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Advances in clinical research in urologic neoplasms with machine learning-based radiomics technology
ZHANG Yunfeng  WANG Chao  QIAO Xiaoni  WANG Mengyu  ZHOU Fenghai 

Cite this article as: ZHANG Y F, WANG C, QIAO X N, et al. Advances in clinical research in urologic neoplasms with machine learning-based radiomics technology[J]. Chin J Magn Reson Imaging, 2023, 14(2): 197-202. DOI:10.12015/issn.1674-8034.2023.02.035.


[Abstract] In recent years, the incidence of tumors of the urinary system has increased year by year, kidney cancer, bladder cancer (BCa), prostate cancer (PCa) have become important factors threatening the health of middle-aged and elderly people. The early detection and prognosis monitoring of urinary malignant tumors have increasingly become the hot spot of current research. Radiomics is an emerging diagnostic method in recent years, it enables non-invasive and quantitative evaluation of tissues by extracting and analyzing the characteristics of tissue heterogeneity, compared with traditional imaging, it can diagnose and differentiate lesions more accurately. From urology clinician's perspective, this paper reviews the current research progress of radiomics in preoperative diagnosis, efficacy evaluation, prognosis evaluation, and gene expression of urologic tumors.
[Keywords] adrenal gland neoplasms；kidney neoplasms；urinary bladder neoplasms；prostatic neoplasms；radiomics；magnetic resonance imaging

ZHANG Yunfeng1, 2   WANG Chao2   QIAO Xiaoni3   WANG Mengyu4   ZHOU Fenghai2*  

1 The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China

2 Department of Urology, Gansu Provincial People's Hospital, Lanzhou 730000, China

3 Department of Information Management, Gansu Provincial People's Hospital, Lanzhou 730000, China

4 School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

*Correspondence to: Zhou FH, E-mail: zhoufengh@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Gansu Province Key R&D Program Funding Project (No. 21YF5FA016).
Received  2022-10-08
Accepted  2023-01-17
DOI: 10.12015/issn.1674-8034.2023.02.035
Cite this article as: ZHANG Y F, WANG C, QIAO X N, et al. Advances in clinical research in urologic neoplasms with machine learning-based radiomics technology[J]. Chin J Magn Reson Imaging, 2023, 14(2): 197-202. DOI:10.12015/issn.1674-8034.2023.02.035.

