Geometric Mapping Evaluation for Real-Time Local Sensor Simulation

Medical augmented reality and simulated test environments struggle in accurately simulating local sensor measurements across large spatial domains while maintaining the proper resolution of information required and real time capability. Here, a simple method for real-time simulation of intraoperative sensors is presented to aid with medical sensor development and professional training. During a surgical intervention, the interaction between medical sensor systems and tissue leads to mechanical deformation of the tissue. Through the inclusion of detailed finite element simulations in a real-time augmented reality system the method presented will allow for more accurate simulation of intraoperative sensor measurements that are independent of the mechanical state of the tissue. This concept uses a coarse, macro-level deformation mesh to maintain both computational speed and the illusion of reality and a simple geometric point mapping method to include detailed fine mesh information. The resulting system allows for flexible simulation of different types of localized sensor measurement techniques. Preliminary simulation results are provided using a real-time capable simulation environment and prove the feasibility of the method.

PIxel-Level Segmentation of Bladder Tumors on MR Images Using a Random Forest Classifier

Objectives: Regional bladder wall thickening on noninvasive magnetic resonance (MR) images is an important sign of developing urinary bladder cancer (BCa), and precise segmentation of the tumor mass is an essential step toward noninvasive identification of the pathological stage and grade, which is of critical importance for the clinical management of patients with BCa. Methods: In this paper, we proposed a new method based on the high-throughput pixel-level features and a random forest (RF) classifier for the BCa segmentation. First, regions of interest (ROIs) including tumor and wall ROIs were used in the training set for feature extraction and segmentation model development. Then, candidate regions containing both bladder tumor and its neighboring wall tissue in the testing set were segmented. Results: Experimental results were evaluated on a retrospective database containing 56 patients postoperatively confirmed with BCa from the affiliated hospital. The Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) of the tumor regions were adopted to quantitatively assess the overall performance of this approach. The results showed that the mean DSC was 0.906 (95% confidential interval [CI]: 0.852-0.959), and the mean ASSD was 1.190 mm (95% CI: 1.727-2.449), which were higher than those of the state-of-the-art methods for tumor region separation. Conclusion: The proposed Pixel-level BCa segmentation method can achieve good performance for the accurate segmentation of BCa lesion on MR images.

Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer

Urinary bladder cancer (BCa) is a highly prevalent disease among aged males. Precise diagnosis of tumor phenotypes and recurrence risk is of vital importance in the clinical management of BCa. Although imaging modalities such as CT and multiparametric MRI have played an essential role in the noninvasive diagnosis and prognosis of BCa, radiomics has also shown great potential in the precise diagnosis of BCa and preoperative prediction of the recurrence risk. Radiomics-empowered image interpretation can amplify the differences in tumor heterogeneity between different phenotypes, i.e., high-grade vs. low-grade, early-stage vs. advanced-stage, and nonmuscle-invasive vs. muscle-invasive. With a multimodal radiomics strategy, the recurrence risk of BCa can be preoperatively predicted, providing critical information for the clinical decision making. We thus reviewed the rapid progress in the field of medical imaging empowered by the radiomics for decoding the phenotype and recurrence risk of BCa during the past 20 years, summarizing the entire pipeline of the radiomics strategy for the definition of BCa phenotype and recurrence risk including region of interest definition, radiomics feature extraction, tumor phenotype prediction and recurrence risk stratification. We particularly focus on current pitfalls, challenges and opportunities to promote massive clinical applications of radiomics pipeline in the near future.

Comprehensive non-invasive analysis of lower urinary tract anatomy using MRI

PURPOSE

Anatomic changes that coincide with aging including benign prostatic hyperplasia (BPH) and lower urinary tract symptoms (LUTS) negatively impact quality of life. Use of MRI with its exquisite soft tissue contrast, full field-of-view capabilities, and lack of radiation is uniquely suited for quantifying specific lower urinary tract features and providing comprehensive measurements such as total bladder wall volume (BWV), bladder wall thickness (BWT), and prostate volume (PV). We present a technique for generating 3D anatomical renderings from MRI to perform quantitative analysis of lower urinary tract anatomy.

METHODS

T2-weighted fast-spin echo MRI of the pelvis in 117 subjects (59F;58 M) aged 30-69 (49.5 ± 11.3) without known lower urinary tract symptoms was retrospectively segmented using Materialise software. Virtual 3D models were used to measure BWV, BWT, and PV.

RESULTS

BWV increased significantly between the 30-39 and 60-69 year age group in women (p = 0.01), but not men (p = 0.32). BWV was higher in men than women aged 30-39 and 40-49 (p = 0.02, 0.05, respectively) ,but not 50-59 or 60-69 (p = 0.18, 0.16, respectively). BWT was thicker in men than women across all age groups. Regional differences in BWT were observed both between men and women and between opposing bladder wall halves (anterior/posterior, dome/base, left/right) within each sex in the 50-59 and 60-69 year groups. PV increased from the 30-39 to 60-69 year groups (p = 0.05). BWT was higher in subjects with enlarged prostates (> 40cm³) (p = 0.05).

CONCLUSION

Virtual 3D MRI models of the lower urinary tract reliably quantify sex-specific and age-associated changes of the bladder wall and prostate.

The invasion depth measurement of bladder cancer using T2-weighted magnetic resonance imaging

BACKGROUND

Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region.

RESULTS

The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The "soft boundary" defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm.

