Clinical effectiveness of combined whole body hyperthermia and external beam radiation therapy (EBRT) versus EBRT alone in patients with painful bony metastases: A phase III clinical trial study.

PURPOSE: To evaluate the response rate, pain relief duration, and time it took for pain to decline or resolve after radiation therapy (RT) with or without fever-range Whole Body Hyperthermia (WBH) in bony metastatic patients with mainly primary tumor of prostate and breast cancer leading to bone pain. MATERIALS & METHODS: Bony metastatic patients with pain score ≥4 on the Brief Pain Inventory (BPI) underwent RT of 30 Gy in 10 fractions in combination with WBH with nursing care under medical supervision versus RT-alone. WBH application time was 3-4 h in three fractions with at least 48-h intervals. All patients were stratified primary site, breast or prostate cancer vs others, BPI score, and exclusion criteria. The primary endpoint was complete response (CR) (BPI equal to zero with no increase of analgesics) within two months of follow-up. RESULTS: Based on this study, the RT-alone group showed the worst pain. The study was terminated after the enrollment of a total of 61 patients, 5 years after the first enrollment (April 2016 to February 2021). Finally, the CR rate in RT + WBH revealed the most significant difference with RT-alone, 47.4% versus 5.3% respectively within 2 months post-treatment (P-value <0.05). The time of complete pain relief was 10 days for RT + WBH, while the endpoint was not reached during the RT-alone arm. Pain progression or stable disease was observed in half of the patients in RT-alone group within 4 weeks after treatment. However, this score was near zero in RT + WBHT patients in two months post-treatment. CONCLUSIONS: WBH plus RT showed significant increases in pain relief and shorter response time in comparison with RT-alone for patients with bone metastatic lesions.

Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study.

OBJECTIVES: Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features. METHODS: We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep-radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep-radiomics features were fed to 9 common machine-learning algorithms to choose the best-performing classifier. The 2 best-performing AI models were then externally validated. RESULTS: Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively. CONCLUSION: Our proposed AI models fed with deep-radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.

Examining the effect of Helicobacter pylori cagPAI variety on gene expression pattern related to gastric cancer.

OBJECTIVES: We aimed to determine possible association between heterogeneity of Helicobacter pylori cytotoxin-associated gene pathogenicity island and gene expression profiles in patients with distinct histopathological changes. METHODS: Gastric biopsies were obtained from seventy five patients. Microbiological and pathological examinations were done and intactness of Helicobacter pylori cagPAI was determined by PCR using 11 pairs of primers flanking cagζ-cagA regions and cagPAI empty site. Alterations at mRNA levels of eight genes were investigated by real-time PCR and their association with cagPAI intactness and histopathological changes examined statistically. RESULTS: A larger proportion of cagPAI positive strains colonized patients with SAG (52.4%), followed by CG (33.3%), and IM (14.3%). Intact cagPAI was found in 87.5% of the strains obtained from patients with SAG, while significantly lower frequency was detected among those with CG (12.5%) and IM (0%). No significant difference was found among the studied histological groups and fold changes in gene expression of gastric biopsies of Helicobacter pylori infected patients with distinct cagPAI status. However, in each histological group, the strains with more complete gene cluster induced (ErbB2, CCNE1, CTNNB1, and MMP7 in SAG and IM groups) or reduced (TP53, in CG group) expression of the GC associated genes in relatively higher levels. APC, TP53 and E-cadherin were down-regulated in patients with SAG and IM compared with CG patients, irrespective to the status of cagPAI integrity. CONCLUSIONS: Helicobacter pylori strains that carry more complete cagPAI segment could induce remarkably higher levels of mRNA changes of GC associated genes in all histopathological groups.

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS: An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS: Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION: A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts.

PURPOSE: To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images. METHOD: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized. RESULTS: The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005). CONCLUSIONS: These results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.

Assessing the Changes of Cortical Thickness in Alzheimer Disease With MRI Using Freesurfer Software.

