Introducing fitting models for estimating age-specific dose and effective dose in paediatric patients undergoing head, chest and abdomen-pelvis imaging protocols: a patient study.

INTRODUCTION: Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient-specific variables. The aim of this study is to present fitting models for the estimation of age-specific dose estimates (ASDE), in the same direction of size-specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen-pelvis imaging. METHODS: A total of 583 paediatric patients were included in the study. Radiometric data were gathered from DICOM files. The patients were categorised into five distinct groups (under 15 years of age), and the effective dose, organ dose and ASDE were computed for the CT examinations involving the head, chest and abdomen-pelvis. Finally, the best fitting models were presented for estimation of ASDE and effective doses based on patient age, gender and the type of examination. RESULTS: The ASDE in head, chest, and abdomen-pelvis CT examinations increases with increasing age. As age increases, the effective dose in head and abdomen-pelvis CT scans decreased. However, for chest scans, the effective dose initially showed a decreasing trend until the first year of life; after that, it increases in correlation with age. CONCLUSIONS: Based on the presented fitting model for the ASDE, these CT scan quantities depend on factors such as patient age and the type of CT examination. For the effective dose, the gender was also included in the fitting model. By utilising the information about the scan type, region and age, it becomes feasible to estimate the ASDE and effective dose using the models provided in this study.

Corpus Callosum Functional Activities in Children with Cerebral Palsy.

Background: Since cerebral palsy (CP) is a corollary to brain damage, persistent treatment should accompany an alteration in brain functional activity in line with clinical improvements. In this regard, the corpus callosum (CC), as a connecting bridge between the two hemispheres, plays an essential role. Objective: This study aimed to investigate the therapeutic effects of occupational therapy (OT) on CC functional activity and walking capacity in children with cerebral palsy. Material and Methods: In this clinical trial study, 4 children with CP (8.25±1.71 years) received 45 min OT sessions 3 times weekly for 8 weeks. Functional magnetic resonance imaging (fMRI) was acquired while conducting passive motor tasks to quantify CC activation. The pre-post activation changes in CC following therapy were quantified in terms of activated voxels. Walking capacity was evaluated using the timed-up-and-go (TUG), 6-minute walk test (6 MWT), and 10-meter walk test (10 MWT) in pre-and post-treatment. Results: The number of activated voxels in CC indicated significant improvement in participants. Post-treatment activated voxels substantially exceeded pre-treatment active voxels. Clinical measures, including TUG, 6 MWT, and 10 MWT are improved by 11.9%, 12.6%, and 25.4%, respectively. Conclusion: Passive task-based fMRI can detect the effects of OT on CC functional activity in children with CP. According to the results, OT improves CC functional activity in addition to gait and balance performance.

Incidence risk assessment of secondary cancer due to radiotherapy of women with rectal cancer using BEIR VII, EPA, and ICRP models.

Background: Radiotherapy has a significant side effect known as radiation-induced secondary cancer. This study aims to evaluate the dose and secondary cancer risk for women with rectal cancer treated with three-dimensional conformal radiation therapy (3D-CRT) to the organs at risk (OARs) and some sensitive organs using different types of radiation-induced cancer risk prediction models, including Biological Effects of Ionizing Radiation (BEIRVII), Environmental Protection Agency (EPA) and International Commission on Radiological Protection (ICRP), and compare the results of the different models for same organs. Materials and methods: Thirty female patients with rectal cancer were considered and dose calculations were based on the PCRT-3D treatment planning system, while the radiotherapy of the patients had been performed using Shinva linear accelerator with a total dose of 45 Gy at 25 fractions. Planning target volume (PTV), OARs, and some sensitive organs were contoured, three models were used to evaluate secondary cancer risk (SCR) using the excess relative risk (ERR) and excess absolute risk (EAR). Results: The bladder presents the highest risk, in terms of ERR, and the femur head and uterus in terms of EAR from the three models (BEIR VII, EPA, and ICRP). Conclusion: Based on the obtained results, radiotherapy of rectal cancer is relatively higher for the bladder and femur head, compared to the risk for other organs, the kidney risk is significantly lower. It was observed that the SCR from the ICRP model was higher compared to BEIR VII and EPA models.

