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Yu H, Tang B, Fu Y, Wei W, He Y, Dai G, Xiao Q. Quantifying the reproducibility and longitudinal repeatability of radiomics features in magnetic resonance Image-Guide accelerator Imaging: A phantom study. Eur J Radiol 2024; 181:111735. [PMID: 39276402 DOI: 10.1016/j.ejrad.2024.111735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/22/2024] [Accepted: 09/09/2024] [Indexed: 09/17/2024]
Abstract
OBJECTIVE This study aimed to quantitatively evaluate the inter-platform reproducibility and longitudinal acquisition repeatability of MRI radiomics features in Fluid-Attenuated Inversion Recovery (FLAIR), T2-weighted (T2W), and T1-weighted (T1W) sequences on MR-Linac systems using an American College of Radiology (ACR) phantom. MATERIALS AND METHODS This study used two MR-Linac systems (A and B) in different cancer centers. The ACR phantom was scanned on system A daily for 30 consecutive days to evaluate longitudinal repeatability. Additionally, retest data were collected after repositioning the phantom. Inter-platform reproducibility was assessed by conducting scans under identical conditions using system B. Regions of interest were delineated on the T1W sequence from system A and mapped to other sequences via rigid registration. Intra-observer and inter-observer comparisons were conducted. Repeatability and reproducibility were assessed using the intraclass correlation coefficient (ICC) and coefficient of variation (CV). Robust radiomics features were identified based on ICC>0.9 and CV<10 %. RESULTS Analysis showed that a higher proportion of radiomics features derived from longitudinal FLAIR sequence (51.65 %) met robustness criteria compared to T2W (48.35 %) and T1W (43.96 %). Additionally, more inter-platform features from the FLAIR sequence (62.64 %) were robust compared to T2W (42.86 %) and T1W (39.56 %). Test-retest and intra-observer repeatability were excellent across all sequences, with a median ICC of 0.99 and CV<5%. However, inter-observer reproducibility was inferior, especially for the T1W sequence. CONCLUSIONS Different sequences show variations in repeatability and reproducibility. The FLAIR sequence demonstrated advantages in both longitudinal repeatability and inter-platform reproducibility. Caution is warranted when interpreting data, particularly in longitudinal or multiplatform radiomics studies.
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Affiliation(s)
- Hang Yu
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, PR China
| | - Bin Tang
- Department of Radiation Oncology, Radiation Oncology Key Laboratory Of Sichuan Province, Sichuan Cancer Hospital& Institute, Chengdu, Sichuan, China
| | - Yuchuan Fu
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, PR China.
| | - Weige Wei
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, PR China
| | - Yisong He
- Medical Physics Laboratory, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, China
| | - Guyu Dai
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, PR China
| | - Qing Xiao
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, PR China
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Montin E, Kijowski R, Youm T, Lattanzi R. Radiomics features outperform standard radiological measurements in detecting femoroacetabular impingement on three-dimensional magnetic resonance imaging. J Orthop Res 2024. [PMID: 39127895 DOI: 10.1002/jor.25952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 07/01/2024] [Accepted: 07/28/2024] [Indexed: 08/12/2024]
Abstract
Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three-dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water-only, fat-only, in-phase, and out-of-phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross-validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis.
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Affiliation(s)
- Eros Montin
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York, New York, USA
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York, New York, USA
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Thomas Youm
- Department of Orthopedic Surgery, New York University Grossman School of Medicine, New York, New York, USA
| | - Riccardo Lattanzi
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York, New York, USA
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York, New York, USA
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Trojani V, Bassi MC, Verzellesi L, Bertolini M. Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review. Cancers (Basel) 2024; 16:2668. [PMID: 39123396 PMCID: PMC11311340 DOI: 10.3390/cancers16152668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. MATERIALS AND METHODS We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. RESULTS After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. CONCLUSIONS From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.
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Affiliation(s)
- Valeria Trojani
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | | | - Laura Verzellesi
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | - Marco Bertolini
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
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Michalet M, Valenzuela G, Debuire P, Riou O, Azria D, Nougaret S, Tardieu M. Robustness of radiomics features on 0.35 T magnetic resonance imaging for magnetic resonance-guided radiotherapy. Phys Imaging Radiat Oncol 2024; 31:100613. [PMID: 39140002 PMCID: PMC11320460 DOI: 10.1016/j.phro.2024.100613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 08/15/2024] Open
Abstract
Background and purpose MR-guided radiotherapy adds the precision of magnetic resonance imaging (MRI) to the therapeutic benefits of a linear accelerator. Prior to each therapeutic session, an MRI generates a significant volume of imaging data ripe for analysis. Radiomics stands at the forefront of medical imaging and oncology research, dedicated to mining quantitative imaging attributes to forge predictive models. However, the robustness of these models is often challenged. Materials and methods To assess the robustness of feature extraction, we conducted reproducibility studies using a 0.35 T MR-linac system, employing both a specialized phantom and patient-derived images, focusing on cases of pancreatic cancer. We extracted shape-based, first-order and textural features from patient-derived images and only first-order and textural features from phantom-derived images. The impact of the delay between simulation and first fraction images was also assessed with an equivalence test. Results From 107 features evaluated, 58 (54 %) were considered as non-reproducible: 18 were uniformly inconsistent across both phantom and patient images, 9 were specific to phantom-based analysis, and 31 to patient-derived data. Conclusion Our findings show that a significant proportion of radiomic features extracted from this dual dataset were unreliable. It is essential to discard these non-reproducible elements to refine and enhance radiomic model development, particularly for MR-guided radiotherapy in pancreatic cancer.
