Skip to main content
SpringerLink
Log in

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review

  • Pelvis
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature.

Methods

We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores.

Results

We identified 33 studies—22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science.

Conclusion

Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7–33. doi: https://doi.org/10.3322/caac.21708.

    Article  PubMed  Google Scholar 

  2. Sanda MG, Cadeddu JA, Kirkby E, Chen RC, Crispino T, Fontanarosa J, et al. Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline. Part II: Recommended Approaches and Details of Specific Care Options. J Urol. 2018;199(4):990–7. doi: https://doi.org/10.1016/j.juro.2018.01.002.

    Article  PubMed  Google Scholar 

  3. Mikel Hubanks J, Boorjian SA, Frank I, Gettman MT, Houston Thompson R, Rangel LJ, et al. The presence of extracapsular extension is associated with an increased risk of death from prostate cancer after radical prostatectomy for patients with seminal vesicle invasion and negative lymph nodes. Urol Oncol. 2014;32(1):26.e1-7. doi: https://doi.org/10.1016/j.urolonc.2012.09.002.

    Article  CAS  PubMed  Google Scholar 

  4. Sayyid R, Perlis N, Ahmad A, Evans A, Toi A, Horrigan M, et al. Development and external validation of a biopsy-derived nomogram to predict risk of ipsilateral extraprostatic extension. BJU International. 2017;120(1):76–82. doi: https://doi.org/10.1111/bju.13733.

    Article  CAS  PubMed  Google Scholar 

  5. Bill-Axelson A, Holmberg L, Garmo H, Taari K, Busch C, Nordling S, et al. Radical Prostatectomy or Watchful Waiting in Prostate Cancer—29-Year Follow-up. New England Journal of Medicine. 2018;379(24):2319–29. doi: https://doi.org/10.1056/NEJMoa1807801.

    Article  PubMed  Google Scholar 

  6. Jeong BC, Chalfin HJ, Lee SB, Feng Z, Epstein JI, Trock BJ, et al. The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. Eur Urol. 2015;67(2):342–6. doi: https://doi.org/10.1016/j.eururo.2014.06.015.

    Article  PubMed  Google Scholar 

  7. Litwin MS, Tan HJ. The Diagnosis and Treatment of Prostate Cancer: A Review. JAMA. 2017;317(24):2532–42. doi: https://doi.org/10.1001/jama.2017.7248.

    Article  PubMed  Google Scholar 

  8. Partin AW, Yoo J, Carter HB, Pearson JD, Chan DW, Epstein JI, et al. The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. J Urol. 1993;150(1):110–4. doi: https://doi.org/10.1016/s0022-5347(17)35410-1.

    Article  CAS  PubMed  Google Scholar 

  9. Ohori M, Kattan MW, Koh H, Maru N, Slawin KM, Shariat S, et al. Predicting the presence and side of extracapsular extension: A nomogram for staging prostate cancer. Journal of Urology. 2004;171(5):1844–9. doi: https://doi.org/10.1097/01.ju.0000121693.05077.3d.

    Article  PubMed  Google Scholar 

  10. Chung JS, Choi HY, Song HR, Byun SS, Seo S, Song C, et al. Preoperative nomograms for predicting extracapsular extension in Korean men with localized prostate cancer: A multi-institutional clinicopathologic study. Journal of Korean Medical Science. 2010;25(10):1443–8. doi: https://doi.org/10.3346/jkms.2010.25.10.1443.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Satake N, Ohori M, Yu C, Kattan MW, Ohno Y, Miyakawa A, et al. Development and internal validation of a nomogram predicting extracapsular extension in radical prostatectomy specimens: Original article: Clinical investigation. International Journal of Urology. 2010;17(3):267–72. doi: https://doi.org/10.1111/j.1442-2042.2010.02452.x.

