Ioannis Tsougos

Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T

Abstract Purpose: To assess the contribution of 1H-magnetic resonance spectroscopy (1H-MRS), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic susceptibility contrast-enhanced (DSCE) imaging metrics in the differentiation of glioblastomas from solitary metastasis, and particularly to clarify the controversial reports regarding the hypothesis that there should be a significant differentiation between the intratumoral and peritumoral areas. Methods: Conventional MR imaging, 1H-MRS, DWI, DTI and DSCE MRI was performed on 49 patients (35 glioblastomas multiforme, 14 metastases) using a 3.0-T MR unit. Metabolite ratios, apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV) were measured in the intratumoral and peritumoral regions of the lesions. Receiver-operating characteristic analysis was used to obtain the cut-off values for the parameters presenting a statistical difference between the two tumor groups. Furthermore, we investigated the potential effect of the region of interest (ROI) size on the quantification of diffusion properties in the intratumoral region of the lesions, by applying two different ROI methods. Results: Peritumoral N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, Cho/NAA and rCBV significantly differentiated glioblastomas from intracranial metastases. ADC and FA presented no significant difference between the two tumor groups. Conclusions: 1H-MRS and dynamic susceptibility measurements in the peritumoral regions may definitely aid in the differentiation of glioblastomas and solitary metastases. The quantification of the diffusion properties in the intratumoral region is independent of the ROI size placed.

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques.

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann-Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.

The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

NTCP modelling and pulmonary function tests evaluation for the prediction of radiation induced pneumonitis in non-small-cell lung cancer radiotherapy

This work aims to evaluate the predictive strength of the relative seriality, parallel and Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis (RP), in a group of patients following lung cancer radiotherapy and also to examine their correlation with pulmonary function tests (PFTs). The study was based on 47 patients who received radiation therapy for stage III non-small-cell lung cancer. For each patient, lung dose volume histograms (DVHs) and the clinical treatment outcome were available. Clinical symptoms, radiological findings and pulmonary function tests incorporated in a post-treatment follow-up period of 18 months were used to assess the manifestation of radiation induced complications. Thirteen of the 47 patients were scored as having radiation induced pneumonitis, with RTOG criteria grade 3 and 28 of the 47 with RTOG criteria grade 2. Using this material, different methods of estimating the likelihood of radiation effects were evaluated, by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Lungs were evaluated as a paired organ as well as individual lungs. Of the NTCP models examined in the overall group considering the dose distribution in the ipsilateral lung, all models were able to predict radiation induced pneumonitis only in the case of grade 2 radiation pneumonitis score, with the LKB model giving the best results (chi2-test: probability of agreement between the observed and predicted results Pchi(chi2)=0.524 using the 0.05 significance level). The NTCP modelling considering lungs as a paired organ did not give statistically acceptable results. In the case of lung cancer radiotherapy, the application of different published radiobiological parameters alters the NTCP results, but not excessively as in the case of breast cancer radiotherapy. In this relatively small group of lung cancer patients, no positive statistical correlation could be established between the incidence of radiation pneumonitis as estimated by NTCP models and the pulmonary function test evaluation. However, the use of PFTs as markers or predictors for the incidence or severity of radiation induced pneumonitis must be investigated further.

The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T.

Background Conventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography (DCE-MRM), may lead to ambiguous diagnosis and unnecessary biopsies. Purpose To investigate the contribution of proton MR spectroscopy (1H-MRS) combined with diffusion tensor imaging (DTI) metrics in the discrimination between benign and malignant breast lesions. Material and Methods Fifty-one women with known breast abnormalities from conventional imaging were examined on a 3T MR scanner. DTI was performed during breast MRI, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were measured in the breast lesions and the contralateral normal breast. FA and ADC were compared between malignant lesions, benign lesions, and normal tissue. 1H-MRS was performed after gadolinium administration and choline peak was qualitatively evaluated. Results In our study 1H-MRS showed a sensitivity of 93.5%, specificity 80%, and accuracy 88.2%. FA was significantly higher in breast carcinomas compared to benign lesions. However, no significant difference was observed in ADC between benign and malignant lesions. The combination of Cho presence and FA achieved higher levels of accuracy and specificity in discriminating malignant from benign lesions over Cho presence or FA alone. Conclusion In conclusion, applying DTI and 1H-MRS together, adds incremental diagnostic value in the characterization of breast lesions and may sufficiently improve the low specificity of conventional breast MRI.

Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy.

The purpose of this work is to evaluate the predictive strength of the relative seriality, parallel and LKB normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis, in a large group of patients following breast cancer radiotherapy, and furthermore, to illustrate statistical methods for examining whether certain published radiobiological parameters are compatible with a clinical treatment methodology and patient group characteristics. The study is based on 150 consecutive patients who received radiation therapy for breast cancer. For each patient, the 3D dose distribution delivered to lung and the clinical treatment outcome were available. Clinical symptoms and radiological findings, along with a patient questionnaire, were used to assess the manifestation of radiation-induced complications. Using this material, different methods of estimating the likelihood of radiation effects were evaluated. This was attempted by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Additionally, the need for an update of the criteria that are being used in the current clinical practice was also examined. The patient material was selected without any conscious bias regarding the radiotherapy treatment technique used. The treatment data of each patient were applied to the relative seriality, LKB and parallel NTCP models, using published parameter sets. Of the 150 patients, 15 experienced radiation-induced pneumonitis (grade 2) according to the radiation pneumonitis scoring criteria used. Of the NTCP models examined, the relative seriality model was able to predict the incidence of radiation pneumonitis with acceptable accuracy, although radiation pneumonitis was developed by only a few patients. In the case of modern breast radiotherapy, radiobiological modelling appears to be very sensitive to model and parameter selection giving clinically acceptable results in certain cases selectively (relative seriality model with Seppenwoolde et al and Gagliardi et al parameter sets). The use of published parameters should be considered as safe only after their examination using local clinical data. The variation of inter-patient radiosensitivity seems to play a significant role in the prediction of such low incidence rate complications. Scoring grades were combined to give stronger evidence of radiation pneumonitis since their differences could not be strictly associated with dose. This obviously reveals a weakness of the scoring related to this endpoint, and implies that the probability of radiation pneumonitis induction may be too low to be statistically analysed with high accuracy, at least with the latest advances of dose delivery in breast radiotherapy.

