This study aims to assess and compare the diagnostic performance of three advanced imaging modalities—multiparametric magnetic resonance imaging (mpMRI), transrectal ultrasound (TRUS), and positron emission tomography/computed tomography (PET/CT)—in detecting prostate cancer in patients with elevated PSA levels and abnormal DRE findings.
Methods
A retrospective analysis was conducted on 150 male patients aged 50–75 years with elevated PSA and abnormal DRE. The diagnostic accuracy of each modality was assessed through sensitivity, specificity, and the area under the curve (AUC) to compare performance in detecting clinically significant prostate cancer (Gleason score ≥ 7).
Results
MpMRI demonstrated the highest diagnostic performance, with a sensitivity of 90%, specificity of 85%, and AUC of 0.92, outperforming both TRUS (sensitivity 76%, specificity 78%, AUC 0.77) and PET/CT (sensitivity 82%, specificity 80%, AUC 0.81). MpMRI detected clinically significant tumors in 80% of cases. Although TRUS and PET/CT had similar detection rates for significant tumors, their overall accuracy was lower. Minor adverse events occurred in 5% of patients undergoing TRUS, while no significant complications were associated with mpMRI or PET/CT.
Conclusion
These findings suggest that mpMRI is the most reliable imaging modality for early detection of clinically significant prostate cancer. It reduces the need for unnecessary biopsies and optimizes patient management.
Nguyen-NielsenMBorreM. Diagnostic and therapeutic strategies for prostate cancer. Semin Nucl Med2016 Nov; 46: 484–490.
2.
TanejaSS. Re: focal ablation targeted to the index lesion in multifocal localised prostate cancer: a prospective development study. J Urol2016 Aug; 196: 14.
3.
DongDXJiZG. Current progress and controversies in prostate cancer management. Chin Med J (Engl)2017 Dec; 130: 2991–2995.
4.
ChatterjeeADevarajAMathewM, et al.Performance of T2 maps in the detection of prostate cancer. Acad Radiol2019; 26: 15–21.
5.
FiorentinoVMartiniMDell’AquilaM, et al.Histopathological ratios to predict gleason score agreement between biopsy and radical prostatectomy. Diagnostics2021; 11: 10.
6.
MoonDHEfstathiouJAChenRC. What is the best way to radiate the prostate in 2016?Urol Oncol2017 Feb; 35: 59–68.
7.
FilellaXFojL. Prostate cancer detection and prognosis: from prostate specific antigen (PSA) to exosomal biomarkers. Int J Mol Sci2016; 17: 1–22.
8.
DearnaleyDPHallELawrenceD, et al.Phase III pilot study of dose escalation using conformal radiotherapy in prostate cancer: PSA control and side effects. Br J Cancer2005 Feb; 92: 488–498.
9.
KhooVS. Radiotherapeutic techniques for prostate cancer, dose escalation and brachytherapy. Clin Oncol (R Coll Radiol)2005 Oct; 17: 560–571.
10.
CarterHBAlbertsenPCBarryMJ, et al.Early detection of prostate cancer: AUA guideline. J Urol2013 Aug; 190: 419–426.
11.
ZelefskyMJPeiXChouJF, et al.Dose escalation for prostate cancer radiotherapy: predictors of long-term biochemical tumor control and distant metastases-free survival outcomes. Eur Urol2011 Dec; 60: 1133–1139.
12.
MansouriNMsakniIGargouriF, et al.Evaluation of concordance of Gleason score between prostate biopsy and radical prostatectomy. Tunis Med2018 Jul; 96: 430–436.
13.
NilssonSNorlenBJWidmarkA. A systematic overview of radiation therapy effects in prostate cancer. Acta Oncol2004; 43: 316–381.
14.
GavadeABNerliRKanwalN, et al.Automated diagnosis of prostate cancer using mpmri images: a deep learning approach for clinical decision support. Computers2023; 12: 152.
15.
JahnenMHauslerTMeissnerVH, et al.Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center. World J Urol2024; 42: 1–7.
16.
HarderFNHemingCAMHaiderMA. mpMRI interpretation in active surveillance for prostate cancer—an overview of the PRECISE score. Abdom Radiol2023; 48: 2449–2455.
17.