Introduction: In this study, we intend to determine the correlation between the thickness of the cerebral cortex and the severity of the cognitive disorder in Alzheimer disease (AD). Methods: A total of 20 (14 women and 6 men) patients diagnosed with AD with a Mean age of 72.95 years, and 10 (7 women and 3 men) cognitively normal (CN) subjects with a Mean age of 70.50 years were included in the study. Of the AD patient and CN subjects, 70% were female, and 30% were male. All individuals underwent 1.5 T Magnetic resonance imaging (MRI). The MRI scanning protocol included 3D MPRAGE (3D-T1W) sequence. All images were analyzed using Freesurfer v5.3, and then the brain cortical thickness in 7 cortical areas (inferior temporal, middle temporal, superior temporal, parahippocampal, pars triangularis, rostral middle frontal, and superior frontal) was calculated. Results: The analysis of covariance (ANCOVA) was conducted to compare the mean thickness of each region between the patient and the control group. There was a significant difference in the mean cortical thickness in all regions. In all cases, the mean cortical thickness in CN subjects was greater than in AD patients. However, the mean thickness of pars triangularis left hand in CN subjects was not significantly greater than that in AD patients. The receiver operating characteristic system (ROC) was designed to evaluate the predictive power of the patients and the healthy people. We have selected a thousand cut-off points from 1.5 to 3.5 mm for cortical thickness. When the cut-off points were within 2.276878-2.299680 mm in the left hemisphere, Youden's index was maximum. The sensitivity and specificity, in this case, were 80%. Also, when the cut-off points were within the range of 2.263278-2.282278 mm in the right hemisphere, the sensitivity and specificity were 90% and 80%, respectively. Conclusion: This study demonstrates the importance of quantifying the cortical thickness changes in the early diagnosis of AD. In addition, examining the pattern of changes and quantifying the reduction in the thickness of the cortex is a crucial tool for displaying the local and global atrophy of the brain. Also, this pattern can be used as an alternative marker for the diagnosis of dementia. Finally, to the best of our knowledge, our study is the first to report finding on the cortical thickness that would help the clinician have a better differential diagnosis. Also, this study has checked the possibility of early diagnosis of the disease. Highlights: The correlation between the thickness of cerebral cortex and the severity of cognitive disorder in Alzheimer's disease was determined.The cortical thickness change is an important factor in early diagnosis of Alzheimers disease.The pattern of reduction in the thickness changes is a crucial tool for displaying the local and global atrophy of the brain. Plain Language Summary: The neurodegenerative disorder Alzheimer's disease (AD) is a fast-growing epidemic in aging populations worldwide. In 2050, one new case of AD is estimated to increase up to every 33 seconds. So the diagnosis of AD in the early stage considerably decreases the progress of dementia and helps identify a correct treatment approach. The cortical thickness measured by structural neuroimaging has received a significant surrogate biomarker that could provide powerful tools for the early diagnosis of AD. Since the sensitivity and specificity of MRI are higher, it offers essential advantages for identifying brain atrophy patterns. The manual cortical thickness measurement methods are time-consuming and require experienced anatomists compared with automated methods. In this regard, Freesurfer software, which is a freely available program and provides information for quantifying the functional and structural features of the brain, is used. The current study demonstrates examining the pattern of changes and quantifying the reduction in the thickness of the cortex. This can also be used as an alternative marker for the early diagnosis of dementia using cortical thickness measurment that would help the physicians.

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

OBJECTIVES: The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning. METHODS: A total of 229 breast lesions and 220 cervical lymph nodes were included in the study. A group of expert radiologists first performed medical imaging and segmented the lesions, after which a rectangular mask was drawn, encompassing all of the contouring. The mask was extended in each axis up to 50%, and 29 radiomics features were extracted from each mask. Radiomics features that showed a significant difference in each contour were used to develop a support vector machine (SVM) classifier for benign and malignant lesions in breast and lymph node images separately. RESULTS: Single radiomics features extracted from extended contours outperformed radiologists' contours in both breast and lymph node lesions. Furthermore, when fed into the SVM model, the extended models also outperformed the radiologist's contour, achieving an area under the receiver operating characteristic curve of 0.887 and 0.970 in differentiating breast and lymph node lesions, respectively. CONCLUSIONS: Our results provide convincing evidence regarding the importance of the tumor periphery and TME in medical imaging diagnosis. We propose that the immediate tumor periphery should be considered for differentiating benign and malignant lesions in image quantification studies.

Metabolite Alternations in the Dopamine Circuit Associated with Methamphetamine-Related Psychotic Symptoms: A Proton Magnetic Resonance Spectroscopy Study.

