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• Clinical Article •
Predictive value of positive margins after radical prostatectomy for prostate cancer based on Bp-MRI radiomics
GUO Sheng  ZHOU Chuan  WANG Chao  ZHANG Yunfeng  WANG Dong  ZHOU Fenghai 

Cite this article as: GUO S, ZHOU C, WANG C, et al. Predictive value of positive margins after radical prostatectomy for prostate cancer based on Bp-MRI radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(12): 54-59. DOI:10.12015/issn.1674-8034.2023.12.009.


[Abstract] Objective To establish and evaluate a predictive model of bi-parameter magnetic resonance imaging (Bp-MRI) radiomics for positive surgical margin (PSM) after radical prostatectomy for prostate cancer (PCa).Materials and Methods The imaging and clinical data of 105 patients who underwent laparoscopic radical prostatectomy via extraperitoneal route in Gansu Provincial People's Hospital were retrospectively analyzed, and they were classified into 40 cases with positive postoperative margins and 65 cases with negative postoperative margins by postoperative pathological findings. The dataset was partitioned into a training set (n=73) and a test set (n=32) in a 7∶3 ratio. Subgroup analysis was performed within both the training and test sets, comparing clinical and MRI data. Region of interest (ROI) was delineated using the ITK-SNAP software from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) sequences. The "Pyradiomics" package was utilized to extract a total of 312 features from these ROIs. The features were then subjected to dimensionality reduction and model construction using the least absolute shrinkage and selection operator (LASSO) algorithm. A predictive model was built based on a logistic regression (LR) classifier. The efficacy of the imaging model in predicting PSM after radical prostatectomy for PCa was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC). Additionally, decision curve analysis (DCA) was employed to assess the clinical net benefit of the model.Results Ten radiomic features closely related to PSMs were ultimately selected. The LR model achieved an AUC of 0.869 (95% CI: 0.786-0.952) in the training set and an AUC of 0.858 (95% CI: 0.726-0.991) in the test set. The DCA indicated that the model offers a significant clinical net benefit.Conclusions The predictive assessment of positive margins after radical prostatectomy for PCa based on Bp-MRI radiomics is informative and helpful for clinical preoperative risk stratification and postoperative treatment.
[Keywords] prostate cancer；positive surgical margin；machine learning；radiomics；magnetic resonance imaging

GUO Sheng1   ZHOU Chuan2   WANG Chao2   ZHANG Yunfeng1   WANG Dong1   ZHOU Fenghai3*  

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

2 The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China

3 Department of Urology, Gansu Provincial People's Hospital, Lanzhou 730050, China

Corresponding author: ZHOU F H, E-mail: zhoufengh@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Gansu Provincial Key Research and Development Program Fund (No. 21YF5FA016); Gansu Provincial Natural Science Foundation (No. 22JR5RA650); Gansu Provincial People's Hospital Intramural Research Fund (No. 22GSSYD-15).
Received  2023-07-24
Accepted  2023-11-27
DOI: 10.12015/issn.1674-8034.2023.12.009
Cite this article as: GUO S, ZHOU C, WANG C, et al. Predictive value of positive margins after radical prostatectomy for prostate cancer based on Bp-MRI radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(12): 54-59. DOI:10.12015/issn.1674-8034.2023.12.009.