[1]
SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
FASSNACHT M, ARLT W, BANCOS I, et al. Management of adrenal incidentalomas: European Society of Endocrinology Clinical Practice Guideline in collaboration with the European Network for the Study of Adrenal Tumors[J]. Eur J Endocrinol, 2016, 175(2): G1-G34. DOI: 10.1530/EJE-16-0467.
[3]
WINKELMANN M T, GASSENMAIER S, WALTER S S, et al. Differentiation of adrenal adenomas from adrenal metastases in single-phased staging dual-energy CT and radiomics[J]. Diagn Interv Radiol, 2022, 28(3): 208-216. DOI: 10.5152/dir.2022.21691.
[4]
TORRESAN F, CRIMÌ F, CECCATO F, et al. Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma[J/OL]. BJS Open, 2021, 5(1): zraa061 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35748202/. DOI: 10.1093/bjsopen/zraa061.
[5]
ZHANG B, ZHANG H, LI X, et al. Can Radiomics Provide Additional Diagnostic Value for Identifying Adrenal Lipid-Poor Adenomas From Non-Adenomas on Unenhanced CT?[J/OL]. Front Oncol, 2022, 12: 888778 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35574405/. DOI: 10.3389/fonc.2022.888778.
[6]
YI X P, GUAN X, ZHANG Y M, et al. Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas[J]. EPMA J, 2018, 9(4): 421-429. DOI: 10.1007/s13167-018-0149-3.
[7]
DAYE D, STAZIAKI P V, FURTADO V F, et al. CT texture analysis and machine learning improve post-ablation prognostication in patients with adrenal metastases: a proof of concept[J]. Cardiovasc Intervent Radiol, 2019, 42(12): 1771-1776. DOI: 10.1007/s00270-019-02336-0.
[8]
MAGGIO R, MESSINA F, D'ARRIGO B, et al. Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan[J/OL]. Front Endocrinol (Lausanne), 2022, 13: 873189 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35784576/. DOI: 10.3389/fendo.2022.873189.
[9]
GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
[10]
YAP F Y, VARGHESE B A, CEN S Y, et al. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses[J]. Eur Radiol, 2021, 31(2): 1011-1021. DOI: 10.1007/s00330-020-07158-0.
[11]
XU Q, ZHU Q Q, LIU H, et al. Differentiating benign from malignant renal tumors using T2- and diffusion-weighted images: a comparison of deep learning and radiomics models versus assessment from radiologists[J]. J Magn Reson Imaging, 2022, 55(4): 1251-1259. DOI: 10.1002/jmri.27900.
[12]
LI Y J, HUANG X L, XIA Y W, et al. Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma[J]. Abdom Radiol (NY), 2020, 45(10): 3193-3201. DOI: 10.1007/s00261-019-02269-9.
[13]
ZHU S, XU H, SHEN C Y, et al. Differential diagnostic ability of 18F-FDG PET/CT radiomics features between renal cell carcinoma and renal lymphoma[J]. Q J Nucl Med Mol Imaging, 2021, 65(1): 72-78. DOI: 10.23736/S1824-4785.19.03137-6.
[14]
MENG X, SHU J, XIA Y, et al. A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma[J/OL]. Biomed Res Int, 2020, 2020: 7103647 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32775436/. DOI: 10.1155/2020/7103647.
[15]
CUI E M, LI Z Y, MA C Y, et al. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics[J]. Eur Radiol, 2020, 30(5): 2912-2921. DOI: 10.1007/s00330-019-06601-1.
[16]
DEMIRJIAN N L, VARGHESE B A, CEN S Y, et al. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma[J]. Eur Radiol, 2022, 32(4): 2552-2563. DOI: 10.1007/s00330-021-08344-4.
[17]
RALLIS K S, KLEEMAN S O, GRANT M, et al. Radiomics for renal cell carcinoma: predicting outcomes from immunotherapy and targeted therapies-a narrative review[J]. Eur Urol Focus, 2021, 7(4): 717-721. DOI: 10.1016/j.euf.2021.04.024.
[18]
HAN D, YU N, YU Y, et al. Performance of CT radiomics in predicting the overall survival of patients with stage III clear cell renal carcinoma after radical nephrectomy[J]. Radiol Med, 2022, 127(8): 837-847. DOI: 10.1007/s11547-022-01526-0.
[19]
QI Y N, ZHAO T T, HAN M Y. The application of radiomics in predicting gene mutations in cancer[J]. Eur Radiol, 2022, 32(6): 4014-4024. DOI: 10.1007/s00330-021-08520-6.
[20]
FAN T W, MALHI H, VARGHESE B, et al. Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma[J]. Abdom Radiol (NY), 2019, 44(1): 201-208. DOI: 10.1007/s00261-018-1694-x.
[21]
ZHANG G, XU L L, ZHAO L, et al. CT-based radiomics to predict the pathological grade of bladder cancer[J]. Eur Radiol, 2020, 30(12): 6749-6756. DOI: 10.1007/s00330-020-06893-8.
[22]
ZHANG X, XU X P, TIAN Q, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging[J]. J Magn Reson Imaging, 2017, 46(5): 1281-1288. DOI: 10.1002/jmri.25669.
[23]
EVRIMLER S, GEDIK M ALI, AHMET SEREL T, et al. Bladder Urothelial Carcinoma: Machine Learning-based Computed Tomography Radiomics for Prediction of Histological Variant[J/OL]. Acad Radiol, 2022, 26: S1076-6332(22)00116-7 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35351362/. DOI: 10.1016/j.acra.2022.02.007.
[24]
WU S X, ZHENG J J, LI Y, et al. Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer[J]. EBioMedicine, 2018, 34: 76-84. DOI: 10.1016/j.ebiom.2018.07.029.
[25]
PINAR U, PRADERE B, ROUPRET M. Artificial intelligence in bladder cancer prognosis: a pathway for personalized medicine[J]. Curr Opin Urol, 2021, 31(4): 404-408. DOI: 10.1097/MOU.0000000000000882.
[26]
XU X P, WANG H J, DU P, et al. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors[J]. J Magn Reson Imaging, 2019, 50(6): 1893-1904. DOI: 10.1002/jmri.26749.
[27]
TANG X, QIAN W L, YAN W F, et al. Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study[J]. BMC Cancer, 2021, 21(1): 823 [2022-09-30]. https://bmccancer.biomedcentral.com/articles/10.1186/s12885-021-08569-y. DOI: 10.1186/s12885-021-08569-y.
[28]
