Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 106 C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.

PURPOSE: A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters. METHODS: The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. RESULTS: The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve . CONCLUSION: It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. METHODS: The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized RMS error, the concordance correlation coefficient, and the structural similarity index. RESULTS: The CD + r reconstructions, chosen using the stopping condition, yielded structural similarity indexs that were similar to the CD (p = 0.47) and LR structural similarity indexs (p = 0.95) across R and that were significantly higher than the L1 structural similarity indexs (p = 0.04). The concordance correlation coefficient values for the CD + r T1 maps across all R and subjects were greater than those corresponding to the L1 (p = 0.15) and LR (p = 0.13) T1 maps, respectively. For R ≥ 12 (≤4.2 min scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD + r. CONCLUSION: The use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.

A study of flavonoid composition and antimicrobial activity of Scutellaria altissima L. from different floristic regions of Bulgaria.

INTRODUCTION: Scutellaria extracts and its constituents, especially the characteristic flavonoids such as baicalein and its glycoside baicalin, exhibit significant antimicrobial activity. AIM: To perform a comparative analysis of flavonoid composition and antimicrobial activity of extracts obtained from aerial parts and roots of Scutellaria altissima from the regions of Mezek and Bachkovo, Bulgaria. MATERIALS AND METHODS: 70% ethanol and aqueous extracts of aerial parts and roots of S. altissima were used. HPLC analysis of S. altissima extracts was performed. Microbiological tests were done on clinical isolates of Streptococcus mitis, Staphylococcus aureus, Escherichia coli, and Candida albicans. Minimal bactericidal and minimal bacteriostatic concentrations of S. altissima extracts were determined by the agar method. RESULTS: The ethanol extracts contain flavonoids approximately twice as much in comparison with the aqueous extracts. The baicalin content in the aerial parts of S. altissima from the region of Mezek is 5 times higher than that in the roots. S. altissima extracts have effective antimicrobial activity against S. mitis only. The minimal bactericidal concentration of ethanol extracts of S. altissima aerial parts and S. altissima roots is 2000 µg/ml and 8000 µg/ml at 24 hours, respectively. The bactericidal effect of aqueous extracts occurs at 48 hours at minimal bactericidal concentration of S. altissima aerial parts - 2000 µg/ml and of S. altissima roots - 6000 µg/ml. CONCLUSIONS: The finding that extracts of Bulgarian S. altissima possess effective antimicrobial properties against S. mitis suggests that it can be used as a potential source for the development of natural antimicrobial agents to suppress oral pathogens and prevent some oral infections.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Hemispheric infiltrative grade II gliomas in adults: association of residual tumour volume and extent of tumour resection with malignant transformation.

OBJECTIVES: Evaluation of the impact of surgical treatment on malignant transformation (MT) of adult supratentorial infiltrative grade II gliomas (G2G) in a series of chemotherapy and radiotherapy-naïve patients. BACKGROUND: Despite G2G are slow-growing tumours, they typically undergo MT with a subsequent fatal disease course. An extensive resection alone likely changes their biological behaviour and defers MT; however, this impact is not unequivocally confirmed. METHODS: Thirty-eight chemotherapy and radiotherapy-naïve adult patients operated from 2005 till 2014 for a G2G were investigated. Based on postoperative magnetic resonance imaging (MRI) and/or positron emission tomography follow-up (FU) scans, the patients were classified as "transformers" (15 patients in whom MT occurred during the FU-period) and "non-transformers" (23 patients). RESULTS: The follow-up period of "non-transformers" was longer (p <0.0001). After adjustment for known risk factors - age, male sex, astrocytoma histology, preoperative tumour volume, preoperative contrast enhancement and positive isocitrate dehydrogenase 1 gene mutation status - a larger log postoperative tumour volume (p=0.031) and a smaller extent of resection (p=0.0086) were associated with a shorter MT-free survival. CONCLUSION: In our series, less extensive resections were associated with a shorter time to MT. Our data support an adoption of techniques enabling extensive G2G resections, such as intraoperative imaging and awake resections, into everyday routine (Tab. 1, Fig. 2, Ref. 40).

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting.

BACKGROUND: The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. METHODS: Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. RESULTS: Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. CONCLUSIONS: Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer.

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

Post-operative imaging anatomy in liver transplantation.

The article describes and illustrates the surgical techniques and the post-operative imaging anatomy in liver transplantation. Special attention is paid to the variant vascular and biliary anatomy that are important for surgical planning. Considering the ever-growing number of liver transplants performed and the key role that imaging plays in the pre-operative planning and post-operative assessment, it is important for the radiologist to be familiar with the surgical techniques and the normal post-operative appearance in these patients.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor.

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Synaptophysin negative central neurocytoma.