    Article  PubMed  Google Scholar 

  12. Ravi C, Sanjeevan KV, Thomas A, Pooleri GK. Development of an Indian nomogram for predicting extracapsular extension in prostate cancer. Indian J Urol. 2021;37(1):65–71. doi: https://doi.org/10.4103/iju.IJU_200_20.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Merder E, Arıman A, Altunrende F. A Modified Partın Table to Better Predict Extracapsular Extensıon in Clinically Localized Prostate Cancer. Urol J. 2021;18(1):74–80. doi: https://doi.org/10.22037/uj.v16i7.6477.

    Article  PubMed  Google Scholar 

  14. Mendhiratta N, Taneja SS, Rosenkrantz AB. The role of MRI in prostate cancer diagnosis and management. Future Oncol. 2016;12(21):2431–43. doi: https://doi.org/10.2217/fon-2016-0169.

    Article  CAS  PubMed  Google Scholar 

  15. Feng TS, Sharif-Afshar AR, Wu J, Li Q, Luthringer D, Saouaf R, et al. Multiparametric MRI Improves Accuracy of Clinical Nomograms for Predicting Extracapsular Extension of Prostate Cancer. Urology. 2015;86(2):332–7. doi: https://doi.org/10.1016/j.urology.2015.06.003.

    Article  PubMed  Google Scholar 

  16. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A. 2015;112(46):E6265–73. doi: https://doi.org/10.1073/pnas.1505935112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nketiah G, Elschot M, Kim E, Teruel JR, Scheenen TW, Bathen TF, et al. T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol. 2017;27(7):3050–9. doi: https://doi.org/10.1007/s00330-016-4663-1.

    Article  PubMed  Google Scholar 

  18. Penzias G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, et al. Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS ONE. 2018;13(8):e0200730. doi: https://doi.org/10.1371/journal.pone.0200730.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. de Rooij M, Hamoen EH, Witjes JA, Barentsz JO, Rovers MM. Accuracy of Magnetic Resonance Imaging for Local Staging of Prostate Cancer: A Diagnostic Meta-analysis. Eur Urol. 2016;70(2):233–45. doi: https://doi.org/10.1016/j.eururo.2015.07.029.

    Article  PubMed  Google Scholar 

  20. Heidenreich A. Consensus criteria for the use of magnetic resonance imaging in the diagnosis and staging of prostate cancer: not ready for routine use. Eur Urol. 2011;59(4):495–7. doi: https://doi.org/10.1016/j.eururo.2011.01.013.

    Article  PubMed  Google Scholar 

  21. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. doi: https://doi.org/10.1038/ncomms5006.

    Article  CAS  PubMed  Google Scholar 

  22. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563–77. doi: https://doi.org/10.1148/radiol.2015151169.

    Article  PubMed  Google Scholar 

  23. Vignati A, Mazzetti S, Giannini V, Russo F, Bollito E, Porpiglia F, et al. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol. 2015;60(7):2685–701. doi: https://doi.org/10.1088/0031-9155/60/7/2685.

    Article  CAS  PubMed  Google Scholar 

  24. Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol. 2015;25(10):2840–50. doi: https://doi.org/10.1007/s00330-015-3701-8.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Gnep K, Fargeas A, Gutiérrez-Carvajal RE, Commandeur F, Mathieu R, Ospina JD, et al. Haralick textural features on T(2)-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging. 2017;45(1):103–17. doi: https://doi.org/10.1002/jmri.25335.

    Article  PubMed  Google Scholar 

  26. Qi Y, Zhang S, Wei J, Zhang G, Lei J, Yan W, et al. Multiparametric MRI-Based Radiomics for Prostate Cancer Screening with PSA in 4–10 ng/mL to Reduce Unnecessary Biopsies. J Magn Reson Imaging. 2020;51(6):1890–9. doi: https://doi.org/10.1002/jmri.27008.

    Article  PubMed  Google Scholar 

  27. Cameron A, Khalvati F, Haider MA, Wong A. MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection. IEEE Trans Biomed Eng. 2016;63(6):1145–56. doi: https://doi.org/10.1109/tbme.2015.2485779.

    Article  PubMed  Google Scholar 

  28. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;48(4):441–6. doi: https://doi.org/https://doi.org/10.1016/j.ejca.2011.11.036.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine Learning in oncology: A clinical appraisal. Cancer Letters. 2020;481:55–62. doi: https://doi.org/https://doi.org/10.1016/j.canlet.2020.03.032.

