The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Evidence Acquisition
2.1. Literature Search
2.2. Eligibility Criteria
2.3. Quality Assessment
2.4. Artificial Intelligence Quality Assessment
2.5. Data Collection
3. Evidence Synthesis
3.1. Study Selection
3.2. QUADAS-2 Risk of Bias Assessment
3.3. Quality Assessment Based on RQS and CLAIM
3.4. Study Characteristics
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Reference, Year | Data Source, n, Country | Dataset Year/s | csPCa, n (%) | PZ, n (%) | MRI Vendor, Tesla | PI-RADS lesions | Reference Standard, MRI to Procedure | Biopsy Technique |
---|---|---|---|---|---|---|---|---|
Dominguez et al. 2023 [24] | Single-center, 86, Chile | 2017–2021 | 66 (0.77) | 81 (0.94) | Philips, 3 | ≥3 | PB, NR | MRI/US fusion, TRUS |
Prata et al. 2023 [25] | Single-center, 91, Italy | 2019–2020 | 39 (0.43) | NR | Siemens, 1.5 | ≥3 | PB, within 4 weeks | MRI/US fusion, TRUS |
Jin et al. 2023 [26] | Multicenter, 463, China | 2018–2019 | 100 (0.22) | 216 (0.47) | Siemens/Philips, 3 | 3 | PB, within 4 weeks | MRI/US fusion, TRUS |
Hamm et al. 2023 a [27] | Single-center, 1224, GER | 2012–2017 | 595 (0.49) | 1935 (0.59) b | Siemens, 3 | ≥1 | PB, within 6 months | NR |
Hong et al. 2023 a [28] | Single-center, 171, Korea | 2018–2022 | 40 (0.37) | 81 (0.47) | Multivendor, 3 | ≥3 | RP, NR | NA |
Jing et al. 2022 [29] | Multicenter, 389, China | 2016–2021 | 270 (0.69) | 190 (0.49) | GE/UI, 3 | ≥2 | RP, within 12 weeks | NA |
Zhu et al. 2022 [30] | Single-center, 347, China | 2017–2020 | 235 (0.68) | 212 (0.68) c | GE, 3 | NR | PB, within 12 weeks | CT, TRUS |
Jiang et al. 2022 a [31] | Single-center, 1230, China | 2012–2019 | 856 (0.63) b | 853 (0.63) b | Siemens/UI, 3 | ≥1 | PB, within 4 weeks | MRI/US fusion, TRUS |
Liu et al. 2021 [32] | Single-center, 402, USA | 2010–2018 | 303 (0.75) b | 364 (0.78) b | NR, 3 | ≥3 | RP, NR | NA |
Lim et al. 2021 [33] | Multicenter, 158, Canada | 2015–2018 | 29 (0.18) b | 79 (0.49) b | Siemens/GE, 3 | 3 | PB, NR | CT, TRUS |
Hectors et al. 2021 [34] | Single-center, 240, USA | 2015–2020 | 28 (0.12) | NR | Siemens/GE, 3 | 3 | PB, within 12 months | MRI/US fusion, TRUS |
Castillo et al. 2021 [35] | Multicenter, 107, NL | 2011–2014 | 112 (0.55) b | 137 (0.67) b | GE/Siemens/Philips, 3/1.5 | ≥1 | RP, NR | NA |
Li et al. 2020 [36] | Single-center, 381, China | 2014–2017 | 142 (0.37) | NR | Philips, 3 | NR | PB, NR | TRUS |
Zhong et al. 2019 [37] | Single-center, 140, USA | 2010–2016 | 105 (0.49) b | NR | Siemens, 3 | NR | RP, NR | NA |
Reference, Year | Sequences | Segmentation | Feature Extraction | Image Preprocessing | Data Imbalance techniques, Data Augmentation | Feature Selection | Train/Test (%) b | Algorithm |
---|---|---|---|---|---|---|---|---|
Dominguez et al. 2023 [24] | T2, ADC | Lesion | Shape, FO, HTF | Not performed | NR | RFE | 80 (CV)/20 | LR |
Prata et al. 2023 [25] | T2, ADC | Lesion | FO, HTF, BLP | NR | NR | Wrapper (RF) | CV | RF |
Jin et al. 2023 [26] | T2, ADC, DWI (b2000) | Lesion | FO, HTF, wavelet features | IN, grey-level quantization, resampling, IR | SMOTE, NR | ANOVA | 70/30 | SVM |
Hamm et al. 2023 [27] | T2, ADC, DWI (high-b value) | Lesion, prostate, PZ, TZ | Deep radiomics | IN, resampling, lesion cropping | NR, Yes | NA | 80 (CV)/20 | Visual Geometry Group Net-based CNN |