Objective: Chronic METH use results in neurodegenerative alternations in the human brain. The present study aimed to assess the long-term METH impact on brain metabolite concentrations in cases meeting the DSM-5 criteria regarding METH use. Method: We recruited 42 METH users meeting the DSM-5 criteria and 21 healthy controls. Psychotic signs were measured using the Positive and Negative Syndrome Scale (PANSS). Proton magnetic resonance spectroscopy (1HMRS) evaluating Myo-inositol (Ml), Choline (Cho), Glutamine plus Glutamate (Glx), N-acetyl aspartate (NAA), and Creatine (Cre) were obtained in the dopaminergic pathway (Frontal Cortex, Substantia nigra, Ventral Tegmental Area (VTA), Nucleus Accumbens (NAc), Hippocampus, Striatum,) the subjects. All participants collected urine specimens for 24 hours to measure presence of specific metabolites including METH metabolite level, 5-Hydroxy indoleacetic acid metabolite (for serotonin level monitoring), and metanephrine metabolite (for dopamine level monitoring). Results: Dopamine and Serotonin increased in the METH group (P < 0.001). METH caused an increase in the Cre (P < 0.001) and a decline in the Glx (P < 0.001), NAA (P = 0.008), and MI (P < 0.001) metabolite concentrations of dopamine circuits in METH users in comparison with healthy subjects. We found no change in Cho metabolite concentration. Psychological data and the neurometabolite concentrations in the studied area of the brain were significantly correlated. Conclusion: There is an association between METH use and active neurodegeneration in the dopamine circuit, and it causes serious mental illness. 1HMRS can detect patient's deterioration and progression of disease as well as follow-up management in patients with METH use disorder.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists' assessment.

PURPOSE: Ultrasonography is the most common imaging modality used to diagnose carpal tunnel syndrome (CTS). Recently artificial intelligence algorithms have been used to diagnose musculoskeletal diseases accurately without human errors using medical images. In this work, a computer-aided diagnosis (CAD) system is developed using radiomics features extracted from median nerves (MN) to diagnose CTS accurately. METHOD: This study is performed on 228 wrists from 65 patients and 57 controls, with an equal number of control and CTS wrists. Nerve conduction study (NCS) is considered as the gold standard in this study. Two radiologists used two guides to evaluate and categorize the pattern and echogenicity of MNs. Radiomics features are extracted from B-mode ultrasound images (Ultrasomics), and the robust features are fed into support vector machine classifier for automated classification. The diagnostic performances of two radiologists and the CAD system are evaluated using ROC analysis. RESULTS: The agreement of two radiologists was excellent for both guide 1 and 2. The honey-comb pattern clearly appeared in control wrists (based on guide 1). In addition, CTS wrists indicated significantly lower number of fascicles in MNs (based on guide 2). The area under ROC curve (AUC) of the radiologist 1 and 2 are 0.658 and 0.667 based on guide 1 and 0.736 and 0.721 based on guide 2, respectively. The CAD system indicated higher performance than two radiologists with AUC of 0.926. CONCLUSION: The proposed CAD system shows the benefit of using ultrasomics features and can assist radiologists to diagnose CTS accurately.

Clinical Significance of Diffusion-weighted Magnetic Resonance Imaging on Treatment Efficacy in MS Patients With Acute Attacks.

