Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Bản đồ thông số của mô hình không gian hai ngăn cho MRI tăng cường tương phản động tuyến tiền liệt - so sánh với mô hình Tofts chuẩn trong chẩn đoán ung thư tuyến tiền liệt
Tóm tắt
Mô hình không gian hai ngăn (2TCM) được sử dụng để phân tích dữ liệu MRI tăng cường tương phản động (DCE) của tuyến tiền liệt và được so sánh với mô hình Tofts chuẩn. Tổng cộng có 29 bệnh nhân mắc ung thư tuyến tiền liệt do sinh thiết xác nhận đã được đưa vào nghiên cứu được phê duyệt bởi IRB này. Dữ liệu MRI được thu thập trên máy quét Philips Achieva 3T-TX. Sau khi thực hiện hình ảnh trọng số T2 và hình ảnh khuếch tán, dữ liệu DCE được thu thập bằng chuỗi 3D T1-FFE mDIXON trước và sau khi tiêm thuốc cản quang (0.1 mmol/kg Multihance) cho 60 lần quét động với độ phân giải tạm thời là 8.3s/hình. Mô hình 2TCM có một ngăn trao đổi nhanh ($${\text{K}}_{\text{1}}^{\text{trans}}$$ và $${\text{k}}_{\text{ep}}^{\text{1}}$$) và một ngăn trao đổi chậm ($${\text{K}}_{\text{2}}^{\text{trans}}$$ và $${\text{k}}_{\text{ep}}^{\text{2}}$$), so với các tham số của mô hình Tofts chuẩn (Ktrans và kep). Trung bình, ung thư tuyến tiền liệt có giá trị cao hơn đáng kể (p < 0.01) so với mô hình tuyến tiền liệt bình thường cho tất cả các tham số được tính toán. Có một mối tương quan mạnh (r = 0.94, p < 0.001) giữa Ktrans và $${\text{K}}_{\text{1}}^{\text{trans}}$$ đối với ung thư, nhưng mối tương quan yếu (r = 0.28, p < 0.05) giữa kep và $${\text{k}}_{\text{ep}}^{\text{1}}$$. Sai số bình quân căn bậc hai (RMSE) trong việc phù hợp từ mô hình 2TCM nhỏ hơn đáng kể (p < 0.001) so với RMSE trong việc phù hợp từ mô hình Tofts. Phân tích đặc trưng hoạt động của người nhận (ROC) cho thấy $${\text{K}}_{\text{1}}^{\text{trans}}$$ nhanh có diện tích dưới đường cong (AUC) cao nhất so với bất kỳ tham số đơn lẻ nào khác. Tổng hợp bốn tham số từ mô hình 2TCM có giá trị AUC cao hơn đáng kể so với tổng hợp hai tham số từ mô hình Tofts. Mô hình 2TCM hữu ích cho phân tích định lượng dữ liệu DCE-MRI tuyến tiền liệt và cung cấp thông tin mới trong chẩn đoán ung thư tuyến tiền liệt.
Từ khóa
#mô hình hai ngăn #MRI tăng cường tương phản động #tuyến tiền liệt #mô hình Tofts #ung thư tuyến tiền liệtTài liệu tham khảo
Gaunay G, Patel V, Shah P, Moreira D, Hall SJ, Vira MA, Schwartz M, Kreshover J, Ben-Levi E, Villani R, Rastinehad A, Richstone L (2017) Role of multi-parametric MRI of the prostate for screening and staging: experience with over 1500 cases. Asian J Urol 4(1):68–74. https://doi.org/10.1016/j.ajur.2016.09.011
Stabile A, Giganti F, Rosenkrantz AB, Taneja SS, Villeirs G, Gill IS, Allen C, Emberton M, Moore CM, Kasivisvanathan V (2020) Multiparametric MRI for prostate cancer diagnosis: current status and future directions. Nat Rev Urol 17(1):41–61. https://doi.org/10.1038/s41585-019-0212-4
Chatterjee A, He D, Fan X, Antic T, Jiang Y, Eggener S, Karczmar GS, Oto A (2019) Diagnosis of prostate cancer by use of MRI-derived quantitative risk maps: a feasibility study. AJR Am J Roentgenol 213(2):W66–W75. https://doi.org/10.2214/ajr.18.20702
Belue MJ, Yilmaz EC, Daryanani A, Turkbey B (2022) Current status of biparametric MRI in prostate cancer diagnosis: literature analysis. Life (Basel) 12(6):804. https://doi.org/10.3390/life12060804
Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76(3):340–351. https://doi.org/10.1016/j.eururo.2019.02.033
Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, Thoeny HC, Verma S (2016) PI-RADS prostate imaging—reporting and data system: 2015, version 2. Eur Urol 69(1):16–40. https://doi.org/10.1016/j.eururo.2015.08.052
Thestrup KC, Logager V, Baslev I, Møller JM, Hansen RH, Thomsen HS (2016) Biparametric versus multiparametric MRI in the diagnosis of prostate cancer. Acta Radiol Open 5(8):2058460116663046. https://doi.org/10.1177/2058460116663046
