Model Of Tumor Response Research Articles

TH-C-220-03: Extraction of Radiosensitivity Coefficients from Multiparametric Molecular Imaging Data through Computational Tumor Modeling

Purpose: Patient‐specific differences in tumor radiosensitivity are generally disregarded in radiation therapy (RT), an approach that overlooks subgroups with different therapeutic responses. In this study, we extracted voxel‐based radiosensitivity coefficients from multiparametric molecular imaging data using a computational tumor growth and response model. Methods: Thirteen canine patients with sinonasal tumors underwent pre‐therapy PET/CT imaging of metabolic activity ([18F]FDG), proliferation ([18F]FLT), and hypoxia ([61Cu]Cu‐ATSM) using bite‐block immobilization. Following two RT fractions and two off‐treatment days, proliferative response was assessed via [18F]FLT‐PET/CT. Using a previously developed tumor simulation model, voxel‐based ‘effective’ linear radiosensitivity coefficients (alpha) were determined by simulating treatment response based upon pre‐therapy PET data and iteratively minimizing differences between simulated and imaged proliferative response. Resulting alpha values were investigated for inter‐ and intra‐patient heterogeneity, histology dependence, and spatial correlations with pre‐therapy PET data. Results: Alpha values in carcinomas (mean=0.061/Gy, SD=0.051/Gy) were higher than those found in sarcomas (mean=0.042/Gy, SD=0.024/Gy). Carcinomas were more heterogeneous in response (range=0.554/Gy) than sarcomas (range=0.110/Gy), which correlated with a more heterogeneous pre‐therapy uptake of all three PET imaging surrogate markers. Increased [61Cu]Cu‐ATSM uptake (SUV>4) alone indicated radioresistance, while simultaneous [18F]FLT uptake lowered effective alpha values. Using Akaike's information criterion, five carcinomas were identified to exhibit bimodal or trimodal alpha distributions, while all sarcomas were found to be unimodal. Conclusion: Within this patient population, carcinomas were more radiosensitive and heterogeneous in alpha and PET tracer uptake. Extracted alpha values and their correlations with pre‐therapy PET data differed between tumor histologies. Results suggest that imaged hypoxia might not necessarily indicate increased levels of radioresistance as high proliferation potentially offsets this effect. After correlating early treatment response to late outcome, this model could be used to identify potential biological targets within tumors, stratify candidate dose painting patients, and to transform voxel‐ based information into biological treatment planning objectives.

Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaptation of a multiscale cancer model.

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem.

Computational Modeling of Tumor Response to Vascular‐Targeting Therapies—Part I: Validation

Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled. In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy. In particular, this model is used to test the antitumor activity of a clinically used angiogenesis inhibitor (both in isolation, and with a cytotoxic chemotherapeutic) and a vascular disrupting agent currently undergoing clinical trial testing. I demonstrate that the mathematical model can make predictions in agreement with preclinical/clinical data and can also be used to gain more insight into these treatment protocols. The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.

An advanced discrete state–discrete event multiscale simulation model of the response of a solid tumor to chemotherapy: Mimicking a clinical study

In this paper an advanced, clinically oriented multiscale cancer model of breast tumor response to chemotherapy is presented. The paradigm of early breast cancer treated by epirubicin according to a branch of an actual clinical trial (the Trial of Principle, TOP trial) has been addressed. The model, stemming from previous work of the In Silico Oncology Group, National Technical University of Athens, is characterized by several crucial new features, such as the explicit distinction of proliferating cells into stem cells of infinite mitotic potential and cells of limited proliferative capacity, an advanced generic cytokinetic model and an improved tumor constitution initialization technique. A sensitivity analysis regarding critical parameters of the model has revealed their effect on the behavior of the biological system. The favorable outcome of an initial step towards the clinical adaptation and validation of the simulation model, based on the use of anonymized data from the TOP clinical trial, is presented and discussed. Two real clinical cases from the TOP trial with variable molecular profile have been simulated. A realistic time course of the tumor diameter and a reduction in tumor size in agreement with the clinical data has been achieved for both cases by selection of reasonable model parameter values, thus demonstrating a possible adaptation process of the model to real clinical trial data. Available imaging, histological, molecular and treatment data are exploited by the model in order to strengthen patient individualization modeling. The expected use of the model following thorough clinical adaptation, optimization and validation is to simulate either several candidate treatment schemes for a particular patient and support the selection of the optimal one or to simulate the expected extent of tumor shrinkage for a given time instant and decide on the adequacy or not of the simulated scheme.