Introduction: Despite various imaging methods, the accurate diagnosis of numerous neurodegenerative diseases remains controversial. Using advanced imaging techniques, like diffusion-weighted imaging, can help the early detection of Multiple Sclerosis (MS) and evaluation of the treatment efficacy in these patients. Methods: In total, 24 MS patients with acute attack and 30 healthy subjects were considered in our study. Region of Interest (ROI) was defined for acute and chronic plaques and Normal-Appearing White Matter (NAWM) in the patients' group. In the normal group, ROI only was mapped in the white matter in the same regions of the patient. All MS patients were receiving Methylprednisolone for 3 to 5 days. The rate of clinical disability in these patients was also evaluated based on the Expanded Disability Status Scale (EDSS) index. Finally evaluate changes of ADC values of plaques and NAWM before and after treatment. Results: The Apparent Diffusion Coefficient (ADC) values of acute plaques, the ADC values of NAWM, the number of enhancement in T1w, and EDSS values suggested a significant difference after treatment compared to before treatment. However, the ADC values of chronic plaques revealed no significant difference after treatment. There was a significant positive correlation between the difference in EDSS values before and after treatment. Conclusion: The study results demonstrated that using diffusion technique and ADC values analysis is a proper non-invasive method for MS diagnosis and evaluating treatment efficacy in these patients. Highlights: The obtained results suggested that the mean ADC for acute plaques and normal white matter significantly decreased after methylprednisolone treatment.Our study indicated a strong correlation between variations in EDSS, the mean ADC for acute plaques, and normal white matter.The collected results indicated that the number of enhanced plaques decreased after treatment. Besides, there was a positive correlation between its variations and EDSS. Plain Language Summary: Multiple Sclerosis (MS) is a common inflammatory disorder of the central nervous system that could result in physical and mental disabilities in patients. Disease progression usually manifests as a series of attacks. Although there is no proven cure for MS, different treatment strategies aim to modify the cause of the disease, manage its symptoms, and prevent and postpone disability. The most common therapy in acute attacks is using corticosteroid drugs. In addition to the treatment, evaluating the success rate of treatment was also challenging. Historically, clinical assessments method (e.g. EDSS) have been used as the baseline for measuring the therapy's efficiency. Several supplementary methods, including imaging techniques, are introduced to address this issue. Conventional MRI imaging with injection has been widely accepted to assess the treatment. However, because of the modest sensitivity of conventional MRI to detect subtle pathological changes, there is a poor correlation between its findings and patients' disability. This study moved from conventional MRI to advanced techniques, such as DWI and its quantitative index named ADC value. This technique can provide information about microstructural changes in MS patients. This method does not require injection, so there are no probable adverse effects and lower scan time. This study emphasizes changes in ADC value and EDSS before and after treatment with methylprednisolone. Our results suggested s that ADC values and EDSS after treatment are significantly different from their typical values. ADC values can be used as a biomarker to evaluate treatment efficiency, yet it is not objective enough to use it alone. So, the combination of DWI imaging with conventional methods might be beneficial in assessing treatment efficiency in MS patients.

Multiparametric MRI for the Diagnosis of Tumor Type in Patients Suspicious of Inner Gland Prostate Cancer.

PURPOSE: The current study aimed to evaluate multiparametric MRI for the diagnosis of type of tumor (benign ormalignant) in patients suspicious of inner gland prostate cancer. MATERIALS AND METHODS: This cross-sectional study was conducted on 44 consecutive patients with a clinicalimpression of prostate cancer who were referred to the MRI department of Payambaran Hospital, Tehran, Iranfor confirmative diagnostic evaluation. Cases suspected of tumor relapse and those who previously underwenttreatment for prostate cancer were excluded. Multiparametric MRI was performed for every patient by using a 1.5Tesla device with an integrated endorectal and pelvic-phased array coil. All patients subsequently underwent MRItransrectalultrasound fusion biopsy. The diagnostic value of each sequence was then investigated individually andin combination with other techniques by comparing the results with histological findings from MRI-TRUS fusionbiopsy. RESULTS: Among the techniques, T2-weighted imaging (T2W) had the highest sensitivity and specificity whiledynamic contrast enhanced (DCE) technique had the least. Diffusion-weighted imaging (DWI) and magnetic resonancespectroscopy (MRS) had a similar sensitivity and specificity and did not significantly differ from T2W.Adding functional techniques to T2W did not improve diagnostic indices compared to T2W alone. Quantitativeevaluation of apparent diffusion coefficient (ADC), DWI, and MRS showed that all techniques were able to differentiatebetween benign and malignant tumors. However, the quantitative combination of these sequences decreaseddiagnostic performance. CONCLUSION: T2W is the best technique for the diagnosis of type of tumor in terms of benignancy or malignancyin patients suspicious of inner gland prostate cancer. Adding functional imaging measurements to T2W does notimprove its diagnostic value.

Diagnostic Value of Diffusion Weighted Magnetic Resonance Imaging in Evaluation of Metastatic Neck Lymph Nodes in Head and Neck Cancer: A Sample of Iranian Patient.

