Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks

Purpose: Accurate delineation of the prostate gland and intraprostatic lesions (ILs) is essential for prostate cancer dose-escalated radiation therapy. The aim of this study was to develop a sophisticated deep neural network approach to magnetic resonance image analysis that will help IL detection and delineation for clinicians. Methods and Materials: We trained and evaluated mask region-based convolutional neural networks to perform the prostate gland and IL segmentation. There were 2 cohorts in this study: 78 public patients (cohort 1) and 42 private patients from our institution (cohort 2). Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches cropped out. The IL segmentation model was extended to select 5 highly suspicious volumetric lesions within the entire prostate. Results: The mask region-based convolutional neural networks model was able to segment the prostate with dice similarity coefficient (DSC) of 0.88 ± 0.04, 0.86 ± 0.04, and 0.82 ± 0.05; sensitivity (Sens.) of 0.93, 0.95, and 0.95; and specificity (Spec.) of 0.98, 0.85, and 0.90. However, ILs were segmented with DSC of 0.62 ± 0.17, 0.59 ± 0.14, and 0.38 ± 0.19; Sens. of 0.55 ± 0.30, 0.63 ± 0.28, and 0.22 ± 0.24; and Spec. of 0.974 ± 0.010, 0.964 ± 0.015, and 0.972 ± 0.015 in public validation/public testing/private testing patients when trained with patients from cohort 1 only. When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009, 0.969 ± 0.016, and 0.977 ± 0.013. Conclusions: Our research framework is able to perform as an end-to-end system that automatically segmented the prostate gland and identified and delineated highly suspicious ILs within the entire prostate. Therefore, this system demonstrated the potential for assisting the clinicians in tumor delineation

Characterizing Sensitive Cardiac Substructure Excursion Due to Respiration

PURPOSE: Whole-heart dose metrics are not as strongly linked to late cardiac morbidities as radiation doses to individual cardiac substructures. Our aim was to characterize the excursion and dosimetric variation throughout respiration of sensitive cardiac substructures for future robust safety margin design. METHODS AND MATERIALS: Eleven patients with cancer treatments in the thorax underwent 4-phase noncontrast 4-dimensional computed tomography (4DCT) with T2-weighted magnetic resonance imaging in end-exhale. The end-exhale phase of the 4DCT was rigidly registered with the magnetic resonance imaging and refined with an assisted alignment surrounding the heart from which 13 substructures (chambers, great vessels, coronary arteries, etc) were contoured by a radiation oncologist on the 4DCT. Contours were deformed to the other respiratory phases via an intensity-based deformable registration for radiation oncologist verification. Measurements of centroid and volume were evaluated between phases. Mean and maximum dose to substructures were evaluated across respiratory phases for the breast (n = 8) and thoracic cancer (n = 3) cohorts. RESULTS: Paired t tests revealed reasonable maintenance of geometric and anatomic properties (P \u3c .05 for 4/39 volume comparisons). Maximum displacements \u3e5 mm were found for 24.8%, 8.5%, and 64.5% of the cases in the left-right, anterior-posterior, and superior-inferior axes, respectively. Vector displacements were largest for the inferior vena cava and the right coronary artery, with displacements up to 17.9 mm. In breast, the left anterior descending artery D(mean) varied 3.03 ± 1.75 Gy (range, 0.53-5.18 Gy) throughout respiration whereas lung showed patient-specific results. Across all patients, whole heart metrics were insensitive to breathing phase (mean and maximum dose variations \u3c0.5 Gy). CONCLUSIONS: This study characterized the intrafraction displacement of the cardiac substructures through the respiratory cycle and highlighted their increased dosimetric sensitivity to local dose changes not captured by whole heart metrics. Results suggest value of cardiac substructure margin generation to enable more robust cardiac sparing and to reduce the effect of respiration on overall treatment plan quality

Ductal Carcinoma in Situ of the Breast: MR Imaging Findings With Histopathologic Correlation.