Junker D, Steinkohl F, Fritz V, Bektic J, Tokas T, Aigner F, Herrmann TRW, Rieger M, Nagele U (2019) Comparison of multiparametric and biparametric MRI of the prostate: are gadolinium-based contrast agents needed for routine examinations? World J Urol 37(4):691–699. https://doi.org/10.1007/s00345-018-2428-y
Tamada T, Kido A, Yamamoto A, Takeuchi M, Miyaji Y, Moriya T, Sone T (2021) Comparison of biparametric and multiparametric mri for clinically significant prostate cancer detection with PI-RADS version 2.1. J Magn Reson Imaging 53(1):283–291. https://doi.org/10.1002/jmri.27283
Efiloglu O, Gunduz N, Iplikci A, Dogan MB, Cakici MC, Turan T, Yildirim A (2022) Comparison of biparametric and multiparametric prostate magnetic resonance imaging in predicting oncologic outcomes after radical prostatectomy. Medeni Med J 37(4):313–319. https://doi.org/10.4274/MMJ.galenos.2022.78785
Zhang J, Xu L, Zhang G, Zhang X, Bai X, Ji Z, Xiao Y, Sun H, Jin Z (2022) Comparison between biparametric and multiparametric MRI diagnosis strategy for prostate cancer in the peripheral zone using PI-RADS version 2.1. Abdom Radiol 47(8):2905–2916. https://doi.org/10.1007/s00261-022-03553-x
Sherrer RL, Glaser ZA, Gordetsky JB, Nix JW, Porter KK, Rais-Bahrami S (2019) Comparison of biparametric MRI to full multiparametric MRI for detection of clinically significant prostate cancer. Prostate Cancer Prostatic Dis 22(2):331–336. https://doi.org/10.1038/s41391-018-0107-0
Song J, Zhao C, Zhang F, Yuan Y, Wang LM, Sah V, Zhang J, Weng W, Yang Z, Wang Z, Wang L (2023) The diagnostic performance in clinically significant prostate cancer with PI-RADS version 2.1: simplified bpMRI versus standard mpMRI. Abdom Radiol 48(2):704–712. https://doi.org/10.1007/s00261-022-03750-8
Mason J, Adiotomre E, Bownes P, Carey B, Henry A (2018) Importance of dynamic contrast enhanced magnetic resonance imaging for targeting biopsy and salvage treatments after prostate cancer recurrence. J Contemp Brachyther 10(6):570–572. https://doi.org/10.5114/jcb.2018.79667
Muller BG, van den Bos W, Brausi M, Fütterer JJ, Ghai S, Pinto PA, Popeneciu IV, de Reijke TM, Robertson C, de la Rosette JJ, Scionti S, Turkbey B, Wijkstra H, Ukimura O, Polascik TJ (2015) Follow-up modalities in focal therapy for prostate cancer: results from a delphi consensus project. World J Urol 33(10):1503–1509. https://doi.org/10.1007/s00345-014-1475-2
Verma S, Turkbey B, Muradyan N, Rajesh A, Cornud F, Haider MA, Choyke PL, Harisinghani M (2012) Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol 198(6):1277–1288. https://doi.org/10.2214/ajr.12.8510
Wu LM, Xu JR, Gu HY, Hua J, Zhu J, Chen J, Zhang W, Hu J (2013) Role of magnetic resonance imaging in the detection of local prostate cancer recurrence after external beam radiotherapy and radical prostatectomy. Clin Oncol 25(4):252–264. https://doi.org/10.1016/j.clon.2012.11.010
Wu X, Reinikainen P, Kapanen M, Vierikko T, Ryymin P, Kellokumpu-Lehtinen PL (2018) Dynamic contrast-enhanced imaging as a prognostic tool in early diagnosis of prostate cancer: correlation with psa and clinical stage. Contrast Media Mol Imaging 2018:3181258. https://doi.org/10.1155/2018/3181258
Chatterjee A, He D, Fan X, Wang S, Szasz T, Yousuf A, Pineda F, Antic T, Mathew M, Karczmar GS, Oto A (2018) Performance of ultrafast DCE-MRI for diagnosis of prostate cancer. Acad Radiol 25(3):349–358. https://doi.org/10.1016/j.acra.2017.10.004
Sun C, Chatterjee A, Yousuf A, Antic T, Eggener S, Karczmar GS, Oto A (2019) Comparison of T2-weighted imaging, DWI, and dynamic contrast-enhanced MRI for calculation of prostate cancer index lesion volume: correlation with whole-mount pathology. AJR Am J Roentgenol 212(2):351–356. https://doi.org/10.2214/ajr.18.20147