Comparison of Accuracy in Calculation of Absorbed Dose to the Kidneys Following Radioligand Therapy with 177Lu-DKFZ-PSMA-617 by Two Different Background Correction Methods.

Background: To improve the accuracy of activity image quality, scatter correction is a critical method. The aim of this study is to compare the accuracy in calculation of absorbed dose to patients following radioligand therapy (RLT) with 177Lu-DKFZ-PSMA-617 by two different methods of background correction in the conjugate view method. Materials and Methods: This study involved 10 patients. The individualized patient dosimetry calculations were based on whole-body planar scintigraphy images acquired in 10 patients with a mean age of 71.4 ± 6.07 years (range 63-85 years) at approximately 0-2 h, 4-6 h, 18-24 h, and 36-48 h after administration of the mean 6253 ± 826.4 MBq (range 5500-7400 MBq) of 177Lu-DKFZ-PSMA-617. Organ activities were calculated using the conjugate view method by Buijs and conventional background correction. Eventually, the absorbed dose of radiation was calculated using Medical Internal Radiation Dose formalism. Results: The dose per unit of injected activity (mGy/MBq) ± standard deviation for kidney using Buijs and conventional methods was 1.05 ± 0.11 and 0.63 ± 0.14, respectively. Conclusion: The Buijs background correction method was more accurate than the conventional method.

Monte Carlo evaluation of effective dose and risk of exposure induced cancer death (REID) for common examinations in stereo radiography (EOS) imaging: Considering age and gender.

INTRODUCTION: The aim of this study is to evaluate the effective dose and cancer risk of examinations in EOS imaging system in different age and gender groups. MATERIALS AND METHODS: In total, 555 patients who had undergone common EOS imaging examinations were entered into the study. Exposure parameters and patients' characteristics for lower limb, full spine and full body imaging techniques, at different gender and age groups, were evaluated. Finally, effective dose and risk of exposure induced cancer death (REID) was calculated with the Monte Carlo based PCXMC software. RESULTS: The difference between average effective doses of male and female was not significant (p ≥ 0.05), however, the corresponding REID showed statistically significant difference (p ≤ 0.001). The average effective dose of patients (without considering technique, age and gender) was obtained as 0.13 mSv. The corresponding average REID was 8.84 per million. The maximum average effective dose value was obtained for patients over 10 years of age with the full body technique (0.17 ± 0.05 mSv). The maximum average REID value was obtained for full body technique and for patient with 0-10 years old (15.20 ± 10.00 per million). CONCLUSION: In common EOS imaging examinations, the effective dose and REID values of patients in both genders in all age groups are less than the corresponding values in other imaging modalities (according to previous studies). However, according to stochastic effects of ionizing radiation and based on the As Low As Reasonably Achievable (ALARA) principle, more considerations are necessary, especially in the full body technique and for female examinations.

Electronic portal image enhancement based on nonuniformity correction in wavelet domain.

PURPOSE: Electronic portal images are one of the most important tools to verify the ongoing radiotherapy treatment through comparison with a reference image generated during treatment planning. In this procedure, two images are geometrically matched by means of visible bone or other landmarks of interest such as implanted fiducials. However, the intrinsically poor contrast and low spatial resolution of portal images can limit image quality. METHODS: In this study, we have provided a multiresolution approach to enhance the quality of portal images acquired from the pelvis treatment fields. The main idea behind this work aims at removing some of the image artifacts that conceal the anatomical information. For this purpose, we have applied the homomorphic filtering on the approximation sub-band of wavelet decomposition to enhance local information. Moreover, in order to sharpen the bone edges, wavelet detail sub-bands were weighted to amplify important image details in the reconstruction of the desired enhanced image. The most appropriate image quality measure was chosen according to the image's characteristics in the spatial domain. By considering the characteristics of portal images as the random and nonperiodic texture, high level of noise, and a nonuniform background, three suitable quality measures of images were assessed: edge content, measure of enhancement, and measure of enhancement by entropy. RESULTS: The higher values of these measures indicate the quality improvement in the processed images through our proposed algorithm. Moreover, the subjective evaluation results indicate that the proposed multiresolution approach significantly enhances the perceived quality of images in comparison with original and the similar approach ( p < 0.001 $p < 0.001$ ). CONCLUSIONS: Our proposed wavelet-based enhancement algorithm successfully reduced image intensity nonuniformity and enhanced anatomical featured information, which drastically improved the objective metrics values. Subjective evaluation of enhanced image confirmed this quality improvement.

