Abstract
Purpose
To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions.
Methods
This multi-institutional review board-approved retrospective case–control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI with histopathology diagnosis by MRI-TRUS-guided targeted biopsy. A blinded radiologist confirmed PI-RADS v2.1 score and segmented lesions on axial T2W and ADC images using 3D Slicer, extracting radiomic features with an open-source software (Pyradiomics). Diagnostic accuracy for (1) any PCa and (2) clinically significant (CS; International Society of Urogenital Pathology Grade Group ≥ 2) PCa was assessed using XGBoost with tenfold cross -validation.
Results
From 160 PI-RADS 3 lesions, there were 50.0% (80/160) PCa, including 36.3% (29/80) CS-PCa (63.8% [51/80] ISUP 1, 23.8% [19/80] ISUP 2, 8.8% [7/80] ISUP 3, 3.8% [3/80] ISUP 4). The remaining 50.0% (80/160) lesions were benign. ML of all radiomic features from T2W and ADC achieved area under receiver operating characteristic curve (AUC) for diagnosis of (1) CS-PCa 0.547 (95% Confidence Intervals 0.510–0.584) for T2W and 0.684 (CI 0.652–0.715) for ADC and (2) any PCa 0.608 (CI 0.579–0.636) for T2W and 0.642 (CI 0.614–0.0.670) for ADC.
Conclusion
Our results indicate ML of radiomic features extracted from T2W and ADC achieved at best moderate accuracy for determining which PI-RADS category 3 lesions represent PCa.
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Christopher S. Lim has cooperative research development agreements with IBM Watson Health Imaging. All other authors have no relevant disclosures.
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Appendices
Appendix 1: MRI protocols for both institution
Institution 1 techniquea,b | Imaging plane | Field of view (mm) | Matrix size | Slice thickness/gap (mm) | TR/TE (ms) | Echo train length | Flip angle | Acceleration factor | Receiver bandwidth (Hz/voxel) | Acquisition time (min) | Number of signals averaged |
---|---|---|---|---|---|---|---|---|---|---|---|
T2 TSE | Coronal Sagittal Axial | 220 × 220 | 320 × 256 | 4.0/0 3.0/0 3.0/0 | 3890–5250/ 105–125 | 27–35 | 111 | N/A | 122 | 4 min 4 min 4 min | 1–2 |
DWIe | Axial | 280 × 280 | 128 × 80 | 3–5.0/0 | 4200/ 90 | 1 | 90 | 2 | 1950 | 5 min | 4–10 |
T1 GREf dynamic contrast | Axial | 220 × 220 | 128 × 128 | 4.0/0 | 4.3/1.3 | N/A | 12 | 2 | 488 | 2 min | 1 |
Institution 2 techniquea,b | Imaging plane | Field of view (mm) | Matrix size | Slice thickness/gap (mm) | TR/TE (ms) | Echo train length | Flip angle | Acceleration factor | Receiver bandwidth (Hz/voxel) | Approximate acquisition time (min) | Number of signals averaged |
---|---|---|---|---|---|---|---|---|---|---|---|
T2 TSEc | Coronal Sagittal Axial | 220 × 220 | 320 × 256 | 3.0/0 3.0/0 3.0/0 | 3890–5250/ 105–125 | 27–35 | 111 | N/A | 122 | 4 min 4 min 4 min | 1–2 |
DWId | Axial | 220 × 220 | 128 × 80 | 3.0/0 | 4200/ 90 | 1 | 90 | 2 | 1950 | 5 min | 4–15 |
T1 GREf dynamic contrast | Axial | 220 × 220 | 128 × 128 | 3.0/0 | 4.3/1.3 | N/A | 12 | 2 | 488 | 5 min | 1 |
Appendix 2: Cognitive fusion biopsy technique used at both institutions
Targeted biopsies were performed using TRUS guidance with cognitive fusion of MRI data onto real time 2-Dimensional TRUS images. At institution 1, transrectal US-guided examinations were performed using modern Ultrasound equipment (Aloka Prosound Alpha 10, Aloka Hitachi Medical or General Electric Logiq E9, General Electric Healthcare) and endoluminal 4–8 MHz end-fire probes. Biopsies were performed by a core-group of five fellowship-trained abdominal radiologists all with 6 years of experience in cognitive fusion-targeted biopsy of the prostate. At institution 2, transrectal US-guided examinations were performed using one system (Phillips IU 22, Philips medical) with endocavitary 5–9 MHz end-fire probes. Biopsies were performed by a core-group of three fellowship-trained abdominal radiologists with mean of 8.3 years of experience [range 3–15 years] in cognitive fusion-targeted biopsy of the prostate.
The TRUS-guided biopsy system used for all biopsies employed an 18-gauge side-cutting needle. All of the biopsy suites are equipped with monitors which enable display of mp-MRI which can be reviewed before and during the biopsy procedure. Anesthesia is provided using 1–2% Lidocaine nerve block. A fleet enema was prescribed prior to the procedure and antibiotic prophylaxis to prevent infection. Core-needle biopsy specimens are submitted for laboratory processing and interpretation in separate pathology specimen containers according to the site of sampling. Tissues from biopsy specimens are fixed overnight in 10% neutral buffered formalin. Three histological slides are prepared from each block, each with three serial sections cut at 3 μm in thickness and stained with hematoxylin and eosin (H&E). Biopsy results are reported for each core specimen individually.
Appendix 3: Accuracy and area under receiver operating characteristic curve (AUC) for the diagnosis of CS-PCa and any cancer for all features and the top 5 T2W and ADC least interdependent features in the combined and institution 2 data
Combined data | Institution 2 data | |||||
---|---|---|---|---|---|---|
Accuracy (%) | AUC (95% confidence interval) | Accuracy (%) | AUC (95% confidence interval) | |||
T2W | CSPCa | All | 81.9 | 0.547 (0.510–0.584) | 83.9 | 0.550 (0.507–0.591) |
Top 5 | 81.9 | 0.594 (0.560–0.628) | 83.9 | 0.607 (0.571–0.643) | ||
Any cancer | All | 60.1 | 0.608 (0.579–0.636) | 53.6 | 0.535 (0.462–0.608) | |
Top 5 | 61.8 | 0.632 (0.605–0.660) | 54.8 | 0.608 (0.537–0.680) | ||
ADC | CSPCa | All | 81.4 | 0.684 (0.652–0.715) | 82.3 | 0.715 (0.677–0.753) |
Top 5 | 81.9 | 0.620 (0.585–0.654) | 83.9 | 0.605 (0.565–0.645) | ||
Any cancer | All | 60.6 | 0.642 (0.614–0.670) | 62.5 | 0.660 (0.590–0.728) | |
Top 5 | 66.3 | 0.695 (0.668–0.720) | 68.2 | 0.725 (0.659–0.791) | ||
Top 5 T2W and ADC | CSPCa | 81.9 | 0.595 (0.560–0.630) | 83.9 | 0.634 (0.599–0.669) | |
Any cancer | 61.7 | 0.682 (0.655–0.707) | 64.1 | 0.694 (0.626–0.757) |
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Lim, C.S., Abreu-Gomez, J., Thornhill, R. et al. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdom Radiol 46, 5647–5658 (2021). https://doi.org/10.1007/s00261-021-03235-0
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DOI: https://doi.org/10.1007/s00261-021-03235-0