Numerical Simulation and Microstructure Analysis of 30CrMnMoRe High-Strength Steel Welding.

Welding experiments were conducted under different currents for single-pass butt welding of high-strength steel flat plates. The microstructure of welded joints was characterized using OM, SEM, and EBSD, and the welding process was numerically simulated using a finite element method. According to the grain size obtained by electron microscope characterization and the temperature data obtained by simulation, the microstructure and mechanical properties of coarse grain and fine grain areas of the heat-affected zone were predicted by using the material microstructure and property simulation software. Finally, the results of mechanical properties simulation were verified through mechanical property testing.

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma.

Objectives: To explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC). Methods: Data from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis. Results: In the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05). Conclusion: The effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Background: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods: We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results: The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions: AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.

Background: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers. Methods: We retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models' performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models. Results: The LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164). Conclusions: The radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.

Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of Gleason Grade Group from MRI.

BACKGROUND: The Gleason Grade Group (GG) is essential in assessing the malignancy of prostate cancer (PCa) and is typically obtained by invasive biopsy procedures in which sampling errors could lead to inaccurately scored GGs. With the gradually recognized value of bi-parametric magnetic resonance imaging (bpMRI) in PCa, it is beneficial to noninvasively predict GGs from bpMRI for early diagnosis and treatment planning of PCa. However, it is challenging to establish the connection between bpMRI features and GGs. PURPOSE: In this study, we propose a dual attention-guided multiscale neural network (DAMS-Net) to predict the 5-scored GG from bpMRI and design a training curriculum to further improve the prediction performance. METHODS: The proposed DAMS-Net incorporates a feature pyramid network (FPN) to fully extract the multiscale features for lesions of varying sizes and a dual attention module to focus on lesion and surrounding regions while avoiding the influence of irrelevant ones. Furthermore, to enhance the differential ability for lesions with the inter-grade similarity and intra-grade variation in bpMRI, the training process employs a specially designed curriculum based on the differences between the radiological evaluations and the ground truth GGs. RESULTS: Extensive experiments were conducted on a private dataset of 382 patients and the public PROSTATEx-2 dataset. For the private dataset, the experimental results showed that the proposed network performed better than the plain baseline model for GG prediction, achieving a mean quadratic weighted Kappa (Kw ) of 0.4902 and a mean positive predictive value of 0.9098 for predicting clinically significant cancer (PPVGG>1 ). With the application of curriculum learning, the mean Kw and PPVGG>1 further increased to 0.5144 and 0.9118, respectively. For the public dataset, the proposed method achieved state-of-the-art results of 0.5413 Kw and 0.9747 PPVGG>1 . CONCLUSION: The proposed DAMS-Net trained with curriculum learning can effectively predict GGs from bpMRI, which may assist clinicians in early diagnosis and treatment planning for PCa patients.

Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry.

BACKGROUND: To develop an end-to-end deep learning method for automated quantitative assessment of pediatric blunt hepatic trauma based on contrast-enhanced computed tomography (CT). METHODS: This retrospective study included 170 children with blunt hepatic trauma between May 1, 2015, and August 30, 2021, who had undergone contrast-enhanced CT. Both liver parenchyma and liver trauma regions were manually segmented from CT images. Two deep convolutional neural networks (CNNs) were trained on 118 cases between May 1, 2015, and December 31, 2019, for liver segmentation and liver trauma segmentation. Liver volume and trauma volume were automatically calculated based on the segmentation results, and the liver parenchymal disruption index (LPDI) was computed as the ratio of liver trauma volume to liver volume. The segmentation performance was tested on 52 cases between January 1, 2020, and August 30, 2021. Correlation analysis among the LPDI, trauma volume, and the American Association for the Surgery of Trauma (AAST) liver injury grade was performed using the Spearman rank correlation. The performance of severity assessment of pediatric blunt hepatic trauma based on the LPDI and trauma volume was evaluated using receiver operating characteristic (ROC) analysis. RESULTS: The Dice, precision, and recall of the developed deep learning framework were 94.75, 94.11, and 95.46% in segmenting the liver and 72.91, 72.40, and 76.80% in segmenting the trauma regions. The LPDI and trauma volume were significantly correlated with AAST grade (rho = 0.823 and rho = 0.831, respectively; p < 0.001 for both). The area under the ROC curve (AUC) values for the LPDI and trauma volume to distinguish between high-grade and low-grade pediatric blunt hepatic trauma were 0.942 (95% CI, 0.882-1.000) and 0.952 (95% CI, 0.895-1.000), respectively. CONCLUSIONS: The developed end-to-end deep learning method is able to automatically and accurately segment the liver and trauma regions from contrast-enhanced CT images. The automated LDPI and liver trauma volume can act as objective and quantitative indexes to supplement the current AAST grading of pediatric blunt hepatic trauma.

A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.

