Prostate Volume Measurement by Ultrasonography Over 40 Years Age of Bangladeshi Population

Background: Benign prostatic hyperplasia (BPH) or benign prostatic hypertrophy, is a histologic diagnosis status characterized by proliferation of the ‘glandular elements’ of the prostate, which may lead to an enlarged prostate gland. In many studies, people over the age of 40 years found as the most vulnerable for BPH. Ultrasonography is a prominent method to determine prostate volume or size. Aim of the study: The aim of the present study was to evaluate the prostate volume measurement for the Bangladeshi population over the age of 40 years by ultrasonography.Methods:This prospective, observational study was conducted in the Department of Anatomy, Chattogram Medical College Hospital, Chattogram, Bangladesh during the period from January 2019 to December 2020. In total 157 suspected patients of benign prostatic hyperplasia were selected as the study population. All patients were clinically diagnosed for BPH, based on the present prostate symptoms and digital rectal examination. To measure the prostate volume, abdominal ultrasonography was performed for all the patients. After enucleation, another ultrasonogram was performed for all the patients to measure the existing sizes of the prostates of the patients. All the data were processed, analyzed, and disseminated by MS-word and SPSS programs as per need.Results:Finally, in this study in analyzing the volumes of the prostates of the participants according to the abdominal ultra-sonographic reports of pre-operative stage we observed, in 9%, 34%, 31%, 30%, 21% and 32% patients, the prostate sizes (In cc) were <20, 21-40, 41-60, 61-80, 81-100 and >100 cc respectively. On the other hand, after enucleation, in 11.46%, 24.20%, 28.66%, 27.39%, 7.01% and 1.27% patients, the prostate sizes (In cc) were found <20, 21-40, 41-60, 61-80, 81-100 and >100 cc respectively. The mean changes of prostate sizes between pre- and post-operative stages among the participant was not significant where the P value was found 0.464.Conclusion:The findings of this study support the applications of abdominal ultrasonographic evaluation for suspected benign prostatic hyperplasia patients to know about the exact volumes of their prostates for selecting the appropriate surgical approach.

Usefulness of 99mTc–pertechnetate SPECT-CT in thyroid tissue volumetry: phantom studies and a clinical case series

Background: An accurate measurement of the target volume is of primary importance in theragnostics of hyperthyroidism Objective: Our purpose was to evaluate the accuracy of a threshold–based isocontour extraction procedure for thyroid tissue volumetry from SPECT-CT. Methods: Cylindrical vials with a fixed volume of 99mTcO4 at different activities were inserted into a neck phantom in two different thickness settings. Images were acquired by orienting the phantom in different positions, i.e., 40 planar images and 40 SPECT-CT. The fixed values of the iso-contouring threshold for SPECT and SPECT-CT were calculated by means of linear and spline regression models. Mean, Median, Standard Deviation, Standard Error, Mean Absolute Percentage Error and Root Mean-Square Error were computed. Any difference between the planar method, SPECT and SPECT-CT and the effective volume was evaluated by means of ANOVA and post-hoc tests. Moreover, planar and SPECT-CT acquisitions were performed in 8 patients with hyperthyroidism, considering relevant percentage differences greater than > 20 % from CT gold standard. Results: Concerning phantom studies, the planar method shows higher values of each parameter than the other two methods. SPECT-CT shows lower variability. However, no significant differences were observed between SPECT and SPECT-CT measurements. In patients, relevant differences were found in 7 out of 9 lesions with the planar method, in 6 lesions with SPECT, but in only one with SPECT-CT. Conclution: Our study confirms the superiority of SPECT in volume measurement if compared with the planar method. A more accurate measurement can be obtained from SPECT-CT.

Deep learning-based pancreas volume assessment in individuals with type 1 diabetes

Pancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). Studies investigating pancreas volume are underway to assess pancreas volume in large clinical databases and studies, but manual pancreas annotation is time-consuming and subjective, preventing extension to large studies and databases. This study develops deep learning for automated pancreas volume measurement in individuals with diabetes. A convolutional neural network was trained using manual pancreas annotation on 160 abdominal magnetic resonance imaging (MRI) scans from individuals with T1D, controls, or a combination thereof. Models trained using each cohort were then tested on scans of 25 individuals with T1D. Deep learning and manual segmentations of the pancreas displayed high overlap (Dice coefficient = 0.81) and excellent correlation of pancreas volume measurements (R2 = 0.94). Correlation was highest when training data included individuals both with and without T1D. The pancreas of individuals with T1D can be automatically segmented to measure pancreas volume. This algorithm can be applied to large imaging datasets to quantify the spectrum of human pancreas volume.

Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability Analysis

Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ = 0.75 and 0.63) and moderate agreement on apex and base segmentation (κ = 0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93 ± 0.02, significantly higher than other baseline methods, such as DeepLab v3+ and UNet (both p values &lt; 0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.

Clinical Implication of Individually Tailored Segmentation Method for Distorted Hypothalamus in Craniopharyngioma

ObjectiveSeveral attempts have been done to capture damaged hypothalamus (HT) using volumetric measurements to predict the development of hypothalamic obesity in patients with craniopharyngioma (CP). This study was to develop a novel method of HT volume measurement and examine the associations between postoperative HT volume and clinical parameters in patients with CP.MethodsWe included 78 patients with adult-onset CP who underwent surgical resection. Postoperative HT volume was measured using T1- and T2-weighted magnetic resonance imaging (MRI) with a slice thickness of 3 mm, and corrected for temporal lobe volume. We collected data on pre- and postoperative body weights, which were measured at the time of HT volume measurements.ResultsThe corrected postoperative HT volume measured using T1- and T2-weighted images was significantly correlated (r=0.51 [95% confidence interval (CI) 0.32 to 0.67], P&lt;0.01). However, HT volume was overestimated using T1-weighted images owing to obscured MR signal of the thalamus in patients with severe HT damage. Therefore, we used T2-weighted images to evaluate its clinical implications in 72 patients with available medical data. Postoperative HT volume was negatively associated with preoperative body weight and preoperative tumor volume (r=–0.25 [95% CI -0.45 to -0.04], P=0.04 and r=–0.26 [95% CI -0.40 to -0.15], P=0.03, respectively). In the subgroup analysis of CP patients who underwent primary surgery (n=56), pre- and postoperative body weights were negatively associated with HT volume (r=–0.30 [95% CI -0.53 to -0.03], P=0.03 and r=–0.29 [95% CI -0.53 to -0.02], P=0.03, respectively).ConclusionsAdult-onset CP patients showed negative associations between postoperative HT volume and preoperative/postoperative body weight using a new method of HT volume measurement based on T2-weighted images.

Polymerization Shrinkage, Hygroscopic Expansion, Elastic Modulus and Degree of Conversion of Different Composites for Dental Application

Objectives: To characterize the mechanical properties of different resin-composites for dental application. Methods: Thirteen universal dentin shade composites (n = 10) from different manufacturers were evaluated (4 Seasons, Grandio, Venus, Amelogen Plus, P90, Z350, Esthet-X, Amaris, Vita-l-escence, Natural-Look, Charisma, Z250 and Opallis). The polymerization shrinkage percentage was calculated using a video-image recording device (ACUVOL—Bisco Dental) and the hygroscopic expansion was measured after thermocycling aging in the same equipment. Equal volumes of material were used and, after 5 min of relaxation, baseline measurements were calculated with 18 J of energy delivered from the light-curing unit. Specimens were stored in a dry-dark environment for 24 h then thermocycled in distilled water (5–55 °C for 20,000 cycles) with volume measurement at each 5000 cycles. In addition, the pulse-excitatory method was applied to calculate the elastic modulus and Poisson ratio for each resin material and the degree of conversion was evaluated using Fourier transform infrared spectroscopy. Results: The ANOVA showed that all composite volumes were influenced by the number of cycles (α = 0.05). Volumes at 5 min post-polymerization (12.47 ± 0.08 cm3) were significantly lower than those at baseline (12.80 ± 0.09 cm3). With regard to the impact of aging, all resin materials showed a statistically significant increase in volume after 5000 cycles (13.04 ± 0.22 cm3). There was no statistical difference between volumes measured at the other cycle steps. The elastic modulus ranged from 22.15 to 10.06 GPa and the Poisson ratio from 0.54 to 0.22 with a significant difference between the evaluated materials (α = 0.05). The degree of conversion was higher than 60% for all evaluated resin composites.

