Share:
Share this content in WeChat
X
Clinical Article
Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI
LI Mengjuan  ZHANG Caiyuan  ZHAO Wenlu  WEI Chaogang  ZHANG Yueyue  DING Ning  WANG Chengcheng  JI Yiding  SHEN Junkang 

Cite this article as: Li MJ, Zhang CY, Zhao WL, et al. Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI[J]. Chin J Magn Reson Imaging, 2022, 13(11): 76-81. DOI:10.12015/issn.1674-8034.2022.11.014.


[Abstract] Objective To evaluate the radiomics model constructed based on biparametric MRI for predicting clinically significant prostate cancer (csPCa).Materials and Methods The clinical, pathological and imaging data of 381 patients (non-csPCa group 239, csPCa group 142) were analyzed retrospectively. Through image preprocessing and segmentation, feature extraction and selection, the radiomics model was established and its diagnostic value was evaluated.Results The radiomics model based on biparametric MRI showed good intra-observer and inter-observer consistency, and the constructed radiomics model had high diagnostic value for csPCa. The area under the curve (AUC) values of the training group and the test group were 0.991 and 0.983, respectively.Conclusions The biparametric MRI is an effective method to detect csPCa. The radiomics model constructed by training and testing has high diagnostic value for csPCa, which is relatively objective and accurate. It can be used as an auxiliary method for clinical diagnosis of csPCa, and provide an important reference for clinical decision-making of patient diagnosis and treatment.
[Keywords] prostate cancer;magnetic resonance imaging;radiomics;diagnostic efficacy

LI Mengjuan1   ZHANG Caiyuan2*   ZHAO Wenlu2   WEI Chaogang2   ZHANG Yueyue2   DING Ning1   WANG Chengcheng1   JI Yiding1   SHEN Junkang2  

1 Department of Imaging, Suzhou Ninth People's Hospital, Suzhou 215004, China

2 Department of Imaging, Second Affiliated Hospital of Soochow University, Suzhou 215004, China

Zhang CY, E-mail: zcy2002yy@aliyun.com

Conflicts of interest   None.

Received  2021-11-25
Accepted  2022-11-04
DOI: 10.12015/issn.1674-8034.2022.11.014
Cite this article as: Li MJ, Zhang CY, Zhao WL, et al. Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI[J]. Chin J Magn Reson Imaging, 2022, 13(11): 76-81.DOI:10.12015/issn.1674-8034.2022.11.014