Objective: To evaluate the diagnostic value of DWI in assessment of metastatic neck lymph node in a sample of Iranian patients with Head and Neck cancer. Methods: 25 patients with 80 neck lymph nodes were analyzed using 1.5 T MRI. DWI was performed with b values of 0 and 1,000 s/mm2. Short axis diameter and ADC values (min, max and mean) were calculated for metastatic and non-metastatic neck LNs and results were compared with histopathological findings. The optimal ADC thresholds were analyzed using receiver coefficient characteristic (ROC) curves for discriminating between metastatic and benign neck lymph nodes. Result: Histopathological findings revealed that there are 45% (n=36) metastatic and 55% (n=44) non-metastatic neck LNs respectively. There was no statistically significant difference in short axis diameter between the two groups (p = 0.346). However, The ADC values of metastatic neck LNs were significantly lower than those of non-metastatic neck LNs (p < 0.001); 0.90 ± 0.10 × 10-3 mm2/s vs 1.06 ± 0.12 × 10-3 mm2/s ( ADC mean ), 0.78 ± 0.08 × 10-3 mm2/s vs 0.92 ± 0.20× 10-3 mm2/s ( ADC min ) and 1.02 ± 0.12 × 10-3 mm2/s vs 1.24 ± 0.15 × 10-3 mm2/s (ADC max ). The optimal mean ADC threshold value was equal to 0.996 × 10-3 mm2/s for differentiating malignant from benign lymph nodes with sensitivity, specificity and accuracy of 80.56 %, 77.27 % and 71.59 % respectively. Conclusion: MR diffusion imaging and ADC values as a non-invasive technique can assess metastatic neck LNs in head and neck cancer with higher sensitivity, specificity and accuracy.

Evaluation of iron deposition in brain basal ganglia of patients with Parkinson's disease using quantitative susceptibility mapping.

AIM OF THE STUDY: Parkinson's disease is associated with iron deposition in the brain. The QSM (quantitative susceptibility mapping) is more sensitive than T2-weighted imaging, T2* and R2. Few studies have been used QSM to evaluate the iron in the basal ganglia of patients with Parkinson's disease. Our aim was to evaluate the iron deposition in the basal ganglia using QSM and determination of diagnostic value of this method and evaluation of the association between disease stage with QSM and age with QSM in all nuclei, separately. MATERIALS AND METHODS: Thirty patients were tested using Hoehn and Yahr test in three different stages. Fifteen healthy subjects were considered as control group. MRI sequences were performed using SIEMENS 3 T scanner.The Signal Processing in NMR software was used to process and analyze the images. The QSM in every of the basal ganglia was measured separately. RESULTS: There was a significant difference for QSM in the Subtania Nigera, Red Nucleus, Thalamic Nucleus and Globus Pallidus nucleus between two groups. The relationship between disease stage with QSM was significant in Subtania Nigera, Red Nucleus, and Globus Pallidus nucleus. The QSM values had a significant association with disease stage in all nuclei. The results showed that QSM has a higher accuracy in Subtania Nigera, Globus Pallidus, Red Nucleus and Thalamic Nucleus, respectively. CONCLUSIONS: Using QSM in Red Nucleus, Subtania Nigera, and Globus Pallidus nuclei can help diagnosis and staging the patients with Parkinson's disease. In future, studies with emphasis on the disease stage can be helpful in evaluation the different parts of these three nuclei.

Real-time mapping of heat generation and distribution in a laser irradiated agar phantom loaded with gold nanoparticles using MR temperature imaging.

Gold nanoparticles (AuNPs) have shown potential strength in photothermal therapy of cancer. Several techniques have been developed to investigate local heat generation by AuNPs. However, a sensitive thermal imaging technology with high temporal resolution, minimum invasiveness and high spatial resolution is still lacking. In this research study, by using magnetic resonance thermal imaging (MRTI), we reported a technique for monitoring of heat generation and distribution in an AuNPs loaded agar phantom irradiated by laser. Three different agar phantoms with various AuNPs concentrations (0, 8 and 16 µg/ml) were produced and studied. The phantoms were exposed to an external laser [532 nm; 4 min] under MRTI. For real-time temperature monitoring, we employed the theory of proton resonance frequency (PRF) shift. Infrared (IR) camera was employed to measure the actual temperature of each point on the surface of irradiated agar gel. Finally, the correlation between the temperatures obtained by IR camera and MRTI was evaluated. We observed that temperature of the gels loaded by AuNPs at concentration of 0, 8 and 16 µg/ml reached 27.2, 37.8, 45 °C with a total area of heat distribution of 94.98, 452.16, and 907.34 mm2 (from the point of irradiation). During the process of laser irradiation, we observed: (i) a significant rise in temperature, (ii) a dependency between the rate of temperature rise and concentration of AuNPs, and (iii) a direct correlation between temperature change and MR image phase. In addition, statistical analysis showed that the variation of temperatures measured by IR camera and temperatures computed by MRTI had acceptable correlation (R > 0.9). In conclusion, MRTI has a good sensitivity and precision that can be employed for nano-photothermal therapy planning and may be considered for real-time mapping of heat generation and distribution in a laser irradiated tissue loaded by AuNPs.

