Share：
Share this content in WeChat
X
[Chinese][PDF]92196
• CLINICAL ARTICLE •
Predicting clinically significant prostate cancer based on perilesional and intralesional MRI radiomics features
ZHANG Han  MAO Ning  XIE Haizhu  LI Tianping  LUO Xunrong  LI Xianglin 

Cite this article as: Zhang H, Mao N, Xie HZ, et al.Citation: Predicting clinically significant prostate cancer based on perilesional and intralesional MRI radiomics features[J]. Chin J Magn Reson Imaging, 2021, 12(1): 48-52. DOI:10.12015/issn.1674-8034.2021.01.010.


[Abstract] Objective To explore value of perilesional volume (ILV) and intralesional volume (PLV) MRI radiomics features for the diagnosis of clinically significant prostate cancer (csPCa).Materials and Methods One hundred and forty patients (train set: 112, testing set: 28) who underwent prostate MRI examination were included in that retrospective study. ILV and PLV were manually segmented on T2WI (T2 weighted imaging), ADC map and radiomics features were extracted. Radiomics features were selected via univariate analysis and least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross validation. Prediction model was built based on LASSO regression and evaluated by ROC curve analysis, decision curve analysis (DCA).Results AUC and accuracy of model in train set were 0.93 (95% CI: 0.88—0.98, specificity: 0.87, sensitivity: 0.89), 0.84 (95% CI: 0.76—0.90), respectively. AUC and accuracy of model in test set were 0.92 (95% CI: 0.81—1, specificity: 0.95, sensitivity: 0.69), 0.89 (95% CI: 0.72—0.98), respectively. Decision curve showed that if the cut-off point is between 0.01 and 0.83 or between 0.87 and 0.98, using that model has more net benefit than either the “positive-all” model or the “negative-all” model.Conclusions ILV and PLV based MRI radiomics features is valuable for diagnosis of csPCa.
[Keywords] radiomics；magnetic resonance imaging；clinically significant prostate cancer；machine learning；predict

ZHANG Han1   MAO Ning2   XIE Haizhu2   LI Tianping1   LUO Xunrong1   LI Xianglin2*  

1 School of Medical Imaging, Binzhou Medical University, Shandong Province, Yantai 264003, China

2 Department of Radiology, Yantai Yuhuangding Hospital, Yantai 264000, China

*Corresponding author: Li XL, E-mail: xlli163@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by Natural Science Foundation of Shandong Province of China No. ZR2016HL40 and Shandong Key R & D Plan No. 2017GSF18121, 2018YFJH0501
Received  2020-09-11
Accepted  2020-12-01
DOI: 10.12015/issn.1674-8034.2021.01.010
Cite this article as: Zhang H, Mao N, Xie HZ, et al.Citation: Predicting clinically significant prostate cancer based on perilesional and intralesional MRI radiomics features[J]. Chin J Magn Reson Imaging, 2021, 12(1): 48-52. DOI:10.12015/issn.1674-8034.2021.01.010.

1
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68 (6): 394-424. DOI: 10.3322/caac.21492
2
Mohler JL, Antonarakis ES, Armstrong AJ, et al. Prostate cancer, version 2.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2019, 17(5): 479-505. DOI: 10.6004/jnccn.2019.0023
3
Wang B. More attention should be paid to prostate imaging. J Med Imaging, 2007, 17(10): 1011-1012. DOI: 10.3969/j.issn.1006-9011.2007.10.001
4
Sun BX, Zhang H, Zhang XX, et al. Advances in magnetic resonance imaging in the diagnosis of prostate cancer. Chin J Magn Reson Imaging, 2019, 10(12): 947-50. DOI: 10.12015/issn.1674-8034.2019.12.017
5
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. J Nucl Med, 2020, 61 (4): 488-495. DOI: 10.2967/jnumed.118.222893
6
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278 (2): 563-577. DOI: 10.1148/radiol.2015151169
7
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Exp Rev Precis Med Drug Dev, 2016, 1 (2): 207-226. DOI: 10.1080/23808993.2016.1164013
8
Li M, Chen T, Zhao W, Et al. Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI. Quant Imaging Med Surg, 2020, 10 (2): 368-379. DOI: 10.21037/qims.2019.12.06
9
Wang J, Wu CJ, Bao ML, et al. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol, 2017, 27 (10): 4082-4090. DOI: 10.1007/s00330-017-4800-5
10
Cuocolo R, Stanzione A, Ponsiglione A, et al. Clinically significant prostate cancer detection on MRI: a radiomic shape features study. Eur J Radiol, 2019, 116: 144-149. DOI: 10.1016/j.ejrad.2019.05.006
11
Zhang Y, Chen W, Yue X, et al. Development of a novel, multi- parametric, MRI-based radiomic nomogram for differentiating between clinically significant and insignificant prostate cancer. Front Oncol, 2020, 10: 888. DOI: 10.3389/fonc.2020.00888
12
Bernatz S, Ackermann J, Mandel P, et al. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features. Eur Radiol, 2020, 30 (12): 6757-6769. DOI: 10.1007/s00330-020-07064-5
13
Min X, Li M, Dong D, et al. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method. Eur J Radiol, 2019, 115: 16-21. DOI: 10.1016/j.ejrad.2019.03.010
14
Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging, 2012, 30 (9): 1323-1341. DOI: 10.1016/j.mri.2012.05.001
15
Liljequist D, Elfving B, Roaldsen KS. Intraclass correlation: a discussion and demonstration of basic features. PloS one, 2019, 14 (7): e0219854. DOI: 10.1371/journal.pone.0219854
16
Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS prostate imaging - reporting and data system: 2015, version 2. Eur Urol, 2016, 69 (1): 16-40. DOI: 10.1016/j.eururo.2015.08.052
17
Kim SM, Kim Y, Jeong K, et al. Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography. Ultrasonography, 2018, 37 (1): 36-42. DOI: 10.14366/usg.16045
18
Yip SS, Liu Y, Parmar C, et al. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Report, 2017, 7 (1): 3519. DOI: 10.1038/s41598-017-02425-5
19
Wang X, Zhao X, Li Q, et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol, 2019, 29 (11): 6049-6058. DOI: 10.1007/s00330-019-06084-0
20
Fan M, Zhang P, Wang Y, et al. Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. Eur Radiol, 2019, 29 (8): 4456-4467. DOI: 10.1007/s00330-018-5891-3
21
D'antonoli TA, Farchione A, Lenkowicz J, et al. CT radiomics signature of tumor and peritumoral lung parenchyma to predict nonsmall cell lung cancer postsurgical recurrence risk. Acad Radiol, 2020, 27 (4): 497-507. DOI: 10.1016/j.acra.2019.05.019
22
Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res, 2017, 19 (1): 57. DOI: 10.1186/s13058-017-0846-1

PREV Correlation between pancreatic fat deposition and abdominal fat distribution and essential hypertension based on MRI
NEXT Probe of the central mechanism of analgesic effect of ozone water on patients with knee osteoarthritis pain based on rs-fMRI techniqueTIAN Mingyue
  


LINK


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