Multimodality-multiparametric brain tumors evaluation.

Brain tumors represent a vast group of lesions, originating from different neuronal cells with different degrees of aggressiveness. Despite some technological advances either pre or post-treatment, these tumors may share similar imaging findings and properties, rendering diagnosis/prognosis, an ambiguous process. Gadolinium-enhanced magnetic resonance imaging remains the gold standard for providing detailed morphologic information, but presents several limitations due to the overlap of findings, in cases such as progressive tumor and post-radiation related effects. Tumor cellularity, vascularity, proliferative activity, metabolic and functional profiles are a few of many characteristics that may further support tumor classification, but cannot be assessed by conventional imaging alone. We review the aforementioned factors and indicate how they improve tumor characterization and grading in order to design the optimal treatment strategy and better evaluate post treatment efficacy.

Addressing the post-irradiation hypothalamic-pituitary endocrine abnormalities of brain tumors in pediatric patients.

PURPOSE: Hypothalamic-pituitary axis is susceptible to radiotherapy, causing endocrine disorders to childhood cancer survivors. We conducted a systematic review in order to assess the radiation-induced toxicity that leads to hormone secretion abnormalities and their severity in children with brain tumors. METHODS: The data were collected by relevant studies on PubMed and EMBASE. Articles up to December 2016 were included. We selected studies which focused on children patients (<18 yr old) with brain tumors treated with radiotherapy and the consequences for their endocrine system. RESULTS: Growth hormone (GH) deficiency was the most common post-irradiation abnormality among children cancer survivors, followed by gonadotrophin (GT), thyroid stimulating hormone (TSH), corticotropin (ACTH) and prolactin (PRL) disorders. CONCLUSIONS: The age of the patient, total radiotherapy dose, number of fractions, fraction size and the duration of treatment seem to determine the severity of these disturbances.

Quantification of normal CSF flow through the aqueduct using PC-cine MRI at 3T.

INTRODUCTION: Quantification of cerebrospinal fluid (CSF) flow through the cerebral aqueduct is of paramount importance in patients with hydrocephalus. The purpose of this study was to evaluate the normal CSF flow measurements at three different anatomical levels of the aqueduct utilizing 3-Tesla (3 T) magnetic resonance imaging. MATERIALS AND METHODS: The CSF hydrodynamics in 22 healthy volunteers were evaluated. Phase-contrast cine MRI was performed on a 3 T General Electric MR system (GE Medical Systems, Milwaukee, WI, USA). A cardiac-gated, flow-compensated GRE sequence with flow encoding was used, and the aqueduct was visualized using a sagittal T1 FLAIR sequence. Velocity maps were acquired at three different anatomical levels. Region-of-interest (ROI) analysis was performed. RESULTS: CSF flow velocities were slightly increased at the upper in comparison with the lower part of the aqueduct. The mean values for the peak positive and negative velocity and the mean average flow were calculated for both ROIs. DISCUSSION/CONCLUSIONS: CSF peak positive velocity, peak negative velocity, and mean flow through the aqueduct were calculated in 22 young healthy volunteers performed at 3 T. Our measurements did not show significant difference compared with the reported measurements obtained at 1.5 T. Slight differences were observed in the CSF hydrodynamic measurements, depending on the anatomical level of the aqueduct; however, they did not vary significantly.

Psychosocial correlates of insomnia in an adolescent population.

This study examines the nature of the relationship between psychosocial factors and insomnia complaints in an adolescent non-clinical population. It is a cross-sectional study of a stratified sample of 2,195 Greek adolescent high-school students. Subjects were given the Athens insomnia scale, the Symptom Checklist scale (SCL-90-R) and a questionnaire concerning demographic characteristics. None of the subjects had received help for insomnia complaints or other overt psychopathology. Adolescents classified as suffering from insomnia presented higher levels of general psychopathology. Age, tobacco and alcohol use, self-reported patterns of communication in the family, perceived economic status and school performance were identified as correlates of the insomnia complaints. A significant number of adolescents fail to receive appropriate treatment for insomnia. Psychosocial correlates are important factors to consider when faced with insomnia complaints in this age group. More research is needed in important timelines in the developmental history of a young adult.

A free software for the evaluation and comparison of dose response models in clinical radiotherapy (DORES).