Organ dose in cardiac dual-energy computed tomography: a Monte Carlo study.

Dual-energy computed tomography (DECT) has appeared as a novel approach with the aim of evaluating artery-related diseases. With the advent of DECT, concerns have been raised about the induction of diseases such as cancer due to high radiation exposure of patients. Therefore, the dose received by patients in DECT should be considered. The parameter most commonly used for patient dosimetry is the effective dose (ED). The purpose of this study is to model and validate a DECT scanner by a developed MCNP Monte Carlo code and to calculate the organ doses, the ED, and the conversion factor (k-factor) used in determining ED in the cardiac imaging protocol. To validate the DECT scanner simulation, a standard dosimetry body phantom was modeled in two radiation modes of single energy CT and DECT. The results of simulated CT dose index (CTDI) were compared with those of ImPACT or measurement data. Then dosimetry phantom was replaced by the male and female ORNL phantoms and the organ doses were calculated. The organ doses were also calculated by ImPACT dose software. In the initial validation stage, the minimum and maximum observed relative differences between results of MNCP simulation and measured were 2.77% and 5.79% for the central CTDI and 1.91% and 5.83% for the averaged peripheral CTDI, respectively. The mean ED of simulation and the ImPACT were 3.23 and 5.55 mSv/100 mAs, and the mean k-factor was 0.016 and 0.032 mSv mGy-1 cm-1 in the male and female phantoms, respectively. The k-factor obtained for males is close to the currently used k-factor, but the k-factor for females is almost twice.

Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection.

OBJECTIVE: Robust differentiation between infarcted and normal tissue is important for clinical diagnosis and precision medicine. The aim of this work is to investigate the radiomic features and to develop a machine learning algorithm for the differentiation of myocardial infarction (MI) and viable tissues/normal cases in the left ventricular myocardium on non-contrast Cine Cardiac Magnetic Resonance (Cine-CMR) images. METHODS: Seventy-two patients (52 with MI and 20 healthy control patients) were enrolled in this study. MR imaging was performed on a 1.5 T MRI using the following parameters: TR = 43.35 ms, TE = 1.22 ms, flip angle = 65°, temporal resolution of 30-40 ms. N4 bias field correction algorithm was applied to correct the inhomogeneity of images. All images were segmented and verified simultaneously by two cardiac imaging experts in consensus. Subsequently, features extraction was performed within the whole left ventricular myocardium (3D volume) in end-diastolic volume phase. Re-sampling to 1 × 1 × 1 mm3 voxels was performed for MR images. All intensities within the VOI of MR images were discretized to 64 bins. Radiomic features were normalized to obtain Z-scores, followed by Student's t-test statistical analysis for comparison. A p-value < 0.05 was used as a threshold for statistically significant differences and false discovery rate (FDR) correction performed to report q-value (FDR adjusted p-value). The extracted features were ranked using the MSVM-RFE algorithm, then Spearman correlation between features was performed to eliminate highly correlated features (R2 > 0.80). Ten different machine learning algorithms were used for classification and different metrics used for evaluation and various parameters used for models' evaluation. RESULTS: In univariate analysis, the highest area under the curve (AUC) of receiver operating characteristic (ROC) value was achieved for the Maximum 2D diameter slice (M2DS) shape feature (AUC = 0.88, q-value = 1.02E-7), while the average of univariate AUCs was 0.62 ± 0.08. In multivariate analysis, Logistic Regression (AUC = 0.93 ± 0.03, Accuracy = 0.86 ± 0.05, Recall = 0.87 ± 0.1, Precision = 0.93 ± 0.03 and F1 Score = 0.90 ± 0.04) and SVM (AUC = 0.92 ± 0.05, Accuracy = 0.85 ± 0.04, Recall = 0.92 ± 0.01, Precision = 0.88 ± 0.04 and F1 Score = 0.90 ± 0.02) yielded optimal performance as the best machine learning algorithm for this radiomics analysis. CONCLUSION: This study demonstrated that using radiomics analysis on non-contrast Cine-CMR images enables to accurately detect MI, which could potentially be used as an alternative diagnostic method for Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR).

Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm.

AIMS: Despite the promising results achieved by radiomics prognostic models for various clinical applications, multiple challenges still need to be addressed. The two main limitations of radiomics prognostic models include information limitation owing to single imaging modalities and the selection of optimum machine learning and feature selection methods for the considered modality and clinical outcome. In this work, we applied several feature selection and machine learning methods to single-modality positron emission tomography (PET) and computed tomography (CT) and multimodality PET/CT fusion to identify the best combinations for different radiomics modalities towards overall survival prediction in non-small cell lung cancer patients. MATERIALS AND METHODS: A PET/CT dataset from The Cancer Imaging Archive, including subjects from two independent institutions (87 and 95 patients), was used in this study. Each cohort was used once as training and once as a test, followed by averaging of the results. ComBat harmonisation was used to address the centre effect. In our proposed radiomics framework, apart from single-modality PET and CT models, multimodality radiomics models were developed using multilevel (feature and image levels) fusion. Two different methods were considered for the feature-level strategy, including concatenating PET and CT features into a single feature set and alternatively averaging them. For image-level fusion, we used three different fusion methods, namely wavelet fusion, guided filtering-based fusion and latent low-rank representation fusion. In the proposed prognostic modelling framework, combinations of four feature selection and seven machine learning methods were applied to all radiomics modalities (two single and five multimodalities), machine learning hyper-parameters were optimised and finally the models were evaluated in the test cohort with 1000 repetitions via bootstrapping. Feature selection and machine learning methods were selected as popular techniques in the literature, supported by open source software in the public domain and their ability to cope with continuous time-to-event survival data. Multifactor ANOVA was used to carry out variability analysis and the proportion of total variance explained by radiomics modality, feature selection and machine learning methods was calculated by a bias-corrected effect size estimate known as ω2. RESULTS: Optimum feature selection and machine learning methods differed owing to the applied radiomics modality. However, minimum depth (MD) as feature selection and Lasso and Elastic-Net regularized generalized linear model (glmnet) as machine learning method had the highest average results. Results from the ANOVA test indicated that the variability that each factor (radiomics modality, feature selection and machine learning methods) introduces to the performance of models is case specific, i.e. variances differ regarding different radiomics modalities and fusion strategies. Overall, the greatest proportion of variance was explained by machine learning, except for models in feature-level fusion strategy. CONCLUSION: The identification of optimal feature selection and machine learning methods is a crucial step in developing sound and accurate radiomics risk models. Furthermore, optimum methods are case specific, differing due to the radiomics modality and fusion strategy used.

Radiation Dose to Medical Staff from Administering 177Lu-PSMA-DKFZ-617 Therapy.

The number of radioligand therapy applications for metastatic castration-resistant prostate cancer has been continuously rising in most nuclear medicine departments in Iran, but to our knowledge, no one has studied the dose to staff who perform treatment procedures. The current study aimed to determine the external radiation dose received by staff who, using or not using a lead shield, treat patients with 177Lu-prostate-specific membrane antigen therapy. Methods: This study used a personal thermoluminescent digital survey meter to measure dose rates to staff at various distances from patients and determined the average time spent by staff at these distances. The deep-dose equivalent to staff was obtained. Results: The measured deep-dose equivalent to staff per patient was within the range of 1.8-5.2 mSv using a 2-mm lead shield and 3.3-8.1 mSv not using the shield. The shield markedly reduced the external dose to staff. Conclusion: The skill and accuracy of staff, and the speed with which they act, can directly affect their received dose.

