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] 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
  • [42] The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment
    Gaia Spadarella
    Lorenzo Ugga
    Giuseppina Calareso
    Rossella Villa
    Serena D’Aniello
    Renato Cuocolo
    Neuroradiology, 2022, 64 : 1639 - 1647
  • [43] The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment
    Spadarella, Gaia
    Ugga, Lorenzo
    Calareso, Giuseppina
    Villa, Rossella
    D'Aniello, Serena
    Cuocolo, Renato
    NEURORADIOLOGY, 2022, 64 (08) : 1639 - 1647
  • [44] MRI radiomics predicts progression-free survival in prostate cancer (vol 12, 974257, 2023)
    Jia, Yushan
    Quan, Shuai
    Ren, Jialiang
    Wu, Hui
    Liu, Aishi
    Gao, Yang
    Hao, Fene
    Yang, Zhenxing
    Zhang, Tong
    Hu, He
    FRONTIERS IN ONCOLOGY, 2023, 12
  • [45] A dynamic-static combination model based on radiomics features for prostate cancer using multiparametric MRI
    Li, Shuqin
    Zheng, Tingting
    Fan, Zhou
    Qu, Hui
    Wang, Jianfeng
    Bi, Jianbin
    Lv, Qingjie
    Zhang, Gejun
    Cui, Xiaoyu
    Zhao, Yue
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (01)
  • [46] MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
    Zhu, Xuehua
    Shao, Lizhi
    Liu, Zhenyu
    Liu, Zenan
    He, Jide
    Liu, Jiangang
    Ping, Hao
    Lu, Jian
    JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B, 2023, 24 (08): : 663 - 681
  • [47] Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in breast cancer
    Netti, Sofia
    D'Ecclesiis, Oriana
    Corso, Federica
    Botta, Francesca
    Origgi, Daniela
    Pesapane, Filippo
    Agazzi, Giorgio Maria
    Rotili, Anna
    Gaeta, Aurora
    Scalco, Elisa
    Rizzo, Giovanna
    Jereczek-Fossa, Barbara Alicja
    Cassano, Enrico
    Curigliano, Giuseppe
    Gandini, Sara
    Raimondi, Sara
    EUROPEAN RADIOLOGY, 2024,
  • [48] Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics
    Rodrigues, Ana
    Santinha, Joao
    Galvao, Bernardo
    Matos, Celso
    Couto, Francisco M.
    Papanikolaou, Nickolas
    CANCERS, 2021, 13 (23)
  • [49] MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment
    Spadarella, Gaia
    Calareso, Giuseppina
    Garanzini, Enrico
    Ugga, Lorenzo
    Cuocolo, Alberto
    Cuocolo, Renato
    EUROPEAN JOURNAL OF RADIOLOGY, 2021, 140
  • [50] Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics
    Gong, Lixin
    Xu, Min
    Fang, Mengjie
    Zou, Jian
    Yang, Shudong
    Yu, Xinyi
    Xu, Dandan
    Zhou, Lijuan
    Li, Hailin
    He, Bingxi
    Wang, Yan
    Fang, Xiangming
    Dong, Di
    Tian, Jie
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2020, 52 (04) : 1102 - 1109