WindischOValerioMYeeCH, et al.Biopsy strategies in the era of mpMRI: a comprehensive review. Prostate Cancer Prostatic Dis2024; 25: 1–10.
18.
LopciELazzeriMColomboP, et al.Diagnostic performance and clinical impact of PSMA PET/CT versus mpMRI in patients with a high suspicion of prostate cancer and previously negative biopsy: a prospective trial (PROSPET-BX). Urol Int2023; 107: 433–439.
19.
SongJZhaoCZhangF, et al.The diagnostic performance in clinically significant prostate cancer with PI-RADS version 2.1: simplified bpMRI versus standard mpMRI. Abdom Radiol2023; 48: 704–712.
20.
PepePPennisiM. Targeted biopsy in men high risk for prostate cancer: 68Ga-PSMA PET/CT versus mpMRI. Clin Genitourin Cancer2023; 21: 639–642.
21.
RiedelAMichaelMGrünbergJ, et al.The role of multiparametric MRI (mpMRI) in the prediction of adverse prostate cancer pathology in radical prostatectomy specimen. Urol Int2024; 108: 146–152.
22.
MendhirattaNMengXTanejaSS. Using multiparametric MRI to “personalize” biopsy for men. Curr Opin Urol2015 Nov; 25: 498–503.
23.
MasiMLandoniVFaiellaA, et al.Comparison of rigid and deformable coregistration between mpMRI and CT images in radiotherapy of prostate bed cancer recurrence. Phys Medica2021; 92: 32–39.
24.
KhanfariHMehranfarSChekiM, et al.Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI. BMC Med Imaging2023; 23: 195.
CaglicIKovacVBarrettT. Multiparametric MRI - local staging of prostate cancer and beyond. Radiol Oncol2019 May; 53: 159–170.
27.
SantiBDSalviMGianniniV, et al.Multimodal T2w and DWI prostate gland automated registration. Conf Proc . Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf2019 Jul; 2019: 4427–4430.
28.
HaiderMAYaoXLoblawA,et al.Multiparametric magnetic resonance imaging in the diagnosis of prostate cancer: a systematic review. Clin Oncol (R Coll Radiol)2016 Sep; 28: 550–567.
29.
IsraelBLeestMSedelaarM, et al.Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 2: interpretation. Eur Urol2020 Apr; 77: 469–480.
30.
CosmaITennstedt-SchenkCWinzlerS, et al.The role of gadolinium in magnetic resonance imaging for early prostate cancer diagnosis: a diagnostic accuracy study. PLoS One2019; 14: e0227031.
31.
ZawaidehJPSalaEShaidaN, et al.Diagnostic accuracy of biparametric versus multiparametric prostate MRI: assessment of contrast benefit in clinical practice. Eur Radiol2020; 30: 4039–4049.
32.
PatelU. TRUS and prostate biopsy: current status. Prostate Cancer Prostatic Dis2004; 7: 208–210.
33.
ToreiniEAitkenMCoopamootooK, et al.The relationship between trust in AI and trustworthy machine learning technologies. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, 2020, pp. 272–83.
34.
BoesenL. Multiparametric MRI in detection and staging of prostate cancer. Scand J Urol2015; 49: 25–34.
35.
BijariSSayfollahiSMardokh-RouhaniS, et al.Radiomics and deep features: robust classification of brain hemorrhages and reproducibility analysis using a 3D autoencoder neural network. Bioengineering2024; 11: 643.
36.
RezaeijoSMRezaeiMPoursheikhaniA, et al.Integrative bioinformatics analysis of miRNA and mRNA expression profiles identified some potential biomarkers for breast cancer. Egypt J Med Hum Genet2023; 24: 62.
37.
MahboubisarighiehAShahverdiHJafarpoor NesheliS, et al.Assessing the efficacy of 3D dual-CycleGAN model for multi-contrast MRI synthesis. Egypt J Radiol Nucl Med2024; 55: 1–12.
38.
SalmanpourMRHosseinzadehMAkbariA, et al. Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features. In: Medical imaging 2022: computer-aided diagnosis. Vol. 12033. SPIE. SPIE, 2022, pp.662–667.
39.
HarsiniSWilsonDSaprunoffH, et al.Outcome of patients with biochemical recurrence of prostate cancer after PSMA PET/CT-directed radiotherapy or surgery without systemic therapy. Cancer Imaging2023; 23: 27.
