The Clinical Role of CXCR4-Targeted PET on Lymphoproliferative Disorders: A Systematic Review.

Background/Objectives: We conducted a comprehensive investigation to explore the pathological expression of the CXCR4 receptor in lymphoproliferative disorders (LPDs) using [68Ga]Ga-Pentixafor PET/CT or PET/MRI technology. The PICO question was as follows: What is the diagnostic role (outcome) of [68Ga]Ga-Pentixafor PET (intervention) in patients with LPDs (problem/population)? Methods: The study was written based on the reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and it was registered on the prospective register of systematic reviews (PROSPERO) website (CRD42024506866). A comprehensive computer literature search of Scopus, MEDLINE, Scholar, and Embase databases was conducted, including articles indexed up to February 2024. To the methodological evaluation of the studies used the quality assessment of diagnosis accuracy studies-2 (QUADAS-2) tool. Results: Of the 8380 records discovered, 23 were suitable for systematic review. Fifteen studies (on 571 LPD patients) focused on diagnosis and staging, and eight trials (194 LPD patients) assessed treatment response. Conclusions: The main conclusions that can be inferred from the published studies are as follows: (a) [68Ga]Ga-Pentixafor PET may have excellent diagnostic performance in the study of several LPDs; (b) [68Ga]Ga-Pentixafor PET may be superior to [18F]FDG or complementary in some LPDs variants and settings; (c) multiple myeloma seems to have a high uptake of [68Ga]Ga-Pentixafor. Overall, this technique is probably suitable for imaging, staging, and follow-up on patients with LPD. Due to limited data, further studies are warranted to confirm the promising role of [68Ga]Ga-Pantixafor in this context.

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation.

INTRODUCTION: Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. DATA DESCRIPTION: To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative.

Deep-neural network approaches for predicting 3D dose distribution in intensity-modulated radiotherapy of the brain tumors.

PURPOSE: The aim of this study is to reduce treatment planning time by predicting the intensity-modulated radiotherapy 3D dose distribution using deep learning for brain cancer patients. "For this purpose, two different approaches in dose prediction, i.e., first only planning target volume (PTV) and second PTV with organs at risk (OARs) as input of the U-net model, are employed and their results are compared." METHODS AND MATERIALS: The data of 99 patients with glioma tumors referred for IMRT treatment were used so that the images of 90 patients were regarded as training datasets and the others were for the test. All patients were manually planned and treated with sixth-field IMRT; the photon energy was 6MV. The treatment plans were done with the Collapsed Cone Convolution algorithm to deliver 60 Gy in 30 fractions. RESULTS: The obtained accuracy and similarity for the proposed methods in dose prediction when compared to the clinical dose distributions on test patients according to MSE, dice metric and SSIM for the Only-PTV and PTV-OARs methods are on average (0.05, 0.851, 0.83) and (0.056, 0.842, 0.82) respectively. Also, dose prediction is done in an extremely short time. CONCLUSION: The same results of the two proposed methods prove that the presence of OARs in addition to PTV does not provide new knowledge to the network and only by defining the PTV and its location in the imaging slices, does the dose distribution become predictable. Therefore, the Only-PTV method by eliminating the process of introducing OARs can reduce the overall designing time of treatment by IMRT in patients with glioma tumors.

Diagnostic Accuracy of PET with Different Radiotracers versus Bone Scintigraphy for Detecting Bone Metastases of Breast Cancer: A Systematic Review and a Meta-Analysis.

Due to the importance of correct and timely diagnosis of bone metastases in advanced breast cancer (BrC), we performed a meta-analysis evaluating the diagnostic accuracy of [18F]FDG, or Na[18F]F PET, PET(/CT), and (/MRI) versus [99mTc]Tc-diphosphonates bone scintigraphy (BS). The PubMed, Embase, Scopus, and Scholar electronic databases were searched. The results of the selected studies were analyzed using pooled sensitivity and specificity, diagnostic odds ratio (DOR), positive-negative likelihood ratio (LR+-LR-), and summary receiver-operating characteristic (SROC) curves. Eleven studies including 753 BrC patients were included in the meta-analysis. The patient-based pooled values of sensitivity, specificity, and area under the SROC curve (AUC) for BS (with 95% confidence interval values) were 90% (86-93), 91% (87-94), and 0.93, respectively. These indices for [18F]FDG PET(/CT) were 92% (88-95), 99% (96-100), and 0.99, respectively, and for Na[18F]F PET(/CT) were 96% (90-99), 81% (72-88), and 0.99, respectively. BS has good diagnostic performance in detecting BrC bone metastases. However, due to the higher and balanced sensitivity and specificity of [18F]FDG PET(/CT) compared to BS and Na[18F]F PET(/CT), and its advantage in evaluating extra-skeletal lesions, [18F]FDG PET(/CT) should be the preferred multimodal imaging method for evaluating bone metastases of BrC, if available.