Ductal carcinoma in situ (DCIS) is a noninvasive malignancy that is commonly encountered at routine breast imaging. It may be a primary tumor or may be seen in association with other focal higher-grade tumors. Early detection is important because of the large proportion of DCIS that can progress to invasive carcinoma. The extent of DCIS involvement is frequently underestimated at mammography, which can reliably help detect only calcified DCIS; consequently, magnetic resonance (MR) imaging evaluation can alter the course of treatment. Seven biopsy-proved cases of DCIS were evaluated with T2-weighted MR imaging sequences, as well as T1-weighted sequences performed both before and after contrast material administration. The signal intensity and enhancement patterns of the tumors were analyzed, and the findings were correlated with the relevant underlying histopathologic features. Common enhancement patterns of DCIS include clumped linear-ductal enhancement, clumped focal enhancement, and masslike enhancement. The most common enhancement distribution pattern is segmental, followed by focal, diffuse, linear-ductal, and regional patterns. At T2-weighted MR imaging, DCIS is typically isointense relative to breast parenchyma; less commonly, it is hypointense or hyperintense. The use of MR imaging in the evaluation of DCIS is controversial, and many questions remain with regard to treatment and management. However, breast MR imaging can be extremely useful in the preoperative diagnosis and evaluation of DCIS when used in conjunction with other imaging modalities

Examination and evaluation of MR radiomics features for characterization of dominant intraprostatic lesions.

Purpose: This pilot study investigates a set of radiomics features extracted from fast relaxation fast spin echo (FRFSE) T2 pulse sequences for normal tissue and Dominant Intraprostatic Lesions (DILs) in twenty prostate cancer patients. Material and Methods: Twenty patients with prostate cancer were studied. All patients had axial FSRFSE T2 scans using a 3 Tesla scanner. A radiologist interpreted MR examinations, and contoured the suspicious DIL and the contralateral section of the prostate gland (normal) on the T2 weighted MR images. Patients underwent a 14-core transrectal Ultrasound Guided Biopsy and localization of positive cores, Gleason score and clinical tumor stage were recorded. 167 radiomics features were extracted from normal and DIL zones. These features were categorized into 8 different sets as following: Intensity Histogram Based (IHB), Gray Level Run Length (GLRL), Law\u27s Textural Information (LAWS), Discrete Orthonormal Stockwell Transform (DOST), Local Binary Pattern (LBP), Two Dimensional Wavelet Transform (2DWT), Two Dimensional Gabor Filter (2DGF), and Gray Level Co-Occurrence Matrix (GLCM) with 8, 7, 18, 18, 6, 48, 40, and 22 features in each category respectively. A Welch\u27s test and the Fisher method were used to test for significant differences among the 167 radiomics features and their subcategories. For all patients, correlation coefficients between the extracted features in the normal and DIL zones were also calculated. Results: According to the Fisher combined p-values, among the eight categories of radiomics features, only 5 feature categories showed a significant difference (IHB, GLRL, DOST, LBPF and GLCM with pFisher= 2.0×10-6, 0.02, 12 ×10-4, 3.7 ×10-3, and 1.5 ×10-6 respectively). Among all 167 features, only 7 showed a significant difference (D=100x[DIL/NP-1]) and small correlation between normal and DIL zones: IHB-Skewness (r=0.19, p=0.03, and D=50.3%), GLCM-Contrast (r=0.12, p=0.03, and D=-67.5%), GLCM-Dissimilarity (r=0.12, p=0.01, and D=-67.5%), GLCM-Entropy (r=0.07, p=0.01, and D=-67.1%), GLCM-Difference-Variance (r=0.12, p=0.01, and D=-67.1%), GLCM-Difference-Entropy (r=0.10, p=0.01, and D=-60.4%), and GLCM-Information-Measure-of-Correlation (r=0.25, p=0.01, and D=-65.1%). Conclusion and Discussion: This pilot study demonstrates the feasibility of using radiomics features from MR images to characterize DILs in prostate cancer patients. Among 167 radiomics features extracted from axial MR T2 FRFSE, 7 features were shown to be potentially significant for distinguishing normal tissue from DILs. This research supports an integrated decision making system, combining clinical factors and radiomics features extracted from MR images, for increasing the DIL detection performance in prostate cancer studies

Examination of zone-based radiomic features for characterization of dominant intraprostatic lesions using MR multi-modal information.