40.
HagensMJLuiningWIJagerA, et al.The diagnostic value of PSMA PET/CT in men with newly diagnosed unfavorable intermediate-risk prostate cancer. J Nucl Med2023; 64: 1238–1243.
41.
HuangSOngSMcKenzieD, et al.Comparison of 18F-based PSMA radiotracers with [68Ga] Ga-PSMA-11 in PET/CT imaging of prostate cancer—a systematic review and meta-analysis. Prostate Cancer Prostatic Dis2023; 28: 1–11.
42.
UrsoLFilippiLCastelloA, et al.PSMA PET/CT in castration-resistant prostate cancer: myth or reality?J Clin Med2023; 12: 7130.
43.
GafitaADjailebLRauscherI, et al.Response evaluation criteria in PSMA PET/CT (RECIP 1.0) in metastatic castration-resistant prostate cancer. Radiology2023; 308: e222148.
44.
AwiwiMOGjoniMVikramR, et al.MRI and PSMA PET/CT of biochemical recurrence of prostate cancer. Radiographics2023; 43: e230112.
45.
PepePPepeLTamburoM, et al.68Ga-PSMA PET/CT and prostate cancer diagnosis: which SUVmax value?In Vivo (Brooklyn)2023; 37: 1318–1322.
46.
LawalIONdlovuHKgatleM, et al.Prognostic value of PSMA PET/CT in prostate cancer. Semin Nucl Med2024; 54: 46–59.
47.
JochumsenMRBoucheloucheK. PSMA PET/CT for primary staging of prostate cancer-an updated overview. Semin Nucl Med2024; 54: 39–45.
48.
BelalSLFrantzSMinarikD, et al.Applications of artificial intelligence in PSMA PET/CT for prostate cancer imaging. Semin Nucl Med2024; 54: 141–149.
49.
de JongACSegbersMLingSW, et al.68Ga-PSMA PET/CT for response evaluation of 223Ra treatment in metastatic prostate cancer. J Nucl Med2023; 64: 1556–1562.
50.
SutherlandDEKAzadAAMurphyDG, et al.Role of FDG PET/CT in management of patients with prostate cancer. Semin Nucl Med2024; 54: 4–13.
51.
MariACadenarAGiudiciS, et al.A systematic review and meta-analysis to evaluate the diagnostic accuracy of PSMA PET/CT in the initial staging of prostate cancer. Prostate Cancer Prostatic Dis2024; 31: 1–14.
52.
ChenMFuYPengS, et al.The association between [68Ga] PSMA PET/CT response and biochemical progression in patients with high-risk prostate cancer receiving neoadjuvant therapy. J Nucl Med2023; 64: 1550–1555.
53.
SachpekidisCPanLGroezingerM, et al.Combined whole-body dynamic and static PET/CT with low-dose [18F] PSMA-1007 in prostate cancer patients. Eur J Nucl Med Mol Imaging2024; 51: 1–14.
54.
XuYKlyuzhinIHarsiniS, et al.Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss. Comput Biol Med2023; 158: 106882.
55.
SubramanianKMartinezJHuicochea CastellanosS, et al.Complex implementation factors demonstrated when evaluating cost-effectiveness and monitoring racial disparities associated with [18F] DCFPyL PET/CT in prostate cancer men. Sci Rep2023; 13: 8321.
56.
DitonnoFFrancoAManfrediC, et al.Novel non-MRI imaging techniques for primary diagnosis of prostate cancer: micro-ultrasound, contrast-enhanced ultrasound, elastography, multiparametric ultrasound, and PSMA PET/CT. Prostate Cancer Prostatic Dis2024; 27: 29–36.
57.
SalmanpourMRHajianfarGLvW, et al.Multitask outcome prediction using hybrid machine learning and PET-CT fusion radiomics. Soc Nuclear Med2021; 62: 1424.
58.
CaoCYangPXuY, et al.Feasibility of multiparametric imaging with PET/MR in nasopharyngeal carcinoma: a pilot study. Oral Oncol2019 Jun; 93: 91–95.
59.
AltaziBAZhangGGFernandezDC, et al.Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms. J Appl Clin Med Phys2017; 18: 32–48.
60.