    Article  CAS  PubMed  Google Scholar 

  30. Mayerhoefer ME, Szomolanyi P, Jirak D, Berg A, Materka A, Dirisamer A, et al. Effects of magnetic resonance image interpolation on the results of texture-based pattern classification a phantom study. Invest Radiol. 2009;44(7):405–11. doi: https://doi.org/10.1097/RLI.0b013e3181a50a66.

    Article  PubMed  Google Scholar 

  31. Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magnetic Resonance Imaging. 2004;22(1):81–91. doi: https://doi.org/https://doi.org/10.1016/j.mri.2003.09.001.

    Article  CAS  PubMed  Google Scholar 

  32. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. 2017;14(12):749–62. doi: https://doi.org/10.1038/nrclinonc.2017.141.

    Article  PubMed  Google Scholar 

  33. Veerman H, Heymans MW, van der Poel HG. External Validation of a Prediction Model for Side-Specific Extraprostatic Extension of Prostate Cancer at Robot-assisted Radical Prostatectomy. European Urology Open Science. 2022;37:50–2. doi: https://doi.org/10.1016/j.euros.2021.12.010.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Losnegård A, Reisæter LAR, Halvorsen OJ, Jurek J, Assmus J, Arnes JB, et al. Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients. Acta Radiol. 2020;61(11):1570–9. doi: https://doi.org/10.1177/0284185120905066.

    Article  PubMed  Google Scholar 

  35. Shiradkar R, Zuo R, Mahran A, Ponsky L, Tirumani SH, Madabhushi A. Radiomic features derived from periprostatic fat on pre-surgical T2W MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary results. In: Hahn HK, Mazurowski MA, editors. Progress in Biomedical Optics and Imaging—Proceedings of SPIE: SPIE; 2020.

  36. Damascelli A, Gallivanone F, Cristel G, Cava C, Interlenghi M, Esposito A, et al. Advanced imaging analysis in prostate MRI: Building a radiomic signature to predict tumor aggressiveness. Diagnosis 2021;11(4). doi: https://doi.org/10.3390/diagnostics11040594.

  37. Cuocolo R, Stanzione A, Faletti R, Gatti M, Calleris G, Fornari A, et al. MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. European Radiology. 2021;31(10):7575–83. doi: https://doi.org/10.1007/s00330-021-07856-3.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Xu L, Zhang G, Zhao L, Mao L, Li X, Yan W, et al. Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer. Front Oncol. 2020;10:940. doi: https://doi.org/10.3389/fonc.2020.00940.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ma S, Xie H, Wang H, Han C, Yang J, Lin Z, et al. MRI-Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer. J Magn Reson Imaging. 2019;50(6):1914–25. doi: https://doi.org/10.1002/jmri.26777.

    Article  PubMed  Google Scholar 

  40. Fütterer JJ, Heijmink SWTPJ, Scheenen TWJ, Jager GJ, Hulsbergen–Van de Kaa CA, Witjes JA, et al. Prostate Cancer: Local Staging at 3-T Endorectal MR Imaging—Early Experience. Radiology. 2006;238(1):184–91. doi: https://doi.org/10.1148/radiol.2381041832.

    Article  PubMed  Google Scholar 

  41. Akin O, Riedl CC, Ishill NM, Moskowitz CS, Zhang J, Hricak H. Interactive dedicated training curriculum improves accuracy in the interpretation of MR imaging of prostate cancer. Eur Radiol. 2010;20(4):995–1002. doi: https://doi.org/10.1007/s00330-009-1625-x.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wibmer A, Vargas HA, Donahue TF, Zheng J, Moskowitz C, Eastham J, et al. Diagnosis of Extracapsular Extension of Prostate Cancer on Prostate MRI: Impact of Second-Opinion Readings by Subspecialized Genitourinary Oncologic Radiologists. Am J Roentgenol. 2015;205(1):W73–W8. doi: https://doi.org/10.2214/AJR.14.13600.