Purpose: We investigated radiomic features extracted from dominant intraprostatic lesions (DILs) of the peripheral zone (PZ) and central gland (CG) from MR multi-modal images of 20 patients with prostate cancer (PCa). Remaining prostate gland (RPG) were included in the analysis. Material and Methods: 20 biopsy-proven PCa patients with no prior radiation treatment were studied. Axial T2 weighted images (T2WI) and diffusion weighted images (DWI) were acquired of the pelvis using a 3T MR scanner. ADC maps were constructed from DWIs. Region of interests delineating DILs and RPGs were contoured on each MR modality. 168 radiomic features were extracted from DIL and RPG volumes (15 pairs from PZ and 5 pairs from CG). Radiomic features were categorized into 8 different sets: Intensity Based Histogram (IBH, 9 features), Gray Level Run Length (GLRL, 7 features), Law\u27s Textural Information (LAWS, 18 features), Discrete Orthonormal Stockwell Transform (DOST, 18 features), Local Binary Pattern (LBP, 6 features), 2D Wavelet Transform (2DWT, 48 features), 2D Gabor Filter (2DGF, 40 features), and Gray Level Co- Occurrence Matrix (GLCM, 22 features). ANOVA (with Bonferroni adjustment), overall mean percent difference (OMPD), and the Fisher combined probability were used to test the following 7 hypotheses: (1) DILs of PZ and CG from T2WI (2) DILs of PZ and CG from ADC (3) DILs and RPGs of PZ from T2WI (4) DILs and RPGs of PZ from ADC (5) DILs and RPGs of CG from T2WI (6) DILs and RPGs of CG from ADC (7) DILs of PZ for T2WI and ADC. Results: Results imply that among 168 radiomics features, only 5 (DOST, and 2DGF, OMPD=107.8%), 2 (2DWT, and 2DGF, OMPD=141.7%), 13 (IBH, 2DWT, and GLCM, OMPD=%226.6), 17 (IBH, 2DWT, and GLCM, OMPD=179.7%), 18 (IBH, 2DWT, and GLCM, OMPD=321.9%), 18 (IBH, 2DWT, and GLCM, OMPD=726.1%), and 74 (IBH, GLRL, LAWS, DOST, LBP, 2DWT, 2DGF, GLCM, OMPD=1564%) features are discriminant (p \u3c0.050 with Confidence Level of 95%) for hypotheses no. 1 through 7 respectively. Conclusion and Discussion: Results for the discriminant features identified from hypotheses no. 1 and 2 can be used to construct a predictive model with a higher performance that benefits from the zone-based information (PZ and CG) as a-priori knowledge. As most of the discriminant features in tests no. 1 to 7 are primarily entropy-based, this suggests that potential feature-based biomarkers of DILs in PCa patients are more associated with spatial-locality, and frequency-based characteristics of MR images. The high value of the OMPD for test no. 7, strongly supports the use of the two MR modalities for increasing the information gain in perfecting predictive models for detection of DILs in PCa studies. The results of this pilot study, albeit subject to confirmation in a larger patient population, suggest a potential role for the use of zone-based radiomics information in models developed for detection of DILs in PCa patients

An equilibrium model for characterization and classification of dominant intraprostatic lesions and normal tissues in patients with prostate cancer.

