Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy.

PURPOSE: Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS: We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS: The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION: A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized 90Y glass microspheres SIRT: a preliminary machine learning study.

BACKGROUND: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing 90Y selective internal radiation therapy (SIRT). MATERIALS/METHODS: This preliminary and retrospective analysis included 17 patients with hepatocellular carcinoma (HCC) treated with 90Y SIRT. The patients underwent personalized treatment planning and voxel-wise dosimetry. After the procedure, the OS and PFS were evaluated. Three structures were delineated including tumoral liver (TL), normal perfused liver (NPL), and whole normal liver (WNL). 289 dose-volume constraints (DVCs) were extracted from dose-volume histograms of physical and biological effective dose (BED) maps calculated on 99mTc-MAA and 90Y SPECT/CT images. Subsequently, the DVCs and 16 clinical biomarkers were used as features for univariate and multivariate analysis. Cox proportional hazard ratio (HR) was employed for univariate analysis. HR and the concordance index (C-Index) were calculated for each feature. Using eight different strategies, a cross-combination of various models and feature selection (FS) methods was applied for multivariate analysis. The performance of each model was assessed using an averaged C-Index on a three-fold nested cross-validation framework. The Kaplan-Meier (KM) curve was employed for univariate and machine learning (ML) model performance assessment. RESULTS: The median OS was 11 months [95% CI: 8.5, 13.09], whereas the PFS was seven months [95% CI: 5.6, 10.98]. Univariate analysis demonstrated the presence of Ascites (HR: 9.2[1.8,47]) and the aim of SIRT (segmentectomy, lobectomy, palliative) (HR: 0.066 [0.0057, 0.78]), Aspartate aminotransferase (AST) level (HR:0.1 [0.012-0.86]), and MAA-Dose-V205(%)-TL (HR:8.5[1,72]) as predictors for OS. 90Y-derived parameters were associated with PFS but not with OS. MAA-Dose-V205(%)-WNL, MAA-BED-V400(%)-WNL with (HR:13 [1.5-120]) and 90Y-Dose-mean-TL, 90Y-D50-TL-Gy, 90Y-Dose-V205(%)-TL, 90Y-Dose- D50-TL-Gy, and 90Y-BED-V400(%)-TL (HR:15 [1.8-120]) were highly associated with PFS among dosimetry parameters. The highest C-index observed in multivariate analysis using ML was 0.94 ± 0.13 obtained from Variable Hunting-variable-importance (VH.VIMP) FS and Cox Proportional Hazard model predicting OS, using clinical features. However, the combination of VH. VIMP FS method with a Generalized Linear Model Network model predicting OS using Therapy strategy features outperformed the other models in terms of both C-index and stratification of KM curves (C-Index: 0.93 ± 0.14 and log-rank p-value of 0.023 for KM curve stratification). CONCLUSION: This preliminary study confirmed the role played by baseline clinical biomarkers and dosimetry parameters in predicting the treatment outcome, paving the way for the establishment of a dose-effect relationship. In addition, the feasibility of using ML along with these features was demonstrated as a helpful tool in the clinical management of patients, both prior to and following 90Y-SIRT.

Source Identification of Urban dust Heavy Metals Using in Situ and Satellite Data in Three Mega Cities of Iran.

The aim of this study was an integrative assessment of heavy metals associated with urban dust data in Iran (Ahvaz, Isfahan, and Shiraz). Samples of urban dust from four sites (traffic, industrial, residential, and Greenland) were collected, and ten heavy metal concentrations were determined using ICP_MS in each sample. The highest average concentrations of metals were at the traffic site for the Mn, Zn, and Cr metals. The PMF model indicates a higher percentage of Pb participation, which shows the importance of traffic resources. The highest non-carcinogenic risk (HI) was for the Cr and the carcinogenic risk was tolerable. To evaluate aerosol and its effects on urban dust, Aerosol Optical Depth (AOD) data were used during 2003-2023. According to the Mankendall test, the trend of AOD has been increasing in Esfahan (p_value < 0.05) and Shiraz. Although Ahvaz's AOD is about two times greater than other cities, the aerosol trend in Ahvaz is decreasing.

Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks.