Predicting Outcomes in Cervical Cancer: A Kinetic Model of Tumor Regression during Radiation Therapy

Applications of mathematical modeling can improve outcome predictions of cancer therapy. Here we present a kinetic model incorporating effects of radiosensitivity, tumor repopulation, and dead-cell resolving on the analysis of tumor volume regression data of 80 cervical cancer patients (stages 1B2-IVA) who underwent radiation therapy. Regression rates and derived model parameters correlated significantly with clinical outcome (P < 0.001; median follow-up: 6.2 years). The 6-year local tumor control rate was 87% versus 54% using radiosensitivity (2-Gy surviving fraction S(2) < 0.70 vs. S(2) > or = 0.70) as a predictor (P = 0.001) and 89% vs. 57% using dead-cell resolving time (T(1/2) < 22 days versus T(1/2) > or = 22 days, P < 0.001). The 6-year disease-specific survival was 73% versus 41% with S(2) < 0.70 versus S(2) > or = 0.70 (P = 0.025), and 87% vs. 52% with T(1/2) < 22 days versus T(1/2) > or = 22 days (P = 0.002). Our approach illustrates the promise of volume-based tumor response modeling to improve early outcome predictions that can be used to enable personalized adaptive therapy.

A mathematical model of brain tumour response to radiotherapy and chemotherapy considering radiobiological aspects

Glioblastoma is the most frequent and malignant brain tumour. For many years, the conventional treatment has been maximal surgical resection followed by radiotherapy (RT), with a median survival time of less than 10 months. Previously, the use of adjuvant chemotherapy (given after RT) has failed to demonstrate a statistically significant survival advantage. Recently, a randomized phase III trial has confirmed the benefit of temozolomide (TMZ) and has defined a new standard of care for the treatment of patients with high-grade brain tumours. The results showed an increase of 2.5 months in median survival, and 16.1% in 2 year survival, for patients receiving RT with TMZ compared with RT alone. It is not clear whether the major benefit of TMZ comes from either concomitant administration of TMZ with RT, or from six cycles of adjuvant TMZ, or both. The objectives were to develop our original model, which addressed survival after RT, to construct a new module to assess the potential role of TMZ from clinical data, and to explore its synergistic contribution in addition to radiation. The model has been extended to include radiobiological parameters. The addition of the linear quadratic equation to describe cellular response to treatment has enabled us to quantify the effects of radiation and TMZ in radiobiological terms. The results indicate that the model achieves an excellent fit to the clinical data, with the assumption that TMZ given concomitantly with RT synergistically increases radiosensitivity. The alternative, that the effect of TMZ is due only to direct cell killing, does not fit the clinical data so well. The addition of concomitant TMZ appears to change the radiobiological parameters. This aspect of our results suggests possible treatment developments. Our observations need further evaluations in real clinical trials, may suggest treatment strategies for new trials, and inform their design.

