Jörg Sabczynski

Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm

The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.

A Gaussian approach for the calculation of the accuracy of stereotactic frame systems

Stereotactic frame systems are widely used in neurosurgery. The accuracy of frame devices is considered as a gold standard to which the accuracy of new frameless stereotactic navigation systems is compared. The purpose of this study is to develop a general approach for the prediction of the application accuracy of stereotactic systems. The approach will be applied to the frame-based biopsy performed with three frame devices: Leksell G, Cosman–Roberts–Wells (CRW), and Brown–Roberts–Wells (BRW). A work-flow analysis will be carried out demonstrating that the accuracy relevant for a clinical application comprises several error sources including imaging, target and entry point selection, image to frame coordinates registration, and the setting of mechanical parameters of the frame. These error sources will be postulated to obey a Gaussian distribution probability density. The linear, i.e., Gaussian, error propagation, will be used to link all error contributions thus to calculate the cumulative accuracy of the frame used in the application. Although the Gaussian approach is an approximation, it allows for an analytical treatment of the accuracy. Both the accuracy at the target point and the accuracy of the probe needle guidance along the planned trajectory have been investigated. Of great significance is the relationship found between accuracy, pixel dimension, and image slice thickness, the latter being the dominant factor for slices of more than 1.5 mm thickness, yielding inaccuracies larger than 1.5 mm. For target points the predictions for the application accuracy have been compared to the results of measurements, showing good agreement with the experimental data.

4D rotational x-ray imaging of wrist joint dynamic motion

Current methods for imaging joint motion are limited to either two-dimensional (2D) video fluoroscopy, or to animated motions from a series of static three-dimensional (3D) images. 3D movement patterns can be detected from biplane fluoroscopy images matched with computed tomography images. This involves several x-ray modalities and sophisticated 2D to 3D matching for the complex wrist joint. We present a method for the acquisition of dynamic 3D images of a moving joint. In our method a 3D-rotational x-ray (3D-RX) system is used to image a cyclically moving joint. The cyclic motion is synchronized to the x-ray acquisition to yield multiple sets of projection images, which are reconstructed to a series of time resolved 3D images, i.e., four-dimensional rotational x ray (4D-RX). To investigate the obtained image quality parameters the full width at half maximum (FWHM) of the point spread function (PSF) via the edge spread function and the contrast to noise ratio between air and phantom were determined on reconstructions of a bullet and rod phantom, using 4D-RX as well as stationary 3D-RX images. The CNR in volume reconstructions based on 251 projection images in the static situation and on 41 and 34 projection images of a moving phantom were 6.9, 3.0, and 2.9, respectively. The average FWHM of the PSF of these same images was, respectively, 1.1, 1.7, and 2.2 mm orthogonal to the motion and parallel to direction of motion 0.6, 0.7, and 1.0 mm. The main deterioration of 4D-RX images compared to 3D-RX images is due to the low number of projection images used and not to the motion of the object. Using 41 projection images seems the best setting for the current system. Experiments on a postmortem wrist show the feasibility of the method for imaging 3D dynamic joint motion. We expect that 4D-RX will pave the way to improved assessment of joint disorders by detection of 3D dynamic motion patterns in joints.

Effect of Localization Devices and Registration Methods on the Accuracy of Stereotactic Frame Systems Predicted by the Gaussian Approach

A qualitative work-flow analysis of a neurosurgical procedure indicates that the resolution of the image used to plan the intervention is the major source of inaccuracy. Quantitative experimental measurements confirm this observation. They fail, however, to explain the relationship between the accuracy of the frame components involved in a stereotactic procedure and the overall application accuracy. This investigation shows that the novel Gaussian approach is a flexible framework for the calculation of the application accuracy of frame systems. Therefore, the Gaussian approach provides a detailed understanding of the interplay between the various factors affecting accuracy. The basic ideas and limitations of the Gaussian approach are briefly explained. The effect of fiducial marker distribution and registration is investigated and shown to introduce a spatial dependence to the accuracy. The results of the Gaussian approach are compared with experimental data for three stereotactic frame devices: Leksell G, Cosman-Roberts-Wells, and Brown-Roberts-Wells. Although the Gaussian approach is an approximation, it reproduces the accuracy measured in the experiment within the statistical error of that experiment.

In silico oncology: Exploiting clinical studies to clinically adapt and validate multiscale oncosimulators

This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer preoperatively treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the planned clinical validation procedure since the same type of multiscale data - although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature-based values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.

Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. However, dealing simultaneously with several PET/CT scans poses a serious workflow problem. It can be a difficult and tedious task to extract response criteria based upon an integrated analysis of PET and CT images and to track these criteria over time. In order to improve the workflow for serial analysis of PET/CT scans we introduce in this paper a fast lesion tracking algorithm. We combine a global multi-resolution rigid registration algorithm with a local block matching and a local region growing algorithm. Whenever the user clicks on a lesion in the base-line PET scan the course of standardized uptake values (SUV) is automatically identified and shown to the user as a graph plot. We have validated our method by a data collection from 7 patients. Each patient underwent two or three PET/CT scans during the course of a cancer therapy. An experienced nuclear medicine physician manually measured the courses of the maximum SUVs for altogether 18 lesions. As a result we obtained that the automatic detection of the corresponding lesions resulted in SUV measurements which are nearly identical to the manually measured SUVs. Between 38 measured maximum SUVs derived from manual and automatic detected lesions we observed a correlation of 0.9994 and a average error of 0.4 SUV units.

In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model

The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions.

Image-Guided Therapy (IGT): New CT and Hybrid Imaging Technologies

Integrated multi-modality IGT prototypes are described for IR and OR suites of the future including procedures guided with CT and hybrid imaging devices. Radiofrequency Ablation (RFA) is an exemplary application, and the prototypes can be adapted to other procedures. To investigate pre-planning 1 , a software prototype enables virtual electrode placement. Intra-procedurally, a CT-integrated robot aligns its laser to planned trajectories. Interventional tools and mini-imaging devices are registered to imagers and 3-D data sets by attaching them as robot hands, or by tracking them with electromagnetic or optical systems. Tissue response is monitored with new 3-D real-time imaging. The ultimate goal is to simplify treatment and provide benefit to the patient.

Automatic assessment of the quality of patient positioning in mammography

Quality assurance has been recognized as crucial for the success of population-based breast cancer screening programs using x-ray mammography. Quality guidelines and criteria have been defined in the US as well as the European Union in order to ensure the quality of breast cancer screening. Taplin et al. report that incorrect positioning of the breast is the major image quality issue in screening mammography. Consequently, guidelines and criteria for correct positioning and for the assessment of the positioning quality in mammograms play an important role in the quality standards. In this paper we present a system for the automatic evaluation of positioning quality in mammography according to the existing standardized criteria. This involves the automatic detection of anatomic landmarks in medio- lateral oblique (MLO) and cranio-caudal (CC) mammograms, namely the pectoral muscle, the mammilla and the infra-mammary fold. Furthermore, the detected landmarks are assessed with respect to their proper presentation in the image. Finally, the geometric relations between the detected landmarks are investigated to assess the positioning quality. This includes the evaluation whether the pectoral muscle is imaged down to the mammilla level, and whether the posterior nipple line diameter of the breast is consistent between the different views (MLO and CC) of the same breast. Results of the computerized assessment are compared to ground truth collected from two expert readers.

Follow-up segmentation of lung tumors in PET and CT data

Early response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. We have developed algorithms which allow the user to track both tumor volume and standardized uptake value (SUV) measurements during the therapy from series of CT and PET images, respectively. To prepare for tumor volume estimation we have developed a new technique for a fast, flexible, and intuitive 3D definition of meshes. This initial surface is then automatically adapted by means of a model-based segmentation algorithm and propagated to each follow-up scan. If necessary, manual corrections can be added by the user. To determine SUV measurements a prioritized region growing algorithm is employed. For an improved workflow all algorithms are embedded in a PET/CT therapy monitoring software suite giving the clinician a unified and immediate access to all data sets. Whenever the user clicks on a tumor in a base-line scan, the courses of segmented tumor volumes and SUV measurements are automatically identified and displayed to the user as a graph plot. According to each course, the therapy progress can be classified as complete or partial response or as progressive or stable disease. We have tested our methods with series of PET/CT data from 9 lung cancer patients acquired at Princess Margaret Hospital in Toronto. Each patient underwent three PET/CT scans during a radiation therapy. Our results indicate that a combination of mean metabolic activity in the tumor with the PET-based tumor volume can lead to an earlier response detection than a purely volume based (CT diameter) or purely functional based (e.g. SUV max or SUV mean) response measures. The new software seems applicable for easy, faster, and reproducible quantification to routinely monitor tumor therapy.