Supervised machine learning methods are usually used to build a custom model for disease diagnosis and auxiliary prognosis in radiomics studies. A classical machine learning pipeline involves a series of steps and multiple algorithms, which leads to a great challenge to find an appropriate combination of algorithms and an optimal hyper-parameter set for radiomics model building. We developed a freely available software package for radiomics model building. It can be used to lesion labeling, feature extraction, feature selection, classifier training and statistic result visualization. This software provides a user-friendly graphic interface and flexible IOs for radiologists and researchers to automatically develop radiomics models. Moreover, this software can extract features from corresponding lesion regions in multi-modality images, which is labeled by semi-automatic or full-automatic segmentation algorithms. It is designed in a loosely coupled architecture, programmed with Qt, VTK, and Python. In order to evaluate the availability and effectiveness of the software, we utilized it to build a CT-based radiomics model containing peritumoral features for malignancy grading of cell renal cell carcinoma. The final model got a good performance of grading study with AUC=0.848 on independent validation dataset.Clinical Relevance-the developed provides convenient and powerful toolboxes to build radiomics models for radiologists and researchers on clinical studies.

[Automatic segmentation of kidney tumor based on cascaded multiscale convolutional neural networks].

The background of abdominal computed tomography (CT) images is complex, and kidney tumors have different shapes, sizes and unclear edges. Consequently, the segmentation methods applying to the whole CT images are often unable to effectively segment the kidney tumors. To solve these problems, this paper proposes a multi-scale network based on cascaded 3D U-Net and DeepLabV3+ for kidney tumor segmentation, which uses atrous convolution feature pyramid to adaptively control receptive field. Through the fusion of high-level and low-level features, the segmented edges of large tumors and the segmentation accuracies of small tumors are effectively improved. A total of 210 CT data published by Kits2019 were used for five-fold cross validation, and 30 CT volume data collected from Suzhou Science and Technology Town Hospital were independently tested by trained segmentation models. The results of five-fold cross validation experiments showed that the Dice coefficient, sensitivity and precision were 0.796 2 ± 0.274 1, 0.824 5 ± 0.276 3, and 0.805 1 ± 0.284 0, respectively. On the external test set, the Dice coefficient, sensitivity and precision were 0.817 2 ± 0.110 0, 0.829 6 ± 0.150 7, and 0.831 8 ± 0.116 8, respectively. The results show a great improvement in the segmentation accuracy compared with other semantic segmentation methods.

Machine learning-based quantitative analysis of barium enema and clinical features for early diagnosis of short-segment Hirschsprung disease in neonate.

OBJECTIVE: To develop a mathematical model based on a combination of clinical and radiologic features (barium enema) for early diagnosis of short-segment Hirschsprung disease (SHSCR) in neonate. METHODS: The analysis included 54 neonates with biopsy-confirmed SHSCR (the cases) and 59 neonates undergoing barium enema for abdominal symptoms but no Hirschsprung disease (the control). Colon shape features extracted from barium enema images and clinical features were used to develop diagnostic models using support vector machine (SVM) and L2-regularized logistic regression (LR). The training cohort included 32 cases and 37 controls; testing cohort consisted 22 cases and 22 controls. Results were compared to interpretation by 2 radiologists. RESULTS: In the analysis by radiologists, 87 out of 113 cases were correctly classified. Six SHSCR cases were mis-classified into the non-HSCR group. In the remaining 20 cases, radiologists were unable to make a decision. Both the SVM and LR classifiers contained five clinical features and four shape features. The performance of the two classifiers was similar. The best model had 86.36% accuracy, 81.82% sensitivity, and 90.91% specificity. The AUC was 0.9132 for the best-performing SVM classifier and 0.9318 for the best-performing LR classifier. CONCLUSION: A combination of clinical features and colon shape features extracted from barium enemas can be used to improve early diagnosis of SHSCR in neonate.

CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma.

OBJECTIVE: To evaluate the efficiency of CT-based peritumoral radiomics signatures of clear cell renal cell carcinoma (ccRCC) for malignancy grading in preoperative prediction. MATERIALS AND METHODS: 203 patients with pathologically confirmed as ccRCC were retrospectively enrolled in this study. All patients were categorized into training set (n = 122) and validation set (n = 81). For each patient, two types of volumes of interest (VOI) were masked on CT images. One type of VOIs was defined as the tumor mass volume (TMV), which was masked by radiologists delineating the outline of all contiguous slices of the entire tumor, while the other type defined as the peritumoral tumor volume (PTV), which was automatically created by an image morphological method. 1760 radiomics features were calculated from each VOI, and then the discriminative radiomics features were selected by Pearson correlation analysis for reproducibility and redundancy. These selected features were investigated their validity for building radiomics signatures by mRMR feature ranking method. Finally, the top ranked features, which were used as radiomics signatures, were input into a classifier for malignancy grading. The prediction performance was evaluated by receiver operating characteristic (ROC) curve in an independent validation cohort. RESULTS: The radiomics signatures of PTV showed a better performance on malignancy grade prediction of ccRCC with AUC of 0.807 (95% CI 0.800-0.834) in train data and 0.848 (95% CI 0.760-0.936) in validation data, while the radiomics signatures of TMV with AUC of 0.773 (95% CI 0.744-0.802) in train data and 0.810 (95% CI 0.706-0.914) in validation data. CONCLUSION: The CT-based peritumoral radiomics signature is a potential way to be used as a noninvasive tool to preoperatively predict the malignancy grades of ccRCC.

MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI.

PURPOSE: To compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI). METHODS: A total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis. RESULTS: The radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated. CONCLUSIONS: Our study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.