Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning
Abstract
:Simple Summary
Abstract
1. Introduction
First Author (Year)  Evaluation Granularity  Radiologist Input Required  Patient Number  Method  AUC 

Hou (2021) [7]  Per index lesion  Tumor segmentation  849  Radiologists CNN  0.63–0.74 0.73–0.81 
Cuocolo (2021) [17]  Per index lesion  Tumor segmentation  193  Radiologists Radiomics + SVM  81–83% acc 0.73–0.80 74–79% acc 
Eurboonyanun (2021) [27]  Per index  Measure TCL  95  Logistic regression w/  
lesion  absolute TCL (euclidean)  0.80  
actual TCL (curvilinear)  0.74  
Losnegard (2020) [28]  Per index lesion  Tumor segmentation  228  Radiologists Radiomics + Random forest  0.75 0.74 
Park (2020) [29]  Per patient  Measure TCL  301  Radiologists using MRIbased EPE grade, ESUR score, Likert scale, TCL  0.77–0.81 0.79–0.81 0.78–0.79 0.78–0.85 
Xu (2020) [19]  Per lesion (all those MRI visible)  Tumor segmentation  95  Radiomics + Regression algorithm  0.87 
Shiradkar (2020) [18]  Per index lesion  Tumor and periprostatic fat segmentation  45  Radiomics + SVM  0.88 
Mehralivand (2019) [30]  Per index lesion  Measure TCL  553  Logistic regression w/ MRIbased EPE grade + clinical features  0.77 0.81 
Ma (2019) [31]  Per index lesion  Tumor segmentation  210  Radiologists Radiomics + Regression algorithm  0.60–0.70 0.88 
Stanzione (2019) [20]  Per index lesion  Tumor segmentation  39  Radiomics + Bayesian Network  0.88 
Krishna (2017) [32]  Per lesion (all those MRI visible)  Tumor segmentation  149  Radiologists Logistic regression w/ PIRADS scores, tumor size, TCL, ADC entropy  0.61–0.67, 0.61–0.72, 0.73, 0.69, 0.76 
2. Materials and Methods
2.1. Dataset
2.1.1. Population Characteristics
2.1.2. Image Acquisition
2.1.3. Labels
2.2. Data PreProcessing
2.2.1. Histopathology PreProcessing
 Registration: Each digital histopathology image was aligned with its corresponding T2w MR image using the automated affine and deformable registration method RAPSODI [33]. This enabled accurate mapping of pixellevel cancer and extraprostatic extension labels from digital histopathology images onto MRI. For details on this process, refer to [26,33].
 Smoothing: Images were smoothed with a Gaussian filter with $\sigma =0.25$ mm to avoid downsampling artifacts.
 Resampling: The Gaussian smoothed images were downsampled to an XY size of $224\times 224$ pixels, resulting in an inplane pixel size of $0.29\times 0.29$ mm^{2}.
 Intensity normalization: Each RGB channel of the resulting digital histopathology images was Zscore normalized.
2.2.2. MRI PreProcessing
 Affine Registration: The T2w images and ADC images were manually registered using an affine transformation driven by the prostate segmentations on both modalities.
 Resampling: The T2w images, ADC images, prostate masks and cancer labels were projected and resampled on the corresponding histopathology images, resulting in images of $224\times 224$ pixels, with pixel size of $0.29\times 0.29$ mm^{2}.
 Intensity standardization: We followed the procedure by Nyul et al. [34]. Using the training dataset, we learned a set of intensity histogram landmarks for T2w and ADC sequences independently. Then, we transformed the image histograms to align with the learned mean histogram of each MRI sequence. The histogram average learned in the training set was also used to align the cases in the test set. This histogram alignment intensity standardization method helps ensure similar MRI intensity distribution for all patients irrespective of scanners and scanning protocols.
 Intensity normalization: Finally, Zscore normalization was applied to the prostate regions of T2w and ADC images.
2.3. Proposed Approach
2.3.1. Step 1: Deep Learning Models for Cancer Detection
2.3.2. Step 2: PostProcessing Pipeline
 Dilated prostate mask. The deep learning cancer predictions become less reliable the further we look outside the prostate, since other anatomical features may drive false positives. To prevent this, we applied a dilated prostate mask to the cancer probability map. Based on the diameter of the largest extraprostatic extension lesions in our cohort, we chose to dilate the original prostate mask using kernels of size $64\times 64$ pixels (corresponding to 1.86 cm × 1.86 cm):$${\mathcal{M}}_{pr}(x,y)=dilate\left({\mathcal{L}}_{pr}(x,y)\right),$$$$p(x,y)={p}_{Ca}(x,y)\ast {\mathcal{M}}_{pr}(x,y),$$
 Binary threshold. All pixels in the prediction map with probability $p(x,y)$ greater than a fixed threshold, $\alpha $, were considered to be cancer, and the rest were set to zero; $\alpha $ is a hyperparameter:$${p}_{\alpha}(x,y)=\left\{\begin{array}{cc}p(x,y)\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}p(x,y)>\alpha \hfill \\ 0\hfill & \mathrm{otherwise},\hfill \end{array}\right.$$
 Connected components. Next, we computed all 3D connected components in the ${P}_{\alpha}(x,y,z)$ volume with connectivity value 26 using the python cc3d library [36]:$$\left\{{\mathcal{C}}_{\alpha}^{\left(i\right)}(x,y,z)\right\}=\mathtt{cc}\mathtt{3}\mathtt{d}.\mathtt{connected}\_\mathtt{components}\left({P}_{\alpha}(x,y,z)\right).$$Each component ${\mathcal{C}}_{\alpha}^{\left(i\right)}(x,y,z)$ is a lesion candidate (Figure 2f). Note that the connected components function returns binary mask objects, i.e., each pixel in ${\mathcal{C}}_{\alpha}^{\left(i\right)}(x,y,z)$ is either 0 or 1, and all the pixels with value 1 are connected.
 Logical rules: We used logical rules to prune these components and determine the final predictions for extraprostatic extension:
 Rule I: Component must predict cancer both inside:$${\mathcal{C}}_{\alpha}^{\left(i\right)}\cap {\mathcal{L}}_{pr}\ne 0,$$$${\mathcal{C}}_{\alpha}^{\left(i\right)}\setminus {\mathcal{L}}_{pr}\ne 0,$$
 Rule II: For each viable lesion candidate, compute tumor–capsule contact line length (TCL) and compare with threshold $t{h}_{TCL}$. The overlap between a candidate ${\mathcal{C}}_{\alpha}^{\left(i\right)}$ and the prostate boundary ${\mathcal{L}}_{pr}$ defines a curvilinear segment (shown in pink in Figure 2g), and ${l}_{TCL}^{\left(i\right)}$ is the length of this segment.$${l}_{TCL}^{\left(i\right)}=\mathtt{tumor}\_\mathtt{capsule}\_\mathtt{contact}\_\mathtt{line}\_\mathtt{length}({\mathcal{C}}_{\alpha}^{\left(i\right)},{\mathcal{L}}_{pr}).$$Lesion candidates with ${l}_{TCL}^{\left(i\right)}<t{h}_{TCL}$ are discarded. Candidates with ${l}_{TCL}^{\left(i\right)}\ge t{h}_{TCL}$ constitute our final predictions for cancer lesions with extraprostatic extension. Each final candidate ${\mathcal{C}}_{\alpha}^{\left(i\right)}$ is a binary mask; multiplying it elementwise with the probability map ${P}_{\alpha}$ gives the probability map for cancer with extraprostatic extension (Figure 2h).$${q}^{\left(i\right)}(x,y,z)={\mathcal{C}}_{\alpha}^{\left(i\right)}(x,y,z)\ast {P}_{\alpha}(x,y,z).$$We denote the final extraprostatic extension probability map for the entire case volume $\mathcal{Q}(x,y,z)$.
Algorithm 1 Steps for predicting lesions with extraprostatic extension 

