Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer

Ileana Montoya Perez, Harri Merisaari, Ivan Jambor, Otto Ettala, Pekka Taimen, Juha Knaapila, Henna Kekki, Ferdhos L. Khan, Elise Syrjälä, Aida Steiner, Kari T. Syvänen, Janne Verho, Marjo Seppänen, Antti Rannikko, Jarno Riikonen, Tuomas Mirtti, Tarja Lamminen, Jani Saunavaara, Ugo Falagario, Alberto MartiniTapio Pahikkala, Kim Pettersson, Peter J. Boström, Hannu J. Aronen

    Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

    3 Lataukset (Pure)

    Abstrakti

    Background: Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group ≥ 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). Purpose: To develop and validate radiomics and kallikrein models for the detection of csPCa. Study Type: Retrospective. Population: A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated. Field strength/Sequence: A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. Assessment: In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. Statistical Tests: For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant. Results: The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488). Data Conclusion: The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains. Level of Evidence: 1. Technical Efficacy: Stage 2.

    AlkuperäiskieliEnglanti
    JulkaisuJournal of Magnetic Resonance Imaging
    Vuosikerta55
    Numero2
    Varhainen verkossa julkaisun päivämääräheinäk. 2021
    DOI - pysyväislinkit
    TilaJulkaistu - 2022
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

    Julkaisufoorumi-taso

    • Jufo-taso 1

    !!ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

    Sormenjälki

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