BIOSUITE, NEW SOFTWARE FOR RADIOBIOLOGICAL CUSTOMISATION OF DOSE AND FRACTON SIZE IN EBRT

Purpose: BioSuite is a program developed to help clinicians to identify cases eligible for escalation and/or altered fractionation and to quantify this potential using biological models of normal tissue and tumour response to EBRT. Materials: BioSuite consists in a single executable file developed in C++. The input data are DVHs from a TPS (Philips Pinnacle) as well as a more general DVH format. Plans are defined by parameters such as the number of fractions or a default prescription dose. These DVHs are in turn associated with an endpoint defined by a biological model and a set of parameters. From these models (LKB, Relative Seriality and enhanced LQ Poisson TCP), evaluation of NTCP or TCP can be performed forwardly or inversely. The forward evaluation is made in real-time as the operator modifies plan or endpoint parameters. The inverse evaluation (or optimisation) finds the dose multiplication factor (DMP) rescaling the DVH or number of fractions of a given size which take the complication risk to a prior set level. An isotoxic can also be done, which consists of computing for a fraction-number range the highest DMP (and thus TCP) for a constant NTCP. An upper TCP limit can be set (e.g. 99%) to avoid excessive doses in particular cases. The operator can finally generate dose-response curves to visually assess plan quality. Isotoxic optimisation was applied to 33 plans from our institution whose DVHs were exported to BIOSUITE. Results: For most of these plans, scope for escalation and/or altered fractionation was found, suggesting these patients were undertreated. In cases with a very small tumour and consequently small volume of irradiated normal tissue, the models implemented in BioSuite suggested treatments very similar to those currently done in SBRT i.e. TCP close to 100% for as low as 3 fractions. Serial organ DVHs in the plan can drastically affect the BIOSUITE fractionation strategy, making hypofractionation no longer viable.

CLINICAL IMPLEMENTATION OF RADIOBIOLOGICAL TREATMENT PLAN OPTIMIZATION USING A DATABASE AND CALCULATION MODULE INTEGRATED TO A TREATMENT PLANNING SYSTEM

Purpose: BioSuite is a program developed to help clinicians to identify cases eligible for escalation and/or altered fractionation and to quantify this potential using biological models of normal tissue and tumour response to EBRT. Materials: BioSuite consists in a single executable file developed in C++. The input data are DVHs from a TPS (Philips Pinnacle) as well as a more general DVH format. Plans are defined by parameters such as the number of fractions or a default prescription dose. These DVHs are in turn associated with an endpoint defined by a biological model and a set of parameters. From these models (LKB, Relative Seriality and enhanced LQ Poisson TCP), evaluation of NTCP or TCP can be performed forwardly or inversely. The forward evaluation is made in real-time as the operator modifies plan or endpoint parameters. The inverse evaluation (or optimisation) finds the dose multiplication factor (DMP) rescaling the DVH or number of fractions of a given size which take the complication risk to a prior set level. An isotoxic can also be done, which consists of computing for a fraction-number range the highest DMP (and thus TCP) for a constant NTCP. An upper TCP limit can be set (e.g. 99%) to avoid excessive doses in particular cases. The operator can finally generate dose-response curves to visually assess plan quality. Isotoxic optimisation was applied to 33 plans from our institution whose DVHs were exported to BIOSUITE. Results: For most of these plans, scope for escalation and/or altered fractionation was found, suggesting these patients were undertreated. In cases with a very small tumour and consequently small volume of irradiated normal tissue, the models implemented in BioSuite suggested treatments very similar to those currently done in SBRT i.e. TCP close to 100% for as low as 3 fractions. Serial organ DVHs in the plan can drastically affect the BIOSUITE fractionation strategy, making hypofractionation no longer viable.