VanderstraetenBDuthoyWDe GersemW, et al.[18f]fluoro-deoxy-glucose positron emission tomography ([18F]FDG-PET) voxel intensity-based intensity-modulated radiation therapy (IMRT) for head and neck cancer. Radiother Oncol2006 Jun; 79: 249–258.
61.
SalmanpourMRHosseinzadehMRezaeijoSM, et al.Robustness and reproducibility of radiomics features from fusions of PET-CT images. Soc Nuclear Med2022; 31: 3179–3179.
62.
SeppäläJSeppänenMArponenE, et al.Carbon-11 acetate PET/CT based dose escalated IMRT in prostate cancer. Radiother Oncol2009; 93: 234–240.
63.
RahmimAZaidiH. PET Versus SPECT: strengths, limitations and challenges. Nucl Med Commun2008; 29: 193–207.
64.
RundoLStefanoAMilitelloC, et al.A fully automatic approach for multimodal PET and MR image segmentation in Gamma Knife treatment planning. Comput Methods Programs Biomed2017; 144: 77–96.
65.
ChangJHWadaMAndersonNJ, et al.Hypoxia-targeted radiotherapy dose painting for head and neck cancer using 18 F-FMISO PET: a biological modeling study. Acta Oncol (Madr) [Internet]2013 Nov 15 [cited 2018 May 13]; 52: 1723–1729, http://www.tandfonline.com/doi/full/10.3109/0284186X.2012.759273.
66.
JeongHBaekCSonY, et al.Use of integrated 18F-FDG PET/CT to improve the accuracy of initial cervical nodal evaluation in patients with head and neck squamous cell carcinoma. Head Neck2007; 29: 203–210.
67.
NiyaziMBartensteinPBelkaC,et al.Choline PET based dose-painting in prostate cancer–modelling of dose effects. Radiat Oncol2010 Mar; 5: 23.
68.
RogaschJMMHofheinzFvan HeekL, et al.Influences on PET quantification and interpretation. Diagnostics2022; 12: 451.
69.
HeydarheydariSHaghparastA. Diagnostic value of PET/CT in comparison with other conventional imaging modalities for the evaluation of breast cancer recurrence: a systematic review of the literature. Arch Breast Cancer2016; 3: 77–82.
70.
Veit-HaibachPLuczakCWankeI, et al.TNM Staging with FDG-PET/CT in patients with primary head and neck cancer. Eur J Nucl Med Mol Imaging2007; 34: 1953–1962.
71.
GorjiAJouzdaniAFSanatiN, et al.PET-CT fusion based outcome prediction in lung cancer using deep and handcrafted radiomics features and machine learning. Soc Nuclear Med2023; 64: 1164–1196.
72.
TixierFHattMVallaC, et al.Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non–small cell lung cancer. J Nucl Med2014; 55: 1235–1241.
73.
RenJEriksenJGNijkampJ,et al.Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation. Acta Oncol (Madr)2021; 60: 1399–1406.
74.
El NaqaIGrigsbyPWApteA, et al.Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit2009; 42: 1162–1171.
75.
LiRRavizziniGCGorinMA, et al.The use of PET/CT in prostate cancer. Prostate Cancer Prostatic Dis2018; 21: 4–21.
76.
AndrearczykVOreillerVBoughdadS, et al.Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In: 3D Head and neck tumor segmentation in PET/CT challenge. Springer International Publishing, 2021, pp.1–37.
77.
MartensRMKoopmanTLaviniC, et al.Multiparametric functional MRI and 18 F-FDG-PET for survival prediction in patients with head and neck squamous cell carcinoma treated with (chemo) radiation. Eur Radiol2021; 31: 616–628.
78.
ChenYJRathTMohanS. PET–Computed tomography in head and neck cancer: current evidence and future directions. Magn Reson Imaging Clin2018; 26: 37–49.
79.
BoellaardR. Standards for PET image acquisition and quantitative data analysis. J Nucl Med2009; 50: 11S–20S.
80.
HouwelingACWolfALVogelWV, et al.FDG-PET and diffusion-weighted MRI in head-and-neck cancer patients : implications for dose painting. Radiot Oncol2013; 106: 250–254.
81.
NieDTrulloRLianJ, et al.Medical image synthesis with deep convolutional adversarial networks. IEEE Trans Biomed Eng2018; 65: 2720–2730.