Patient-Specific Dosimetry in Radioligand Therapy (RLT) for Metastatic Prostate Cancer Using 177Lu-DKFZ-PSMA-617.

PURPOSE: 177Lu-DKFZ-PSMA-617 is a promising treatment for patients with metastatic prostate cancer. Specific dosimetry for each patient is an important factor in planning the patient's treatment process. This study aimed to perform an image-based absorbed dose calculation for the treatment of metastatic prostate cancer with 177Lu-DKFZ-PSMA-617. METHODS: The individualized patient dosimetry calculations were based on whole-body planar scintigraphy images acquired in 10 patients with a mean age of 71.4 ± 6.07 years (range 63-85 years) at approximately 0-2 h, 4-6 h, 18-24 h, and 36-48 h after administration of the mean 6253 ± 826.4 MBq (range 5500-7400 MBq) of 177Lu-DKFZ-PSMA-617. Time-activity curves were generated for various organs. For count conversion to activities, calibration factors were calculated. Finally, the absorbed dose for an individual cycle was calculated using IDIAC-DOSE 2.1 software. RESULTS: On average, the calculated absorbed dose for the kidneys and salivary glands were 0.46 ± 0.09 mGy/MBq and 0.62 ± 0.07 mGy/MBq, respectively. CONCLUSIONS: Based on the results, the177Lu-PSMA-617 therapy is a safe method for the treatment of castration-resistant prostate cancer patients. Large inter-individual variations in organ dose were found, demonstrating the need for patient-specific dosimetry and treatment planning.

A New Method on Kerma Estimation in Mammography Screenings.

BACKGROUND: Given the extensive use and preferred diagnostic method in common mammography tests for screening and diagnosis of breast cancer, there is concern about the increased dose absorbed by the patient due to the sensitivity of the breast tissue. OBJECTIVE: This study aims to evaluate the entrance surface air kerma (ESAK) before irradiation to the patient through its estimation. MATERIAL AND METHODS: In this descriptive paper, firstly, a phantom was used to measure some data, including ESAK, Kvp, mAs, HVL, and type of filter/target. Secondly, the MultiLayer Perceptron (MLP) neural network model was trained with Levenberg-Marquardt (LM) backpropagation training algorithm and finally, ESAK was estimated. RESULTS: Based on results obtained from the program in different neuron numbers, it was found that the number of 35 neurons is the most optimal value, offering a regression coefficient of 95.7%. The Mean Squared Error (MSE) for all data was 0.437 mGy and accounting for 4.8% of the output range changes, predicting 95.2% accuracy in the present research. CONCLUSION: Using neural networks in ESAK prediction, the method proposed in the present research leads to the possible ESAK estimation of patients before X-Ray. The results suggested that the regression coefficient represented 4.3% difference between the kerma measured by solid-state dosimeter in the radiation field and the value predicted in the research. In comparison with the Monte-Carlo simulation method, this method has better accuracy.

OPTIMIZATION OF LUNG CT PROTOCOL FOR THE DIAGNOSTIC EVALUATION OF COVID-19 LUNG DISEASE.

This study intends to evaluate the different lung CT scan protocols used for the diagnostic evaluation of COVID-19-induced lung disease in Iranian imaging centers in terms of radiation dose and image quality. After data collecting, subjective image quality, radiation dose and objective image quality such as noise, SNR and CNR were assessed. Statistically significant differences in effective dose and image quality were evident among different lung CT protocols. Lowest and highest effective dose was1.31 ± 0.53 mSv related to a protocol with activated AEC (reference mAs = 20) and 6.15 ± 0.57 mSv related to a protocol with Fixed mAs (mAs = 100), respectively. A protocol with enabled tube current modulation with 70 mAs as a reference mAs, and protocol with 20 mAs and enabled AEC had the best and lowest image quality, respectively. To optimize the scan parameters, AEC must be used, and a range of tube currents (between 20 and 50 mAs) can produce acceptable images in terms of diagnostic quality and radiation dose for the diagnosis of COVID-19-induced lung disease.

Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma.