[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]
SONG X Y, ZHANG T, WANG Y Y, et al. Clinic, MRI radiomics and deep learning combined model for predicting bone metastases of initial prostate cancer[J]. Chin J Interv Imag Ther, 2023, 20(4): 212-217. DOI: 10.13929/j.issn.1672-8475.2023.04.006.
[3]
TAO J Y, LIANG C Z, ZHOU J. Progress in diagnosis and treatment of positive surgical margins after radical prostatectomy[J]. J Clin Urol, 2023, 38(3): 232-237. DOI: 10.13201/j.issn.1001-1420.2023.03.017.
[4]
MARTINI A, GANDAGLIA G, FOSSATI N, et al. Defining clinically meaningful positive surgical margins in patients undergoing radical prostatectomy for localised prostate cancer[J]. Eur Urol Oncol, 2021, 4(1): 42-48. DOI: 10.1016/j.euo.2019.03.006.
[5]
STABILE A, GIGANTI F, ROSENKRANTZ A B, et al. Multiparametric MRI for prostate cancer diagnosis: current status and future directions[J]. Nat Rev Urol, 2020, 17(1): 41-61. DOI: 10.1038/s41585-019-0212-4.
[6]
JI J Z, ZHANG Q, NIU M, et al. Nomogram based on clinical, MR T2WI and apparent diffusion coefficient map radiomics features for predicting bone metastasis of incipient prostate cancer[J]. Chin J Med Imag Technol, 2022, 38(7): 1050-1055. DOI: 10.13929/j.issn.1003-3289.2022.07.020.
[7]
NEMATOLLAHI H, MOSLEHI M, AMINOLROAYAEI F, et al. Diagnostic performance evaluation of multiparametric magnetic resonance imaging in the detection of prostate cancer with supervised machine learning methods[J/OL]. Diagnostics, 2023, 13(4): 806 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/36832294/. DOI: 10.3390/diagnostics13040806.
[8]
WANG Y, WU Y, ZHU M L, et al. The diagnostic performance of tumor stage on MRI for predicting prostate cancer-positive surgical margins: a systematic review and meta-analysis[J/OL]. Diagnostics, 2023, 13(15): 2497 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/37568860/. DOI: 10.3390/diagnostics13152497.
[9]
AERTS H J, VELAZQUEZ E R, LEIJENAAR R T, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J/OL]. Nat Commun, 2014, 5: 4006 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/24892406/. DOI: 10.1038/ncomms5006.
[10]
NAKANISHI R, AKIYOSHI T, TODA S, et al. Radiomics approach outperforms diameter criteria for predicting pathological lateral lymph node metastasis after neoadjuvant (chemo)radiotherapy in advanced low rectal cancer[J]. Ann Surg Oncol, 2020, 27(11): 4273-4283. DOI: 10.1245/s10434-020-08974-w.
[11]
ZHANG Z, ZHANG K N, HONG B A, et al. Risk factors for positive surgical margin after laparoscopic radical prostatectomy[J]. Chin J Clin Oncol, 2019, 46(6): 299-302. DOI: 10.3969/j.issn.1000-8179.2019.06.276.
[12]
POOLI A, SALMASI A, JOHNSON D C, et al. Positive surgical margins at radical prostatectomy in the United States: institutional variations and predictive factors[J/OL]. Urol Oncol, 2020, 38(1): 1.e17-1.e23 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/31537483/. DOI: 10.1016/j.urolonc.2019.08.016.
[13]
JIN T T, WU W J, FU H, et al. Risk factors for positive surgical margins after radical prostatectomy in Chinese men: a meta-analysis[J]. Chin Gen Pract, 2023, 26(17): 2147-2154. DOI: 10.12114/j.issn.1007-9572.2022.0568.
[14]
ZHANG P X, WANG W G, MA J, et al. Influencing factors of positive surgical margin after extraperitoneal laparoscopic radical prostatectomy[J]. J Mod Urol, 2020, 25(3): 247-251. DOI: 10.3969/j.issn.1009-8291.2020.03.00.
[15]
ZHOU C, ZHANG Y F, GUO S, et al. Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study[J/OL]. Discov Oncol, 2023, 14(1): 133 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/37470865/. DOI: 10.1007/s12672-023-00752-w.
[16]
LI L, SHIRADKAR R, LEO P, et al. A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI[J/OL]. EBioMedicine, 2021, 63: 103163 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/33321450/. DOI: 10.1016/j.ebiom.2020.103163.
[17]