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurements and pulmonary veins was constructed. Combining the correspondence between ECG signals and echocardiographic signals in the time domain, a series of preprocessing such as denoising, feature point localization, and segmentation of the cardiac cycle was realized by wavelet transform and threshold method to complete the data collection. This paper proposes a comparative model based on artificial intelligence, adapts to the characteristics of one-dimensional time-series echocardiographic signals, automatically extracts the deep features of echocardiographic signals, effectively reduces the subjective influence of manual feature selection, and realizes the automatic classification and evaluation of human left atrial volume measurement and pulmonary veins under different states. The experimental results show that the proposed BP neural network model has good adaptability and classification performance in the tasks of LV volume measurement and pulmonary vein automatic classification evaluation and achieves an average test accuracy of over 96.58%. The average root-mean-square error percentage of signal compression is only 0.65% by extracting the coding features of the original echocardiographic signal through the convolutional autoencoder, which completes the signal compression with low loss. Comparing the training time and classification accuracy of the LSTM network with the original signal and encoded features, the experimental results show that the AI model can greatly reduce the model training time cost and achieve an average accuracy of 97.97% in the test set and increase the real-time performance of the left atrial volume measurement and pulmonary vein evaluation as well as the security of the data transmission process, which is very important for the comparison of left atrial volume measurement and pulmonary vein. It is of great practical importance to compare left atrial volume measurements with pulmonary veins.

314 Automated left atrial volume measurement by two-dimensional speckle-tracking echocardiograpy. Feasibility, accuracy, and reproducibility

Aims A by-product of left atrial (LA) strain analysis is the automated measurement of LA maximal volume (LAVmax), which may decrease the time of echocardiography reporting, and increase the reproducibility of the LAVmax measurement. However, the automated measurement of LAVmax by two-dimensional speckle-tracking analysis (2DSTE) has never been validated. Accordingly, we sought to: (i) assess the feasibility of automated LAVmax measurement by 2DSTE; (ii) compare the automated LAVmax by 2DSTE with conventional two-dimensional (2DE) biplane and three-dimensional echocardiography (3DE) measurements; and (iii) evaluate the accuracy and reproducibility of the three echocardiography techniques. Methods and results LAVmax (34–197 ml) were obtained from 198/210 (feasibility 94%) consecutive patients with various cardiac diseases (median age 67 years, 126 men) by 2DSTE, 2DE, and 3DE. 2DE and 2DSTE measurements resulted in similar LAVmax values (bias = 1.5 ml, limits of agreement, LOA ± 7.5 ml), and slightly underestimated 3DE LAVmax (biases = −5 ml, LOA ± 17 ml, and −6 ml, LOA ± 16 ml, respectively). LAVmax by 2DSTE and 2DE were strongly correlated to those obtained by cardiac magnetic resonance (CMR) (r = 0.946, and r = 0.935, respectively; P &lt; 0.001). However, LAVmax obtained by 2DSTE (bias = −9.5 ml, LOA ± 16 ml) and 2DE (bias = −8 ml, LOA ± 17 ml) were significantly smaller than those measured by CMR. Conversely, 3DE LAVmax were similar to CMR (bias = −2 ml, LOA ± 10 ml). Excellent intra- and inter-observer intraclass correlations were found for 3DE (0.995 and 0.995), 2DE (0.990 and 0.988), and 2DSTE (0.990 and 0.989). Conclusions Automated LAVmax measurement by 2DSTE is highly feasible, highly reproducible, and provided similar values to conventional 2DE calculations in consecutive patients with a wide range of LAVmax.

COT-16 Development of automatic lesion extraction application using artificial intelligence for the purpose of simplifying tumor volume measurement of meningioma

BACKGROUND: With the widespread use of MRI equipment and brain scans, opportunities to perform follow-up examinations for meningiomas have increased. On the other hand, an objective evaluation index for meningiomas characterized by slow changes on imaging has not been established. To establish a volume-based evaluation index for meningoceles, we are developing an application for automatic lesion extraction using artificial intelligence as a highly reproducible tumor volume measurement technique that enables large volume image data processing. METHODS: In this study, 195 patients with meningioma who underwent contrast-enhanced MRI imaging at Osaka University Hospital were included. The images were manually extracted by three neurosurgeons and used as supervised data. deeplabV3 was used as the learning network. All the supervised data were randomly divided into training (80%) and testing (20%) data, and the application was constructed by deep learning and validation with 5-fold cross-validation. The matching rate of the area of the region automatically extracted by the device against the test data and the mean square error rate of the calculated tumor volume were used as indices of the product measurement performance. RESULTS: The matching rate using the automatic extraction application for the correct data(Dice index) was 91.5% on average. The mean squared error rate of the tumor volume calculated from these extracted regions was 8.84%. CONCLUSION: We consider that this application using artificial intelligence has a certain degree of validity in terms of the accuracy of extracted lesions. In the future, it is necessary not only to improve the performance of the equipment but also to clarify the clinical significance of the new imaging biomarkers based on tumor volume that can be obtained from these lesion extraction techniques.