COMPARISON OF THE BIOLOGICALLY EFFECTIVE UNIFORM DOSE, EQUIVALENT UNIFORM DOSE, EFFECTIVE DOSE AND GENERALIZED EQUIVALENT UNIFORM DOSE CONCEPTS

Purpose: BioSuite is a program developed to help clinicians to identify cases eligible for escalation and/or altered fractionation and to quantify this potential using biological models of normal tissue and tumour response to EBRT. Materials: BioSuite consists in a single executable file developed in C++. The input data are DVHs from a TPS (Philips Pinnacle) as well as a more general DVH format. Plans are defined by parameters such as the number of fractions or a default prescription dose. These DVHs are in turn associated with an endpoint defined by a biological model and a set of parameters. From these models (LKB, Relative Seriality and enhanced LQ Poisson TCP), evaluation of NTCP or TCP can be performed forwardly or inversely. The forward evaluation is made in real-time as the operator modifies plan or endpoint parameters. The inverse evaluation (or optimisation) finds the dose multiplication factor (DMP) rescaling the DVH or number of fractions of a given size which take the complication risk to a prior set level. An isotoxic can also be done, which consists of computing for a fraction-number range the highest DMP (and thus TCP) for a constant NTCP. An upper TCP limit can be set (e.g. 99%) to avoid excessive doses in particular cases. The operator can finally generate dose-response curves to visually assess plan quality. Isotoxic optimisation was applied to 33 plans from our institution whose DVHs were exported to BIOSUITE. Results: For most of these plans, scope for escalation and/or altered fractionation was found, suggesting these patients were undertreated. In cases with a very small tumour and consequently small volume of irradiated normal tissue, the models implemented in BioSuite suggested treatments very similar to those currently done in SBRT i.e. TCP close to 100% for as low as 3 fractions. Serial organ DVHs in the plan can drastically affect the BIOSUITE fractionation strategy, making hypofractionation no longer viable.

TU‐C‐BRB‐01: Junior Investigator Winner: Outcome Prediction of Cervical Cancer: Kinetic Model of Tumor Regression During Radiation Therapy

Purpose: A kinetic model on describing tumor response to radiation therapy (RT) was developed to analyze the tumor regression data and predict the RT outcome for cervical cancer. Methods and Materials: Eighty patients of cervical cancer (stages IB2‐IVA) completed four sequential magnetic resonance imaging (MRI) scans before, during and after RT. The median follow‐up time is 5.5 years (range 0.2–9.4 years). A kinetic model incorporating three major effects: radiation cell killing, dead‐cell resolving, and tumor repopulation, was developed to fit the volumetric regression data measured by serial MRI scans. The derived radiobiological parameters were correlated with long‐term clinical outcome.Results: The tumor regression kinetics was significantly different for the two outcome groups: local control vs. local failure. The tumor regression rate and model parameters correlated significantly with the treatment outcome (p&lt;0.001), with a median 2‐Gy surviving fraction (S2) of 0.65 and a median half‐time of dead‐cell resolving (T1/2) of 7.9 days for locally controlled tumors, vs. S2=0.70 and T1/2=18 days for locally recurrent tumors. The 6‐year local tumor control rate was 87% vs. 54% for patients with S2&lt;0.70 vs. ⩾0.70 (p=0.001), and 95% vs. 57% for patients with T1/2 &lt;22 days vs. ⩾22 days (p&lt;0.001), respectively. Similarly the 6‐year disease‐free survival was 73% vs. 41% for the patients with S2&lt;0.70 vs. ⩾0.70 (p=0.025), and 87% vs. 52% for patients with T1/2&lt;22 days vs. ⩾22 days (p=0.002), respectively. Conclusion: The kinetic model well fits the temporal change of 3D tumor volume measured by serial MRI. Our data demonstrated that the locally recurrent tumors were not only radioresistant, but also had slow dead‐cell resolving probably due to the poor tumor microcirculation. This approach shows promise for volume‐based tumor response modeling and potential to refine outcome prediction.