OBJECTIVE: We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions. METHODS: The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed. RESULTS: The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was - 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, - 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were - 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, - 1.11 ± 2.90%, 2.34 ± 2.03%, respectively. CONCLUSION: Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation. CLINICAL RELEVANCE STATEMENT: We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations. KEY POINTS: • We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.

Fully automated accurate patient positioning in computed tomography using anterior-posterior localizer images and a deep neural network: a dual-center study.

OBJECTIVES: This study aimed to improve patient positioning accuracy by relying on a CT localizer and a deep neural network to optimize image quality and radiation dose. METHODS: We included 5754 chest CT axial and anterior-posterior (AP) images from two different centers, C1 and C2. After pre-processing, images were split into training (80%) and test (20%) datasets. A deep neural network was trained to generate 3D axial images from the AP localizer. The geometric centerlines of patient bodies were indicated by creating a bounding box on the predicted images. The distance between the body centerline, estimated by the deep learning model and ground truth (BCAP), was compared with patient mis-centering during manual positioning (BCMP). We evaluated the performance of our model in terms of distance between the lung centerline estimated by the deep learning model and the ground truth (LCAP). RESULTS: The error in terms of BCAP was - 0.75 ± 7.73 mm and 2.06 ± 10.61 mm for C1 and C2, respectively. This error was significantly lower than BCMP, which achieved an error of 9.35 ± 14.94 and 13.98 ± 14.5 mm for C1 and C2, respectively. The absolute BCAP was 5.7 ± 5.26 and 8.26 ± 6.96 mm for C1 and C2, respectively. The LCAP metric was 1.56 ± 10.8 and -0.27 ± 16.29 mm for C1 and C2, respectively. The error in terms of BCAP and LCAP was higher for larger patients (p value < 0.01). CONCLUSION: The accuracy of the proposed method was comparable to available alternative methods, carrying the advantage of being free from errors related to objects blocking the camera visibility. KEY POINTS: • Patient mis-centering in the anterior-posterior direction (AP) is a common problem in clinical practice which can degrade image quality and increase patient radiation dose. • We proposed a deep neural network for automatic patient positioning using only the CT image localizer, achieving a performance comparable to alternative techniques, such as the external 3D visual camera. • The advantage of the proposed method is that it is free from errors related to objects blocking the camera visibility and that it could be implemented on imaging consoles as a patient positioning support tool.

Evaluation of recurrence, mortality and treatment complications of oral squamous cell carcinoma in public health centers in Shiraz during 2010 to 2020.

INTRODUCTION: Oral Squamous cell Carcinoma (OSCC) is the most common oral cancer and is treated with surgery, radiotherapy and chemotherapy. Various complications of treatment include xerostomia, mucositis, and trismus, which affect patients' quality of life. The aim of this study is to evaluate the mortality, recurrence rate and prevalence of oral complications in treated patients. METHOD AND MATERIALS: This cross-sectional study reviewed 326 cases of patients with OSCC who were referred to public health centers in Shiraz (Khalili Hospital and Dental School) from 2010 to 2020. All patients were contacted, and the survivors were called and examined by an oral physician. A medical record was created for them, including demographic information, location of the lesion, type of treatment, history of recurrence, metastasis and oral complications. RESULTS: 53.5% of patients were male and 46.5% were female. The mean age of patients was 58.68 years. Mortality and recurrence rate was respectively 49.8% and 17.8%. The most common location of the lesion was tongue (64%). Surgery was done for all patients. 97.4% of patients complained of xerostomia, 46.2% of mucositis and 44.3% of trismus. CONCLUSION: The most common complications of treatment are xerostomia, mucositis, and trismus, respectively. Frequent and regular follow-ups and supportive therapies reduce these complications and improve patients' quality of life.

OXR1 signaling pathway as a possible mechanism of elastase-induced oxidative damage in pulmonary cells: the protective role of ellagic acid.

