Theoretical investigation of the efficacy of antiangiogenic drugs combined to chemotherapy in xenografted mice.

Antiangiogenic drugs were developed with the aim to inhibit the formation of intratumoral blood vessels and in consequence the growth of solid tumors. As these drugs are generally combined with classical cytotoxic drugs in the treatment of cancer patients, finding the optimal combinations remains a complex challenge due to possible interactions of the antiangiogenic compound with the hemodynamic property of the treated tumor. To analyze this problem, we developed a multi-scale model of vascular tumor growth combining a molecular model of VEGF signaling pathways and a tissue model of the tumor expansion including the dynamics of cellular and tissue processes of tumor growth and response to treatments. We addressed the potential impact of antiangiogenic drug by defining a new index of vasculature quality which depends on the balance between stable and unstable vessels within the tumor mass. Our goal was to investigate the interactions between a chemotherapy and a antiangiogenic treatment, and, by simulating the model, to identify the optimal delay of chemotherapy delivery after the administration of the antiangiogenic compound. This theoretical analysis could be used in the future to optimize antiangiogenic drug delivery in preclinical settings and to facilitate the translation from preclinical to clinical studies.

Precision of manual two-dimensional segmentations of lung and liver metastases and its impact on tumour response assessment using RECIST 1.1.

BACKGROUND: Response evaluation criteria in solid tumours (RECIST) has significant limitations in terms of variability and reproducibility, which may not be independent. The aim of the study was to evaluate the precision of manual bi-dimensional segmentation of lung, liver metastases, and to quantify the uncertainty in tumour response assessment. METHODS: A total of 520 segmentations of metastases from six livers and seven lungs were independently performed by ten physicians and ten scientists on CT images, reflecting the variability encountered in clinical practice. Operators manually contoured the tumours, firstly independently according to the RECIST and secondly on a preselected slice. Diameters and areas were extracted from the segmentations. Mean standard deviations were used to build regression models and 95% confidence intervals (95% CI) were calculated for each tumour size and for limits of progressive disease (PD) and partial response (PR) derived from RECIST 1.1. RESULTS: Thirteen aberrant segmentations (2.5%) were observed without significant differences between the physicians and scientists; only the mean area of liver tumours (p = 0.034) and mean diameter of lung tumours (p = 0.021) differed significantly. No difference was observed between the methods. Inter-observer agreement was excellent (intra-class correlation >0.90) for all variables. In liver, overlaps of the 95% CI with the 95% CI of limits of PD or PR were observed for diameters above 22.7 and 37.9 mm, respectively. An overlap of 95% CIs was systematically observed for area. No overlaps were observed in lung. CONCLUSIONS: Although the experience of readers might not affect the precision of segmentation in lung and liver, the results of manual segmentation performed for tumour response assessment remain uncertain for large liver metastases.

[Adaptive radiotherapy in routine: The radiation oncologist's point of view]. / Radiothérapie adaptative en routine : point de vue de l'oncologue radiothérapeute.

Adaptive radiotherapy is defined as all processes leading to the modification of a treatment plan on the basis of patient-specific variations observed during the course of a treatment. This concept is currently of particular relevance due to the development of onboard volumetric imaging systems, which allow for daily viewing of variations in both tumour and organs at risk in terms of position, shape or volume. However, its application in routine clinical practice is limited due to the demanding nature of the processes involved (re-delineation and replanning) and increased dependence on available human resources. Even if "online" strategies, based on deformable image registration (DIR) algorithms, could lead to a reduction in both work and calculation time, for the moment their use is limited to the research field due to uncertainties surrounding the validity of results gathered. Other strategies without DIR can be used as "offline" or "hybrid offline-online" strategies that seem to offer a compromise between time consumption and therapeutic gain for the patient.

In vivo mathematical modeling of tumor growth from imaging data: soon to come in the future?

The future challenges in oncology imaging are to assess the response to treatment even earlier. As an addition to functional imaging, mathematical modeling based on the imaging is an alternative, cross-disciplinary area of development. Modeling was developed in oncology not only in order to understand and predict tumor growth, but also to anticipate the effects of targeted and untargeted therapies. A very wide range of these models exist, involving many stages in the progression of tumors. Few models, however, have been proposed to reproduce in vivo tumor growth because of the complexity of the mechanisms involved. Morphological imaging combined with "spatial" models appears to perform well although functioning imaging could still provide further information on metabolism and the micro-architecture. The combination of imaging and modeling can resolve complex problems and describe many facets of tumor growth or response to treatment. It is now possible to consider its clinical use in the medium term. This review describes the basic principles of mathematical modeling and describes the advantages, limitations and future prospects for this in vivo approach based on imaging data.

A multiscale mathematical model of avascular tumor growth to investigate the therapeutic benefit of anti-invasive agents.

With the aim of inhibiting cancer growth and reducing the risk of metastasis, pharmaceutical companies in the early 1990s developed anti-metastatic agents called inhibitors of metalloproteinases (MMPi). Despite the promising results obtained in pre-clinical studies, results of Phase III trials have been somewhat disappointing for late stage cancer patients. With the aim of mathematically investigating this therapeutic failure, we developed a mechanistically based model which integrates cell cycle regulation and macroscopic tumor dynamics. By simulating the model, we evaluated the efficacy of MMPi therapy. Simulation results predict the lack of efficacy of MMPi in advanced cancer patients. The theoretical model may aid in evaluating the efficacy of anti-metastatic therapies, thus benefiting the design of prospective clinical trials.