdisciplinary) approach, based on the formalism of the Phenomenological Universalities (PUN).

Methods

The main PUN class investigated to date, i.e. UN, can only account for the overall growth pattern. For a realistic description it is necessary to add to it one or more “spurts”, as expected on biological grounds, due to the stimulation of growth and sex hormones.

Results

A new PUN class (UN + FM) is generated and shown to be able to provide excellent agreement with standard auxological datasets. The accuracy of the fitting and reliability of the model suggest applications both at the diagnostic and therapeutic level.

Conclusions

The developed formalism can be suitably related to the biological description of bone plate growth under selective hormonal stimulation on the bone epiphysis; i.e., the additional increase of stature is the “macroscopic” response to a well defined biological signal.

Phenomenological universalities: a novel tool for the analysis of dynamic contrast enhancement in magnetic resonance imaging

Dynamic contrast enhancement in magnetic resonance imaging (DCE-MRI) is a promising tool for the clinical diagnosis of tumors, whose implementation may be improved through the use of suitable hemodynamic models. If one prefers to avoid assumptions about the tumor physiology, empirical fitting functions may be adopted. For this purpose, in this paper we discuss the exploitation of a recently proposed phenomenological universalities (PUN) formalism. In fact, we show that a novel PUN class may be used to describe the time-signal intensity curves in both healthy and tumoral tissues, discriminating between the two cases and thus potentially providing a convenient diagnostic tool. The proposed approach is applied to analysis of the DCE-MRI data relative to a study group composed of ten patients with spine tumors.