Prostate cancer MRI methodological radiomics score: a EuSoMII radiomics auditing group initiative

被引:1
|
作者
Cavallo, Armando Ugo [1 ]
Stanzione, Arnaldo [2 ]
Ponsiglione, Andrea [2 ]
Trotta, Romina [3 ]
Fanni, Salvatore Claudio [4 ]
Ghezzo, Samuele [5 ]
Vernuccio, Federica [6 ]
Klontzas, Michail E. [7 ,8 ,9 ]
Triantafyllou, Matthaios [7 ,8 ]
Ugga, Lorenzo [2 ]
Kalarakis, Georgios [9 ,10 ]
Cannella, Roberto [6 ]
Cuocolo, Renato [11 ]
机构
[1] Ist Dermopat Immacolata IDI IRCCS, Rome, Italy
[2] Univ Naples Federico II, Dept Adv Biomed Sci, Naples, Italy
[3] Hosp Fatima, Dept Cardiol, Seville, Spain
[4] Univ Pisa, Dept Translat Res, Acad Radiol, Pisa, Italy
[5] Univ Vita Salute San Raffaele, Milan, Italy
[6] Univ Palermo, Dept Biomed Neurosci & Adv Diagnost BiND, Sect Radiol, Palermo, Italy
[7] Univ Crete, Sch Med, Dept Radiol, Iraklion, Greece
[8] Univ Hosp Heraklion, Dept Med Imaging, Iraklion, Greece
[9] Karolinska Inst, Dept Clin Sci Intervent & Technol CLINTEC, Div Radiol, Stockholm, Sweden
[10] Karolinska Univ Hosp, Dept Neuroradiol, Stockholm, Sweden
[11] Univ Salerno, Dept Med Surg & Dent, Baronissi, Italy
关键词
Prostate; Radiomics; Magnetic resonance imaging; Systematic review;
D O I
10.1007/s00330-024-11299-x
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesTo evaluate the quality of radiomics research in prostate MRI for the evaluation of prostate cancer (PCa) through the assessment of METhodological RadiomICs (METRICS) score, a new scoring tool recently introduced with the goal of fostering further improvement in radiomics and machine learning methodology.Materials and methodsA literature search was conducted from July 1st, 2019, to November 30th, 2023, to identify original investigations assessing MRI-based radiomics in the setting of PCa. Seven readers with varying expertise underwent a quality assessment using METRICS. Subgroup analyses were performed to assess whether the quality score varied according to papers' categories (diagnosis, staging, prognosis, technical) and quality ratings among these latter.ResultsFrom a total of 1106 records, 185 manuscripts were available. Overall, the average METRICS total score was 52% +/- 16%. ANOVA and chi-square tests revealed no statistically significant differences between subgroups. Items with the lowest positive scores were adherence to guidelines/checklists (4.9%), handling of confounding factors (14.1%), external testing (15.1%), and the availability of data (15.7%), code (4.3%), and models (1.6%). Conversely, most studies clearly defined patient selection criteria (86.5%), employed a high-quality reference standard (89.2%), and utilized a well-described (85.9%) and clinically applicable (87%) imaging protocol as a radiomics data source.ConclusionThe quality of MRI-based radiomics research for PCa in recent studies demonstrated good homogeneity and overall moderate quality.Key PointsQuestionTo evaluate the quality of MRI-based radiomics research for PCa, assessed through the METRICS score.FindingsThe average METRICS total score was 52%, reflecting moderate quality in MRI-based radiomics research for PCa, with no statistically significant differences between subgroups.Clinical relevanceEnhancing the quality of radiomics research can improve diagnostic accuracy for PCa, leading to better patient outcomes and more informed clinical decision-making.Key PointsQuestionTo evaluate the quality of MRI-based radiomics research for PCa, assessed through the METRICS score.FindingsThe average METRICS total score was 52%, reflecting moderate quality in MRI-based radiomics research for PCa, with no statistically significant differences between subgroups.Clinical relevanceEnhancing the quality of radiomics research can improve diagnostic accuracy for PCa, leading to better patient outcomes and more informed clinical decision-making.Key PointsQuestionTo evaluate the quality of MRI-based radiomics research for PCa, assessed through the METRICS score.FindingsThe average METRICS total score was 52%, reflecting moderate quality in MRI-based radiomics research for PCa, with no statistically significant differences between subgroups.Clinical relevanceEnhancing the quality of radiomics research can improve diagnostic accuracy for PCa, leading to better patient outcomes and more informed clinical decision-making.