    Article  Google Scholar 

  43. Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, Radice L, et al. Machine learning applications in prostate cancer magnetic resonance imaging. European Radiology Experimental. 2019;3(1):35. doi: https://doi.org/10.1186/s41747-019-0109-2.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Ursprung S, Beer L, Bruining A, Woitek R, Stewart GD, Gallagher FA, et al. Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis. Eur Radiol. 2020;30(6):3558–66. doi: https://doi.org/10.1007/s00330-020-06666-3.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Granzier RWY, van Nijnatten TJA, Woodruff HC, Smidt ML, Lobbes MBI. Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review. European Journal of Radiology. 2019;121:108736. doi: https://doi.org/https://doi.org/10.1016/j.ejrad.2019.108736.

    Article  CAS  PubMed  Google Scholar 

  46. Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol. 2020;30(1):523–36. doi: https://doi.org/10.1007/s00330-019-06360-z.

    Article  PubMed  Google Scholar 

  47. Spadarella G, Stanzione A, Akinci D’Antonoli T, Andreychenko A, Fanni SC, Ugga L, et al. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. European Radiology. 2023;33(3):1884–94. doi: https://doi.org/10.1007/s00330-022-09187-3.

    Article  PubMed  Google Scholar 

  48. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. European Urology. 2019;76(3):340–51. doi: https://doi.org/https://doi.org/10.1016/j.eururo.2019.02.033.

    Article  PubMed  Google Scholar 

  49. Heye T, Merkle EM, Reiner CS, Davenport MS, Horvath JJ, Feuerlein S, et al. Reproducibility of Dynamic Contrast-enhanced MR Imaging. Part II. Comparison of Intra- and Interobserver Variability with Manual Region of Interest Placement Versus Semiautomatic Lesion Segmentation and Histogram Analysis. Radiology. 2013;266(3):812–21. doi: https://doi.org/10.1148/radiol.12120255.

    Article  PubMed  Google Scholar 

  50. van Dam IE, van Sörnsen de Koste JR, Hanna GG, Muirhead R, Slotman BJ, Senan S. Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool. Radiotherapy and Oncology. 2010;96(1):67–72. doi: https://doi.org/https://doi.org/10.1016/j.radonc.2010.05.003.

    Article  PubMed  Google Scholar 

  51. Tao L, Ma L, Xie M, Liu X, Tian Z, Fei B. Automatic Segmentation of the Prostate on MR Images Based on Anatomy and Deep Learning. Proc SPIE Int Soc Opt Eng. 2021;11598. doi: https://doi.org/10.1117/12.2581893.

  52. Fave X, Cook M, Frederick A, Zhang L, Yang J, Fried D, et al. Preliminary investigation into sources of uncertainty in quantitative imaging features. Computerized Medical Imaging and Graphics. 2015;44:54–61. doi: https://doi.org/https://doi.org/10.1016/j.compmedimag.2015.04.006.

    Article  PubMed  Google Scholar 

  53. Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis? European Journal of Radiology. 2013;82(2):342–8. doi: https://doi.org/https://doi.org/10.1016/j.ejrad.2012.10.023.

    Article  PubMed  Google Scholar 

  54. Ibrahim A, Vallières M, Woodruff H, Primakov S, Beheshti M, Keek S, et al. Radiomics Analysis for Clinical Decision Support in Nuclear Medicine. Semin Nucl Med. 2019;49(5):438–49. doi: https://doi.org/10.1053/j.semnuclmed.2019.06.005.

    Article  PubMed  Google Scholar 

  55. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015;278(2):563–77. doi: https://doi.org/10.1148/radiol.2015151169.

    Article  PubMed  Google Scholar 

  56. Peerlings J, Woodruff HC, Winfield JM, Ibrahim A, Van Beers BE, Heerschap A, et al. Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Scientific Reports. 2019;9(1):4800. doi: https://doi.org/10.1038/s41598-019-41344-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chaddad A, Kucharczyk MJ, Daniel P, Sabri S, Jean-Claude BJ, Niazi T, et al. Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation. Front Oncol. 2019;9:374. doi: https://doi.org/10.3389/fonc.2019.00374.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board. Radiology. 2019;294(3):487–9. doi: https://doi.org/10.1148/radiol.2019192515.