The Role of Single Voxel MR Spectroscopy, T2 Relaxation Time and Apparent Diffusion Coefficient in Determining the Cellularity of Brain Tumors by MATLAB Software

Introduction: Brain tumors if timely diagnosed are sure to be treated through shorter processes. MRI amongst others is of Para clinical methods greatly effective in diagnosis phase. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps provide some information that could reflect tissue cellularity. Neurosurgeons, in particular to detect the tumor cellularity, must send the specimens taken through biopsy to the pathology unit. This study is aimed at determining the tumor cellularity in brain. Materials and Methods: In this cross-sectional study, 32 patients (18 males and 14 females of the range 18 ­ 77 y/o) between April 2014 and February 2016 who were referred to the neurosurgery department of Shohada-E Tajrish Hospital of Tehran participated. Imaging was made using single voxel MR Spectroscopy, ADC and T2W Multi Echo Pulse Sequence in addition to routine pulse sequences and the images were analyzed using MATLAB software to determine the cellularity of brain tumors in comparison to the biopsy. Results: findings showed that by decreasing T2 relaxation time, the amount of ADC, N-Acetyl Aspartate (NAA) and also, increase Choline metabolite, lead to registering tumors in the lower class on the designed table and these tumors have a higher degree of consistency and cellularity. T2 Relaxation time, the tumors will stand at higher class on the designed table. Also the results indicated that 85% diagnostic weight of T2 relaxation time and 83% diagnostic weight of ADC compared with biopsy could reveal the brain tumor cellularity (P>0.05). Conclusion: some cellular metabolite changes such as NAA and Choline, ADC value and T2 relaxation time feature could effectively be used to distinguish and illustrate the degree of cellularity of brain tumors especially Intra-axial brain tumors (with about 85%. vs. biopsy). We recommend to more data should be used to increase the accuracy percentage of this technique.

Preoperative Evaluation of Tumor Adhesion to Adjacent Brain Tissue in Patients with Meningioma with BSMI Method and Its Comparison with the Width of Edema Around Tumor

Background: This study aims to investigate the ability of BSMI, to preoperative evaluation of tumor adhesion to adjacent brain tissue in patients with meningioma and comparing this method to the width of edema around tumor, using surgery findings as the reference standard. Methods: Thirty patients with meningioma brain tumor who underwent surgery at Loghman hospital were selected for the study between November 2016 and January 2018. The level of edema according to the classification of Ide et al., (u1995) was compared with the surgical findings with blinded results, and neurosurgeons made a qualitative assessment of tumor adhesion at the time of resection. The ability of BSMI and level of edema to predict the surgical assessment of adhesion was tested using the Fisher Exact Test. Results: BSMI method was conducted on patients with meningioma brain tumor, which judged 22 (73.3%) patients as adhesion (+) and 8 (26.66%) patients as adhesion (-). In this case, there was a significant relationship between BSMI judgment and surgical findings (p-value<0.0001). The sensitivity, specificity, precision and accuracy was high, at 91.30%, 85.71%, 95.45% and 90%, respectively. Using T2-Weighted SPACE sequence, of the 30 patients, 13 (43.3%) were judged as adhesion (+) and 17 (56.7%) as adhesion (-) from edema, whereas surgical findings evaluated 23 (76.7%) as adhesion (+) and 7 (23.3%) as adhesion (-).The sensitivity was moderate but the specificity was high, at 52.17% and 85.71%, respectively. Other criteria such as precision and accuracy were 62.31% and 60%, respectively. Conclusions: BSMI evaluated adhesion of the tumor to the adjacent brain tissue with high-accuracy prior to surgery. This method was more effective than Edema method in evaluating adhesion between meningioma and the brain.

Comparison Between Three-Dimensional Diffusion-Weighted PSIF Technique and Routine Imaging Sequences in Evaluation of Peripheral Nerves in Healthy People.