引用
收藏
页码:1157 / 1165
页数:9
相关论文
共 50 条
  • [41] A hybrid classification model with radiomics and CNN for high and low grading of prostate cancer Gleason score on mp-MRI
    Liu, Feng
    Zhao, Yuanshen
    Song, Jukun
    Tu, Guilan
    Liu, Yadong
    Peng, Yunsong
    Mao, Jiahui
    Yan, Chongzhe
    Wang, Rongpin
    DISPLAYS, 2024, 83
  • [42] The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer
    Adriano Basso Dias
    Seyed Ali Mirshahvalad
    Claudia Ortega
    Nathan Perlis
    Alejandro Berlin
    Theodorus van der Kwast
    Sangeet Ghai
    Kartik Jhaveri
    Ur Metser
    Masoom Haider
    Lisa Avery
    Patrick Veit-Haibach
    European Journal of Nuclear Medicine and Molecular Imaging, 2023, 50 : 2167 - 2176
  • [43] The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer
    Dias, Adriano Basso
    Mirshahvalad, Seyed Ali
    Ortega, Claudia
    Perlis, Nathan
    Berlin, Alejandro
    van der Kwast, Theodorus
    Ghai, Sangeet
    Jhaveri, Kartik
    Metser, Ur
    Haider, Masoom
    Avery, Lisa
    Veit-Haibach, Patrick
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 50 (07) : 2167 - 2176
  • [44] Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study
    Zhou, Chuan
    Zhang, Yun-Feng
    Guo, Sheng
    Wang, Dong
    Lv, Hao-Xuan
    Qiao, Xiao-Ni
    Wang, Rong
    Chang, De-Hui
    Zhao, Li-Ming
    Zhou, Feng-Hai
    DISCOVER ONCOLOGY, 2023, 14 (01)
  • [45] Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study
    Chuan Zhou
    Yun-Feng Zhang
    Sheng Guo
    Dong Wang
    Hao-Xuan Lv
    Xiao-Ni Qiao
    Rong Wang
    De-Hui Chang
    Li-Ming Zhao
    Feng-Hai Zhou
    Discover Oncology, 14
  • [46] Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study
    Mirshahvalad, Seyed Ali
    Dias, Adriano B.
    Ghai, Sangeet
    Ortega, Claudia
    Perlis, Nathan
    Berlin, Alejandro
    Avery, Lisa
    van der Kwast, Theodorus
    Metser, Ur
    Veit-Haibach, Patrick
    ACADEMIC RADIOLOGY, 2025, 32 (01) : 250 - 259
  • [47] Added Value Of MRI Radiomics To Predict Pathological Status Of Prostate Cancer Patients
    Vincini, M. G.
    Marvaso, G.
    Isaksson, L. J.
    Zaffaroni, M.
    Pepa, M.
    Corrao, G.
    Summers, P. E.
    Repetto, M.
    Mazzola, G. C.
    Rotondi, M.
    Raimondi, S.
    Gandini, S.
    Volpe, S.
    Haron, Z.
    Alessi, S.
    Pricolo, P.
    Mistretta, F. A.
    Luzzago, S.
    Cattani, F.
    Musi, G.
    De Cobelli, O.
    Cremonesi, M.
    Orecchia, R.
    La Torre, D.
    Petralia, G.
    Jereczek-Fossa, B. A.
    RADIOTHERAPY AND ONCOLOGY, 2023, 182 : S1884 - S1886
  • [48] Added value of MRI radiomics to predict pathological status of prostate cancer patients
    Vincini, M. G.
    Marvaso, G.
    Isaksson, L. J.
    Zaffaroni, M.
    Pepa, M.
    Corrao, G.
    Summers, P. E.
    Repetto, M.
    Mazzola, G. C.
    Rotondi, M.
    Raimondi, S.
    Gandini, S.
    Volpe, S.
    Haron, Z.
    Alessi, S.
    Pricolo, P.
    Mistretta, F. A.
    Luzzago, S.
    Cattani, F.
    Musi, G.
    De Cobelli, O.
    Cremonesi, M.
    La Torre, D.
    Petralia, G.
    Jereczek-Fossa, B. A.
    EUROPEAN UROLOGY, 2023, 83
  • [49] IMPROVING PROSTATE CANCER DETECTION ON MRI WITH DEEP LEARNING, CLINICAL VARIABLES, AND RADIOMICS
    Saunders, Sara
    Li, Xinran
    Vesal, Sulaiman
    Bhattacharya, Indrani
    Soerensen, Simon J. C.
    Fan, Richard E.
    Rusu, Mirabela
    Sonn, Geoffrey A.
    JOURNAL OF UROLOGY, 2023, 209 : E665 - E665
  • [50] Added value of MRI radiomics to predict pathological status of prostate cancer patients
    Marvaso, G.
    Pepa, M.
    Isaksson, L. J.
    Summers, P. E.
    Zaffaroni, M.
    Vincini, M. G.
    Corrao, G.
    Mazzola, G. C.
    Rotondi, M.
    Raimondi, S.
    Gandini, S.
    Volpe, S.
    Haron, Z.
    Alessi, S.
    Pricolo, P.
    Mistretta, F. A.
    Luzzago, S.
    Cattani, F.
    Musi, G.
    De Cobelli, O.
    Cremonesi, M.
    Orecchia, R.
    Petralia, G.
    Jereczek-Fossa, B. A.
    RADIOTHERAPY AND ONCOLOGY, 2022, 170 : S320 - S321