2.4. Evaluation
2.4.1. PatientLevel Evaluation
2.4.2. SextantLevel Evaluation
2.4.3. ROC Analysis
2.5. Experimental Design
HyperParameter Optimization
3. Results
3.1. Qualitative Results
3.2. Quantitative Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ADC  Apparent Diffusion Coefficient 
AUC  Area Under the Curve 
CNN  Convolutional Neural Network 
EPE  Extraprostatic Extension 
MRI  Magnetic Resonance Imaging 
PIRADS  Prostate ImagingReporting and Data System 
ROC  Receiver Operating Characteristic 
SVM  Support Vector Machine 
TCL  Tumor–capsule Contact Line Length (also known as Capsular Contact Length, CCL) 
Appendix A. Details of Deep Learning Models for Cancer Detection
Appendix B. Additional Visual Results
Appendix C. Analysis of False Positive Predictions
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Cohort  Train  Test 

Patient number  74  49 
Lesion count  90  58 
Indolent  9  10 
Aggressive  81  48 
EPE (pathologically proven)  29  10 
Lesion volume (mm^{3})  1541.6 (714.7, 3418.6)  1099.1 (743.2, 2544.7) 
EPE volume (where applicable)  8.6 (3.6, 44.6)  10.6 (5.6, 36.3) 
Number of Patients  123 