We developed multi-modality radiomic models by integrating information extracted from18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.

A Monte Carlo Platform for Characterization of X-Ray Radiation Dose in CT Imaging.

BACKGROUND: Computed tomography (CT) is currently known as a versatile imaging tool in the clinic used for almost all types of cancers. The major issue of CT is the health risk, belonging to X-ray radiation exposure. Concerning this, Monte Carlo (MC) simulation is recognized as a key computational technique for estimating and optimizing radiation dose. CT simulation with MCNP/MCNPX MC code has an inherent problem due to the lack of a fan-beam shaped source model. This limitation increases the run time and highly decreases the number of photons passing the body or phantom. Recently, a beta version of MCNP code called MCNP-FBSM (Fan-Beam Source Model) has been developed to pave the simulation way of CT imaging procedure, removing the need of the collimator. This is a new code, which needs to be validated in all aspects. OBJECTIVE: In this work, we aimed to develop and validate an efficient computational platform based on modified MCNP-FBSM for CT dosimetry purposes. MATERIAL AND METHODS: In this experimental study, a setup is carried out to measure CTDI100 in air and standard dosimetry phantoms. The accuracy of the developed MC CT simulator results has been widely benchmarked through comparison with our measured data, UK's National Health Service's reports (known as ImPACT), manufacturer's data, and other published results. RESULTS: The minimum and maximum observed mean differences of our simulation results and other above-mentioned data were the 1.5%, and 9.79%, respectively. CONCLUSION: The developed FBSM MC computational platform is a beneficial tool for CT dosimetry.

Optimization of dose and image quality in pediatrics chest digital radiography.

INTRODUCTION: In this study, pediatric chest digital radiography (DR) is evaluated in response to the high volume of chest DR examinations and high radiosensitivity of children and young adults. The aim of the study is to optimize irradiation parameters in chest DR to have dose as low as reasonably achievable (ALARA) and simultaneously obtain improved or preserved image quality. MATERIALS AND METHODS: Homogeneous phantoms in terms of density, dimensions, and composition were constructed to produce equivalent chest phantoms with less than 5% error for the 5-10 and 10-15-year-old age groups. The modulation transfer function (MTF), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for both the reference and new conditions for the two age groups. RESULTS: The results indicate that for the 5-10 year old age group, the optimized technique was 75 kVp and 6.3 mAs, in which the dose reduced 10% and the SNR and CNR increased by 0.4% and 6%, respectively, compared to the reference condition. For the 10-15-year-old age group, the 85 kVp and 5 mAs was close to the optimum condition, in which the dose reduced 37% and the SNR and CNR increased by 16% and 4%, respectively, compared to the reference condition. CONCLUSION: The introduced optimized conditions in this study are accompanied by lower dose and higher SNR and CNR; therefore, they can be proposed as guides for optimization in clinical practice for pediatric chest digital radiography.

Lifetime attributable risk of cancer incidence and mortality in routine digital radiology procedures.

PURPOSE: Digital radiography has the potential to improve the practice of radiography but it also has the potential to significantly increase patient doses. Considering rapidly growing digital radiography in many centers, concerns rise about increasing the collective dose of the human population and following health effects. This study aimed to estimate organ and effective doses and calculate the lifetime attributable risk (LAR) of cancer incidence and mortality in digital radiography procedures in Iran. METHODS: Organ and effective doses of 12 routine digital radiography examinations including the skull, cervical spine, chest, thoracic spine, lumbar spine, pelvic and abdomen were estimated using PCXMC software based on Monte Carlo simulation method. Then, LARs of cancer incidence and mortality were estimated using the BEIR VII method. RESULTS: Organ doses ranged from 0.01 to a maximum of 2.5 mGy while effective doses ranged from 0.01 to 0.7 mSv. Radiation risk showed dependence on the X-ray examination type and the patient's sex and age. In skull and cervical X-rays, the thyroid; in the chest and thoracic spine X-rays, the lung, and breast; and in the lumbar spine, pelvic and abdominal X-rays, the colon and bladder had the highest LAR of cancer incidence and mortality. Furthermore, younger patients and also females were at higher radiation risk. CONCLUSION: The lifetime attributable risk of cancer incidence and mortality due to radiation exposure is not trivial. Therefore efforts should be made to reduce patient doses while maintaining image quality.