BACKGROUND AND OBJECTIVE: Oxidative stress is a process that occurs through free radicals on the cell membranes which causes damage to the cell and intracellular organelles, especially mitochondria membranes. H2O2 induced oxidative stress in human cells is of interest in toxicological research since oxidative stress plays a main role in the etiology of several pathological conditions. Neutrophil Elastase (Serine proteinase) is involved in the pathology process of emphysema as a respiratory disease through lung inflammation, and destruction of alveolar walls. The present study investigated the direct oxidative stress effects of Elastase in comparison with H2O2 on human lung epithelial cells (A549 cells) concerning the generation of reactive oxygen species (ROS) and modulation of oxidation resistance 1 (OXR1) and its downstream pathway using the well-known antioxidant Ellagic acid as an activator of antioxidant genes. MATERIALS AND METHODS: The human pulmonary epithelial cells (A549) were divided into the nine groups including Negative control, Positive control (H2O2), Elastase (15, 30, and 60 mU/mL), Ellagic acid (10 µmol/L), and Elastase + Ellagic acid. Cytotoxicity, ROS generation, oxidative stress profile, level of reactive metabolites, and gene expression of OXR1 and its downstream genes were measured in all groups. RESULTS: The obtained data demonstrated that Elastase exposure caused oxidative stress damage in a dose-depended manner which was associated with decreases in antioxidant defense system genes. Conversely, treatment with Ellagic acid as a potent antioxidant showed improved antioxidant enzyme activity and content which was in line with the upregulation of OXR1 signaling pathway genes. CONCLUSIONS: The present findings can highlight the novel mechanism underlying the oxidative stress induced by Neutrophil Elastase through OXR1 and related genes. Moreover, the benefit of Ellagic acid on cytoprotection, resulting from its antioxidant properties was documented.

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network.

OBJECTIVES: The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. METHODS: In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). RESULTS: The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. CONCLUSIONS: The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. KEY POINTS: • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.

Correlates of waterpipe smoking among Iranian university students and the role of religiosity.

Waterpipe smoking among youth and adolescents in Iran has gained in popularity. The aim of this study was to investigate the relationship between waterpipe smoking and different dimensions of religiosity in a sample of students attending two major universities in South East Iran. A total of 682 students completed a waterpipe and cigarette smoking questionnaire along with the Duke University Religion Index. The lifetime prevalence of dual cigarette and waterpipe use was 48.3%, with prevalence of current use (within the last 30 days) of 24.9%. The proportions of lifetime and current waterpipe-only users were 27.0% and 18.8%, respectively. Students who participated more often in private religious activities were less likely to report engaging in waterpipe smoking (odds ratio: 0.82; 95% confidence interval: 0.71-0.98). A higher level of attendance of religious services was negatively associated with dual cigarette and waterpipe smoking (odds ratio: 0.71; 95% confidence interval: 0.54-0.93). Waterpipe-only use was significantly higher among males, students who had lower grade point averages, those who reported having a close friend or a family member who was a waterpipe smoker. To conclude, it is possible that religious observance may have a protective role in lowering waterpipe usage among Iranian university students.

Intranasal CEPO-FC prevents attention deficits in streptozotocin-induced rat model of Alzheimer's disease: Focus on synaptic plasticity-related factors.

Alzheimer's disease remains an issue of great controversy due to its pathology. It is characterized by cognitive impairments and neuropsychiatric symptoms. The FDA approved medications for this disease, can only mitigate the symptoms. One reason for the lack of effective medications is the inaccessibility of the brain which is encompassed by the blood-brain barrier, making intranasal (IN) route of administration potentially advantageous. Male Wistar rats underwent stereotaxic surgery to induce an Alzheimer's disease model via intracerebroventricular (ICV) streptozotocin injection, and Carbamylated Erythropoietin-Fc (CEPO-FC), a derivative of Erythropoietin without its harmful characteristics, was administered intranasally for ten consecutive days. Cognition performance for memory and attention was assessed using the Novel Object Recognition Test and the Object-Based Attention Test respectively. Depression like behavior was evaluated using the Forced Swim Test. Western blotting was done on the extracted hippocampus to quantify STIM proteins. Calbindin, PSD-95, Neuroplastin, Synaptophysin and GAP-43 genes were assessed by Realtime PCR. Behavioral tests demonstrated that IN CEPO-FC could halt cognition deficits and molecular investigations showed that, STIM proteins were decreased in Alzheimer's model, and increased after IN CEPO-FC treatment. Calbindin and PSD-95 were downregulated in our disease model and upregulated when treated with IN CEPO-FC. While Neuroplastin, and GAP-43 expressions remained unchanged. This study suggests that IN CEPO-FC in low doses could be promising for improving cognition and synaptic plasticity deficits in Alzheimer's disease and since IN route of administration is a convenient way, choosing IN CEPO-FC for clinical trial might worth consideration. See also the graphical abstract(Fig. 1).