INTRODUCTION: The present study aims to evaluate the Three-Dimensional Diffusion-Weighted reversed fast imaging with steady state free precession (3D DW-PSIF) sequence with respect to imaging of the peripheral nerves; the tibial, medial, and lateral plantar nerves in the lower extremity, ulnar and median nerve in the upper extremity, sciatic nerve, brachial plexus, and lumbosacral plexus, and also to compare its usefulness with the current two-dimensional sequences on a 1.5 T MR scanner. METHODS: A total of 25 healthy subjects underwent MR imaging of peripheral nerves, 5 subjects in each area. In each imaging sequence, including T2W SPAIR and 3D DW-PSIF, images were evaluated for ability to identify the nerves in the related area using a 3-score scale (0-2). Then, by summing up the conspicuity scores, a total certainty score was recorded for each sequence. RESULTS: With combining the results of all studies, the conspicuity mean (SD) score was 1.57(0.67) on the 3D DW-PSIF images, and 0.74(0.76) on the T2-weighted images (P<0.001). Regarding the lumbosacral plexus, the corresponding certainty mean (SD) scores were 1.80(0.40) and 1.07(0.74) (P<0.001) and with regard to the brachial plexus, they were 1.23(0.83) and 0.75(0.84), (P<0.001). Regarding the ankle/hind foot they were 1.87(0.35) and 0.40(0.50) (P<0.001) and in the wrist/proximal hand, 1.70(0.48) and 0.50(0.52) (P<0.001). Regarding the sciatic nerve, they were 1.80(0.44) and 0.20(0.44) (P=0.003). CONCLUSION: 3D DW PSIF provides better manifestation of nerves compared to routine imaging sequences particularly fat saturated T2W images. This novel imaging technique can be used in MR neurography examination protocol for exact localization of the nerve and evaluation of the nerve pathology.

Evaluation of Respiratory Triggered Diffusion-Weighted MRI with Three b-Values Compared to ADC Map and Fast Spin Echo Heavily T2W in Differential Diagnosis of Hemangioma from Malignant Liver Lesions.

PURPOSE: The goal of this study was to evaluate the ability of diffusion-weighted imaging (DWI) with three different b-values compared to apparent diffusion coefficient (ADC) map and fast spin echo heavily T2 weighted (FSE-HT2W) in differential diagnosis of hemangioma from malignant liver lesions. METHODS: Fifty-four liver lesions in 20 patients (12 females and eight males: mean age of 52 ± 12.3 years) were examined in this study. FSE-HT2W with breath-hold technique and DWI using respiratory triggered with three different b-values (50,400 and 800 s/mm2) were performed on all patients. Mean ADC values were calculated from each lesion. Agreement levels of each sequence with standard of reference were compared by constructing the receiver operating characteristics (ROC) curve and calculation of the area under the ROC curve. RESULTS: ADC maps had the largest area under the ROC curve and also the most agreement with the standard of reference. DWI obtained with high b-value (b-800 s/mm2) and FSE-HT2W technique were ranked next, respectively. Hemangiomas had significantly higher ADC values than malignant liver lesions (P = .001). No significant differences were seen in gender, age, and lesion size between two lesion groups. CONCLUSIONS: ADC maps and DWI in high b-values are more successful than FSE-HT2W technique in differential diagnosis of hemangioma from malignant liver lesions.

Methoxyamine Enhances 5-Fluorouracil-Induced Radiosensitization in Colon Cancer Cell Line HT29.

OBJECTIVE: This study intended to observe the effects of methoxyamine (Mx) on cytotoxic effects and DNA damage caused by 5-Fluorouracil (5-FU) in combination with gamma radiation in a human colon cancer cell line, HT29. MATERIALS AND METHODS: In this experimental study, HT29 cells were cultured as a monolayer and treated with different concentrations of 5-FU along with 1 mM Mx for 24 hours. Next, the cells were irradiated with 2 Gy gamma radiation. After the treatments, we assessed for DNA damage, cytotoxicity, and viability by alkaline comet, clonogenic survival, and trypan blue dye exclusion assays. RESULTS: Cytotoxicity and DNA damage increased with increasing 5-FU concentration. The 1 mM Mx concentration had no significant effect on cytotoxicity and DNA damage from 5-FU; however, it increased the cytotoxic and genotoxic effects of different concentrations of 5-FU when used in combination with 2 Gy gamma radiation. CONCLUSION: Mx combined with 5-FU enhanced the radiosensitivity of colon cancer cells.