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Affiliation(s)
- Morgan Michalet
- Institut du Cancer de Montpellier, Fédération Universitaire d’Oncologie-Radiothérapie d’Occitanie Méditerranée (FOROM), INSERM U1194 IRCM, 208 avenue des apothicaires, 34298 Montpellier, France
- IRCM, Univ Montpellier, ICM, INSERM, 208 avenue des apothicaires, 34298 Montpellier, France
| | - Gladis Valenzuela
- IRCM, Univ Montpellier, ICM, INSERM, 208 avenue des apothicaires, 34298 Montpellier, France
| | - Pierre Debuire
- Institut du Cancer de Montpellier, Fédération Universitaire d’Oncologie-Radiothérapie d’Occitanie Méditerranée (FOROM), INSERM U1194 IRCM, 208 avenue des apothicaires, 34298 Montpellier, France
| | - Olivier Riou
- Institut du Cancer de Montpellier, Fédération Universitaire d’Oncologie-Radiothérapie d’Occitanie Méditerranée (FOROM), INSERM U1194 IRCM, 208 avenue des apothicaires, 34298 Montpellier, France
- IRCM, Univ Montpellier, ICM, INSERM, 208 avenue des apothicaires, 34298 Montpellier, France
| | - David Azria
- Institut du Cancer de Montpellier, Fédération Universitaire d’Oncologie-Radiothérapie d’Occitanie Méditerranée (FOROM), INSERM U1194 IRCM, 208 avenue des apothicaires, 34298 Montpellier, France
- IRCM, Univ Montpellier, ICM, INSERM, 208 avenue des apothicaires, 34298 Montpellier, France
| | - Stéphanie Nougaret
- IRCM, Univ Montpellier, ICM, INSERM, 208 avenue des apothicaires, 34298 Montpellier, France
- Institut du Cancer de Montpellier, Service d’imagerie médicale, 208 avenue des apothicaires, 34298 Montpellier, France
| | - Marion Tardieu
- IRCM, Univ Montpellier, ICM, INSERM, 208 avenue des apothicaires, 34298 Montpellier, France
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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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Zhong J, Liu X, Hu Y, Xing Y, Ding D, Ge X, Song Y, Wang S, Chen L, Zhu Y, Lu W, Zhang H, Yao W. Robustness of Quantitative Diffusion Metrics from Four Models: A Prospective Study on the Influence of Scan-Rescans, Voxel Size, Coils, and Observers. J Magn Reson Imaging 2023. [PMID: 38112305 DOI: 10.1002/jmri.29192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Quantitative diffusion metrics provide additional microstructural information of diseases. The robustness of quantitative diffusion metrics should be established before clinical application. PURPOSE To evaluate the variability and reproducibility of quantitative diffusion MRI metrics. STUDY TYPE Prospective. POPULATION 14 volunteers (7 men; median age, range, 28, 26-59 years). FIELD STRENGTH/SEQUENCE 3.0-T/Diffusion spectrum imaging. ASSESSMENT Brain MRI studies were performed four times per subject: involving different combinations of coil types and voxel sizes. Regions of interest of 13 brain anatomical sites were drawn by one observer twice and another observer once to allow interobserver and intraobserver reproducibility assessment. Twenty-five quantitative metrics were calculated using four diffusion models. STATISTICAL TESTS The variability was evaluated with coefficients of variation (CV), and quartile coefficient of dispersion (QCD). The reproducibility was assessed with intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Wilcoxon signed rank test was used to compare the influence of factors on robustness of quantitative diffusion metrics. A two-tailed P < 0.05 was considered statistically significant. RESULTS The variability of quantitative diffusion metrics showed CV of 2.4%-68.2%, and QCD of 0.6%-48.2%, respectively. The reproducibility of scans using 20-channel coils with voxels of 2 × 2 × 2 mm3 and 3 × 3 × 3 mm3 , respectively (ICC 0.03-0.84, CCC 0.03-0.84) was significantly worse than that of repeated scans using a 20-channel coil with a voxel size of 2 × 2 × 2 mm3 (ICC of 0.74-0.97, CCC 0.74-0.97) and that of scans using 20- and 64-channel coils, respectively, with a voxel size of 2 × 2 × 2 mm3 (ICC 0.59-0.95, CCC 0.59-0.95). The intraobserver reproducibility (ICC 0.49-0.94, CCC 0.49-0.94) was significantly better than the interobserver reproducibility (ICC 0.28-0.91, CCC 0.28-0.91). DATA CONCLUSION Our study indicated that the voxel size has a greater influence on the reproducibility of quantitative diffusion metrics than scan-rescans and coils. The reproducibility within one observer was higher than that between two observers. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xianwei Liu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, China
| | - Silian Wang
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liwei Chen
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Zhu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjie Lu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Kremer LE, Chapman AB, Armato SG. Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype. J Med Imaging (Bellingham) 2023; 10:064503. [PMID: 38156331 PMCID: PMC10752557 DOI: 10.1117/1.jmi.10.6.064503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Purpose Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome. Approach The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z -score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods. Results Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively. Conclusions Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.
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Affiliation(s)
- Linnea E. Kremer
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
| | - Arlene B. Chapman
- The University of Chicago, Department of Medicine, Chicago, Illinois, United States
| | - Samuel G. Armato
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
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