Purpose: This study introduced an Equilibrium-Model (EM) as an innovative approach for MR-based classification of Dominant-Intraprostatic- Lesions (DILs) from normal tissues (NT) in patients with prostate cancer (PCa). Methods: Twenty-one patients with evidence of PCa with no priortreatment underwent MRI study. An ultrasound-guided needle-biopsy was performed to confirm the diagnosis. T2-weighted-images (T2WI) and Diffusion- Weighted-images were acquired from the pelvis of patients using a 3TMR scanner. The Apparent-Diffusion-Coefficient (ADC) map was constructed from Diffusion-Weighted-images. Using the diagnostic report of each patient, a set of ROIs delineating DILs and NTs were drawn on their image modalities. It was hypothesized that arrangement and decoration of voxel-intensities on the outer-layer of tumor can serve as a prescribed-Dirichlet boundary-constrain to derive an EM of its inner-core information under steady-state condition. An iterative method was used to solve the Laplace equation for estimating the EM intensities for inner-core of DILs/NTs measured from T2WI and ADC-map. An EM-distance (EMD) was calculated from normalized difference between the averages of MRI and EM intensities. The EMD was used to classify DILs from NTs and the results were evaluated with one-way ANOVA. Results: The EMD measured from T2WI showed strong classification performance (19/21-90%, p \u3c 0.0002, FC = 4.08, FClass = 17.24) for DILs (EMD = 0.0576 ± 0.0733) and NTs (EMD = -0.0187 ± 0.0415). The EMD measured from ADC also showed a high classification performance (19/21-90%, p \u3c 0.0007, FC = 4.08, FClass = 13.56) for DILs (EMD = 0.0228 ± 0.0284) and NTs (EMD = -0.0055 ± 0.0208). Also, the low Correlations (r = 0.37, p \u3c 0.09 for T2WI and r = 0.51, p \u3c 0.015 for ADC-map) between the DILs-EMDs and NTs-EMDs confirmed their robustness. Conclusion: This study demonstrated the feasibility of using an EM to construct a matric for classification of DILs and TNs from multi-modal MR information in PCa patients. The performance of the classifier may be improved if the two EMDs measured from T2WI and ADC-map are combined and DILs-EMDs are adjusted with the inter-variation of the NTs-EMDs

Detection of dominant intraprostatic lesions in patients with prostate cancer using an artificial neural network and MR multimodal radiomics analysis

Purpose/Objective(s): Detection and classification of Dominant Intraprostatic Lesions (DILs) play an important role in diagnosis and radiation treatment response assessment of patients with prostate cancer (PCa). The aim of this pilot study was to construct an adaptive model for characterization and detection of DILs from normal tissue using radiomics features extracted from T2-weighted images and Apparent Diffusion Coefficient (ADC) maps. Purpose/Objective(s): Twenty-one patients with evidence of PCa with no prior treatment underwent MRI study. An ultrasound guided needle biopsy was performed to confirm the diagnosis. Two image modalities were acquired from the pelvis of all patients using a 3.0 T MR scanner: Axial T2 Weighted (T2W) fast spin echo (TE/TR = 4389/110 ms, FA = 90°, voxel size = 0.42 X 0.42 X 2.4 mm3) and axial diffusion weighted (DW) imaging (TE/TR = 4000/85 ms, FA = 90°, voxel size = 1.79 X 1.79 X 0.56 mm3, with b-values = 0 and 1000 [sec-mm-2] for constructing ADC maps). Using the diagnostic report of each PCa patient, a set of ROIs delineating DILs and normal tissues were drawn on each image modality. One hundred sixty-five radiomics features were extracted from tumor and normal volumes. A Partial Least Square Correlation (PLSC) along with one-way ANOVA were recruited to identify the most discriminant radiomics features (DILs versus normal) from multimodal information (T1WI and ADC). An artificial neural networks (ANN) was constructed based on the optimal feature set to classify the DILs and normal tissue. Using Leave-One-Out Cross Validation (LOOCV) techniques, the ANN was trained, optimized, and finally evaluated. Results: Among 165 radiomics features, 8 features (2 features from Two-Dimensional Wavelet Transform, 4 features from Two-Dimensional Gabor Filter, 2 features from Gray Level Co-occurrence Matrix) were found to be significantly different between the DILs and normal tissue in presence of multimodal information. Using the 8 radiomics features as input, after training and optimization (with 8, 5, and 1 neurons in its input, hidden and output layers, termination error = 0.022) of the ANN with LOOCV, it was able to differentiate the DILs and normal groups with predictive power (Az Test) of 84%. When the training vector was randomly permuted 1000 times, the permutation-invariant efficiency of the ANN was 4.5%. Conclusion: This pilot study demonstrates the feasibility of combining radiomics analysis using multimodal MR information and adaptive model to detect DILs in patients with prostate cancer. The study is limited by the number of patients, which can impact the optimal features selected, and also might render a predictive model susceptible to Type II errors. Additionally, the radiomics features selected from multimodal MRI might be impacted by the intensities and contrast of the T2WI and ADC maps. These factors, and the incorporation of additional MR modalities along with pathological-based information into the adaptive model, are being investigated