    Article  PubMed  Google Scholar 

  59. Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence. 2020;2(2):e200029. doi: https://doi.org/10.1148/ryai.2020200029.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging. 2013;26(6):1045–57. doi: https://doi.org/10.1007/s10278-013-9622-7.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Oakden-Rayner L. Exploring Large-scale Public Medical Image Datasets. Acad Radiol. 2020;27(1):106–12. doi: https://doi.org/https://doi.org/10.1016/j.acra.2019.10.006.

    Article  PubMed  Google Scholar 

  62. Pinto dos Santos D, Dietzel M, Baessler B. A decade of radiomics research: are images really data or just patterns in the noise? Eur Radiol. 2021;31(1):1–4. doi: https://doi.org/10.1007/s00330-020-07108-w.

    Article  PubMed  Google Scholar 

  63. Hou Y, Zhang YH, Bao J, Bao ML, Yang G, Shi HB, et al. Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study. European Journal of Nuclear Medicine and Molecular Imaging. 2021;48(12):3805–16. doi: https://doi.org/10.1007/s00259-021-05381-5.

    Article  CAS  PubMed  Google Scholar 

  64. Moroianu ŞL, Bhattacharya I, Seetharaman A, Shao W, Kunder CA, Sharma A, et al. Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning. Cancers (Basel). 2022;14(12). doi: https://doi.org/10.3390/cancers14122821.

  65. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328–38. doi: https://doi.org/10.1148/radiol.2020191145.

    Article  PubMed  Google Scholar 

  66. Giganti F, Coppola A, Ambrosi A, Ravelli S, Esposito A, Freschi M, et al. Apparent diffusion coefficient in the evaluation of side-specific extracapsular extension in prostate cancer: Development and external validation of a nomogram of clinical use. Urologic Oncology: Seminars and Original Investigations. 2016;34(7):291.e9-e17. doi: https://doi.org/10.1016/j.urolonc.2016.02.015.

    Article  PubMed  Google Scholar 

  67. Chen Y, Yu W, Fan Y, Zhou L, Yang Y, Wang H, et al. Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer. Oncotarget. 2017;8(13):22095–103. doi: https://doi.org/10.18632/oncotarget.11559.

    Article  PubMed  Google Scholar 

  68. Lebacle C, Roudot-Thoraval F, Moktefi A, Bouanane M, De La Taille A, Salomon L. Integration of MRI to clinical nomogram for predicting pathological stage before radical prostatectomy. World J Urol. 2017;35(9):1409–15. doi: https://doi.org/10.1007/s00345-016-1981-5.

    Article  PubMed  Google Scholar 

  69. Morlacco A, Sharma V, Viers BR, Rangel LJ, Carlson RE, Froemming AT, et al. The Incremental Role of Magnetic Resonance Imaging for Prostate Cancer Staging before Radical Prostatectomy. European Urology. 2017;71(5):701–4. doi: https://doi.org/10.1016/j.eururo.2016.08.015.

    Article  PubMed  Google Scholar 

  70. Martini A, Gupta A, Cumarasamy S, Beksac A, Lewis SC, Haines K, et al. A Novel side specific mpMRI-based nomogram for the prediction of extra capsular extension of prostate cancer and update of the incremental nerve sparing algorithm. Journal of Endourology. 2018;32:A178. doi: https://doi.org/10.1089/end.2018.29043.abstracts.

    Article  Google Scholar 

  71. Weaver JK, Kim EH, Vetter JM, Shetty A, Grubb RL, Strope SA, et al. Prostate Magnetic Resonance Imaging Provides Limited Incremental Value over the Memorial Sloan Kettering Cancer Center Preradical Prostatectomy Nomogram. Urology. 2018;113:119–28. doi: https://doi.org/10.1016/j.urology.2017.10.051.