CONCLUSION

The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

Delayed 18F FDG PET/CT Imaging in the Assessment of Residual Tumors after Transurethral Resection of Bladder Cancer

Background Delayed fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/CT is used to diagnose bladder cancer. However, it remains difficult to determine whether a lesion with abnormal ¹⁸F FDG uptake is tumor residue or recurrence or if it is an inflammatory reaction in patients with bladder cancer after oncologic treatment. Purpose To determine the diagnostic performance of delayed ¹⁸F FDG PET/CT in the differentiation of residual tumors from postoperative inflammatory reactions in patients with bladder cancer after initial transurethral resection of bladder tumor (TURBT). Materials and Methods A retrospective clinical study between January 2015 and April 2018 was performed in 79 patients with bladder cancer who had undergone ¹⁸F FDG PET/CT within 1 month after initial TURBT. After PET/CT, all patients underwent a second surgery within 2 weeks to confirm the histologic nature of the suspicious lesion and to remove residual tumors. Uni- and multivariable analysis were used to identify predictive factors for residual bladder tumors. Results A total of 79 patients (61 men, 18 women; mean age, 63 years ± 11 [standard deviation]) were enrolled in this study. A total of 98 lesions was studied, 64 (65.3%) of which were residual tumors after initial TURBT. When compared with inflammatory reactions, residual tumors had higher mean standardized uptake value (SUV_mean) (mean, 5.8 ± 2.0 vs 9.3 ± 5.4; P < .001), higher maximum standardized uptake value (SUV_max) (mean, 15.5 ± 9.8 vs 22.2 ± 13.6, P = .01), and greater lesion thickness (mean, 9.6 mm ± 4.1 vs 17.9 mm ± 11.1, P < .001) at ¹⁸F FDG PET/CT. SUV_mean (odds ratio [OR], 1.2; 95% confidence interval [CI]: 1.0, 1.5; P = .049) and lesion thickness (OR, 1.2; 95% CI: 1.0, 1.3; P = .006 or OR, 1.2; 95% CI: 1.1, 1.3; P = .001) were identified as independent predictors for residual tumors with multivariable logistic regression analysis. On the basis of the threshold values of SUV_mean and lesion thickness, we revealed a prediction rate of 37.5% (17 of 47), 85.4% (26 of 29), and 98.3% (21 of 22) for residual tumors in low-, moderate-, and high-risk subgroups, respectively. Conclusion Use of fluorine 18 fluorodeoxyglucose PET/CT to differentiate lesions after transurethral resection of bladder tumor indicates that higher mean standardized uptake values and greater lesion thickness are predictive factors for residual tumors in patients with bladder cancer after oncologic treatment. Published under a CC BY 4.0 license.

Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps

PURPOSE

To determine radiomic features which are capable of reflecting muscular invasiveness of bladder cancer (BC) and propose a non-invasive strategy for the differentiation of muscular invasiveness preoperatively.

METHODS

Sixty-eight patients with clinicopathologically confirmed BC were included in this retrospective study. A total of 118 cancerous volumes of interest (VOI) were segmented from patients' T2 weighted MR images (T2WI), including 34 non-muscle invasive bladder carcinomas (NMIBCs, stage <T2) and 84 muscle invasive ones (MIBCs, stage ≥T2). The radiomic features quantifying tumor signal intensity and textures were extracted from each VOI and its high-order derivative maps to characterize heterogeneity of tumor tissues. Statistical analysis was used to build radiomic signatures with significant inter-group differences of NMIBCs and MIBCs. The synthetic minority oversampling technique (SMOTE) and a support vector machine (SVM)-based feature selection and classification strategy were proposed to first rebalance the imbalanced sample size and then further select the most predictive and compact signature subset to verify its differentiation capability.

RESULTS

From each tumor VOI, a total of 63 radiomic features were derived and 30 of them showed significant inter-group differences (P ≤ 0.01). By using the SVM-based feature selection algorithm with rebalanced samples, an optimal subset including 13 radiomic signatures was determined. The area under receiver operating characteristic curve and Youden index were improved to 0.8610 and 0.7192, respectively.

CONCLUSION

3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscular invasiveness in patients with bladder cancer.

Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging

PURPOSE

To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features.

MATERIALS AND METHODS

In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T magnetic resonance imaging (MRI). A Mann-Whitney U-test was applied to select features with significant differences between low- and high-grade groups (P < 0.05). Then support vector machine with recursive feature elimination (SVM-RFE) and classification strategy was adopted to find an optimal feature subset and then to establish a classification model for grading.

RESULTS

A total 102 features were derived from each VOI and among them, 47 candidate features were selected, which showed significant intergroup differences (P < 0.05). By the SVM-RFE method, an optimal feature subset including 22 features was further selected from candidate features. The SVM classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.861, 82.9%, 78.4%, and 87.1%, respectively.

CONCLUSION

Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1281-1288.

Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

PURPOSE

This study aims to determine the three-dimensional (3D) texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues, and propose a noninvasive, image-based strategy for bladder tumor differentiation preoperatively.

METHODS

A total of 62 cancerous and 62 wall volumes of interest (VOI) were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer. To better reflect heterogeneous distribution of tumor tissues, 3D high-order derivative maps (the gradient and curvature maps) were calculated from each VOI. Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI. Statistical analysis and recursive feature elimination-based support vector machine classifier (RFE-SVM) was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance.

RESULTS

From each VOI, a total of 58 texture features were derived. Among them, 37 features showed significant inter-class differences ([Formula: see text]). With 29 optimal features selected by RFE-SVM, the classification results namely the sensitivity, specificity, accuracy and area under the curve (AUC) of the receiver operating characteristics were 0.9032, 0.8548, 0.8790 and 0.9045, respectively. By using synthetic minority oversampling technique to augment the sample number of each group to 200, the sensitivity, specificity, accuracy an AUC value of the feature selection-based classification were improved to 0.8967, 0.8780, 0.8874 and 0.9416, respectively.

CONCLUSIONS

Our results suggest that 3D texture features derived from intensity and high-order derivative maps can better reflect heterogeneous distribution of cancerous tissues. Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.