T2w  
Repetition time (TR, range) (s)  $[3.9,6.3]$ 
Echo time (TE, range) (ms)  $[122,130]$ 
Pixel size (range) (mm)  $[0.27,0.94]$ 
Distance between slices (mm)  $[3,5.2]$ 
Matrix size  $[256,512]$ 
Number of slices  $[20,44]$ 
ADC  
bvalues (s/mm^{2})  $0,50,800,1000,1200$ 
Pixel size (range) (mm)  $[0.78,1.50]$ 
Distance between slices (mm)  $[3,4.5]$ 
Matrix size  $[50,256]$ 
Number of slices  $[15,40]$ 
Mode  Threshold $\mathit{\alpha}$  Cohort  Sensitivity  Specificity  Sensitivity  Specificity 

(Patient)  (Patient)  (Sextant)  (Sextant)  
%  %  %  %  
EPENet  0.10  crossval  $100.0\pm 0.0$  $1.7\pm 3.3$  $96.5\pm 7.1$  $17.9\pm 8.3$ 
test  90.0  0.0  88.9  13.0  
EPENet  0.15  crossval  $100.0\pm 0.0$  $11.1\pm 6.7$  $95.3\pm 9.4$  $29.2\pm 9.5$ 
test  90.0  0.0  83.3  23.9  
EPENet  0.20  crossval  $100.0\pm 0.0$  $14.3\pm 9.6$  $88.8\pm 13.0$  $36.8\pm 11.6$ 
test  80.0  5.1  83.3  34.4  
EPENet  0.25  crossval  $97.5\pm 5.0$  $19.2\pm 13.4$  $71.8\pm 19.1$  $45.7\pm 11.5$ 
test  80.0  12.8  77.8  45.7  
EPENet  0.30  crossval  95.0 ± 10.0  26.8 ± 8.8  64.4 ± 21.6  54.6 ± 8.1 
test  80.0  28.2  61.1  58.3  
EPENet  0.35  crossval  $\mathbf{85}.\mathbf{0}\pm \mathbf{20}.\mathbf{0}$  $\mathbf{33}.\mathbf{2}\pm \mathbf{13}.\mathbf{8}$  $\mathbf{59}.\mathbf{3}\pm \mathbf{21}.\mathbf{3}$  $\mathbf{63}.\mathbf{2}\pm \mathbf{7}.\mathbf{7}$ 
test  50.0  41.0  55.6  67.8  
EPENet  0.40  crossval  $81.0\pm 18.5$  $39.9\pm 15.5$  $43.2\pm 31.7$  $71.2\pm 8.8$ 
test  50.0  51.3  50.0  77.2  
EPENet  0.45  crossval  $74.5\pm 21.2$  $51.5\pm 23.3$  $31.4\pm 24.6$  $79.0\pm 8.7$ 
test  40.0  61.5  38.9  83.7  
EPENet  0.50  crossval  $68.7\pm 19.8$  $54.3\pm 22.4$  $25.7\pm 25.0$  $82.6\pm 6.8$ 
test  40.0  74.4  27.8  90.9  
EPENet  0.55  crossval  $56.6\pm 16.2$  $65.0\pm 21.6$  $19.7\pm 22.7$  $87.6\pm 6.0$ 
test  40.0  87.2  27.8  95.7  
EPENet  0.60  crossval  $53.8\pm 11.3$  $79.6\pm 14.0$  $17.9\pm 17.2$  $92.1\pm 3.9$ 
test  40.0  89.7  16.7  96.7  
Radiologists  —  test  50.0  76.9  —  – 
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Moroianu, Ş.L.; Bhattacharya, I.; Seetharaman, A.; Shao, W.; Kunder, C.A.; Sharma, A.; Ghanouni, P.; Fan, R.E.; Sonn, G.A.; Rusu, M. Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning. Cancers 2022, 14, 2821. https://doi.org/10.3390/cancers14122821
Moroianu ŞL, Bhattacharya I, Seetharaman A, Shao W, Kunder CA, Sharma A, Ghanouni P, Fan RE, Sonn GA, Rusu M. Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning. Cancers. 2022; 14(12):2821. https://doi.org/10.3390/cancers14122821
Chicago/Turabian StyleMoroianu, Ştefania L., Indrani Bhattacharya, Arun Seetharaman, Wei Shao, Christian A. Kunder, Avishkar Sharma, Pejman Ghanouni, Richard E. Fan, Geoffrey A. Sonn, and Mirabela Rusu. 2022. "Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning" Cancers 14, no. 12: 2821. https://doi.org/10.3390/cancers14122821