Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies
Abstract
:Simple Summary
Abstract
1. Introduction
2. Multiparametric (mp)MRI and VI-RADS Score in Bladder Cancer
2.1. Qualitative mpMRI
2.2. Quantitative (q)MRI
2.2.1. DW-MRI
Parameter | T2w | DW | DCE |
---|---|---|---|
Field strength | 1.5 T/3 T | 1.5 T/3 T | 1.5 T/3 T |
Sequence * | FSE | SS-EPI | FSPGR |
Plane orientation | Multiplanar | Axial | Axial |
FOV (mm) | 220–250 | 250–300 | 250–300 |
TR (ms) | 4000–5000 | 4500–6000 | 3.5–4.5 |
TE (ms) | 80–120 | 60–80 (minimum) | 1.2–2.2 |
Acquisition matrix | 256 × 192–256 | 128 × 128 | 256 × 192–214 |
Slice thickness/gap (mm) | 3–4/0 | 3–4/0 | 3–4/0 |
Number of excitations | 1–2 | 4–12 + | 1 |
Flip angles (FAs) (degree) | 90 | 90 | 15 |
b-values (s/mm2) | NA | 0 and 800–1000, up to 2000 optional | NA |
2.2.2. DCE-MRI
2.2.3. Radiomics
3. mpMRI for Clinical Consideration
3.1. mpMRI for Staging, Characterization, and Prognosis in MIBC
3.2. mpMRI for Prediction of Treatment Response in MIBC
3.3. Artificial Intelligence in Bladder Cancer
# | Application | Reference | Dataset | Methods | Conclusion |
---|---|---|---|---|---|
1 | Segmentation | Dolz et al. (2018) [123] | 60 patients (training 40, validation 5, test 15) | U-Net yields precise segmentation of bladder walls and tumors on T2w. | Higher accuracy than standard CNN, especially for tumors. |
2 | Li et al. (2020) [122] | 1092 MR images | U-Net with priors is applied to segment bladder walls and tumors on T2w. | The method improved the accuracy of bladder wall segmentation. | |
3 | Yu et al. (2022) [126] | 245 patients (training 220, test 25) | Path augmentation U-Net segmentation for bladder walls and tumors on T2w. | It can precisely extract bladder structures, especially small tumors. | |
4 | Coroamă et al. (2023) [127] | 33 patients | A low-complexity 3D U-Net with less than five layers for segmentation of bladder walls and tumors on T2w. | System for automated diagnosis of bladder tumors that can lead to higher reporting accuracy. | |
5 | Moribata et al. (2023) [128] | 170 patients (training 140, test 30) | U-Net could segment bladder cancer, and robust high-order radiomics features were extracted from ADC maps. | The model performed accurate segmentation of bladder cancer, and the extracted radiomics exhibited high reproducibility. | |
6 | Classification | Zou et al. (2022) [130] | 468 patients | Inception V3, CNN on T2w, recognizes the position of bladder walls and tumors. | Reliable method that can be more focused on features from the surrounding area of the tumor. |
7 | Sevcenco et al. (2018) [131] | 51 patients (training 36, test 15) | A multilayer perceptron with one hidden layer on ADC maps. | Classifier model combining the ADC values with clinical–pathological information can identify patients at high risk for survival. | |
8 | Li et al. (2023) [133] | Multicenter cohort of 89 (121) patients (tumors), 61 (93) from center 1, and 28 (28) from center 2. Tumors for training 93, test 28 | 3D ResNet50 CNN on T2w as a multitask model exhibits good diagnostic performance in predicting MIBC. | The method was lesion-focused and more reliable for clinical decisions. | |
9 | Denoising | Taguchi et al. (2021) [124] | 68 patients | VI-RADS validation CNN, with denoising reconstruction on T2w, discriminates between NMIBC and MIBC. | Combining VI-RADS with denoising CNN might improve diagnostic accuracy. |
10 | Watanabe et al. (2022) [125] | 163 patients | VI-RADS validation CNN with denoising reconstruction on T2w and DW- predicts accurate MIBC without using DCE-MRI. | It achieved a comparable predictive accuracy for MIBC to that of conventional VI-RADS. |