82.
HaarburgerCMüller-FranzesGWeningerL, et al.Radiomics feature reproducibility under inter-rater variability in segmentations of CT images. Sci Rep2020; 10: 12688.
83.
KletzelSLHernandezJMMiskielEF, et al.Evaluating the performance of the Montreal Cognitive Assessment in early stage Parkinson’s disease. Parkinsonism Relat Disord2017; 37: 58–64.
84.
NieDTrulloRLianJ, et al.Medical image synthesis with context-aware generative adversarial networks. In: International conference on medical image computing and computer-assisted intervention. Vol. 25. Springer International Publishing, 2017, pp.417–425.
85.
NahumAESanchez-NietoB. Tumour control probability modelling: basic principles and applications in treatment planning. Phys Medica2001; 17: 13–23.
86.
DinhCVSteenbergenPGhobadiG, et al.Multicenter validation of prostate tumor localization using multiparametric MRI and prior knowledge. Med Phys2017 Mar; 44: 949–961.
87.
WestphalenACFazelFNguyenH, et al.Detection of clinically significant prostate cancer with PI-RADS v2 scores, PSA density, and ADC values in regions with and without mpMRI visible lesions. Int Braz J Urol2019; 45: 713–723.
88.
BarrettTTurkbeyBChoykePL. PI-RADS version 2: what you need to know. Clin Radiol2015; 70: 1165–1176.
89.
MeyerARakrMSchindeleD, et al.Towards patient-individual PI-Rads v2 sector map: CNN for automatic segmentation of prostatic zones from T2-weighted MRI. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, 2019, pp.696–700.
90.
WinkelDJBreitHCShiB, et al.Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores. Quant Imaging Med Surg2020; 10: 808.
91.
RöthkeMBlondinDSchlemmerHP,et al.PI-RADS classification: structured reporting for MRI of the prostate. Rofo2013; 185: 253–261.
92.
Kasel-SeibertMLehmannTAschenbachR, et al.Assessment of PI-RADS v2 for the detection of prostate cancer. Eur J Radiol2016; 85: 726–731.
93.
WangJWuCJBaoML, 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 Radiol2017; 27: 4082–4090.
94.
WeinrebJCBarentszJOChoykePL, et al.PI-RADS prostate imaging - reporting and data system: 2015, version 2. Eur Urol2016 Jan; 69: 16–40.
95.
CuocoloRCipulloMBStanzioneA, et al.Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp2019 Aug; 3: 35.
96.
KimTHKimCKParkBK, et al.Relationship between Gleason score and apparent diffusion coefficients of diffusion-weighted magnetic resonance imaging in prostate cancer patients. Can Urol Assoc J2016; 10: E377.
97.
LysenkoIMoriKMostafaeiH, et al.Prognostic value of gleason score at positive surgical margin in prostate cancer: a systematic review and meta-analysis. Clin Genitourin Cancer2020; 18: 517–522.
98.
BultenWPinckaersHvan BovenH, et al.Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol2020; 21: 233–241.
99.
ADC AND DOSE PAINTING AND GLEASON SCORE.pdf.
100.
ChaddadANiaziTProbstS, et al.Predicting Gleason score of prostate cancer patients using radiomic analysis. Front Oncol2018; 8: 630.
101.
GronlundEJohanssonSNyholmT, et al.Dose painting of prostate cancer based on Gleason score correlations with apparent diffusion coefficients. Acta Oncol2018 May; 57: 574–581.
102.
ToivonenJMerisaariHPesolaM, et al.Mathematical models for diffusion-weighted imaging of prostate cancer using b values up to 2000s/mm2: correlation with Gleason score and repeatability of region of interest analysis. Magn Reson Med2015; 74: 1116–1124.
103.
LvWAshrafiniaSMaJ, et al.Multi-level multi-modality fusion radiomics: application to PET and CT imaging for prognostication of head and neck cancer. IEEE J Biomed Heal Informatics2019; 24: 2268–2277.
104.
ChenHChenHWangL. Iteratively refine the segmentation of head and neck tumor in FDG-PET and CT images. In: 3D Head and neck tumor segmentation in PET/CT challenge. Springer International Publishing, 2020, pp.53–58.