Hong et al. 2023 [28] | ADC | Lesion, prostate | Deep radiomics | IN, resizing, prostate cropping, cut-off filtering | Image allocation, NR | NA | 80/20 | DenseNet 201 |
Jing et al. 2022 [29] | T2, DWI (b1500) | Lesion, prostate | Shape, FO, HTF, higher-order features | IN, Resampling | NR | Variance threshold algorithm, Select K-best, LASSO | 70/30 | LR |
Zhu et al. 2022 [30] | T2, ADC | Lesion | Deep radiomics | IN, resampling, prostate cropping, IR | NR, Yes | NA | 60/40 | Res-UNet |
Jiang et al. 2022 [31] | T2, DWI (b1500), ADC | Lesion, prostate | Deep radiomics | IN, resampling, prostate cropping, IR | NR, Yes | NA | 66.6/33.3 | Attention-Gated TrumpetNet |
Liu et al. 2021 [32] | T2, ADC | Lesion | Deep radiomics | IN, lesion cropping, IR | NR | NA | 70/30 | 3D GLCM extractor + CNN |
Lim et al. 2021 [33] | T2, ADC | Lesion | Shape, FO, HTF | NR | NR | Mann–Whitney U-test | CV | XGBoost |
Hectors et al. 2021 [34] | T2 | Lesion | Shape, FO, HTF | IN, grey-level quantization, resampling | SMOTE, NR | RF | 80 (CV)/20 | RF, LR |
Castillo et al. 2021 [35] | T2, DWI (highest-b value), ADC | Lesion | Shape, FO, HTF, higher-order features | Resampling | WORC Workflow a | WORC Workflow a | 80 (CV)/20 | WORC Workflow a |
Li et al. 2020 [36] | T2, ADC | Lesion | FO, HTF | IN, grey-level quantization, resampling | NR | mRMR, LASSO | 60/40 | LR |
Zhong et al. 2019 [37] | T2, ADC | Lesion | Deep radiomics | IN, resizing, lesion cropping | Not necessary, Yes | NA | 80/20 | ResNet with TL |
Reference, Year | Analysis | Validation | Sequence for the Best Model | Best Radiomic Model [CI, 95%] a | PI-RADS Cut-Off | PI-RADS Model [CI, 95%] a | ||||
---|---|---|---|---|---|---|---|---|---|---|
AUC | Sensitivity | Specificity | AUC | Sensitivity | Specificity | |||||
Dominguez et al. 2023 [24] | Index | CV//Hold-out set | ADC | 0.81 [0.56–0.94]//0.71 | NR | NR | NR | 0.66 [0.57–0.74]//NR | NR | NR |
Prata et al. 2023 [25] | Index | CV | ADC | 0.77 | NR | NR | NR | 0.68 | NR | NR |
Jin et al. 2023 [26] | Index | Hold-out set//External (1 set) | T2 + ADC + DWI (b2000) | 0.80//0.80 | 0.80//0.73 | 0.65//0.92 | NA | NA | NA | NA |
Jing et al. 2022 [29] | Index | Hold-out set//External (2 sets) | T2 (prostate) + DWI b1500 (lesion) | 0.96 [0.90, 1.00]//0.95 [0.87, 1.00]//0.94 [0.90, 0.99] b | 0.95//0.98//0.86 b | 0.94//0.86//0.91 b | NR | 0.84 [0.74, 0.95]//0.82 [0.72, 0.93]//0.80 [0.71, 0.88] | 0.98//0.98//0.50 | 0.56//0.52//0.94 |
Lim et al. 2021 [33] | All | CV | ADC | 0.68 [0.65–0.72] | NR | NR | NA | NA | NR | NR |
Hectors et al. 2021 [34] | Index | Hold-out set | T2 | 0.76 [0.60–0.92] | 0.75 | 0.8 | NA | NA | NA | NA |
Castillo et al. 2021 [35] | Index | CV//External | T2 + ADC + DWI (highest-b value) | 0.72 [0.64, 0.79]//0.75 | 0.76 [0.66, 0.89]//0.88 | 0.55 [0.44, 0.66]//0.63 | ≥3 | 0.50//0.44 (2 radiologists, External Validation) | 0.76//0.88 | 0.25//0 |
Li et al. 2020 [36] | Index | Hold-out set | T2 + ADC | 0.98 [0.97–1.00] | 0.95 | 0.87 | NA | NA | NA | NA |
Reference, Year | Analysis | Validation | Sequence for the Best Model | Best Radiomic Model [CI, 95%] a | PI-RADS Cut-Off | PI-RADS Model [CI, 95%] a | ||||
---|---|---|---|---|---|---|---|---|---|---|
AUC | Sensitivity | Specificity | AUC | Sensitivity | Specificity | |||||
Hamm et al. 2023 [27] | All | Hold-out set//External (PROSTATEx) | T2 + ADC + DWI (high-b value) | 0.89 [0.85, 0.93]//0.87 [0.81, 0.93] | 0.77 [0.69, 0.85]//0.90 [0.83, 0.97] | 0.89 [0.84, 0.95]//0.85 [0.80, 0.90] | NA | NA | NA | NA |
Index | 0.78//NR | 0.98 [0.95, 1.00] b//NR | NR | |||||||
Hong et al. 2023 [28] | Index | Hold-out set//External (1 set) | ADC | NR//0.63 | 0.72//0.84 | 0.74//0.48 | NA | NA | NA | NA |
Zhu et al. 2022 [30] | All | Hold-out set | T2 + ADC | NR | 0.96 [0.89, 0.99] | NR | ≥3 | NR | 0.94 [0.87, 0.98] | NR |
Sextant | NR | 0.96 [0.90, 0.99] | 0.92 [0.89, 0.93] | NR | 0.93 [0.87, 0.97] | 0.92 [0.90, 0.94] | ||||
Index | NR | 0.99 [0.92, 0.99] | 0.65 [0.53, 0.76] | NR | 0.99 [0.92, 0.99] | 0.66 [0.54, 0.77] | ||||
Jiang et al. 2022 [31] | All | Hold-out set//External (PROSTATEx) | T2 + ADC + DWI (b1500) | 0.85 [0.81, 0.88]//0.86 [0.81, 0.91] | 0.93//0.87 | 0.5//0.66 | ≥3 | 0.92 [0.89, 0.95]//0.86 [0.80, 0.90] | 0.94//0.77 | 0.79//0.87 |
Liu et al. 2021 [32] | All | Hold-out set | T2 + ADC | 0.85 [0.79, 0.91] | 0.90 [0.83, 0.96] | 0.70 [0.59, 0.82] | ≥4 | 0.73 [0.65, 0.80] | 0.83 [0.75, 0.92] | 0.47 [0.35, 0.59] |
Index | 0.73 [0.59, 0.88] | 0.90 [0.83, 0.96] | 0.47 [0.21, 0.72] | 0.65 [0.52, 0.78] | 0.83 [0.75, 0.91] | 0.27 [0.04, 0.72] | ||||
Zhong et al. 2019 [37] | All | Hold-out set | T2 + ADC | 0.73 [0.58, 0.88] | 0.64 | 0.8 | ≥4 | 0.71 [0.58, 0.87] | 0.86 | 0.48 |
Reference, Year | Analysis | Validation | PSA-D [CI, 95%]a | Clinical Model [CI, 95%] a | Combined Model [CI, 95%] a | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
AUC | Sensitivity | Specificity | AUC | Sensitivity | Specificity | AUC | Sensitivity | Specificity | |||
Dominguez et al. 2023 [24] | Index | CV//Hold-out set | 0.77 [0.66–0.87]//NR | NR | NR | 0.76 [0.62–0.87]//0.80 (PV-MR, PSA, PSA-D) | NR | NR | 0.91 [0.76–0.99]//0.80 (Clinical Model and Radiomic Model) | NR | NR |
Prata et al. 2023 [25] | Index | Hold-out set | NA | NA | NA | 0.69 (DRE, PI-RADS) | NR | NR | 0.80 (DRE, PI-RADS and Radiomic Model) | 0.915 | 0.844 |
Jin et al. 2023 [26] | Index | Hold-out set//External (1 set) | 0.71//0.69 | 0.84//0.77 | 0.60//0.62 | NA | NA | NA | N/A | NA | NA |
Jing et al. 2022 [29] | Index | Hold-out set//External (2 sets) | NA | NA | NA | NA | N/A | N/A | 0.96 [0.90, 1.00]//0.95 [0.87, 1.00]//0.94 [0.90, 0.99] (Radiomic Model + PI-RADS) | 0.952//0.978//0.861 | 0.944//0.857//0.907 |
Hectors et al. 2021 [34] | Index | Hold-out set | 0.61 [0.41, 0.80] | 0.72 | 0.52 | NA | NA | NA | NA | NA | NA |
Li et al. 2020 [36] | Index | Hold-out set | NA | NA | NA | 0.79 [0.70–0.88] (Age, PSA, PSA-D) | 0.76 | 0.74 | 0.98 [0.97–1.00] (Age, PSA, PSA-D and Radiomic Model) | 0.82 | 0.97 |
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Antolin, A.; Roson, N.; Mast, R.; Arce, J.; Almodovar, R.; Cortada, R.; Maceda, A.; Escobar, M.; Trilla, E.; Morote, J. The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review. Cancers 2024, 16, 2951. https://doi.org/10.3390/cancers16172951
Antolin A, Roson N, Mast R, Arce J, Almodovar R, Cortada R, Maceda A, Escobar M, Trilla E, Morote J. The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review. Cancers. 2024; 16(17):2951. https://doi.org/10.3390/cancers16172951
Chicago/Turabian StyleAntolin, Andreu, Nuria Roson, Richard Mast, Javier Arce, Ramon Almodovar, Roger Cortada, Almudena Maceda, Manuel Escobar, Enrique Trilla, and Juan Morote. 2024. "The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review" Cancers 16, no. 17: 2951. https://doi.org/10.3390/cancers16172951