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
References
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VI-RADS Score | Inferences |
---|---|
1 | Muscle invasion is highly unlikely |
2 | Muscle invasion is unlikely to be present |
3 | Presence of muscle invasion is equivocal |
4 | Muscle invasion is likely |
5 | Invasion of muscle and beyond the bladder is very likely |
# | Reference | MRI/Segmentation/ Tools/Statistical Method | Dataset | Conclusion |
---|---|---|---|---|
1 | Li et al. (2023) [110] | T2w and DW-MRI, manual, PyRadiomics, LASSO | 3148 features, first order, shape and size, texture, wavelet filter, and Laplacian of Gaussian filter in 169 patients (70% training, 30% test); 24 optimal features | Radiomics combined with monograms can differentiate low-from high-grade NMIBCs. |
2 | Zhang et al. (2022) [26] | T2w, DW- and DCE-MRI, manual, and PyRadiomics | 23,688 features, first order, shape, and grey levels (GLCM, GLRLM, GLSZM, GLDM, and NGTDM) in 342 patients (239 training, 68 validation); 43 optimal features | T2w, DW-MRI, and DCE-MRI radiomics models could effectively assess the state of muscular invasion. |
3 | Wang et al. (2020) [111] | T2w and DW-MRI, manual, LASSO, logistic regression, and SVM-RFE | 1404 features, histogram, co-occurrence matrices, run-length matrix, and grey levels (NGTDM and GLRSZM) in 106 patients (64 training, 42 validation), 36 optimal features | Features selected by SVM-RFE reflect the regional heterogeneity of tumor tissues and can better characterize tissue heterogeneity differences between NMIBC and MIBC. |
4 | Xu et al. (2019) [112] | T2w, DW- and DCE-MRI, manual, SVM-RFE and LASSO | 1872 features, histogram, co-occurrence matrices, run-length matrix, and grey levels (NGTDM and GLSZM) in 71 patients (50 training, 21 validation), 24 optimal features | The radiomics–clinical nomogram has potential in the preoperative prediction of the first two years after transurethral resection of the bladder tumor. |
5 | Zheng et al. (2021) [113] | T2w and DCE-MRI, manual, PyRadiomics, and SMOTE-LASSO | 2436 features, 179 patients (70% training, 30% validation), 10 optimal features | The applied model could predict the Ki67 expression status and was associated with survival outcomes. |
6 | Kimura et al. 2022 [114] | ADC maps, manual and LIFEx, LIFEX, RF, and SVM | 46 features: histogram, shape, grey levels (GLCM, GLRLM, GLZLM, and NGLDM) in 45 patients, | The radiomics model can predict the CRT response and serve as a novel imaging biomarker. |
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Akin, O.; Lema-Dopico, A.; Paudyal, R.; Konar, A.S.; Chenevert, T.L.; Malyarenko, D.; Hadjiiski, L.; Al-Ahmadie, H.; Goh, A.C.; Bochner, B.; et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers 2023, 15, 5468. https://doi.org/10.3390/cancers15225468
Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers. 2023; 15(22):5468. https://doi.org/10.3390/cancers15225468
Chicago/Turabian StyleAkin, Oguz, Alfonso Lema-Dopico, Ramesh Paudyal, Amaresha Shridhar Konar, Thomas L. Chenevert, Dariya Malyarenko, Lubomir Hadjiiski, Hikmat Al-Ahmadie, Alvin C. Goh, Bernard Bochner, and et al. 2023. "Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies" Cancers 15, no. 22: 5468. https://doi.org/10.3390/cancers15225468
APA StyleAkin, O., Lema-Dopico, A., Paudyal, R., Konar, A. S., Chenevert, T. L., Malyarenko, D., Hadjiiski, L., Al-Ahmadie, H., Goh, A. C., Bochner, B., Rosenberg, J., Schwartz, L. H., & Shukla-Dave, A. (2023). Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers, 15(22), 5468. https://doi.org/10.3390/cancers15225468