BACKGROUND: Central neurocytoma is a rare primary brain tumour, mostly localised in the lateral ventricles in relation to the foramen of Monro. OBJECTIVES: To report a case of a rare central neurocytoma with a complete loss of Synaptophysin expression and provide the differential diagnosis. METHODS AND RESULTS: We describe a case of a 34-year old man with a headache, unsteady gait and dim vision. MRI demonstrated a tumorous expansion localised in both lateral ventricles. The patient underwent a subtotal resection. Histology showed a picture consistent with central neurocytoma, but tumour was completely negative for Synaptophysin. We describe our approach in such a diagnostically difficult case. CONCLUSIONS: In the rare case of Synaptophysin-negative central neurocytoma, its neuronal differentiation should be substantiated by electron-microscopic examination. Unfortunately in the routine work, biopsy samples are usually fixed in formalin fixative which does not preserve ultrastructure well. In such situations, an accurate diagnosis is disputable and based on careful assessment of the histological features, exclusion of tumours with similar morphology and detailed correlation with MRI pictures (Fig. 4, Ref. 6). Full Text (Free, PDF) www.bmj.sk.

MR imaging of late radiation therapy- and chemotherapy-induced injury: a pictorial essay.

Radiation to the brain and adjuvant chemotherapy may produce late delayed changes from several months to years after treatment of intracranial malignancies with a reported prevalence of 5-24%. The pattern of treatment-related injury may vary from diffuse periventricular white matter lesions to focal or multifocal lesions. Differentiation of treatment-related injury from tumor progression/recurrence may be difficult with conventional MR imaging (MRI). With both disease processes, the characteristic but nonspecific imaging features are vasogenic edema, contrast enhancement, and mass effect. This pictorial essay presents MRI spectra of late therapy-induced injuries in the brain with a particular emphasis on radiation necrosis, the most common and severe form. Novel MRI techniques, such as diffusion-weighted imaging (DWI), proton MR spectroscopy (MRS), and perfusion MRI, improve the possibilities of better characterization of treatment-related changes. Advanced MRI techniques allow for the assessment of metabolism and physiology and may increase specificity for therapy-induced changes.

Post-therapeutical changes in the brain: novel trends in imaging and their infuence on external beam radiotherapy.

UNLABELLED: Presented is the analysis of patients who underwent external beam radiotherapy (EBRT) to the brain in the period from 2003 to 2006 at the department of Radiation Oncology of the St. Elisabeth Cancer Institute.

The aim of our analysis was to identify risk factors of late delayed therapy induced injuries (LDTI) in the brain. The patients were regularly examined with magnetic resonance (MRI), including conventional and advanced techniques: perfusion imaging (pMRI), diffusion weighted imaging (DWI), MRI spectroscopy (MRS). The results from MRI were correlated with 18fluoro-deoxyglucose positron emission tomography (18FDG/PET) scans, as none of the listed method is sufficiently sensitive and specific by itself. Also clinical data records and treatment plans of these patients were analyzed.

In our cohort we found 6 patients with abnormal post-therapeutical changes, 4 of them with MR and 18FDG/PET scans characteristics for LDTI - radiation necrosis. In one patient biopsy was performed and radiation necrosis (RN) was confirmed.

[Nontuberculous mycobacteria and incidence of mycobacterioses in Prague in 1999-2004]. / Netuberkulózní mykobakteria a mykobakteriózy v Praze v letech 1999-2004.

OBJECTIVE: To analyze the incidence of nontuberculous mycobacteria detected in Prague patients in 1999-2004 as either single findings suggestive of clinical insignificance or repetitive findings suggestive of mycobacteriosis when reported together with the corresponding organ damage and symptomatology. MATERIAL AND METHODS: Nontuberculous mycobacteria were isolated and identified according to the Czech recommended methods for mycobacterial diagnosis in two Prague laboratories. The determined incidence rates of Mycobacterium (M.) kansasii, M. avium complex (MAC) and M. xenopi were compared with the respective nationwide rates and those of other nontuberculous bacteria, M. tuberculosis and M. bovis. The data on cases of mycobacterioses reported within the Czech Registry of Tuberculosis were provided by the Institute of Health Information and Statistics of the Czech Republic. RESULTS: In 1999-2004, the annual incidence rates of nontuberculous mycobacteria detected in Prague patients ranged between 169-139 and accounted for 13-25% of the totals of isolated mycobacteria including M. tuberculosis and M. bovis. Over this period, M. kansasii, MAC and M. xenopi were detected in 45, 76 and 43 patients, respectively. The single to repetitive detection ratio was the highest for M. kansasii (1:1.6), followed by MAC (1:1.8) and M. xenopi (1:2.3). Most male excretors were from higher age categories (median of 58-73 years) while the median age of female excretors ranged between 41 and 75 years. As many as 47 cases of mycobacterioses caused by the following agents: M. kansasii (20 cases), MAC (17 cases), M. xenopi (6 cases) and other nontuberculous mycobacteria (4 cases), were reported to the National Registry of Tuberculosis over the study period. CONCLUSIONS: Detection and identification of nontuberculous mycobacteria have become part of diagnostic routine of mycobacteriological laboratories. Compared to conventional tuberculous mycobacteria, detection of nontuberculous mycobacteria often requires the use of different and more cumbersome procedures such as incubation at preferential temperatures, longer incubation for detection of growth in primary cultures, species specific culture media, etc.. More skills and experience are needed for the use of automated detection systems and molecular biological techniques for species identification and interpretation of results. Regular consultations with clinical and outpatient physicians are crucial for the assessment of pathogenetic potential of nontuberculous mycobacteria.