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Clinical Article
Prediction and risk assessment of benign and malignant prostate lesions based on Bp-MRI radiomics
ZHAO Yingying  FANG Chen  WU Shenglian  XU Wei  ZHENG Pengxiang  ZHENG Weilong  CHEN Zhiqiang 

Cite this article as: Zhao YY, Fang C, Wu SL, 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.


[Abstract] Objective To explore the diagnosis,differential diagnosis and risk assessment of benign and malignant prostatic lesions based on biparameter magnetic resonance imaging (Bp-MRI) radiomics and clinical information.Materials and Methods A total of 161 patient cases with pathologically proven prostate disease were retrospectively analyzed and randomly divided into training set and verification set in 7∶3 ratio. The t-test /Wilcoxon rank sum test, the least absolute shrinkage and selection operator (LASSO) algorithm, Spearman correlation analysis, and logistic regression model were used to analyze the clinical and radiographic features, and the radiographic model and the joint model were constructed. The performance of the model was evaluated by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Subsequently, the combined nomograms were constructed based on the radiographic and clinical features and verified.Results The AUC of the radiographic model in predicting prostate cancer in the training and validation sets was 0.946 (95% CI: 0.903-0.982) and 0.902 (95% CI: 0.862-0.958). AUC comparable with pooled models was 0.965 (95% CI: 0.904-0.989) and 0.924 (95% CI: 0.868-0.980), respectively.Conclusions Bp-MRI radiomics model has high diagnostic efficiency for prostate cancer (PCa). The combined nomograms that combine total prostate specific antigen (tPSA), free prostate specific antigen (fPSA)/tPSA (f/t), and radiographic features may provide an effective tool for risk prediction and individualized treatment in patients with prostate disease.
[Keywords] biparameter magnetic resonance imaging;radiomics;prostate cancer;diagnostic efficacy;nomogram;prostate specific antigen

ZHAO Yingying1, 2   FANG Chen3   WU Shenglian1   XU Wei1   ZHENG Pengxiang3   ZHENG Weilong1   CHEN Zhiqiang2*  

1 Department of Radiology, Fuqing City Hospital Affiliated to Fujian Medical University, Fuzhou 350000, China

2 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750004, China

3 Department of Urology, Fuqing City Hospital Affiliated to Fujian Medical University, Fuzhou 350000, China

Chen ZQ, E-mail: zhiqiang_chen99@163.com

Conflicts of interest   None.

Received  2022-02-19
Accepted  2022-07-28
DOI: 10.12015/issn.1674-8034.2022.08.008
Cite this article as: Zhao YY, Fang C, Wu SL, 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