    Article  PubMed  Google Scholar 

  72. Rayn KN, Bloom JB, Gold SA, Hale GR, Baiocco JA, Mehralivand S, et al. Added Value of Multiparametric Magnetic Resonance Imaging to Clinical Nomograms for Predicting Adverse Pathology in Prostate Cancer. Journal of Urology. 2018;200(5):1041–7. doi: https://doi.org/10.1016/j.juro.2018.05.094.

    Article  PubMed  Google Scholar 

  73. Zanelli E, Giannarini G, Cereser L, Zuiani C, Como G, Pizzolitto S, et al. Head-to-head comparison between multiparametric MRI, the Partin tables, Memorial Sloan Kettering Cancer Center nomogram, and CAPRA score in predicting extraprostatic cancer in patients undergoing radical prostatectomy. J Magn Reson Imaging. 2019;50(5):1604–13. doi: https://doi.org/10.1002/jmri.26743.

    Article  PubMed  Google Scholar 

  74. Jansen BHE, Nieuwenhuijzen JA, Oprea-Lager DE, Yska MJ, Lont AP, van Moorselaar RJA, et al. Adding multiparametric MRI to the MSKCC and Partin nomograms for primary prostate cancer: Improving local tumor staging? Urol Oncol. 2019;37(3):181.e1-e6. doi: https://doi.org/10.1016/j.urolonc.2018.10.026.

    Article  PubMed  Google Scholar 

  75. Zapała P, Dybowski B, Bres-Niewada E, Lorenc T, Powała A, Lewandowski Z, et al. Predicting side-specific prostate cancer extracapsular extension: a simple decision rule of PSA, biopsy, and MRI parameters. Int Urol Nephrol. 2019;51(9):1545–52. doi: https://doi.org/10.1007/s11255-019-02195-1.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Nyarangi-Dix J, Wiesenfarth M, Bonekamp D, Hitthaler B, Schütz V, Dieffenbacher S, et al. Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for the Prediction of Extraprostatic Disease—A Risk Model for Patient-Tailored Risk Stratification When Planning Radical Prostatectomy. Eur Urol Focus. 2020;6(6):1205–12. doi: https://doi.org/10.1016/j.euf.2018.11.004.

    Article  PubMed  Google Scholar 

  77. Soeterik TFW, van Melick HHE, Dijksman LM, Küsters-Vandevelde H, Stomps S, Schoots IG, et al. Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy. European Urology Oncology. 2020. doi: https://doi.org/10.1016/j.euo.2020.08.008.

    Article  PubMed  Google Scholar 

  78. Alves JR, Muglia VF, Lucchesi FR, Faria RAOG, Alcantara-Quispe C, Vazquez VL, et al. Independent external validation of nomogram to predict extracapsular extension in patients with prostate cancer. Eur Radiol. 2020;30(9):5004–10. doi: https://doi.org/10.1007/s00330-020-06839-0.

    Article  PubMed  Google Scholar 

  79. Ravi Chandran K, Thomas A, Sanjeevan KV, Ginil KP. Development of a novel Indian nomogram for predicting extracapsular extension in prostate cancer. Indian Journal of Urology. 2020;36(5):S3–S4.

    Google Scholar 

  80. Soeterik TFW, van Melick HHE, Dijksman LM, Küsters-Vandevelde HVN, Biesma DH, Witjes JA, et al. External validation of the Martini nomogram for prediction of side-specific extraprostatic extension of prostate cancer in patients undergoing robot-assisted radical prostatectomy. Urol Oncol. 2020;38(5):372–8. doi: https://doi.org/10.1016/j.urolonc.2019.12.028.

    Article  PubMed  Google Scholar 

  81. Di Trapani E, Luzzago S, Peveri G, Catellani M, Ferro M, Cordima G, et al. A novel nomogram predicting lymph node invasion among patients with prostate cancer: The importance of extracapsular extension at multiparametric magnetic resonance imaging. Urol Oncol. 2021;39(7):431.e15-e22. doi: https://doi.org/10.1016/j.urolonc.2020.11.040.

