CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.

OBJECTIVES: This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). METHODS: CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. RESULTS: There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). CONCLUSIONS: Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. ADVANCES IN KNOWLEDGE: This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

Intelligent Optimization Design of a Phononic Crystal Air-Coupled Ultrasound Transducer.

To further improve the operational performance of a phononic crystal air-coupled ultrasonic transducer while reducing the number of simulations, an intelligent optimization design strategy is proposed by combining finite element simulation analysis and artificial intelligence (AI) methods. In the proposed strategy, the structural design parameters of 1-3 piezoelectric composites and acoustic impedance gradient matching layer are sampled using the optimal Latin hypercube sampling (OLHS) method. Moreover, the COMSOL software is utilized to calculate the performance parameters of the transducer. Based on the simulation data, a radial basis function neural network (RBFNN) model is trained to establish the relationship between the design parameters and the performance parameters. The accuracy of the approximation model is verified through linear regression plots and statistical methods. Finally, the NSGA-II algorithm is used to determine the design parameters of the transducer. After optimization, the band gap widths of the piezoelectric composites and acoustic impedance gradient matching layer are increased by 16 kHz and 13.5 kHz, respectively. Additionally, the -6 dB bandwidth of the transducer is expanded by 11.5%. The simulation results and experimental results are consistent with the design objectives, which confirms the effectiveness of the design strategy. This work provides a feasible strategy for the design of high-performance air-coupled ultrasonic transducers, which is of great significance for the development of non-destructive testing technology.

Design and optimization of two-degree-of-freedom parallel four pure-slide- and four parallel quadrilateral-pair precision positioning platform.

Aiming at the complex structure, small output displacement, and low positioning accuracy of the two-degree-of-freedom (2-DOF) precision positioning platform, theoretical analyses and experimental tests are carried out so that the platform has the characteristics of compact structure, large output stroke, and high positioning accuracy. First, to optimize the structural parameters of the positioning platform, a modeling method to improve the modeling accuracy of the compliant mechanism of the positioning platform is proposed. A static model of the positioning platform based on Euler-Bernoulli beam theory and the sixth-order compliance matrix method is established, and the accuracy of the model is verified by simulation. In addition, the single-objective genetic optimization algorithm is used to optimize the structural size parameters of the positioning platform, and the optimal solution set of the structural size parameters of the positioning platform is obtained by taking the displacement amplification rate of the positioning platform as the optimization target. Finally, according to theoretical and simulation analysis and optimization results, an experimental prototype was fabricated, and a series of experimental tests were carried out on the working stroke, displacement magnification, and output stiffness. The experimental results show that the displacement magnification of the positioning platform reaches 3.39, the positioning stroke is 89.2 × 85.9 µm2, and the displacement resolutions of the x-axis and y-axis are 35 and 31 nm, respectively. The positioning platform designed in this paper meets the requirements of large output stroke and high positioning accuracy.

An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma.

Objectives: To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM). Methods: 175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test. Results: The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p <0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p < 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration. Conclusion: The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.

Design and research on tuning fork piezoelectric actuator based on stick-slip effect.

Aiming at the problems of low output speed, large size, and difficult miniaturization of stacked and sandwich piezoelectric actuators, a patch-type tuning fork piezoelectric actuator model based on the stick-slip effect was designed, in which the dynamic theoretical analysis, the simulation optimization to determine the stator structure parameters, and the experimental research were carried out to obtain the stator structure parameters. The externally applied conditions (the influence model of excitation voltage, excitation frequency, and pre-pressure) on the performance output of piezoelectric actuators will promote the miniaturization and industrialization of tuning fork piezoelectric actuators in the next step. The simulation results show that the integrated output performance of the piezoelectric actuator is best when the angle of the tuning fork is 15°. After optimizing the stator chamfer to 2.5 and 4.5 mm, the tangential amplitude difference of the 15° tuning fork angle actuator is the smallest. The experimental results show that the output speed of the actuator is positively linearly related to the excitation voltage, the maximum output thrust is 8 N when the excitation voltage is 100 V, the excitation frequency is 20.1 kHz, the pre-pressure is 7.5 N, the phase difference of the excitation signal is π/2, and the output speed of the actuator can reach 116 mm/s.

Computed Tomography Image under Three-Dimensional Reconstruction Algorithm Based in Diagnosis of Renal Tumors and Retroperitoneal Laparoscopic Partial Nephrectomy.

This study was to explore the clinical application value of computed tomography (CT) images based on a three-dimensional (3D) reconstruction algorithm for laparoscopic partial nephrectomy (LPN) in patients with renal tumors. 30 cases of renal cell carcinoma (RCC) patients admitted to the hospital were selected as the research objects and were rolled into two groups using a random table method. The patients who received PLN under the three-dimensional reconstruction and laparoscopic technique were included in the experimental group (group A), and the patients who received LPN using CT images only were included in the control group (group B). In addition, the treatment results of the two groups of patients were compared and analyzed. Results. The effective rate of the established model was 93.3%; the total renal arteriovenous variability of group A (13.3%) was higher than that of group B (6.7%), and the operation time (131.5 ± 32.1 minutes) was much lower than that of group B (158.7 ± 36.2 minutes), showing statistical significance (P < 0.05). Conclusion. CT images based on 3D reconstruction algorithms had high clinical application value for LPN in patients with renal tumors, which could improve the efficiency and safety of LPN.