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. PURPOSE: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. METHODS: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics. RESULTS: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent. CONCLUSIONS: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.

Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study.

BACKGROUND: This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients. METHODS: A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event. RESULTS: The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only. CONCLUSION: Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.

Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning.

PURPOSE: Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms. PATIENTS AND METHODS: This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric. RESULTS: For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them. CONCLUSIONS: The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.

Autoimmune thyroid disorders and polycystic ovary syndrome: Tracing links through systematic review and meta-analysis.

Polycystic Ovary Syndrome (PCOS) and Autoimmune Thyroiditis (AIT) are two prevalent endocrine disorders affecting women, often coexisting within the same patient population. This meta-analysis aims to systematically assess and synthesize the existing body of literature to elucidate the intricate relationship between PCOS and AIT. A systematic literature search for relevant observational studies was conducted in electronic databases such as Web of Science, Google Scholar, PubMed, Cochrane, and Scopus until March 2023. All Statistical analyses were performed using CMA Software v3.7 in a random-effects network meta-analysis. In addition, sensitivity and meta-regression analyses were conducted to identify sources of Heterogeneity based on related risk factors. Our meta-analysis included eighteen studies with 3657 participants, which revealed significant differences between PCOS patients and control groups. In particular, a considerable association was detected between PCOS and the presence of AIT (OR = 2.38; 95% CI: 1.63-3.49; P< 0.001) and elevated levels of TSH (SMD = 0.24; 95% CI: 0.06-0.42; P= 0.01), anti-TPO (SMD = 0.36; 95% CI: 0.19-0.53; P< 0.001), anti-TG (SMD = 1.24; 95% CI: 0.37-2.10; P< 0.001), and other positive serum antibodies compared to the control groups. The findings from this meta-analysis may contribute to enhanced diagnostic strategies like complete thyroid function tests, more targeted interventions, and improved patient care for individuals presenting with both PCOS and AIT. Additionally, identifying commonalities between these conditions may pave the way for future research directions, guiding the development of novel therapeutic approaches that address the interconnected nature of PCOS and AIT.

Information fusion for fully automated segmentation of head and neck tumors from PET and CT images.

BACKGROUND: PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi-modal information available are still lacking. PURPOSE: Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output-level voting-based fusions. METHODS: The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image-level fusions were implemented. In addition, a modified U2-Net architecture as DL fusion model baseline was used. Three different input, layer, and decision-level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image-level and network-level fusions), that is, output-level information fusion (voting-based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. RESULTS: In single modalities, PET had a reasonable performance with a Dice score of 0.77 ± 0.09, while CT did not perform acceptably and reached a Dice score of only 0.38 ± 0.22. Conventional fusion algorithms obtained a Dice score range of [0.76-0.81] with guided-filter-based context enhancement (GFCE) at the low-end, and anisotropic diffusion and Karhunen-Loeve transform fusion (ADF), multi-resolution singular value decomposition (MSVD), and multi-level image decomposition based on latent low-rank representation (MDLatLRR) at the high-end. All DL fusion models achieved Dice scores of 0.80. Output-level voting-based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak , SUVmean and SUVmedian . CONCLUSION: PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET-only and CT-only methods. In addition, both conventional image-level and DL fusions achieve competitive results. Meanwhile, output-level voting-based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.

Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.

BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS: The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION: The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.

Cytogenetic analysis of 179 Iranian women with premature ovarian failure.