    Article  PubMed  Google Scholar 

  82. Diamand R, Ploussard G, Roumiguié M, Oderda M, Benamran D, Fiard G, et al. External Validation of a Multiparametric Magnetic Resonance Imaging-Based Nomogram for the Prediction of Extracapsular Extension and Seminal Vesicle Invasion in Prostate Cancer Patients Undergoing Radical Prostatectomy. Eur Urol. 2021;79(2):180–5. doi: https://doi.org/10.1016/j.eururo.2020.09.037.

    Article  PubMed  Google Scholar 

  83. Majchrzak N, Cieśliński P, Głyda M, Karmelita-Katulska K. Prostate Magnetic Resonance Imaging Analyses, Clinical Parameters, and Preoperative Nomograms in the Prediction of Extraprostatic Extension. Clin Pract. 2021;11(4):763–74. doi: https://doi.org/10.3390/clinpract11040091.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Wibmer AG, Kattan MW, Alessandrino F, Baur ADJ, Boesen L, Franco FB, et al. International Multi-site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer. Cancers (Basel). 2021;13(11). doi: https://doi.org/10.3390/cancers13112627.

  85. Zapała P, Kozikowski M, Dybowski B, Zapała Ł, Dobruch J, Radziszewski P. External validation of a magnetic resonance imaging-based algorithm for prediction of side-specific extracapsular extension in prostate cancer. Cent European J Urol. 2021;74(3):327–33. doi: https://doi.org/10.5173/ceju.2021.0128.R2.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Stanzione A, Cuocolo R, Cocozza S, Romeo V, Persico F, Fusco F, et al. Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results. Acad Radiol. 2019;26(10):1338–44. doi: https://doi.org/10.1016/j.acra.2018.12.025.

    Article  PubMed  Google Scholar 

  87. Ma S, Xie H, Wang H, Yang J, Han C, Wang X, et al. Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer. Mol Imaging Biol. 2020;22(3):711–21. doi: https://doi.org/10.1007/s11307-019-01405-7.

    Article  CAS  PubMed  Google Scholar 

  88. He D, Wang X, Fu C, Wei X, Bao J, Ji X, et al. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging. 2021;21(1). doi: https://doi.org/10.1186/s40644-021-00414-6.

  89. Bai H, Xia W, Ji X, He D, Zhao X, Bao J, et al. Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension with Prostate Cancer. J Magn Reson Imaging. 2021;54(4):1222–30. doi: https://doi.org/10.1002/jmri.27678.

    Article  PubMed  Google Scholar 

  90. Fan X, Xie N, Chen J, Li T, Cao R, Yu H, et al. Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer. Front Oncol. 2022;12. doi: https://doi.org/10.3389/fonc.2022.839621.

Download references

Funding

This research was funded by the University of Florida College of Medicine Jacksonville Launchpad Initiative Grant.

Author information

Authors and Affiliations

  1. Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA

    Luis F. Calimano-Ramirez, Mayur K. Virarkar, Mauricio Hernandez, Savas Ozdemir, Sindhu Kumar, Dheeraj R. Gopireddy, Chandana Lall & Kazim Z. Gumus

  2. Department of Urology, University of Florida College of Medicine, Jacksonville, FL, 32209, USA

    K. C. Balaji

  3. Department of Computer Science and Information System, Texas A&M University-Commerce, Commerce, TX, 75428, USA

    Mutlu Mete

Authors
  1. Luis F. Calimano-Ramirez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mayur K. Virarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauricio Hernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Savas Ozdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sindhu Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dheeraj R. Gopireddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chandana Lall
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. K. C. Balaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mutlu Mete
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kazim Z. Gumus
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to this paper with the conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.

Corresponding author

Correspondence to Kazim Z. Gumus.

Ethics declarations

Conflict of interest

Authors declare no conflict of interests for this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 30 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Calimano-Ramirez, L.F., Virarkar, M.K., Hernandez, M. et al. MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdom Radiol 48, 2379–2400 (2023). https://doi.org/10.1007/s00261-023-03924-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-023-03924-y

Keywords

Search

Navigation