CHMMOTv1 - cardiac and hepatic multi-echo (T2*) MRI images and clinical dataset for Iron overload on thalassemia patients.

INTRODUCTION: Regarding deep learning networks in medical sciences for improving diagnosis and treatment purposes and the existence of minimal resources for them, we decided to provide a set of magnetic resonance images of the cardiac and hepatic organs. DATABASE DESCRIPTION: The dataset included 124 patients (67 women and 57 men) with thalassemia (THM), the age range of (5-52) years. Patients were divided into two groups: with follow-up (1-5 times) at time intervals of about (5-6) months and without follow-up. T2* and, R2* values, the results of the Cardiac and Hepatic overload report (normal, mild, moderate, severe), and laboratory tests including Ferritin, Bilirubin (D, and T), AST, ALT, and ALP levels were provided as an Excel file. Also, the details of the patients' Echocardiogram data have been made available. This dataset CHMMOTv1) has been published in Mendeley Dataverse and also is accessible through the web at: http://databiox.com .

Post-chemo-radiotherapy response and pseudo-progression evaluation on glioma cell types by multi-parametric magnetic resonance imaging: a prospective study.

BACKGROUND: We focused on Differentiated pseudoprogression (PPN) of progression (PN) and the response to radiotherapy (RT) or chemoradiotherapy (CRT) using diffusion and metabolic imaging. METHODS: Seventy-five patients with glioma were included in this prospective study (approved by the Iranian Registry of Clinical Trials (IRCT) (IRCT20230904059352N1) in September 2023). Contrast-enhanced lesion volume (CELV), non-enhanced lesion volume (NELV), necrotic tumor volume (NTV), and quantitative values ​​of apparent diffusion coefficient (ADC) and magnetic resonance spectroscopy (Cho/Cr, Cho/NAA and NAA/Cr) were calculated by a neuroradiologist using a semi-automatic method. All patients were followed at one and six months after CRT. RESULTS: The results of the study showed statistically significant changes before and six months after RT-CRT for M-CELV in all glioma types (𝑝 < 0.05). In glioma cell types, the changes in M-ADC, M-Cho/Cr, and Cho/NAA indices for PN were incremental and greater for PPN patients. M-NAA/Cr ratio decreased after six months which was significant only on PN for GBM, and Epn (𝑝 < 0.05). A significant difference was observed between diffusion indices, metabolic ratios, and CELV changes after six months in all types (𝑝 < 0.05). None of the patients were suspected PPN one month after treatment. The DWI/ADC indices had higher sensitivity and specificity (98.25% and 96.57%, respectively). CONCLUSION: The results of the present study showed that ADC values and Cho/Cr and Cho/NAA ratios can be used to differentiate between patients with PPN and PN, although ADC is more sensitive and specific.

Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison.

BACKGROUND: Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomics is a non-invasive and low-cost method for developing models of predicting radiation toxicity that does not have the limitations of previous methods. These models have been developed using individual patients' information and have reliable and acceptable performance. This study was conducted by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods to predict radiation-induced rectal toxicity. METHODS: Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model's performance. RESULTS: The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%. CONCLUSIONS: This research showed that as radiomic signatures for predicting radiation-induced rectal toxicity, MR images outperform CT images.

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a beneficial diagnostic tool in treatment planning. Treatment response assessment in glioma patients is usually based on the Response Assessment in Neuro Oncology (RANO) criteria. The limitation of assessment based on RANO is two-dimensional (2D) manual measurements. Deep learning (DL) has great potential in neuro-oncology to improve the accuracy of response assessment. In the current research, firstly, the BraTS 2018 Challenge dataset included 210 HGG and 75 LGG were applied to train a designed U-Net network for automatic tumor and intra-tumoral segmentation, followed by training of the designed classifier with transfer learning for determining grading HGG and LGG. Then, designed networks were employed for the segmentation and classification of local MRI images of 49 glioma patients pre and post-radiotherapy. The results of tumor segmentation and its intra-tumoral regions were utilized to determine the volume of different regions and treatment response assessment. Treatment response assessment demonstrated that radiotherapy is effective on the whole tumor and enhancing region with p-value ≤ 0.05 with a 95% confidence level, while it did not affect necrosis and peri-tumoral edema regions. This work demonstrated the potential of using deep learning in MRI images to provide a beneficial tool in the automated treatment response assessment so that the patient can obtain the best treatment.

Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice.

Background: This study investigated the feasibility of channelized hoteling observer (CHO) model in computed tomography (CT) protocol optimization regarding the image quality and patient exposure. While the utility of using model observers such as to optimize the clinical protocol is evident, the pitfalls associated with the use of this method in practice require investigation. Materials and Methods: This study was performed using variable tube current and adaptive statistical iterative reconstruction (ASIR) level (ASIR 10% to ASIR 100%). Various criteria including noise, high-contrast spatial resolution, CHOs model were used to compare image quality at different captured levels. For the implementation of CHO, we first tuned the model in a restricted dataset and then it to the evaluation of a large dataset of images obtained with different reconstruction ASIR and filtered back projection (FBP) levels. Results: The results were promising in terms of CHO use for the stated purposes. Comparisons of the noise of reconstructed images with 30% ASIR and higher levels of noise in rebuilding images using the FBP approach showed a significant difference (P < 0.05). The spatial resolution obtained using various ASIR levels and tube currents were 0.8 pairs of lines per millimeter, which did not differ significantly from the FBP method (P > 0.05). Conclusions: Based on the results, using 80% ASIR can reduce the radiation dose on lungs, abdomen, and pelvis CT scans while maintaining image quality. Furthermore using ASIR 60% only for the reconstruction of lungs, abdomen, and pelvis images at standard radiation dose leads to optimal image quality.

Investigating variation of dose gradient index by different grid sizes in intensity-modulated radiation therapy of optic nerve sheath meningioma.

In optic nerve sheath meningioma (ONSM) radiotherapy, vital organs with a low tolerance dose are very close to the target volume. Thus dose fall-off steepness around the target volume is the most critical parameter in intensity-modulated radiation therapy (IMRT) planning for this malignancy. We hypothesized that one of the parameters that can impact the dose fall-off steepness is dose calculation grid sizes. This study aimed to assess the impact of different dose calculation grid sizes on the newly introduced dose gradient indices (DGIs) for the steepness of dose fall-off around the target volume. Results showed that decreasing the dose calculation grid size in IMRT treatment planning of ONSM patients had a significant difference in the dosimetric parameters and DGIs of the target volume and at-risk organs. This study demonstrates that using a finer dose calculation grid size is preferred in treating ONSM patients.

A novel mathematical model to generate semi-automated optimal IMRT treatment plan based on predicted 3D dose distribution and prescribed dose.

BACKGROUND: In recent years, with the development of artificial intelligence and deep learning techniques, it has become possible to predict the three-dimensional distribution dose (3D3 ) of a new patient based on the treatment plans of similar recent patients. Therefore, some new questions have arisen for the above issue: how to make use of the predicted 3D3 obtained from deep learning, to facilitate treatment planning? How to convert the predicted 3D3 to a clinical deliverable Pareto optimal plan? Little research has been done and limited software has been developed in this regard. PURPOSE: In the current research, an attempt was made to contribute the knowledge-based planning by presenting a new mathematical model, and to take a novel step towards optimizing the treatment plan derived from both predicted 3D3 as well as dose prescription to generate a semi-automated clinically applicable optimal IMRT treatment plan. METHODS: The presented model has benefited from both prescribed dose as well as predicted dose and its objective function includes both quadratic and linear phrases, so it was called the QuadLin model. The model has been run on the data of 30 patients with head and neck cancer randomly selected from the Open-KBP dataset. There are 19 sets of dose prediction data for each patient in this database. Therefore, a total of 570 problems have been solved in the CVX framework with commercial solver Mosek and the results have been evaluated by two plan quality approaches (1) DVH points differences, and (2) satisfied clinical criteria. RESULTS: The results of the current study indicate a strong significant improvement in almost all plan evaluation indicators compared to the reference plan of the dataset, 3D3 predictions, as well as the results of previous research, based on the Wilcoxon signed ranks test with a significance level of 0.01. Accordingly, for all regions of interest (ROIs) (or structures) of all 570 problems total clinical indicators have improved by more than 21%, 15%, and at least13%, on average, compared to the predicted dose, the reference plan, and previous research, respectively, with 341 s as the average of solving time. CONCLUSIONS: Evaluation of the research results indicates the significant effect of the QuadLin model on improving the dose delivery to the target volumes while reducing the dose and preserving organs at risk. Based on the literature, the proposed model has generated the best-known treatment plan from the predicted 3D3 so far.