PURPOSE: The aim of this work was to develop a user-friendly and simple tool for fast and accurate estimation of Normal Tissue Complication Probabilities (NTCP) for several radiobiological models, which can be used as a valuable complement to the clinical experience. MATERIALS AND METHODS: The software which has been named DORES (Dose Response Evaluation Software) has been developed in Visual Basic, and includes three NTCP models (Lyman-Kuther-Burman (LKB), Relative Seriality and Parallel). Required input information includes the Dose-Volume Histogram (DVH) for the Organs at Risk (OAR) of each treatment, the number of fractions and the total dose of therapy. RESULTS: NTCP values are computed, and subsequently placed in a spreadsheet file for further analysis. A Dose Response curve for every model is automatically generated. Every patient of the study population can be found on the curve since by definition their corresponding dose-response points fall exactly on the theoretical dose-response curve, when plotted on the same diagram. CONCLUSION: Distributions of absorbed dose alone do not provide information on the biological response of tissues to irradiation, so the use of this software may aid in the comparison of outcomes for different treatment plans or types of treatment, and also aid the evaluation of the sensitivity of different model predictions to uncertainties in parameter values. This was illustrated in a clinical case of breast cancer radiotherapy.

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.

Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy.

The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving [see text]|EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived.

A Retrospective Analysis of Toxicity and Efficacy for 2 Hypofractionated Irradiation Schedules Versus a Conventional One for Post-Mastectomy Adjuvant Radiotherapy in Breast Cancer.

INTRODUCTION: The aim of this analysis was a retrospective evaluation of the efficacy and toxicity of 2 hypofractionated irradiation schedules compared to conventional therapy in post-mastectomy patients. METHODS: 3 irradiation schedules were analyzed: 48.30 Gy in 21 fractions (group A, n = 60), 42.56 Gy in 16 fractions (group B, n = 27) and 50 Gy in 25 fractions (group C, n = 30) of the front chest wall. All groups were also treated with a supraclavicular field, with 39.10 Gy in 17 fractions (group A), 37.24 Gy in 14 fractions (group B) or 45 Gy in 25 fractions (group C). RESULTS: No local recurrences were noted in any group during 36 months of follow-up. Acute skin toxicity presented in all groups, with 58.3%, 70.4% and 60% of grade I; 35%, 25.9% and 40% of grade II; 6.7%, 3.7% and 0% of grade III being seen in groups A, B and C, respectively. Late skin toxicity was noted only as grade I in 16.7%, 25.9% and 26.7% of groups A, B and C, respectively. No significant difference was noted among all groups for either acute or late skin toxicity, or for radio-pneumonitis (chi2 test, p > 0.05). CONCLUSION: All schedules were equally effective with equivalent toxicity. A prospective randomized study is needed to confirm our results.

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.

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data.

INTRODUCTION: A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification. METHODS: MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data. RESULTS: The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible. CONCLUSIONS: FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.

Statistical methods for clinical verification of dose-response parameters related to esophageal stricture and AVM obliteration from radiotherapy.

The purpose of this work is to provide some statistical methods for evaluating the predictive strength of radiobiological models and the validity of dose-response parameters for tumour control and normal tissue complications. This is accomplished by associating the expected complication rates, which are calculated using different models, with the clinical follow-up records. These methods are applied to 77 patients who received radiation treatment for head and neck cancer and 85 patients who were treated for arteriovenous malformation (AVM). The three-dimensional dose distribution delivered to esophagus and AVM nidus and the clinical follow-up results were available for each patient. Dose-response parameters derived by a maximum likelihood fitting were used as a reference to evaluate their compatibility with the examined treatment methodologies. The impact of the parameter uncertainties on the dose-response curves is demonstrated. The clinical utilization of the radiobiological parameters is illustrated. The radiobiological models (relative seriality and linear Poisson) and the reference parameters are validated to prove their suitability in reproducing the treatment outcome pattern of the patient material studied (through the probability of finding a worse fit, area under the ROC curve and chi2 test). The analysis was carried out for the upper 5 cm of the esophagus (proximal esophagus) where all the strictures are formed, and the total volume of AVM. The estimated confidence intervals of the dose-response curves appear to have a significant supporting role on their clinical implementation and use.