TU‐C‐BRD‐07: Translational Research with Small Animal IGRT

Radiation therapy is a critical component in the management of cancer. However, our understanding of radiotherapy must continue to grow with improvements in conformality (e.g., IMRT) and as more targeted agents become available. Maximizing the therapeutic ratio will require a thorough understanding of biology of tumors and normal tissues. Small animal models of tumor response and normal tissue toxicity can provide unique insights into these issues, but have inherent limitations and challenges which must be overcome. As small animal image‐guided radiation therapy (IGRT) becomes feasible, one of the most important issues becomes “What questions do we want to ask?”. Pure radiotherapy questions such as dose, volume, and time/fractionation can be explored in appropriate small animal tumor models while monitoring normal tissue effects. When combined with the ever‐increasing number of targeted agents, small animal IGRT becomes a powerful pre‐clinical tool that may allow optimal selection of agents and improve the timing and sequencing of concurrent therapies. After strong models of tumor control and normal tissue response are established, exploration of response biomarkers including novel imaging modalities becomes feasible. The wide‐spread availability of small animal multi‐modality imaging including CT, MR, optical, and US combined with novel contrast agents makes this an especially rich area of exploration. Some examples of previous small animal radiotherapy experiments with a brain tumor model and ongoing exploration of normal tissues effects in lung will be reviewed and potential future studies attempting to link pre‐clinical and clinical data will be outlined.Learning Objectives:At the end of this lecture, the audience will be able to:1. Describe some of the challenges of small animal models.2. Discuss the range of biologic experiments which may be feasible.

Probability dynamics of a repopulating tumor in case of fractionated external radiotherapy

In this work two analytical methods are developed for computing the probability distribution of the number of surviving cells of a repopulating tumor during a fractionated external radio-treatment. Both methods are developed for the case of pure birth processes. They both allow the description of the tumor dynamics in case of cell radiosensitivity changing in time and for treatment schedules with variable dose per fraction and variable time intervals between fractions. The first method is based on a direct solution of the set of differential equations describing the tumor dynamics. The second method is based on the works of Hanin et al. [Hanin LG, Zaider M, Yakovlev AY. Distribution of the number of clonogens surviving fractionated radiotherapy: a long-standing problem revisited. Int J Radiat Biol 2001;77:205–13; Hanin LG. Iterated birth and death process as a model of radiation cell survival. Math Biosci 2001;169:89–107; Hanin LG. A stochastic model of tumor response to fractionated radiation: limit theorems and rate of convergence. Math Biosci 2004;191:1–17], where probability generating functions are used. In addition a Monte Carlo algorithm for simulating the probability distributions is developed for the same treatment conditions as for the analytical methods. The probability distributions predicted by the three methods are compared graphically for a certain set of values of the model parameters and an excellent agreement is found to exist between all three results, thus proving the correct implementation of the methods. However, numerical difficulties have been encountered with both analytical methods depending on the values of the model parameters. Therefore, the Poisson approximation is also studied and it is compared to the exact methods for several different combinations of the model parameter values. It is concluded that the Poisson approximation works sufficiently well only for slowly repopulating tumors and a low cell survival probability and that it cannot always be used for the description of the tumor dynamics. The two analytical methods and the Monte Carlo method are compared in terms of computational time. The Monte Carlo method is found to be much more time consuming than the analytical methods.

Introduction of Hypermatrix and Operator Notation into a Discrete Mathematics Simulation Model of Malignant Tumour Response to Therapeutic SchemesIn Vivo. Some Operator Properties

The tremendous rate of accumulation of experimental and clinical knowledge pertaining to cancer dictates the development of a theoretical framework for the meaningful integration of such knowledge at all levels of biocomplexity. In this context our research group has developed and partly validated a number of spatiotemporal simulation models of in vivo tumour growth and in particular tumour response to several therapeutic schemes. Most of the modeling modules have been based on discrete mathematics and therefore have been formulated in terms of rather complex algorithms (e.g. in pseudocode and actual computer code). However, such lengthy algorithmic descriptions, although sufficient from the mathematical point of view, may render it difficult for an interested reader to readily identify the sequence of the very basic simulation operations that lie at the heart of the entire model. In order to both alleviate this problem and at the same time provide a bridge to symbolic mathematics, we propose the introduction of the notion of hypermatrix in conjunction with that of a discrete operator into the already developed models. Using a radiotherapy response simulation example we demonstrate how the entire model can be considered as the sequential application of a number of discrete operators to a hypermatrix corresponding to the dynamics of the anatomic area of interest. Subsequently, we investigate the operators' commutativity and outline the "summarize and jump" strategy aiming at efficiently and realistically address multilevel biological problems such as cancer. In order to clarify the actual effect of the composite discrete operator we present further simulation results which are in agreement with the outcome of the clinical study RTOG 83-02, thus strengthening the reliability of the model developed.