DETERMINATION OF DIAGNOSTIC REFERENCE LEVEL IN ROUTINE EXAMINATIONS OF DIGITAL RADIOGRAPHY IN MAZANDARAN PROVINCE.

INTRODUCTION: The main purpose of this study was to determine the diagnostic reference level (DRL) for routine digital radiography examinations in Mazandaran province. MATERIALS AND METHODS: Thirteen digital radiographic examinations at 18 high-patient-load radiography centres were investigated. The indirect dosimetry method was performed based on the IAEA report. Average entrance skin dose (ESD) and the third quartile of ESD as the DRL were evaluated from the measurement made by a semiconductor dosemeter. RESULTS: DRL for the examinations of digital radiography was obtained as: Skull (postero-anterior [PA]): 2.2, skull (lateral [LAT]): 2.4, cervical spine (antero-posterior [AP]): 1.6, cervical spine (LAT): 1.7, thoracic spine (AP): 3.6, thoracic spine (LAT): 9.9, lumbar spine (AP): 5.3, lumbar spine (LAT): 11.8, chest (PA): 1.4, chest (LAT): 2.1, abdomen (AP): 4.3, pelvis (AP): 3.2 and hip (AP): 2.1 mGy. CONCLUSION: Although DRL was not higher compared with the international organisations' levels, it can be reduced by adequate training of radiographers.

ORGAN EQUIVALENT DOSE AND LIFETIME ATTRIBUTABLE RISK OF CANCER INCIDENCE AND MORTALITY ASSOCIATED WITH CARDIAC CT ANGIOGRAPHY.

The aim of this study is the calculation of equivalent organ dose and estimation of lifetime attributable risk (LAR) of cancer incidence and mortality related to cardiac computed tomography angiography (CCTA) because the use of CT angiography as a noninvasive diagnostic method has increased. The organ dose has been calculated by ImPACT software based on the volumetric CT dose index (CTDIvol), and LAR of cancer risk incidence and mortality from CCTA has estimated according to the BEIR VII report. The median value of the effective dose was 13.78 ± 6.88 mSv for both genders. In all scanners, the highest median value for LAR of cancer incidence in males and females for lung cancer was 44.20 and 109.17 per 100 000, respectively. And in infants was 5.89 and 12 for lung cancer in males and breast cancer in females, respectively. Also, the median value of LAR of all cancer incidence from single CCTA in adult patients for males and females was 122 and 238 cases, respectively. Maximum LAR of cancer mortality in adults for lung cancer was 40.28 and 91.84 and in pediatrics was 5.69 and 8.50 in males and females, respectively. Despite many benefits of CTA in the heart disease evaluation, according to a high radiation dose in CCTA, to reduce the cancer risk: CCTA should be used cautiously, especially for pediatric and females.

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.

PURPOSE: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade. MATERIALS AND METHODS: Seventy-one ccRCC patients (31 low grade and 40 high grade) were included in this study. Tumors were manually segmented on CT images followed by the application of three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) on delineated tumor volumes. Overall, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association between each feature and the histological condition. Multivariate analysis involved the use of machine learning (ML) algorithms and the following three feature selection algorithms: the least absolute shrinkage and selection operator, Student's t test, and minimum Redundancy Maximum Relevance. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under the receiver operating characteristic curve (AUC) metric. RESULTS: The univariate analysis demonstrated that among the different image sets, 128 bin-discretized images have statistically significant different texture parameters with a mean AUC of 0.74 ± 3 (q value < 0.05). The three ML-based classifiers showed proficient discrimination between high and low-grade ccRCC. The AUC was 0.78 for logistic regression, 0.62 for random forest, and 0.83 for the SVM model, respectively. CONCLUSION: CT radiomic features can be considered as a useful and promising noninvasive methodology for preoperative evaluation of ccRCC Fuhrman grades.