Palumbo P, Manetta R, Izzo A, Bruno F, Arrigoni F, De Filippo M, Splendiani A, Di Cesare E, Masciocchi C, Barile A (2020) Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement. Gland Surg 9(6):2235–2247. https://doi.org/10.21037/gs-20-547
Lovegrove CE, Matanhelia M, Randeva J, Eldred-Evans D, Tam H, Miah S, Winkler M, Ahmed HU, Shah TT (2018) Prostate imaging features that indicate benign or malignant pathology on biopsy. Transl Androl Urol 7(Suppl 4):S420–S435. https://doi.org/10.21037/tau.2018.07.06
Ziayee F, Ullrich T, Blondin D, Irmer H, Arsov C, Antoch G, Quentin M, Schimmöller L (2021) Impact of qualitative, semi-quantitative, and quantitative analyses of dynamic contrast-enhanced magnet resonance imaging on prostate cancer detection. PLoS ONE 16(4):e0249532. https://doi.org/10.1371/journal.pone.0249532
Cristel G, Esposito A, Damascelli A, Briganti A, Ambrosi A, Brembilla G, Brunetti L, Antunes S, Freschi M, Montorsi F, Del Maschio A, De Cobelli F (2019) Can DCE-MRI reduce the number of PI-RADS v.2 false positive findings? Role of quantitative pharmacokinetic parameters in prostate lesions characterization. Eur J Radiol 118:51–57. https://doi.org/10.1016/j.ejrad.2019.07.002
Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, Larsson HB, Lee TY, Mayr NA, Parker GJ, Port RE, Taylor J, Weisskoff RM (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10(3):223–232
Yang X, Knopp MV (2011) Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol 2011:732848. https://doi.org/10.1155/2011/732848
Chang YC, Ackerstaff E, Tschudi Y, Jimenez B, Foltz W, Fisher C, Lilge L, Cho H, Carlin S, Gillies RJ, Balagurunathan Y, Yechieli RL, Subhawong T, Turkbey B, Pollack A, Stoyanova R (2017) Delineation of tumor habitats based on dynamic contrast enhanced MRI. Sci Rep 7(1):9746. https://doi.org/10.1038/s41598-017-09932-5
Ge R, Wang Z, Cheng L (2022) Tumor microenvironment heterogeneity an important mediator of prostate cancer progression and therapeutic resistance. NPJ Precis Oncol 6(1):31. https://doi.org/10.1038/s41698-022-00272-w
Franiel T, Ludemann L, Rudolph B, Rehbein H, Staack A, Taupitz M, Prochnow D, Beyersdorff D (2008) Evaluation of normal prostate tissue, chronic prostatitis, and prostate cancer by quantitative perfusion analysis using a dynamic contrast-enhanced inversion-prepared dual-contrast gradient echo sequence. Invest Radiol 43(7):481–487. https://doi.org/10.1097/RLI.0b013e31816b2f63
Schimpf O, Hindel S, Ludemann L (2017) Assessment of micronecrotic tumor tissue using dynamic contrast-enhanced magnetic resonance imaging. Phys Med 34:38–47. https://doi.org/10.1016/j.ejmp.2017.01.010
Port RE, Knopp MV, Hoffmann U, Milker-Zabel S, Brix G (1999) Multicompartment analysis of gadolinium chelate kinetics: blood-tissue exchange in mammary tumors as monitored by dynamic MR imaging. J Magn Reson Imaging 10(3):233–241
Li X, Priest RA, Woodward WJ, Tagge IJ, Siddiqui F, Huang W, Rooney WD, Beer TM, Garzotto MG, Springer CS Jr (2013) Feasibility of shutter-speed DCE-MRI for improved prostate cancer detection. Magn Reson Med 69(1):171–178. https://doi.org/10.1002/mrm.24211
Brix G, Zwick S, Kiessling F, Griebel J (2009) Pharmacokinetic analysis of tissue microcirculation using nested models: multimodel inference and parameter identifiability. Med Phys 36(7):2923–2933. https://doi.org/10.1118/1.3147145
Sourbron SP, Buckley DL (2011) On the scope and interpretation of the tofts models for DCE-MRI. Magn Reson Med 66(3):735–745. https://doi.org/10.1002/mrm.22861
Sommer JC, Schmid VJ (2014) Spatial two-tissue compartment model fordynamic contrast-enhanced magnetic resonance imaging. J Royal Stat Soc Ser C 63:695–713
Reich JG (1981) On parameter redundancy in curve fitting of Kinetic Data. In: Endrenyi L (ed) Kinetic data analysis: design and analysis of enzyme and pharmacokinetic experiments. Springer US, Boston, MA, pp 39–50. doi:https://doi.org/10.1007/978-1-4613-3255-8_3
