Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative

被引:15
作者
Ponsiglione, Andrea [1 ]
Stanzione, Arnaldo [1 ]
Spadarella, Gaia [1 ]
Baran, Agah [2 ]
Cappellini, Luca Alessandro [3 ]
Lipman, Kevin Groot [4 ]
Van Ooijen, Peter [5 ,6 ]
Cuocolo, Renato [7 ,8 ]
机构
[1] Univ Naples Federico II, Dept Adv Biomed Sci, Naples, Italy
[2] Univ Cologne, Dept Diagnost & Intervent Radiol, Cologne, Germany
[3] Humanitas Univ, Dept Biomed Sci, Milan, Italy
[4] Netherlands Canc Inst, Dept Radiol, Amsterdam, Netherlands
[5] Univ Med Ctr Groningen, Dept Radiat Oncol, Groningen, Netherlands
[6] Univ Med Ctr Groningen, Data Sci Ctr Hlth, Machine Learning Lab, Groningen, Netherlands
[7] Univ Salerno, Dept Med Surg & Dent, Baronissi, Italy
[8] Univ Naples Federico II, Dept Elect Engn & Informat Technol, Augmented Real Hlth Monitoring Lab ARHeMLab, Naples, Italy
关键词
Machine learning; Ovary; Computed tomography; Magnetic resonance imaging; Positron emission tomography; TEXTURE ANALYSIS;
D O I
10.1007/s00330-022-09180-w
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
Objective To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. Methods Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. Results From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. Conclusions The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making.
引用
收藏
页码:2239 / 2247
页数:9
相关论文
共 40 条
[1]   CT texture analysis in histological classification of epithelial ovarian carcinoma [J].
An, He ;
Wang, Yiang ;
Wong, Esther M. F. ;
Lyu, Shanshan ;
Han, Lujun ;
Perucho, Jose A. U. ;
Cao, Peng ;
Lee, Elaine Y. P. .
EUROPEAN RADIOLOGY, 2021, 31 (07) :5050-5058
[2]  
[Anonymous], CANC GENOME ATLAS PR
[3]   Radiomics Analysis in Ovarian Cancer: A Narrative Review [J].
Arezzo, Francesca ;
Loizzi, Vera ;
La Forgia, Daniele ;
Moschetta, Marco ;
Tagliafico, Alberto Stefano ;
Cataldo, Viviana ;
Kawosha, Adam Abdulwakil ;
Venerito, Vincenzo ;
Cazzato, Gerardo ;
Ingravallo, Giuseppe ;
Resta, Leonardo ;
Cicinelli, Ettore ;
Cormio, Gennaro .
APPLIED SCIENCES-BASEL, 2021, 11 (17)
[4]   Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis [J].
Beer, Lucian ;
Sahin, Hilal ;
Bateman, Nicholas W. ;
Blazic, Ivana ;
Vargas, Hebert Alberto ;
Veeraraghavan, Harini ;
Kirby, Justin ;
Fevrier-Sullivan, Brenda ;
Freymann, John B. ;
Jaffe, C. Carl ;
Brenton, James ;
Micco, Maura ;
Nougaret, Stephanie ;
Darcy, Kathleen M. ;
Maxwell, G. Larry ;
Conrads, Thomas P. ;
Huang, Erich ;
Sala, Evis .
EUROPEAN RADIOLOGY, 2020, 30 (08) :4306-4316
[5]   Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma [J].
Chu, Hongpeng ;
Liu, Zelong ;
Liang, Wen ;
Zhou, Qian ;
Zhang, Ying ;
Lei, Kai ;
Tang, Mimi ;
Cao, Yiheng ;
Chen, Shuling ;
Peng, Sui ;
Kuang, Ming .
EUROPEAN RADIOLOGY, 2021, 31 (04) :2368-2376
[6]   Radiomics in endometrial cancer and beyond - a perspective from the editors of the EJR [J].
dos Santos, Daniel Pinto .
EUROPEAN JOURNAL OF RADIOLOGY, 2022, 150
[7]   ESR Statement on the Validation of Imaging Biomarkers [J].
Alberich-Bayarri A. ;
Sourbron S. ;
Golay X. ;
deSouza N. ;
Smits M. ;
van der Lugt A. ;
Boellard R. .
INSIGHTS INTO IMAGING, 2020, 11 (01)
[8]   NCI Imaging Data Commons [J].
Fedorov, Andrey ;
Longabaugh, William J. R. ;
Pot, David ;
Clunie, David A. ;
Pieper, Steve ;
Aerts, Hugo J. W. L. ;
Homeyer, Andre ;
Lewis, Rob ;
Akbarzadeh, Afshin ;
Bontempi, Dennis ;
Clifford, William ;
Herrmann, Markus D. ;
Hoefener, Henning ;
Octaviano, Igor ;
Osborne, Chad ;
Paquette, Suzanne ;
Petts, James ;
Punzo, Davide ;
Reyes, Madelyn ;
Schacherer, Daniela P. ;
Tian, Mi ;
White, George ;
Ziegler, Erik ;
Shmulevich, Ilya ;
Pihl, Todd ;
Wagner, Ulrike ;
Farahani, Keyvan ;
Kikinis, Ron .
CANCER RESEARCH, 2021, 81 (16) :4188-4193
[9]   Radiomics: Images Are More than Pictures, They Are Data [J].
Gillies, Robert J. ;
Kinahan, Paul E. ;
Hricak, Hedvig .
RADIOLOGY, 2016, 278 (02) :563-577
[10]   Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors [J].
Gitto, Salvatore ;
Cuocolo, Renato ;
Emili, Ilaria ;
Tofanelli, Laura ;
Chianca, Vito ;
Albano, Domenico ;
Messina, Carmelo ;
Imbriaco, Massimo ;
Sconfienza, Luca Maria .
JOURNAL OF DIGITAL IMAGING, 2021, 34 (04) :820-832