Automatic detection of active and inactive multiple sclerosis plaques using the Bayesian approach in susceptibility-weighted imaging.

BACKGROUND: Susceptibility-weighted imaging (SWI) is efficient in detecting multiple sclerosis (MS) plaques and evaluating the level of disease activity. PURPOSE: To automatically detect active and inactive MS plaques in SWI images using a Bayesian approach. MATERIAL AND METHODS: A 1.5-T scanner was used to evaluate 147 patients with MS. The area of the plaques along with their active or inactive status were automatically identified using a Bayesian approach. Plaques were given an orange color if they were active and a blue color if they were inactive, based on the preset signal intensity. RESULTS: Experimental findings show that the proposed method has a high accuracy rate of 91% and a sensitivity rate of 76% for identifying the type and area of plaques. Inactive plaques were properly identified in 87% of cases, and active plaques in 76% of cases. The Kappa analysis revealed an 80% agreement between expert diagnoses based on contrast-enhanced and FLAIR images and Bayesian inferences in SWI. CONCLUSION: The results of our study demonstrated that the proposed method has good accuracy for identifying the MS plaque area as well as for identifying the types of active or inactive plaques in SWI. Therefore, it might be helpful to use the proposed method as a supplemental tool to accelerate the specialist's diagnosis.

Pragmatic Approaches to Reducing Radiation Dose in Brain Computed Tomography Scan using Scan Parameter Modification.

Background: High radiation dose of patients has become a concern in the computed tomography (CT) examinations. The aim of this study is to guide the radiology technician in modifying or optimizing the underlying parameters of the CT scan to reduce the patient radiation dose and produce an acceptable image quality for diagnosis. Methods: The body mass measurement device phantom was repeatedly scanned by changing the scan parameters. To analyze the image quality, software-based and observer-based evaluations were employed. To study the effect of scan parameters such as slice thickness and reconstruction filter on image quality and radiation dose, the structural equation modeling was used. Results: By changing the reconstruction filter from standard to soft and slice thickness from 2.5 mm to 5 mm, low-contrast resolution did not change significantly. In addition, by increasing the slice thickness and changing the reconstruction filter, the spatial resolution at different radiation conditions did not significantly differ from the standard irradiation conditions (P > 0.05). Conclusion: In this study, it was shown that in the brain CT scan imaging, the radiation dose was reduced by 30%-50% by increasing the slice thickness or changing the reconstruction filter. It is necessary to adjust the CT scan protocols according to clinical requirements or the special conditions of some patients while maintaining acceptable image quality.

Semi-experimental assessment of neutron equivalent dose and secondary cancer risk for off-field organs in glioma patients undergoing 18-MV radiotherapy.

Neutron contamination as a source of out-of-field dose in radiotherapy is still of concern. High-energy treatment photons have the potential to overcome the binding energy of neutrons inside the nuclei. Fast neutrons emitting from the accelerator head can directly reach the patient's bed. Considering that modern radiotherapy techniques can increase patient survival, concerns about unwanted doses and the lifetime risk of fatal cancer remain strong or even more prominent, especially in young adult patients. The current study addressed these concerns by quantifying the dose and risk of fatal cancer due to photo-neutrons for glioma patients undergoing 18-MV radiotherapy. In this study, an NRD model rem-meter detector was used to measure neutron ambient dose equivalent, H*(10), at the patient table. Then, the neutron equivalent dose received by each organ was estimated concerning the depth of each organ and by applying depth dose corrections to the measured H*(10). Finally, the effective dose and risk of secondary cancer were determined using NCRP 116 coefficients. Evidence revealed that among all organs, the breast (0.62 mSv/Gy) and gonads (0.58 mSv/Gy) are at risk of photoneutrons more than the other organs in such treatments. The neutron effective dose in the 18-MV conventional radiotherapy of the brain was 13.36 mSv. Among all organs, gonads (6.96 mSv), thyroid (1.86 mSv), and breasts (1.86 mSv) had more contribution to the effective dose, respectively. The total secondary cancer risk was estimated as 281.4 cases (per 1 million persons). The highest risk was related to the breast and gonads with 74.4 and, 34.8 cases per 1 million persons, respectively. Therefore, it is recommended that to prevent late complications (secondary cancer and genetic effects), these organs should be shielded from photoneutrons. This procedure not only improves the quality of the patient's personal life but also the healthy childbearing in the community.