Feilke M, Bischl B, Schmid VJ, Gertheiss J (2016) Boosting in nonlinear regression models with an application to DCE-MRI data. Methods Inf Med 55(1):31–41. https://doi.org/10.3414/me14-01-0131
Duan C, Kallehauge JF, Bretthorst GL, Tanderup K, Ackerman JJ, Garbow JR (2017) Are complex DCE-MRI models supported by clinical data? Magn Reson Med 77(3):1329–1339. https://doi.org/10.1002/mrm.26189
Mustafi D, Gleber SC, Ward J, Dougherty U, Zamora M, Markiewicz E, Binder DC, Antic T, Vogt S, Karczmar GS, Oto A (2015) IV administered gadodiamide enters the lumen of the prostatic glands: X-Ray fluorescence microscopy examination of a mouse model. AJR Am J Roentgenol 205(3):W313-319. https://doi.org/10.2214/ajr.14.14055
Dale BM, Jesberger JA, Lewin JS, Hillenbrand CM, Duerk JL (2003) Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI. J Magn Reson Imaging 18(5):575–584. https://doi.org/10.1002/jmri.10399
Heye AK, Thrippleton MJ, Armitage PA, Valdés Hernández MDC, Makin SD, Glatz A, Sakka E, Wardlaw JM (2016) Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability. NeuroImage 125:446–455. https://doi.org/10.1016/j.neuroimage.2015.10.018
Luypaert R, Ingrisch M, Sourbron S, de Mey J (2012) The Akaike information criterion in DCE-MRI: does it improve the haemodynamic parameter estimates? Phys Med Biol 57(11):3609–3628. https://doi.org/10.1088/0031-9155/57/11/3609
Johnson LM, Turkbey B, Figg WD, Choyke PL (2014) Multiparametric MRI in prostate cancer management. Nat Rev Clin Oncol 11(6):346–353. https://doi.org/10.1038/nrclinonc.2014.69
Peled S, Vangel M, Kikinis R, Tempany CM, Fennessy FM, Fedorov A (2019) Selection of fitting model and arterial input function for repeatability in dynamic contrast-enhanced prostate MRI. Acad Radiol 26(9):e241–e251. https://doi.org/10.1016/j.acra.2018.10.018
Rosenkrantz AB, Sabach A, Babb JS, Matza BW, Taneja SS, Deng FM (2013) Prostate cancer: comparison of dynamic contrast-enhanced MRI techniques for localization of peripheral zone tumor. AJR Am J Roentgenol 201(3):W471–478. https://doi.org/10.2214/ajr.12.9737
Berman RM, Brown AM, Chang SD, Sankineni S, Kadakia M, Wood BJ, Pinto PA, Choyke PL, Turkbey B (2016) DCE MRI of prostate cancer. Abdom Radiol 41(5):844–853. https://doi.org/10.1007/s00261-015-0589-3
Dickinson L, Ahmed HU, Allen C, Barentsz JO, Carey B, Futterer JJ, Heijmink SW, Hoskin PJ, Kirkham A, Padhani AR, Persad R, Puech P, Punwani S, Sohaib AS, Tombal B, Villers A, van der Meulen J, Emberton M (2011) Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: recommendations from a european consensus meeting. Eur Urol 59(4):477–494. https://doi.org/10.1016/j.eururo.2010.12.009
Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel’farb G, Ouseph R, Dwyer AC (2014) Models and methods for analyzing DCE-MRI: a review. Med Phys 41(12):124301. https://doi.org/10.1118/1.4898202
Viswanath S, Bloch BN, Genega E, Rofsky N, Lenkinski R, Chappelow J, Toth R, Madabhushi A (2008) A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI. Med Image Comput Comput Assist Interv 11(Pt 1):662–669. https://doi.org/10.1007/978-3-540-85988-8_79
Jackson AS, Reinsberg SA, Sohaib SA, Charles-Edwards EM, Jhavar S, Christmas TJ, Thompson AC, Bailey MJ, Corbishley CM, Fisher C, Leach MO, Dearnaley DP (2009) Dynamic contrast-enhanced MRI for prostate cancer localization. Br J Radiol 82(974):148–156. https://doi.org/10.1259/bjr/89518905
Reynolds HM, Williams S, Zhang A, Chakravorty R, Rawlinson D, Ong CS, Esteva M, Mitchell C, Parameswaran B, Finnegan M, Liney G, Haworth A (2015) Development of a registration framework to validate MRI with histology for prostate focal therapy. Med Phys 42(12):7078–7089. https://doi.org/10.1118/1.4935343