A Reliable Prediction Model for Renal Cell Carcinoma Subtype Based on Radiomic Features from 3D Multiphase Enhanced CT Images.

BACKGROUND: This study aimed to develop a prediction model to distinguish renal cell carcinoma (RCC) subtypes. METHODS: The radiomic features (RFs) from 5 different computed tomography (CT) phases were used in the prediction models: noncontrast phase (NCP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP), and all-phase (ALL-P). RESULTS: For the ALL-P model, all of the RFs obtained from the 4 single-phase images were combined to 420 RFs. The ALL-P model performed the best of all models, with an accuracy of 0.80; the sensitivity and specificity for clear cell RCC (ccRCC) were 0.85 and 0.83; those for papillary RCC (pRCC) were 0.60 and 0.91; those for chromophobe RCC (cRCC) were 0.66 and 0.91, respectively. Binary classification experiments showed for distinguishing ccRCC vs. not-ccRCC that the area under the receiver operating characteristic curve (AUC) of the ALL-P and CMP models was 0.89, but the overall sensitivity/specificity/accuracy of the ALL-P model was better. For cRCC vs. non-cRCC, the ALL-P model had the best performance. CONCLUSIONS: A reliable prediction model for RCC subtypes was constructed. The performance of the ALL-P prediction model was the best as compared to individual single-phase models and the traditional prediction model.

Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma.

BACKGROUND: Many patients experience recurrence of renal cell carcinoma (RCC) after radical and partial nephrectomy. Radiomics nomogram is a newly used noninvasive tool that could predict tumor phenotypes. OBJECTIVE: To investigate Radiomics Features (RFs) associated with progression-free survival (PFS) of RCC, assessing its incremental value over clinical factors, and to develop a visual nomogram in order to provide reference for individualized treatment. METHODS: The RFs and clinicopathological data of 175 patients (125 in the training set and 50 in the validation set) with clear cell RCC (ccRCC) were retrospectively analyzed. In the training set, RFs were extracted from multiphase enhanced CT tumor volume and selected using the stability LASSO feature selection algorithm. A radiomics nomogram final model was developed that incorporated the RFs weighted sum and selected clinical predictors based on the multivariate Cox proportional hazard regression. The performances of a clinical variables-only model, RFs-only model, and the final model were compared by receiver operator characteristic (ROC) analysis and DeLong test. Nomogram performance was determined and validated with respect to its discrimination, calibration, reclassification, and clinical usefulness. RESULTS: The radiomics nomogram included age, clinical stage, KPS score, and RFs weighted sum, which consisted of 6 selected RFs. The final model showed good discrimination, with a C-index of 0.836 and 0.706 in training and validation, and good calibration. In the training set, the C-index of the final model was significantly larger than the clinical-only model (DeLong test, p = 0.008). From the clinical variables-only model to the final model, the reclassification of net reclassification improvement was 18.03%, and the integrated discrimination improvement was 19.08%. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. CONCLUSION: The CT-based RF is an improvement factor for clinical variables-only model. The radiomics nomogram provides individualized risk assessment of postoperative PFS for patients with RCC.

Adult pancreatoblastoma: A case report and clinicopathological review of the literature.

PURPOSE: Our purpose was to report a case of adult pancreatoblastoma, and review the literature in order to assist clinicians in the management of the disease. MATERIALS AND METHODS: The demographic, clinical, and imaging findings of 41 patients with pathologically proven pancreatoblastoma from 1986 to 2017 identified in PubMed were reviewed. The key words used for searching PubMed were: "pancreatoblastoma", "pancreatic tumor", and "adult pancreatoblastoma." We also reported the details of a case of adult pancreatoblastoma treated at our institution. RESULTS: We identified 41 cases of adult pancreatoblastomas, and the mean age at diagnosis was 41.4 ±â€¯17.4 years. Pancreatoblastomas occurred in the pancreatic head in 48.4% of patients, and in 39.0% of cases the tumor was >8 cm in diameter at diagnosis. Patient age and tumor size were similar between males and females (P = 0.59; P = 0.32, respectively). Metastases was present in 17 of the 41 adult patients (41.5%). No significant difference in age, sex, tumor size, and tumor location was found between patients with and without metastases (P = 0.57, 0.58, 0.64, 0.39, respectively). CONCLUSION: Preoperative diagnosis of adult pancreatoblastoma is difficult because of the heterogeneous, variable cellular differentiation and atypical clinical and imaging features. A pancreatoblastoma should be considered when tumors in the pancreas are solid and cystic.