A radionuclide dosimetry toolkit based on material-specific Monte Carlo dose kernels

ObjectiveWe sought to develop a user-friendly dosimetry toolkit that should aid the improvement of the quality of radionuclide therapy, which is critically dependent on patient-specific planning of each treatment. MethodsIn this work, we present a new toolkit suitable for indicative radionuclide dose calculation. The software is built using open source tools and it uses dose kernels calculated using the Geant4 Application for Tomographic Emission simulation toolkit. In addition, a method that uses kernel data to extract a material-specific dose absorption factor is described and a proof of concept is given. In this work, time dependency and organ sensitivity are not modeled. ResultsThe developed software utilizes Monte Carlo calculated dose kernels and proposes a fast dose calculation method. Using computed tomography or magnetic resonance imaging it can provide a more accurate and personalized indicative dose map. ConclusionDosimetry based on quantitative three-dimensional data is more accurate and allows a more individualized approach in patient therapy. Moreover, the use of this toolkit with the standardization for data collection and processing will increase the accuracy as well as the compatibility of radiation dose.

Long-term prognostic value of heart-rate recovery after treadmill testing in patients with diabetes mellitus

BACKGROUND Heart-rate recovery (HRR) is considered to be an independent predictor of cardiac and all-cause mortality. We examined the long-term prognostic value of HRR in patients suffering from diabetes mellitus. METHODS In this study, we included 258 consecutive patients. Patients whose HRR value or myocardial perfusion imaging could have been influenced by factors other than myocardial ischaemia, were excluded. The value of HRR was defined as the decrease in the heart-rate from peak exercise to 1 min after the termination of the exercise. All patients underwent SPECT myocardial perfusion imaging combined with exercise testing. Cardiovascular death and non-fatal myocardial infarction were considered as hard cardiac events, while late revascularization procedures as soft events. Cox proportional-hazard models were applied to evaluate the association between HRR and the investigated outcome. RESULTS During the follow-up period (30.8+/-6.9 months), hard cardiac events occurred in 21 (8%) patients (15 with abnormal HRR value, p<0.001), while 35 (14%) patients underwent revascularization (31 with abnormal HRR value, p<0.001). Considering it as a continuous variable, HRR was a strong predictor for both hard cardiac (coefficient=-0.41, SE=0.052, p<0.001) and soft cardiac events (coefficient=-0.63, SE=0.058, p<0.001). After adjustments were made for potential confounders, including scintigraphic variables, abnormal HRR remained an independent predictor for hard and soft cardiac events (p<0.001). CONCLUSION Our results suggest that among patients with diabetes, a decreased HRR is a significant independent predictor of hard and soft cardiac events.

Distinct peak at 3.8 ppm observed by 3T MR spectroscopy in meningiomas, while nearly absent in high-grade gliomas and cerebral metastases

The purpose of the present study was to evaluate distinct metabolic features of meningiomas to distinguish them from other brain lesions using proton magnetic resonance spectroscopy. The study was performed on 17 meningiomas, 24 high-grade gliomas and 9 metastases. Elevated signal intensity at 3.8 ppm observed in low TE spectra adequately differentiated meningioma from other brain tumors while alanine was not indicative of meningioma occurrence; the presence of lipids and lactate did not provide a strong index for meningioma malignancy.

Correlation between radiation-induced telomerase activity and human telomerase reverse transcriptase mRNA expression in HeLa cells

Purpose: To quantify and correlate human telomerase reverse transcriptase (hTERT) mRNA expression with telomerase activity (TA) after ionizing irradiation of HeLa cells. Materials and methods: TA and hTERT mRNA expression were evaluated, at 24-h intervals, in HeLa cells cultured for up to 144 h, before and after treatment with increasing doses of 6 MV photon ionizing radiation (5 – 20 Gy), using the telomeric repeat amplification protocol (TRAP) assay and real-time reverse transcriptase polymerase chain reaction (RT-PCR), respectively. Cell viability was determined using the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. A prototype phantom was constructed for accurate irradiation of HeLa cells. Results: Treated cells showed a decrease in viability with increasing radiation dose, and a correlation was observed with post-treatment period. TA and hTERT mRNA expression of HeLa cells increased for the first 24 h after irradiation. The maximal increases were approximately two times the un-irradiated cell levels at 24 h post-irradiation, followed by a decrease and a return to the control levels 72 h post-irradiation. The time-course of telomerase activation after 24 h, differed among radiation doses. A dose-dependent G2/M arrest was observed 24 h post-irradiation, along with an increase in polyploidy 48 h post-irradiation and afterwards. Conclusion: A correlation between TA and hTERT mRNA expression and a radiation induced cell cycle dependent modification of hTERT mRNA expression was established for the first 24 h post-irradiation.