A mathematical model for brain tumor response to radiation therapy

Gliomas are highly invasive primary brain tumors, accounting for nearly 50% of all brain tumors (Alvord and Shaw in The pathology of the aging human nervous system. Lea & Febiger, Philadelphia, pp 210-281, 1991). Their aggressive growth leads to short life expectancies, as well as a fairly algorithmic approach to treatment: diagnostic magnetic resonance image (MRI) followed by biopsy or surgical resection with accompanying second MRI, external beam radiation therapy concurrent with and followed by chemotherapy, with MRIs conducted at various times during treatment as prescribed by the physician. Swanson et al. (Harpold et al. in J Neuropathol Exp Neurol 66:1-9, 2007) have shown that the defining and essential characteristics of gliomas in terms of net rates of proliferation (rho) and invasion (D) can be determined from serial MRIs of individual patients. We present an extension to Swanson's reaction-diffusion model to include the effects of radiation therapy using the classic linear-quadratic radiobiological model (Hall in Radiobiology for the radiologist. Lippincott, Philadelphia, pp 478-480, 1994) for radiation efficacy, along with an investigation of response to various therapy schedules and dose distributions on a virtual tumor (Swanson et al. in AACR annual meeting, Los Angeles, 2007).

Tumor Response Kinetics after Schedule-Dependent Paclitaxel Chemoradiation Treatment for Inoperable Non-small Cell Lung Cancer: A Model for Low-Dose Chemotherapy Radiosensitization

Poor local disease control remains a major obstacle for inoperable non-small cell lung cancer (NSCLC) after radiotherapy. We previously reported results of a phase I/II clinical study based on preclinical investigations of paclitaxel radiation interactions for inoperable locally advanced NSCLC, which yielded remarkable local tumor responses and durable in-field tumor control using schedule-dependent low-dose paclitaxel for radiosensitization. Given our unique results, we analyzed the tumor response kinetics and conducted a statistical modeling of tumor response to characterize this regimen. A total of 104 chest CT scans from 27 patients treated in the clinical trial were evaluated. Tumor volumes were calculated by three-dimensional measurements of pretreatment and serial post-therapy CT scans. A nonlinear mixed effects model was used to model response kinetics. The average tumor volume reduction at 1 month post-therapy was 69.9 +/- 22.6% (standard deviation), and was 80.6 +/- 17.9% at the last follow-up. The nonlinear mixed effects model predicts that tumor volume will ultimately shrink by at least 75% for more than 75% of patients treated by this regimen. The model also suggests that maximum shrinkage is reached within 2 months after treatment. Tumor volume response kinetics revealed a rapid shrinkage of gross tumors using schedule-dependent pulsed low-dose paclitaxel radiosensitization. This is contrary to the protracted tumor regression process observed in radiation alone or other chemoradiation combinations. Statistical modeling may prove useful in characterizing and comparing different therapeutic regimens.