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 Imag, 2023, 14(2): 197-202. DOI: 10.12015/issn.1674-8034.2023.02.035.
[18]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[19]
AVANZO M, WEI L S, STANCANELLO J, et al. Machine and deep learning methods for radiomics[J/OL]. Med Phys, 2020, 47(5): e185-e202 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/32418336/. DOI: 10.1002/mp.13678.
[20]
LIU X Y, ELBANAN M G, LUNA A, et al. Radiomics in abdominopelvic solid-organ oncologic imaging: current status[J]. AJR Am J Roentgenol, 2022, 219(6): 985-995. DOI: 10.2214/AJR.22.27695.
[21]
URRARO F, NARDONE V, REGINELLI A, et al. MRI radiomics in prostate cancer: a reliability study[J/OL]. Front Oncol, 2021, 11: 805137 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/34993153/. DOI: 10.3389/fonc.2021.805137.
[22]
SUGANO D, SANFORD D, ABREU A, et al. Impact of radiomics on prostate cancer detection: a systematic review of clinical applications[J]. Curr Opin Urol, 2020, 30(6): 754-781. DOI: 10.1097/MOU.0000000000000822.
[23]
FERNANDES M C, YILDIRIM O, WOO S, et al. The role of MRI in prostate cancer: current and future directions[J]. MAGMA, 2022, 35(4): 503-521. DOI: 10.1007/s10334-022-01006-6.
[24]
ZHANG L, ZHANG J, TANG M, et al. MRI-based radiomics nomogram for predicting prostate cancer with gray-zone prostate-specific antigen levels to reduce unnecessary biopsies[J/OL]. Diagnostics, 2022, 12(12): 3005 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/36553012/. DOI: 10.3390/diagnostics12123005.
[25]
MIDIRI F, VERNUCCIO F, PURPURA P, et al. Multiparametric MRI and radiomics in prostate cancer: a review of the current literature[J/OL]. Diagnostics, 2021, 11(10): 1829 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/34679527/. DOI: 10.3390/diagnostics11101829.
[26]
SOLARI E L, 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.
[27]
ZHANG W J, MAO N, WANG Y S, et al. A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients[J/OL]. Eur J Radiol, 2020, 128: 109020 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/32371181/. DOI: 10.1016/j.ejrad.2020.109020.
[28]
ZHONG Q Z, LONG L H, LIU A, et al. Radiomics of multiparametric MRI to predict biochemical recurrence of localized prostate cancer after radiation therapy[J/OL]. Front Oncol, 2020, 10: 731 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/32477949/. DOI: 10.3389/fonc.2020.00731.
[29]
AN P, LIN Y, HU Y, et al. Predicting model of biochemical recurrence of prostate carcinoma (PCa-BCR) using MR perfusion-weighted imaging-based radiomics[J/OL]. Technol Cancer Res Treat, 2023, 22: 15330338231166766 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/37016971/. DOI: 10.1177/15330338231166766.
[30]
JIA Y S, QUAN S, REN J L, et al. MRI radiomics predicts progression-free survival in prostate cancer[J/OL]. Front Oncol, 2022, 12: 974257 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/36110963/. DOI: 10.3389/fonc.2022.974257.
[31]
LEECH M, OSMAN S, JAIN S, et al. Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics[J]. Cancer Lett, 2021, 498: 210-216. DOI: 10.1016/j.canlet.2020.10.033.
[32]
QU Y H, HE Y J, ZHU H T, et al. Preoperative MRI radiomics model for predicting the feasibility of treating breast cancer with breast conserving surgery[J]. Chin J Med Imag Technol, 2023, 39(6): 848-852. DOI: 10.13929/j.issn.1003-3289.2023.06.011.
[33]
DING Z S, HUANG T, JIAO B B, et al. Establishment and validation of a predictive nomogram for the risk of positive surgical margin in laparoscopic radical prostatectomy[J]. J China Jpn Friendsh Hosp, 2022, 36(2): 71-75, 80. DOI: 10.3969/j.issn.1001-0025.2022.02.002.
[34]
HE D, WANG X M, FU C 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 [2023-11-21]. https://pubmed.ncbi.nlm.nih.gov/34225808/. DOI: 10.1186/s40644-021-00414-6.

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