CHOI S J, PARK K J, HEO C, et al. Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer[J/OL]. Clin Radiol, 2021, 76(8): 627.e13-627.e21 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/33762138/. DOI: 10.1016/j.crad.2021.03.001.
[29]
PARK K J, LEE J L, YOON S K, et al. Radiomics-based prediction model for outcomes of PD-1/PD-L1 immunotherapy in metastatic urothelial carcinoma[J]. Eur Radiol, 2020, 30(10): 5392-5403. DOI: 10.1007/s00330-020-06847-0.
[30]
RUNDO F, BERSANELLI M, URZIA V, et al. Three-dimensional deep noninvasive radiomics for the prediction of disease control in patients with metastatic urothelial carcinoma treated with immunotherapy[J]. Clin Genitourin Cancer, 2021, 19(5): 396-404. DOI: 10.1016/j.clgc.2021.03.012.
[31]
LIN P, WEN D Y, CHEN L, et al. A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma[J]. Eur Radiol, 2020, 30(1): 547-557. DOI: 10.1007/s00330-019-06371-w.
[32]
ZHENG Z, GU Z, XU F, et al. Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer[J/OL]. Cancer Imaging, 2021, 21(1): 65 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34863282/. DOI: 10.1186/s40644-021-00433-3.
[33]
YE F, HU Y, GAO J, et al. Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer[J/OL]. Front Immunol, 2021, 12: 722642 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34733275/. DOI: 10.3389/fimmu.2021.722642.
[34]
SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708..
[35]
ZHANG G M, HAN Y Q, WEI J W, et al. Radiomics based on MRI as a biomarker to guide therapy by predicting upgrading of prostate cancer from biopsy to radical prostatectomy[J]. J Magn Reson Imaging, 2020, 52(4): 1239-1248. DOI: 10.1002/jmri.27138.
[36]
QI Y F, ZHANG S T, WEI J W, et al. Multiparametric MRI-based radiomics for prostate cancer screening with PSA in 4-10 ng/mL to reduce unnecessary biopsies[J]. J Magn Reson Imaging, 2020, 51(6): 1890-1899. DOI: 10.1002/jmri.27008.
[37]
ZHAO Y Y, FANG C, WU S L, et al. Prediction and risk assessment of benign and malignant prostate lesions based on Bp-MRI radiomics[J]. Chin J Magn Reson Imaging, 2022, 13(8): 43-47. DOI: 10.12015/issn.1674-8034.2022.08.008.
[38]
HE D, WANG X, FU C, et al. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins[J/OL]. Cancer Imaging, 2021, 21(1): 46 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34225808/. DOI: 10.1186/s40644-021-00414-6.
[39]
MA S, XIE H H, WANG H H, et al. MRI-based radiomics signature for the preoperative prediction of extracapsular extension of prostate cancer[J]. J Magn Reson Imaging, 2019, 50(6): 1914-1925. DOI: 10.1002/jmri.26777.
[40]
OSMAN S O S, LEIJENAAR R T H, COLE A J, et al. Computed tomography-based radiomics for risk stratification in prostate cancer[J]. Int J Radiat Oncol Biol Phys, 2019, 105(2): 448-456. DOI: 10.1016/j.ijrobp.2019.06.2504.
[41]
CYSOUW M C F, JANSEN B H E, VAN DE BRUG T, et al. Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer[J]. Eur J Nucl Med Mol Imaging, 2021, 48(2): 340-349. DOI: 10.1007/s00259-020-04971-z.
[42]
ZHANG W, MAO N, WANG Y, et al. A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients[J/OL]. Eur J Radiol, 2020, 128: 109020 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32371181/. DOI: 10.1016/j.ejrad.2020.109020.
[43]
HINZPETER R, BAUMANN L, GUGGENBERGER R, et al. Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study[J]. Eur Radiol, 2022, 32(3): 1823-1832. DOI: 10.1007/s00330-021-08245-6.
[44]
ACAR E, LEBLEBICI A, ELLIDOKUZ B E, et al. Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study[J/OL]. Br J Radiol, 2019, 92(1101): 20190286 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/31219712/. DOI: 10.1259/bjr.20190286.
[45]
THARMALINGAM H, TSANG Y M, ALONZI R, et al. Changes in Magnetic Resonance Imaging Radiomic Features in Response to Androgen Deprivation Therapy in Patients with Intermediate- and High-risk Prostate Cancer[J/OL]. Clin Oncol (R Coll Radiol), 2022, 34(6): e246-e253 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35033410/. DOI: 10.1016/j.clon.2021.12.020.
[46]
TSANG Y M, VIGNARAJAH D, MCWILLIAM A, et al. A pilot study on dosimetric and radiomics analysis of urethral strictures following HDR brachytherapy as monotherapy for localized prostate cancer[J/OL]. Br J Radiol, 2020, 93(1106): 20190760 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/31778319/. DOI: 10.1259/bjr.20190760.
[47]
ABDOLLAHI H, MAHDAVI S R, MOFID B, et al. Rectal wall MRI radiomics in prostate cancer patients: prediction of and correlation with early rectal toxicity[J]. Int J Radiat Biol, 2018, 94(9): 829-837. DOI: 10.1080/09553002.2018.1492756.
[48]
BOURBONNE V, VALLIÈRES M, LUCIA F, et al. MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer[J/OL]. Front Oncol, 2019, 9: 807 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/31508367/. DOI: 10.3389/fonc.2019.00807.
[49]
VARGHESE B, CHEN F, HWANG D, et al. Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images[J/OL]. Sci Rep, 2019, 9(1): 1570 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/30733585/. DOI: 10.1038/s41598-018-38381-x.
[50]
MORRISON G, BUCKLEY J, OSTROW D, et al. Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis[J/OL]. Int J Mol Sci, 2022, 3(5): 2571 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35269713/. DOI: 10.3390/ijms23052571.
[51]
MONTOYA PEREZ I, MERISAARI H, JAMBOR I, et al. Detection of prostate cancer using biparametric prostate MRI, radiomics, and kallikreins: a retrospective multicenter study of men with a clinical suspicion of prostate cancer[J]. J Magn Reson Imaging, 2022, 55(2): 465-477. DOI: 10.1002/jmri.27811.

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