Dose-Dense Therapy in the Treatment of Early-Stage Breast Cancer: An Overview of the Data

Breast cancer represents a significant public health burden with > 200,000 new cases diagnosed in the United States each year. Although a significant proportion of these new diagnoses represent early-stage disease, many of these women will eventually experience a distant relapse and ultimately die of complications of metastatic breast cancer. Consequently, innovations in adjuvant treatment strategies are critical as we strive to further optimize outcomes. One such innovation, the dose-dense approach, is intended to specifically optimize the administration of standard chemotherapy regimens. Specifically, models of tumor growth and response, based on the Norton-Simon hypothesis, were translated into regimens which aim to increase tumor cell kill by decreasing the time intervals between treatments. This strategy, fully evaluated with doxorubicin/cyclophosphamide and paclitaxel in Cancer and Leukemia Group B 9741, demonstrated significant benefits compared with conventionally scheduled adjuvant chemotherapy. Dose density has since been applied to a number of other chemotherapy regimens and evaluated in clinical trials. An overview of the pivotal dose-dense trials will be reviewed herein.

A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: the paradigm of glioblastoma multiforme treated by temozolomide

A novel four-dimensional, patient-specific Monte Carlo simulation model of solid tumor response to chemotherapeutic treatment in vivo is presented. The special case of glioblastoma multiforme treated by temozolomide is addressed as a simulation paradigm. Nevertheless, a considerable number of the involved algorithms are generally applicable. The model is based on the patient's imaging, histopathologic and genetic data. For a given drug administration schedule lying within acceptable toxicity boundaries, the concentration of the prodrug and its metabolites within the tumor is calculated as a function of time based on the drug pharamacokinetics. A discretization mesh is superimposed upon the anatomical region of interest and within each geometrical cell of the mesh the most prominent biological "laws" (cell cycling, necrosis, apoptosis, mechanical restictions, etc.) are applied. The biological cell fates are predicted based on the drug pharmacodynamics. The outcome of the simulation is a prediction of the spatiotemporal activity of the entire tumor and is virtual reality visualized. A good qualitative agreement of the model's predictions with clinical experience supports the applicability of the approach. The proposed model primarily aims at providing a platform for performing patient individualized in silico experiments as a means of chemotherapeutic treatment optimization.

SU‐FF‐J‐97: Modeling of Lung Tumor Response to Image‐Guided Radiation Therapy

Purpose: To employ locally weighted regression (LWR) as an empirical technique for modeling tumor response to radiation treatment. Methods and Materials: Daily Megavoltage CT (MVCT) images were acquired prior to the delivery of 660 lung treatment fractions for 20 lung cancer patients, with contoured tumor volumes in each image. This data was used to develop a LWR model of the tumor progression throughout the treatment. A leave one out cross validation (LOOCV) technique was used to evaluate the performance of the model with varied combinations of days of patient data. For each patient, the model was run using the remaining patient data as training data, but with different test observations for each trial. Multiple simulations using random sampling without replacement were carried out to ensure that all possible combinations were tried. The best combination of days was determined by finding the mean predictive performance. Since this technique will eventually be used to make decisions about a patient's treatment, each prediction was accompanied by a measure of its uncertainty. A large uncertainty value indicates that the prediction is unreliable, most likely due to poor coverage of the training data. Results: The LWR model did not always accurately predict the shape of the tumor response curve, but the model was accurate in its predictions made at the end of a patient's treatment. The average prediction error was 13%, and even tumor volume increases during the course of the treatment were correctly predicted. Conclusions: The LWR modeling technique proved fast and fairly accurate when predicting the tumor volume at the end of treatment, and improvement is expected as additional data is added to the memory matrix. Future research will use expert opinion and data analysis techniques to determine which additional patient information will improve the model.

