A predictive model for prostate cancer constructed by dual-modal ultrasound and biparametric MRI radiomics technology

SUN Ya; BAI Dong; MA Yahui; ZHOU Nan; WANG Jiajun; LIANG Lei

PDF(1922 KB)

Medical Journal of the Chinese People Armed Police Forces ›› 2026, Vol. 37 ›› Issue (2) : 151-155.

ORIGINAL ARTICLES

A predictive model for prostate cancer constructed by dual-modal ultrasound and biparametric MRI radiomics technology

SUN Ya¹, BAI Dong², MA Yahui¹, ZHOU Nan¹, WANG Jiajun¹, LIANG Lei¹

Author information +

History +

Abstract

Objective To construct and validate a combined model for preoperative noninvasive prediction of prostate cancer (PCa) using radiomic features from dual-modal ultrasound (B-mode+shear wave elastography) and biparametric MRI (T₂WI+ADC). Methods A total of 232 patients with prostate mass lesions who visited the Aerospace Center Hospital from January 2023 to October 2025 were selected, including 127 cases of PCa and 105 cases of benign conditions. All patients underwent dual-modal ultrasound and biparametric MRI examinations. After manual delineation of the lesions, high-throughput radiomics features were extracted. Feature selection was performed using LASSO regression, and independent clinical risk factors (age, PSA density) were integrated. Four models were constructed and compared: a clinical model, an ultrasound radiomics model, an MRI radiomics model, and a combined model. The performance was evaluated via five-fold cross-validation. Results The combined model demonstrated the best diagnostic efficacy in the validation set, with an AUC of 0.92 (95% CI: 0.84-0.97), significantly higher than the AUCs of the clinical model (0.76), the ultrasound radiomics model (0.78), and the MRI radiomics model (0.85) (all P<0.05). Decision curve analysis confirmed its greater net clinical benefit across a wide threshold range. Conclusions The validated combined model integrating multimodal radiomics and clinical factors can significantly improve the accuracy of preoperative noninvasive diagnosis of PCa, demonstrating robust performance and high clinical applicability.

Key words

prostate cancer / radiomics / ultrasound / MRI / machine learning / predictive model

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

SUN Ya, BAI Dong, MA Yahui, ZHOU Nan, WANG Jiajun, LIANG Lei. A predictive model for prostate cancer constructed by dual-modal ultrasound and biparametric MRI radiomics technology[J]. Medical Journal of the Chinese People Armed Police Forces. 2026, 37(2): 151-155

References

[1] Pinsky P F, Parnes H. Screening for prostate cancer[J]. N Engl J Med,2023,388: 1405-1414.
[2] Cancer facts & figures 2024[EB/OL]. Accessed: December 16, 2024: https://www.cancer.org/research/ cancer-factsstatistics/all-cancer-facts-figures/2024-cancer-facts-figures.htm
[3] Hugosson J, Månsson M, Wallström J, et al. Prostate cancer screening with PSA and MRI followed by targeted biopsy only[J]. N Engl J Med, 2022, 387: 2126-2137.
[4] Cho H, Oh C K, Cha J, et al. Association of serum prostate-specific antigen (PSA) level and circulating tumor cell-based PSA mRNA in prostate cancer[J]. Prostate Int, 2022,10(1):14-20.
[5] Teramoto Y, Numbere N, Wang Y, et al. Clinical significance of perineural invasion by prostate cancer detected on needle core biopsy[J]. Am J Clin Pathol, 2023,159:116-119.
[6] Nedelcu A, Oerther B, Benkendorff A, et al. PI-RADS Version 2.1 for Prostate MRI interpretation: associations of study quality and cancer detection metrics-a systematic review and meta-analysis[J]. Am J Roentgenol, 2025.Doi: 10.2214/AJR.25.33583.
[7] Lim C S, Abreu-Gomez J, Thornhill R, et al. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis[J]. Abdom Radiol (NY), 2021,46(12):5647-5658.
[8] Shiva M, Wei C, Molana H, et al. Costeffectiveness of prostate cancer detection in biopsy-naïve men: ultrasound shear wave elastography vs. multiparametric diagnostic magnetic resonance imaging[J]. Healthcare (Basel),2022,10(2):254.
[9] Keskin E T, Kaplanoglu V, Senocak C, et al. Transrectal shear wave elastography for detection of prostate cancer[J]. Urologia, 2023,90(2):230-235.
[10] Cavallo A U, Stanzione A, Ponsiglione A, et al.Prostate cancer MRI methodological radiomics score: a EuSoMII radiomics auditing group initiative[J]. Eur Radiol, 2025,35(3):1157-1165.
[11] 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]. Front Oncol, 2021,11:610785.
[12] Sun Y, Fang J, Shi Y, et al. Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection[J]. Abdom Radiol (NY), 2024 ,49(1):141-150.
[13] 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.
[14] Brancato V, Aiello M, Basso L, et al. (2021) Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions[J]. Sci Rep,2021,11(1):643.
[15] Giambelluca D, Cannella R, Vernuccio F, et al. (2021) 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.
[16] Israel B, Leest M V, Sedelaar M, et al. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 2: interpretation[J]. Eur Urol, 2020,77(4):469-480.
[17] Jiang W, Lai B, Li X, et al. Integrating shear wave elastography and multiparametric MRI for accurate prostate cancer diagnosis[J]. Am J Cancer Res, 2025,15(1):348-362.
[18] Liu D, Lin W, Wei C, et al. Exploring the potential clinical value of shear wave elastography in prostate cancer: a meta-analysis[J]. BMC Cancer, 2025,25(1):1032.
[19] Zaurito P, Stabile A, Montorsi F, et al. The prognostic role of prostate MRI in prostate cancer patients[J]. Curr Opin Urol, 2025,35(4):418-425.
[20] Morote J, Borque-Fernando Á, Triquell M, et al. A clinically significant prostate cancer predictive model using digital rectal examination prostate volume category to stratify initial prostate cancer suspicion and reduce magnetic resonance imaging demand[J]. Cancers (Basel), 2022,14(20):5100.