[1]
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157. DOI: 10.3322/caac.21552.
[2]
Liu LZ, Tian ZQ, Zhang ZF, et al. Computer-aided detection of prostate cancer with MRI: technology and applications[J]. Acad Radiol, 2016, 23(8): 1024-1046. DOI: 10.1016/j.acra.2016.03.010.
[3]
Kapoor J, Lamb AD, Murphy DG. Re: diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study[J]. Eur Urol, 2017, 72(1): 151. DOI: 10.1016/j.eururo.2017.02.014.
[4]
Muehlematter UJ, Burger IA, Becker AS, et al. Diagnostic accuracy of multiparametric MRI versus 68Ga-PSMA-11 PET/MRI for extracapsular extension and seminal vesicle invasion in patients with prostate cancer[J]. Radiology, 2019, 293(2): 350-358. DOI: 10.1148/radiol.2019190687.
[5]
Cuocolo R, Cipullo MB, Stanzione A, et al. Machine learning applications in prostate cancer magnetic resonance imaging[J/OL]. Eur Radiol Exp, 2019 [2022-02-19]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686027. DOI: 10.1186/s41747-019-0109-2.
[6]
Li WF, Shakir TM, Zhao YM, et al. Radiomics analysis of [18F] FDG PET/CT for microvascular invasion and prognosis prediction in very-early and early-stage hepatocellular carcinoma[J]. Eur J Nucl Med Mol Imaging, 2021, 48(11): 3353-3354. DOI: 10.1007/s00259-021-05479-w.
[7]
Liu Y, Xu QQ, Lu Y, et al. The study on the value of radiomics model based on T2WI and ADC in distinguishing prostate cancer and benign prostatic hyperplasia[J]. J Clin Radiol, 2021, 40(11): 2168-2173. DOI: 10.13437/j.cnki.jcr.2021.11.026.
[8]
Makowski MR, Bressem KK, Franz L, et al. De novo radiomics approach using image augmentation and features from T1 mapping to predict gleason scores in prostate cancer[J]. Invest Radiol, 2021, 56(10): 661-668. DOI: 10.1097/RLI.0000000000000788.
[9]
Nketiah GA, Elschot M, Scheenen TW, et al. Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study[J/OL]. Sci Rep, 2021 [2022-02-19]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822867. DOI: 10.1038/s41598-021-81272-x.
[10]
Gugliandolo SG, Pepa M, Isaksson LJ, et al. MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase Ⅱ trial on ultra-hypofractionated radiotherapy (AIRC IG-13218)[J]. Eur Radiol, 2021, 31(2): 716-728. DOI: 10.1007/s00330-020-07105-z.
[11]
Sooriakumaran P, Nyberg T, Akre O, et al. Survival among men at high risk of disseminated prostate cancer receiving initial locally directed radical treatment or initial androgen deprivation therapy[J]. Eur Urol, 2017, 72(3): 345-351. DOI: 10.1016/j.eururo.2017.04.002.
[12]
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 [2022-02-19]. https://www.ejradiology.com/article/S0720-048X(20)30209-6/fulltext. DOI: 10.1016/j.ejrad.2020.109020.
[13]
Li ST, Shi M, Zhao JJ, et al. A highly sensitive capillary electrophoresis immunoassay strategy based on dual-labeled gold nanoparticles enhancing chemiluminescence for the detection of prostate-specific antigen[J]. Electrophoresis, 2017, 38(13/14): 1780-1787. DOI: 10.1002/elps.201600396.
[14]
Tamimi W, Dafterdar R, Mansi M, et al. Complexed and total PSA in patients with benign prostatic hyperplasia and prostate cancer[J]. Br J Biomed Sci, 2010, 67(4): 184-188. DOI: 10.1080/09674845.2010.11730317.
[15]
Thakur V, Singh PP, Talwar M, et al. Utility of free/total prostate specific antigen (f/t PSA) ratio in diagnosis of prostate carcinoma[J]. Dis Markers, 2004, 19(6): 287-292. DOI: 10.1155/2004/913870.
[16]
Jiang YM, Yuan QY, Lv WB, et al. Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits[J]. Theranostics, 2018, 8(21): 5915-5928. DOI: 10.7150/thno.28018.
[17]
Xia W, Hu B, Li HQ, et al. Multiparametric-MRI-based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and cross-vendor validation[J]. J Magn Reson Imaging, 2021, 53(1): 242-250. DOI: 10.1002/jmri.27344.
[18]
Bai HL, Xia W, Ji XF, et al. Multiparametric magnetic resonance imaging-based peritumoral radiomics for preoperative prediction of the presence of extracapsular extension with prostate cancer[J]. J Magn Reson Imaging, 2021, 54(4): 1222-1230. DOI: 10.1002/jmri.27678.
[19]
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J/OL]. Nat Commun. 2014 [2022-02-19]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059926. DOI: 10.1038/ncomms5006.
[20]
Gu DS, Xie YS, Wei JW, et al. MRI‐based radiomics signature: a potential biomarker for identifying glypican 3‐positive hepatocellular carcinoma[J]. J Magn Reson Imaging, 2020, 52(6): 1679-1687. DOI: 10.1002/jmri.27199.
[21]
Meng XC, Xia W, Xie PY, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer[J]. Eur Radiol, 2019, 29(6): 3200-3209. DOI: 10.1007/s00330-018-5763-x.
[22]
Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma[J]. Radiother Oncol, 2015, 114(3): 345-350. DOI: 10.1016/j.radonc.2015.02.015.
[23]
Magistri P, Leonard SY, Tang CM, et al. The glypican 3 hepatocellular carcinoma marker regulates human hepatic stellate cells via Hedgehog signaling[J]. J Surg Res, 2014, 187(2): 377-385. DOI: 10.1016/j.jss.2013.12.010.

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