The importance of chromosomal abnormalities in etiology of premature ovarian failure (POF) is well known but in many cases, POF still remains idiopathic. We investigated the frequency and type of chromosomal aberrations in Iranian women diagnosed with idiopathic POF. Standard cytogenetic analysis was carried out in a total of 179 patients. Karyotype analysis of these patients revealed that 161 (89.95%) patients had normal female karyotype and 18 (10.05%) patients had abnormal karyotypes. The abnormal karyotypes included sex reverse sex determining region Y (SRY) negative (five Cases), X chromosome mosaicism (five cases), abnormal X chromosomes (three cases), abnormal autosomes (three cases) and X-autosome translocation (two cases). The overall prevalence of chromosomal abnormalities was 10.05% in this first large-scale report of chromosomal aberrations in Iranian women with POF. The results confirm previous observations and emphasis on the critical role of X chromosome abnormalities as one of the possible etiologies for POF.

Adipose-derived mesenchymal stem cells in emphysema: Comparison of inflammatory markers changes in response to intratracheal and systemic delivery method.

Cytokines are the most important inflammatory mediators and are well-known as the main cause of emphysema. Adipose-derived stem cells (ADSCs) as a cell-based treatment strategy could play a pivotal role in lung regeneration through anti-inflammatory and paracrine properties. Accordingly, the aim of this study was to the comparison of inflammation markers' improvement in response to the intratracheal and systemic delivery method of adipose-derived mesenchymal stem cells in emphysema. Forty-eight rats were divided into five groups including Control, Elastase (25 IU/kg, Intratracheal, at day first and 10th), Elastase+PBS, Intratracheal cell therapy (1 ×107, at day 28th), and Systemic cell therapy groups (1 ×107, Jugular vein, at day 28th). After 3 weeks, the blood gas analysis (PO2, PCO2 and pH), fibrinogen level, and C-reactive protein (CRP) concentrations were measured in all groups. In addition, inflammatory genes expression, and concentration levels of pro and anti-inflammatory cytokines (IL-6, IL-17, TNF-α, and TGF-ß,) were evaluated using Real-time PCR and Elisa kits, respectively. The statistical analysis of our data shows that local administration leads to more significant treatment efficacy with decreased inflammation parameters such as WBC count and pro-inflammatory cytokines in comparison with systemic treatment. Besides, these results were approved by more reduction of CRP and fibrinogen concentration levels in blood samples of intra-tracheal AMSCs-treated rats compare with the systemic group. Moreover, the improvement in histopathology indexes of the local administrated group was significantly better than the systemic group. Accordingly, the obtained results suggest local administration as the most efficacious route for mesenchymal stem cells delivery in patients with emphysema.

Predictive value of 99mTc-MAA-based dosimetry in personalized 90Y-SIRT planning for liver malignancies.

BACKGROUND: Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that the pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation of the 90Y microspheres biodistribution, which is not always the case. Due to the growing interest in theragnostic dosimetry for personalized radionuclide therapy, a robust relationship between the delivered and pre-treatment radiation absorbed doses is required. In this work, we aim to investigate the predictive value of absorbed dose metrics calculated from 99mTc-MAA (simulation) compared to those obtained from 90Y post-therapy SPECT/CT. RESULTS: A total of 79 patients were analysed. Pre- and post-therapy 3D-voxel dosimetry was calculated on 99mTc-MAA and 90Y SPECT/CT, respectively, based on Local Deposition Method. Mean absorbed dose, tumour-to-normal ratio, and absorbed dose distribution in terms of dose-volume histogram (DVH) metrics were obtained and compared for each volume of interest (VOI). Mann-Whitney U-test and Pearson's correlation coefficient were used to assess the correlation between both methods. The effect of the tumoral liver volume on the absorbed dose metrics was also investigated. Strong correlation was found between simulation and therapy mean absorbed doses for all VOIs, although simulation tended to overestimate tumour absorbed doses by 26%. DVH metrics showed good correlation too, but significant differences were found for several metrics, mostly on non-tumoral liver. It was observed that the tumoral liver volume does not significantly affect the differences between simulation and therapy absorbed dose metrics. CONCLUSION: This study supports the strong correlation between absorbed dose metrics from simulation and therapy dosimetry based on 90Y SPECT/CT, highlighting the predictive ability of 99mTc-MAA, not only in terms of mean absorbed dose but also of the dose distribution.

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.

PURPOSE: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. MATERIALS AND METHODS: After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. RESULTS: DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. CONCLUSION: Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.