Prediction of AVM obliteration after stereotactic radiotherapy using radiobiological modelling.

This study was carried out in order to derive the radiobiological parameters of the dose-response relation for the obliteration of arteriovenous malformation (AVM) following single fraction stereotactic radiotherapy. Furthermore, the accuracy by which the linear Poisson model predicts the probability of obliteration and how the haemorrhage history, location and volume of the AVM influence its radiosensitivity are investigated. The study patient material consists of 85 patients who received radiation for AVM therapy. Radiation-induced AVM obliterations were assessed on the basis of post-irradiation angiographies and other radiological findings. For each patient the dose delivered to the clinical target volume and the clinical treatment outcome were available. These data were used in a maximum likelihood analysis to calculate the best estimates of the parameters of the linear Poisson model. The uncertainties of these parameters were also calculated and their individual influence on the dose-response curve was studied. AVM radiosensitivity was assumed to be the same for all the patients. The radiobiological model used was proved suitable for predicting the treatment outcome pattern of the studied patient material. The radiobiological parameters of the model were calculated for different AVM locations, bleeding histories and AVM sizes. The range of parameter variability had considerable effect on the dose-response curve of AVM. The correlation between the dosimetric data and their corresponding clinical effect could be accurately modelled using the linear Poisson model. The derived response parameters can be introduced into the clinical routine with the calculated accuracy assuming the same methodology in target definition and delineation. The known volume dependence of AVM radiosensitivity was confirmed. Moreover, a trend relating AVM location with its radiosensitivity was observed.

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.

Challenges and choices in prostate cancer irradiation: from the three dimensional conformal radiotherapy to the era of intensity modulated, image-guided and adaptive radiation treatment.

In the last decades the status of radiotherapy was tremendously increased in terms of conformity to the target as well as image-guided techniques in conjunction with intensity-modulated radiotherapy (IMRT). The technological improvement had a significant clinical outcome for better response and lower toxicity to the surrounding normal tissues. Nowadays the incidence of rectal toxicity has been significantly decreased, especially with image guided radiation therapy (IGRT), whereas the dose escalation to the prostate has driven the clinical practice to the fact that radical radiotherapy for low or intermediate risk prostate cancer is definitely equivalent to surgery. The treatment volume can be reduced by reducing the size of the necessary margins to count for inaccuracies in target position and patient setup. This can be achieved either by improving the daily localization of the target before treatment or by adapting the treatment in response to feedback. This is the goal of image-guided and adaptive radiotherapy, respectively. These techniques improve the accuracy of dose delivery with a significant impact on clinical outcome and toxicity. 

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques.

In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

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.

Classification methods for the differentiation of atypical meningiomas using diffusion and perfusion techniques at 3-T MRI.

The purpose was to investigate the contribution of machine learning algorithms using diffusion and perfusion techniques in the differentiation of atypical meningiomas from glioblastomas and metastases. Apparent diffusion coefficient, fractional anisotropy, and relative cerebral blood volume were measured in different tumor regions. Naive Bayes, k-Nearest Neighbor, and Support Vector Machine classifiers were used in the classification procedure. The application of classification methods adds incremental differential diagnostic value. Differentiation is mainly achieved using diffusion metrics, while perfusion measurements may provide significant information for the peritumoral regions.

Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data.

PURPOSE: Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data. MATERIALS AND METHODS: Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics. RESULTS: Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology. CONCLUSION: The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

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

PURPOSE: To assess the contribution of (1)H-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, (1)H-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.

Electrochemotherapy in cancer patients: first clinical trial in Greece.

Electrochemotherapy is currently undergoing intensive investigation in the field of local control of cancer. In Greece, five medical centers have co-operated to perform ECT for the efficient management of growing, recurrent or newly emerging cutaneous and subcutaneous tumor nodules. ECT was applied alone or in combination with external beam radiation therapy, brachytherapy and surgery in 52 cancer patients, using bleomycin according to standard protocols. The treatment response for various tumors was 63.83% complete, 31.91% partial, and 95.74% overall of the treated nodules. Patients exerted neither systemic nor local side-effects. The results of ECT performance in Greece provided evidence that this new treatment strategy is safe and permits the effective control of tumors of various origins and histological types.