A new inducible RNAi xenograft model for assessing the staged tumor response to mTOR silencing

Human xenograft tumor models are widely used for efficacy evaluation of potential cancer targets. siRNA is usually stably introduced into tumor cells prior to transplantation. However, silencing of the cancer therapeutic target usually results in reduced cell growth/survival in vitro and/or failure to establish tumors in vivo, thus hindering tumor response-based efficacy evaluation. The present study explored a new tumor response model based on regulated RNAi, which is more relevant from a clinical standpoint. As a proof of principle, an inducible lentiviral RNAi vector was used to silence the known cancer therapeutic target mTOR upon induction with Doxycycline (DOX). The responses to DOX-induced mTOR silencing were tested both in vitro and in vivo for prostate cancer PC3 models. Significant reduction in cancer cell survival was observed due to cell cycle arrest and apoptosis when mTOR silencing was induced in vitro. mTOR silencing also caused tumor regression for the early-staged PC3 tumors (100% tumor regressed and 45% became tumor-free). The advanced-staged tumors also demonstrated significant responses (100% regressed). Therefore, our results demonstrate the powerful utility of this new inducible xenograft tumor model for efficacy evaluation of cancer targets, and it provides a direct in vivo efficacy validation of mTOR as a cancer therapeutic target.

Conventional wisdom and activities of the middle range

CorrespondenceConventional wisdom and activities of the middle rangeB Jones, R G Dale and A CarabeB Jones1Birmingham Cancer Centre, University Hospital Birmingham, B15 2TH, UK 2Department of Radiation Physics & Radiobiology, Charing Cross Hospital, London, W6 8RF, UKSearch for more papers by this author, R G Dale1Birmingham Cancer Centre, University Hospital Birmingham, B15 2TH, UK 2Department of Radiation Physics & Radiobiology, Charing Cross Hospital, London, W6 8RF, UKSearch for more papers by this author and A Carabe1Birmingham Cancer Centre, University Hospital Birmingham, B15 2TH, UK 2Department of Radiation Physics & Radiobiology, Charing Cross Hospital, London, W6 8RF, UKSearch for more papers by this authorPublished Online:28 Jan 2014https://doi.org/10.1259/bjr/22329449SectionsPDF/EPUBFull Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail About"Conventional wisdom and activities of the middle range." The British Journal of Radiology, 78(936), p. 1119 References 1 Munro AJ. The conventional wisdom and the activities of the middle range. Br J Radiol 2005;78:381–3. Link ISI, Google Scholar2 El Hamry AK, Monk J, Plowman PN. Stereotactic radiosurgery at St. Bartholomew's Hospital: third quinquennial review. Br J Radiology 2005;78:384–93. Link ISI, Google Scholar3 Jones B, Dale RG. Mathematical models of tumour and normal tissue response. Acta Oncologica 1999;38:883–93. Crossref Medline ISI, Google Scholar4 Jones B, Dale RG. Radiobiological modelling and clinical trials. Int J Radiat Oncol Biol Phys 2000;48:259–65. Crossref Medline ISI, Google Scholar5 Jones B, Dale RG. Radiobiologically based assessments of the net costs of fractionated focal radiotherapy. Int J Radiat Oncol Biol Phy 1998;41:1139–48. Crossref Medline ISI, Google Scholar6 Jones B, Rosenberg I. Particle Therapy Cooperative Oncology Group (PTCOG40), Insitute Curie 2004. Br J Radiol 2005;78:99–102. Link, Google Scholar7 Tan LT, Jones B, Gee A, Kingston RE. An audit of the treatment of carcinoma of the uterine cervix using external beam radiotherapy and a single line source brachytherapy technique. Br J Radiol 1997;70:1259–69. Link ISI, Google Scholar8 Dale RG, Hendry JH, Jones B, Deehan C, Sinclair J, Robertson G. Practical methods for compensating for missed treatment days in radiotherapy, with particular reference to head & neck schedules. Clin Oncol 2002;14:382–93. Crossref Medline ISI, Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byImplications of quality adjusted survival for clinical trials in radiation oncologyB Jones5 March 2014 | The British Journal of Radiology, Vol. 79, No. 940 Volume 78, Issue 936December 2005Pages: 1069-1141 © The British Institute of Radiology History RevisedJuly 12,2005ReceivedMay 16,2005AcceptedJuly 25,2005Published onlineJanuary 28,2014 Metrics Download PDF