Investigating the number of radiation fields in intensity-modulated radiotherapy plans of optic nerve sheath meningioma patients using dose gradient index.

Purpose: In optic nerve radiotherapy, vital organs are very close to the target volume, they are highly sensitive to radiation and have low dose tolerance. In this regard, evaluating dose fall-off steepness around the target volume is required to assess various intensity-modulated radiation therapy (IMRT) plans in the treatment of the optic nerve sheath meningioma (ONSM) patients.Materials and Methods: Thirteen ONSM patients were analyzed with three IMRT techniques, including three (IMRT-3F), five (IMRT-5F), and seven fields (IMRT-7F). These plans were studied using Dmean, Dmax, D2%, D98%, V100%, uniformity index (UI), homogeneity index (HI), conformity index (CI), and specifically the dose gradient indices (DGIs).Results: The values of Dmaxand Dmeanfor IMRT-3F, IMRT-5F and IMRT-7F were (5637.42 ± 57.08, 5322.84 ± 83.86), (5670.51 ± 67.87, 5383.00 ± 58.45), and (5692.99 ± 31.65, 5405.72 ± 51.73), respectively, which were increased with increment in the number of IMRT fields from 3 to 7. The UI and HI indices were significantly different between IMRT-3F and IMRT-7F (p = 0.010 and p = 0.005, respectively), and CI was close to the ideal value (0.99 ± 0.01) in IMRT-7F. The significant findings of the dose gradient indices represented smaller values in IMRT-7F, which led to a faster dose fall-off, particularly at the 70%-85% isodose levels around the target.Conclusion: Increasing the number of radiation fields in IMRT treatment plans of ONSM patients had a considerable difference in both the dosimetric parameters of the target volume and at-risk organs, as well as the dose gradient indices. Overall, IMRT-7F could be considered as a preferred technique in the treatment of this meningioma.

DETERMINATION OF THE NEUTRON CONTAMINATION DURING BRAIN RADIOTHERAPY USING A MODERATED-BORON TRIFLUORIDE DETECTOR AND THE MCNP MONTE CARLO CODE.

This study aimed to determine the neutron dose equivalent to the thyroid gland and eye lens in brain tumor radiation therapy with 15- and 18-MV three-dimensional conformal methods (3D-CRT). A Monte Carlo simulation was performed using the Monte Carlo N-particle transport code to calculate neutron fluence and ambient dose equivalent (H*(10)). Afterward, these parameters were measured using a model NRD roentgen equivalent in man (REM) neutron detector (Thermo Electron Corporation, USA) equipped with Eberline's ASP-2e rate meter. Finally, the organ neutron dose equivalent was obtained by applying depth corrections to the measured ambient dose equivalent at the distance of the organ center from the central beam axis. The ratio of the out-of-field photon dose equivalent, measured previously, to the neutron dose equivalent in the eye lens was high due to its proximity to the radiation field. In contrast, this ratio remained unexpectedly high in the thyroid gland that is far from the central beam axis (about 15 cm). The calculated neutron parameters agreed with the measurements. The present study findings indicate that external field photon dose is the main source of thyroid gland biological effects in radiotherapy of brain tumors. In addition, it is appropriate to apply the model NRD REM neutron detector for measuring neutron contamination from high-energy linear accelerators inside and outside the treatment field.

A radiobiological comparison of hypo-fractionation versus conventional fractionation for breast cancer 3D-conformal radiation therapy.

BACKGROUND: The present research was aimed to compare the toxicity and effectiveness of conventional fractionated radiotherapy versus hypo-fractionated radiotherapy in breast cancer utilizing a radiobiological model. MATERIALS AND METHODS: Thirty-five left-sided breast cancer patients without involvement of the supraclavicular and axillary lymph nodes (with the nodal stage of N0) that had been treated with conventional or hypo-fractionated were incorporated in this study. A radiobiological model was performed to foretell normal tissue complication probability (NTCP) and tumor control probability (TCP). RESULTS: The data represented that TCP values for conventional and hypo-fractionated regimens were 99.16 ± 0.09 and 95.96 ± 0.48, respectively (p = 0.00). Moreover, the NTCP values of the lung for conventional and hypo-fractionated treatment were 0.024 versus 0.13 (p = 0.035), respectively. Also, NTCP values of the heart were equal to zero for both regimens. CONCLUSION: In summary, hypo-fractionated regimens had comparable efficacy to conventional fraction radiation therapy in the case of dosimetry parameters for patients who had left breast cancer. But, utilizing the radiobiological model, conventional fractionated regimens presented better results compared to hypo-fractionated regimens.

Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.

PURPOSE: Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images. METHODS: PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020. Studies that evaluated the performance of DL-based models in the segmentation, and/or classification and/or grading of HNCs and/or brain tumors were included. Selected studies for each analysis were statistically evaluated based on the diagnostic performance metrics. RESULTS: The search results retrieved 1,664 related studies, of which 30 studies were eligible for meta-analysis. The overall performance of DL models for the complete tumor in terms of the pooled Dice score, sensitivity, and specificity was 0.8965 (95% confidence interval (95% CI): 0.76-0.9994), 0.9132 (95% CI: 0.71-0.994) and 0.9164 (95% CI: 0.78-1.00), respectively. The DL methods achieved the highest performance for classifying three types of glioma, meningioma, and pituitary tumors with overall accuracies of 96.01%, 99.73%, and 96.58%, respectively. Stratification of glioma tumors by high and low grading revealed overall accuracies of 94.32% and 94.23% for the DL methods, respectively. CONCLUSION: Based on the obtained results, we can acknowledge the significant ability of DL methods in the mentioned applications. Poor reporting in these studies challenges the analysis process, so it is recommended that future studies report comprehensive results based on different metrics.

Evaluation of the Lung Dose in Three-dimensional Conformal Radiation Therapy of Left-Sided Breast Cancer: A Phantom Study.

BACKGROUND: Three-dimensional 3D-CRT: conformal radiation therapy is a selective modality in many radiotherapy centers for the treatment of breast cancer. One of the most common side effects of this method is radiation lung injury. Considering such an injury, lung dose deserves to be studied in depth. METHODS: Computed tomography scan of a node-positive left-sided breast cancer woman was used for generating a thorax phantom. Ten thermoluminescent dosimeters (TLDs) were distributed evenly in the left lung of the phantom, and the phantom was scanned. The optimal plan, including supraclavicular and tangential fields, was created by the treatment planning system (TPS). The results of TLD dose measurements at the selected points in the phantom were compared to TPS dose calculations. RESULTS: Lung doses calculated by TPS are significantly different from those measured by the TLDs (P = 0.007). The minimum and maximum differences were -0.91% and 4.46%, respectively. TLDs that were on the inner margin of the lung and breast tissue showed higher dose differences than the TLDs in the lung. CONCLUSION: The results of this study showed that TPS generally overestimated doses compared to TLD measurements due to incorrect beam modeling caused by contaminated electrons in the lung.

Dose perturbation due to dental amalgam in the head and neck radiotherapy: A phantom study.

Dental amalgam, causes perturbation in photon dose distribution of head and neck (H&N) radiotherapy. The aim of this study was to evaluate the effects of dental amalgam on dose distribution of H&N radiotherapy and accuracy of dose calculations algorithm of commercial treatment planning system (TPS). In this study, the measurements were performed using a constructed H&N anthropomorphic. The sample of healthy teeth and teeth filled by amalgam inserted in the desired segment of the phantom in turn. After scanning and organs segmentation of phantom, intensity-modulated radiation therapy (IMRT) plan including 7 fields in the absence (plan 1) and presence (plan 2) of dental amalgam were created separately. Phantom was irradiated using 6 MV linear accelerator (SIMENS-ARTISTE, 5918). Assessment of the effects of dental amalgam on dose distribution and the accuracy of dose calculation algorithm of TPS was done by measurement and comparing of organ's received dose using thermoluminescent dosimeter (TLDs), placed on a phantom and TPS calculations. The scattering and attenuation due to the presence of dental amalgam led to an increase in parotid glands received dose (up to 24.38%) and a decrease in mean dose (up to -6.25%) PTV70. Results of this study revealed that discrepancies between the collapsed cone convolution (CCC) algorithm calculations Prowess Panther TPS and TLD measurements were -19.77% to 27.49% in presence of amalgam and -1.09% to 5.03% in presence of healthy teeth in phantoms. Attenuation and scattering due to amalgam in IMRT of H&N cancer may lead to a significant dose perturbation which is not predictable by dose calculation of TPS.