[1]
Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708.
[2]
Schaeffer E, Srinivas S, Antonarakis ES, et al. NCCN guidelines insights: prostate cancer, version 1.2021[J]. J Natl Compr Canc Netw, 2021, 19(2): 134-143. DOI: 10.6004/jnccn.2021.0008.
[3]
Wu RC, Lebastchi AH, Hadaschik BA, et al. Role of MRI for the detection of prostate cancer[J]. World J Urol, 2021, 39(3): 637-649. DOI: 10.1007/s00345-020-03530-3.
[4]
Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2[J]. Eur Urol, 2019, 76(3): 340-351. DOI: 10.1016/j.eururo.2019.02.033.
[5]
Han SY, Li CM, Liu M, et al. Application of biparametric magnetic resonance imaging in the detection of prostate cancer: a contrastive study based on whole mount section after radical prostatectomy[J]. Chin J Magn Reson Imaging, 2021, 12(5): 30-34. DOI: 10.12015/issn.1674-8034.2021.05.007.
[6]
Chen T, Zhang ZY, Tan SX, et al. MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI[J/OL]. Front Oncol, 2022, 11: 792456 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810653. DOI: 10.3389/fonc.2021.792456.
[7]
De Visschere P, Lumen N, Ost P, et al. Dynamic contrast-enhanced imaging has limited added value over T2-weighted imaging and diffusion-weighted imaging when using PI-RADSv2 for diagnosis of clinically significant prostate cancer in patients with elevated PSA[J]. Clin Radiol, 2017, 72(1): 23-32. DOI: 10.1016/j.crad.2016.09.011.
[8]
Bass EJ, Pantovic A, Connor M, et al. A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk[J]. Prostate Cancer Prostatic Dis, 2021, 24(3): 596-611. DOI: 10.1038/s41391-020-00298-w.
[9]
Huang YQ, Liu ZY, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or Ⅱ) non-small cell lung cancer[J]. Radiology, 2016, 281(3): 947-957. DOI: 10.1148/radiol.2016152234.
[10]
Caruso D, Polici M, Zerunian M, et al. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications[J/OL]. Cancers (Basel), 2021, 13(11): 2681 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197789. DOI: 10.3390/cancers13112681.
[11]
Ferro M, de Cobelli O, Musi G, et al. Radiomics in prostate cancer: an up-to-date review[J/OL]. Ther Adv Urol, 2022, 14: 17562872221109020 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260602. DOI: 10.1177/17562872221109020.
[12]
Wang JY, Shen YJ, Liu XH, et al. Combined usage of diffusion-weighted magnetic resonance imaging and transrectal ultrasound for transrectai prostate biopsy: a preliminary study[J]. Natl Med J China, 2012, 92(8): 512-515. DOI: 10.3760/cma.j.issn.0376-2491.2012.08.004.
[13]
Zhang XP, Zhang YC, Zhang GJ, et al. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential[J/OL]. Front Oncol, 2022, 12: 773840 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891653. DOI: 10.3389/fonc.2022.773840.
[14]
Wei P. Radiomics, deep learning and early diagnosis in oncology[J]. Emerg Top Life Sci, 2021, 5(6): 829-835. DOI: 10.1042/ETLS20210218.
[15]
Castillo TJM, Arif M, Starmans MPA, et al. Classification of clinically significant prostate cancer on multi-parametric MRI: a validation study comparing deep learning and radiomics[J]. Cancers (Basel), 2021, 14(1): 12. DOI: 10.3390/cancers14010012.
[16]
Ou W, Lei JH, Li MH, et al. Ultrasound-based radiomics score for pre-biopsy prediction of prostate cancer to reduce unnecessary biopsies[J/OL]. Prostate, 2022 [2022-10-11]. https://pubmed.ncbi.nlm.nih.gov/36207777. DOI: 10.1002/pros.24442.
[17]
Solari EL, Gafita A, Schachoff S, et al. The added value of PSMA PET/MR radiomics for prostate cancer staging[J]. Eur J Nucl Med Mol Imaging, 2022, 49(2): 527-538. DOI: 10.1007/s00259-021-05430-z.
[18]
Liang L, Zhi X, Sun Y, et al. A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions[J/OL]. Front Oncol, 2021, 11: 610785 [2022-10-11]. https://www.frontiersin.org/articles/10.3389/fonc.2021.610785/full. DOI: 10.3389/fonc.2021.610785.
[19]
Jia YS, Quan S, Ren JL, et al. MRI radiomics predicts progression-free survival in prostate cancer[J/OL]. Front Oncol, 2022, 12: 974257 [2022-10-11]. https://pubmed.ncbi.nlm.nih.gov/36110963. DOI: 10.3389/fonc.2022.974257.
[20]
Xiong H, He XJ, Guo DJ. Value of MRI texture analysis for predicting high-grade prostate cancer[J]. Clin Imaging, 2021, 72: 168-174. DOI: 10.1016/j.clinimag.2020.10.028.
[21]
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, 11(1): 2085 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822867. DOI: 10.1038/s41598-021-81272-x.
[22]
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.
[23]
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.
[24]
Peeken JC, Shouman MA, Kroenke M, et al. A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients[J]. Eur J Nucl Med Mol Imaging, 2020, 47(13): 2968-2977. DOI: 10.1007/s00259-020-04864-1.
[25]
Bleker J, Kwee TC, Dierckx RAJO, et al. Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer[J]. Eur Radiol, 2020, 30(3): 1313-1324. DOI: 10.1007/s00330-019-06488-y.
[26]
Jing GD, Xing PY, Li ZH, et al. Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram[J/OL]. Front Oncol, 2022, 12: 918830 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334707. DOI: 10.3389/fonc.2022.918830.
[27]
Li TP, Sun LN, Li QH, et al. Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions[J/OL]. Front Oncol, 2022, 11: 825429 [2022-01-26]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825569. DOI: 10.3389/fonc.2021.825429.
[28]
Giambelluca D, Cannella R, Vernuccio F, et al. PI-RADS 3 lesions: role of prostate MRI texture analysis in the identification of prostate cancer[J]. Curr Probl Diagn Radiol, 2021, 50(2): 175-185. DOI: 10.1067/j.cpradiol.2019.10.009.
[29]
Chen T, Li MJ, Gu YF, et al. Prostate cancer differentiation and aggressiveness: assessment with a radiomic-based model vs. PI-RADS v2[J]. J Magn Reson Imaging, 2019, 49(3): 875-884. DOI: 10.1002/jmri.26243.
[30]
Brancato V, Aiello M, Basso L, et al. Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions[J/OL]. Sci Rep, 2021, 11(1): 643 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804929. DOI: 10.1038/s41598-020-80749-5.
[31]
Castillo TJM, Starmans MPA, Arif M, et al. A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade[J/OL]. Diagnostics (Basel), 2021, 11(2): 369 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926758. DOI: 10.3390/diagnostics1102036.

PREV Predictive value of Gd-EOB-DTPA enhanced MRI features and hepatobiliary phase histogram parameters in response to transarterial chemoembolization for hepatocellular carcinoma
NEXT MRI radiomics models in rectal cancer to predict pathological complete response of nCRT: Evaluation of different approaches
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn