1
|
Hinzpeter R, Mirshahvalad SA, Kulanthaivelu R, Kohan A, Ortega C, Metser U, Liu A, Farag A, Elimova E, Wong RKS, Yeung J, Jang RWJ, Veit-Haibach P. Gastro-Esophageal Cancer: Can Radiomic Parameters from Baseline 18F-FDG-PET/CT Predict the Development of Distant Metastatic Disease? Diagnostics (Basel) 2024; 14:1205. [PMID: 38893731 PMCID: PMC11171817 DOI: 10.3390/diagnostics14111205] [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: 04/30/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline 18F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent 18F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, 18F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.
Collapse
Affiliation(s)
- Ricarda Hinzpeter
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Seyed Ali Mirshahvalad
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Roshini Kulanthaivelu
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Andres Kohan
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Claudia Ortega
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Ur Metser
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 1X6, Canada;
| | - Adam Farag
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Elena Elimova
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Rebecca K. S. Wong
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (R.K.S.W.); (R.W.-J.J.)
| | - Jonathan Yeung
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Raymond Woo-Jun Jang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (R.K.S.W.); (R.W.-J.J.)
| | - Patrick Veit-Haibach
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| |
Collapse
|
2
|
Fooladi M, Soleymani Y, Rahmim A, Farzanefar S, Aghahosseini F, Seyyedi N, Sh Zadeh P. Impact of different reconstruction algorithms and setting parameters on radiomics features of PSMA PET images: A preliminary study. Eur J Radiol 2024; 172:111349. [PMID: 38310673 DOI: 10.1016/j.ejrad.2024.111349] [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: 10/11/2023] [Revised: 01/09/2024] [Accepted: 01/25/2024] [Indexed: 02/06/2024]
Abstract
PURPOSE Radiomics analysis of oncologic positron emission tomography (PET) images is an area of significant activity and potential. The reproducibility of radiomics features is an important consideration for routine clinical use. This preliminary study investigates the robustness of radiomics features in PSMA-PET images across penalized-likelihood (Q.Clear) and standard ordered subset expectation maximization (OSEM) reconstruction algorithms and their setting parameters in phantom and prostate cancer (PCa) patients. METHOD A NEMA image quality (IQ) phantom and 8 PCa patients were selected for phantom and patient analyses, respectively. PET images were reconstructed using Q.Clear (reconstruction β-value: 100-700, at intervals of 100 for both NEMA IQ phantom and patients) and OSEM (duration: 15sec, 30sec, 1 min, 2 min, 3 min, 4 min and 5 min for NEMA phantom and duration: 30 s, 1 min and 2 min for patients) reconstruction methods. Subsequently, 129 radiomic features were extracted from the reconstructed images. The coefficient of variation (COV) of each feature across reconstruction methods and their parameters was calculated to determine feature robustness. RESULTS The extracted radiomics features showed a different range of variability, depending on the reconstruction algorithms and setting parameters. Specifically, 23.0 % and 53.5 % of features were found as robust against β-value variations in Q.Clear and different durations in OSEM reconstruction algorithms, respectively. Taking into account the two algorithms and their parameters, eleven features (8.5 %) showed COV ≤ 5 % and eighteen (14 %) showed 5 % 20 %. The mean COVs of the extracted radiomics features were significantly different between the two reconstruction methods (p < 0.05) except for the phantom morphological features. CONCLUSIONS All radiomics features were affected by reconstruction methods and parameters, but features with small or very small variations are considered better candidates for reproducible quantification of either tumor or metastatic tissues in clinical trials. There is a need for standardization before the implementation of PET radiomics in clinical practice.
Collapse
Affiliation(s)
- Masoomeh Fooladi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Yunus Soleymani
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - Saeed Farzanefar
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Farahnaz Aghahosseini
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Negisa Seyyedi
- Nursing and Midwifery Care Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Peyman Sh Zadeh
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran; Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
3
|
Yang B, Gao Y, Lu J, Wang Y, Wu R, Shen J, Ren J, Wu F, Xu H. Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules. Front Oncol 2023; 13:1212608. [PMID: 37601669 PMCID: PMC10436991 DOI: 10.3389/fonc.2023.1212608] [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: 04/26/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
Background In this study, we developed and validated machine learning (ML) models by combining radiomic features extracted from magnetic resonance imaging (MRI) with clinicopathological factors to assess pulmonary nodule classification for benign malignant diagnosis. Methods A total of 333 consecutive patients with pulmonary nodules (233 in the training cohort and 100 in the validation cohort) were enrolled. A total of 2,824 radiomic features were extracted from the MRI images (CE T1w and T2w). Logistic regression (LR), Naïve Bayes (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers were used to build the predictive models, and a radiomics score (Rad-score) was obtained for each patient after applying the best prediction model. Clinical factors and Rad-scores were used jointly to build a nomogram model based on multivariate logistic regression analysis, and the diagnostic performance of the five prediction models was evaluated using the area under the receiver operating characteristic curve (AUC). Results A total of 161 women (48.35%) and 172 men (51.65%) with pulmonary nodules were enrolled. Six important features were selected from the 2,145 radiomic features extracted from CE T1w and T2w images. The XGBoost classifier model achieved the highest discrimination performance with AUCs of 0.901, 0.906, and 0.851 in the training, validation, and test cohorts, respectively. The nomogram model improved the performance with AUC values of 0.918, 0.912, and 0.877 in the training, validation, and test cohorts, respectively. Conclusion MRI radiomic ML models demonstrated good nodule classification performance with XGBoost, which was superior to that of the other four models. The nomogram model achieved higher performance with the addition of clinical information.
Collapse
Affiliation(s)
- Bin Yang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yeqi Gao
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jie Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yefu Wang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ren Wu
- Department of Medical Imaging, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Jie Shen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE Healthcare, Beijing, China
| | - Feiyun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
4
|
Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
Collapse
Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| |
Collapse
|
5
|
Qin X, Wang C, Gong G, Wang L, Su Y, Yin Y. Functional MRI radiomics-based assessment of pelvic bone marrow changes after concurrent chemoradiotherapy for cervical cancer. BMC Cancer 2022; 22:1149. [PMID: 36348290 PMCID: PMC9644624 DOI: 10.1186/s12885-022-10254-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Objectives To quantify the dose-response relationship of changes in pelvic bone marrow (PBM) functional MR radiomic features (RF) during concurrent chemoradiotherapy (CCRT) for patients with cervical cancer and establish the correlation with hematologic toxicity to provide a basis for PBM sparing. Methods A total of 54 cervical cancer patients who received CCRT were studied retrospectively. Patients underwent MRI IDEAL IQ and T2 fat suppression (T2fs) scanning pre- and post-CCRT. The PBM RFs were extracted from each region of interest at dose gradients of 5–10 Gy, 10–15 Gy, 15–20 Gy, 20–30 Gy, 30–40 Gy, 40–50 Gy, and > 50 Gy, and changes in peripheral blood cell (PBC) counts during radiotherapy were assessed. The dose-response relationship of RF changes and their correlation with PBC changes were investigated. Results White blood cell, neutrophils (ANC) and lymphocyte counts during treatment were decreased by 49.4%, 41.4%, and 76.3%, respectively. Most firstorder features exhibited a significant dose-response relationship, particularly FatFrac IDEAL IQ, which had a maximum dose-response curve slope of 10.09, and WATER IDEAL IQ had a slope of − 7.93. The firstorder-Range in FAT IDEAL IQ and firstorder-10Percentile in T2fs, showed a significant correlation between the changes in ANC counts under the low dose gradient of 5–10 Gy (r = 0.744, -0.654, respectively, p < 0.05). Conclusion Functional MR radiomics can detect microscopic changes in PBM at various dose gradients and provide an objective reference for bone marrow sparing and dose limitation in cervical cancer CCRT. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10254-7.
Collapse
|
6
|
Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022; 12:16712. [PMID: 36202934 PMCID: PMC9537186 DOI: 10.1038/s41598-022-20703-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.
Collapse
|
7
|
Somasundaram A, Vállez García D, Pfaehler E, van Sluis J, Dierckx RAJO, de Vries EGE, Boellaard R. Mitigation of noise-induced bias of PET radiomic features. PLoS One 2022; 17:e0272643. [PMID: 36006959 PMCID: PMC9409510 DOI: 10.1371/journal.pone.0272643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/22/2022] [Indexed: 11/20/2022] Open
Abstract
Introduction One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias. Methods A phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A “difference image” created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria. Results Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected. Conclusion To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates.
Collapse
Affiliation(s)
- Ananthi Somasundaram
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
| | - David Vállez García
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC–Location VU University Medical Center, Amsterdam, The Netherlands
| | - Elisabeth Pfaehler
- Department of Nuclear Medicine, University Hospital Juelich, Aachen, Germany
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Rudi A. J. O. Dierckx
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Elisabeth G. E. de Vries
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC–Location VU University Medical Center, Amsterdam, The Netherlands
| |
Collapse
|
8
|
Dmytriw AA, Ortega C, Anconina R, Metser U, Liu ZA, Liu Z, Li X, Sananmuang T, Yu E, Joshi S, Waldron J, Huang SH, Bratman S, Hope A, Veit-Haibach P. Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up. Cancers (Basel) 2022; 14:cancers14133105. [PMID: 35804877 PMCID: PMC9264840 DOI: 10.3390/cancers14133105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nasopharyngeal carcinoma (NPC) is a frequent head and neck cancer, especially in Asian countries. Our studies investigated the value of minable data derived from standard of care PET/CT imaging in patients with NPC. The here presented evaluation found that certain specific imaging features in this patient population can be potentially used to predict overall survival and progression free survival at different time points in those patients. Abstract Purpose: We aim determine the value of PET and CT radiomic parameters on survival with serial follow-up PET/CT in patients with nasopharyngeal carcinoma (NPC) for which curative intent therapy is undertaken. Methods: Patients with NPC and available pre-treatment as well as follow up PET/CT were included from 2005 to 2006 and were followed to 2021. Baseline demographic, radiological and outcome data were collected. Univariable Cox proportional hazard models were used to evaluate features from baseline and follow-up time points, and landmark analyses were performed for each time point. Results: Sixty patients were enrolled, and two-hundred and seventy-eight (278) PET/CT were at baseline and during follow-up. Thirty-eight percent (38%) were female, and sixty-two patients were male. All patients underwent curative radiation or chemoradiation therapy. The median follow-up was 11.72 years (1.26–14.86). Five-year and ten-year overall survivals (OSs) were 80.0% and 66.2%, and progression-free survival (PFS) was 90.0% and 74.4%. Time-dependent modelling suggested that, among others, PET gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) (HR 2.74 95% CI 1.06, 7.05) was significantly associated with OS. Landmark analyses suggested that CT parameters were most predictive at 15 month, whereas PET parameters were most predictive at time points 3, 6, 9 and 15 month. Conclusions: This study with long-term follow up data on NPC suggests that mainly PET-derived radiomic features are predictive for OS but not PFS in a time-dependent evaluation. Furthermore, CT radiomic measures may predict OS and PFS best at initial and long-term follow-up time points and PET measures may be more predictive in the interval. These modalities are commonly used in NPC surveillance, and prospective validation should be considered.
Collapse
Affiliation(s)
- Adam A. Dmytriw
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (A.A.D.); (R.A.)
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Claudia Ortega
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (A.A.D.); (R.A.)
| | - Ur Metser
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Zhihui A. Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Zijin Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Xuan Li
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Thiparom Sananmuang
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University,270 Rama VI Road, Ratchathewi, Bangkok 10400, Thailand
| | - Eugene Yu
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Sayali Joshi
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - John Waldron
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Scott Bratman
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Andrew Hope
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
- Correspondence: ; Tel.: +416-340-4800 (ext. 6085); Fax: 416-340-3900
| |
Collapse
|
9
|
Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study. Acad Radiol 2022; 29:806-816. [PMID: 34238656 DOI: 10.1016/j.acra.2021.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve. RESULTS A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical. CONCLUSION The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.
Collapse
Affiliation(s)
- Yancheng Song
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Bo Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Chentong Yuan
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Hao Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Ziwen Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Fanyi Min
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Yu Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong.
| |
Collapse
|
10
|
Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
Collapse
Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| |
Collapse
|
11
|
Beads phantom for evaluating heterogeneity of SUV on 18F-FDG PET images. Ann Nucl Med 2022; 36:495-503. [PMID: 35377093 DOI: 10.1007/s12149-022-01740-w] [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: 01/06/2022] [Accepted: 03/21/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE This study aimed to develop a dedicated phantom using acrylic beads for texture analysis and to represent heterogeneous 18F-fluorodeoxyglucose (FDG) distributions in various acquisition periods. METHODS Images of acrylic spherical beads with or without diameters of 5- and 10-mm representing heterogeneous and homogeneous 18F-FDG distribution in phantoms, respectively, were collected for 20 min in list mode. Phantom data were reconstructed using three-dimensional ordered subset expectation maximization with attenuation and scatter corrections, and the time-of-flight algorithm. The beads phantom images were acquired twice to evaluate the robustness of texture features. Thirty-one texture features were extracted, and the robustness of texture feature values was evaluated by calculating the percentage of coefficient of variation (%COV) and intraclass coefficient of correlation (ICC). Cross-correlation coefficients among texture feature values were clustered to classify the characteristics of these features. RESULTS Heterogeneous 18F-FDG distribution was represented by the beads phantom images. The agreements of %COV between two measurements were acceptable (ICC ≥ 0.71). All texture features were classified into four groups. Among 31 texture features, 24 exhibited significant different values between phantoms with and without beads in 1-, 2-, 3-, 4-, 5-, 20-min image acquisitions. Whereas, the homogeneous and heterogeneous 18F-FDG distribution could not be discriminated by seven texture features: low gray-level run emphasis, high gray-level run emphasis, short-run low gray-level emphasis, low gray-level zone emphasis, high gray-level zone emphasis, short-zone low gray-level emphasis, and coarseness. CONCLUSIONS We have developed the acrylic beads phantom for texture analysis that could represent heterogeneous 18F-FDG distributions in various acquisition periods. Most texture features could discriminate homogeneous and heterogeneous 18F-FDG distributions in the beads phantom images.
Collapse
|
12
|
Önner H, Coşkun N, Erol M, Eren Karanis Mİ. The Role of Histogram-Based Textural Analysis of 18F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma. Mol Imaging Radionucl Ther 2022; 31:33-41. [PMID: 35114750 PMCID: PMC8814553 DOI: 10.4274/mirt.galenos.2021.79037] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Objectives: This study aimed to investigate the contributory role of histogram-based textural features (HBTFs) extracted from 18fluorinefluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in tumoral heterogeneity (TH) evaluation and invasive lung adenocarcinoma (ILA) prognosis prediction. Methods: This retrospective study analyzed the data of 72 patients with ILA who underwent 18F-FDG PET/CT followed by surgical resection. The maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis values were calculated for each tumor. Additionally, HBTFs were extracted from 18F-FDG PET/CT images using the software program. ILA was classified into the following five histopathological subtypes according to the predominant pattern: Lepidic adenocarcinoma (LA), acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma (SA), and micropapillary adenocarcinoma (MA). Differences between 18F-FDG PET/CT parameters and histopathological subtypes were evaluated using non-parametric tests. The study endpoints include overall survival (OS) and progression-free survival (PFS). The prognostic values of clinicopathological factors and 18F-FDG PET/CT parameters were evaluated using the Cox regression analyses. Results: The median SUVmax and entropy values were significantly higher in SA-MA, whereas lower in LA. The median energy-uniformity value of the LA was significantly higher than the others. Among all parameters, only skewness and kurtosis were significantly associated with lymph node involvement status. The median values for follow-up time, PFS, and OS were 31.26, 16.07, and 20.87 months, respectively. The univariate Cox regression analysis showed that lymph node involvement was the only significant predictor for PFS. The multivariate Cox regression analysis revealed that higher SUVmax (≥11.69) and advanced stage (IIB-IIIA) were significantly associated with poorer OS [hazard ratio (HR): 3.580, p=0.024 and HR: 7.608, p=0.007, respectively]. Conclusion: HBTFs were tightly associated with clinicopathological factors causing TH. Among the 18F-FDG PET/CT parameters, only skewness and kurtosis were associated with lymph node involvement, whereas SUVmax was the only independent predictor of OS. TH measurement with HBTFs may contribute to conventional metabolic parameters in guiding precision medicine for ILA.
Collapse
Affiliation(s)
- Hasan Önner
- University of Health Sciences Turkey, Konya City Hospital, Clinic of Nuclear Medicine, Konya, Turkey
| | - Nazım Coşkun
- University of Health Sciences Turkey, Ankara City Hospital, Clinic of Nuclear Medicine, Ankara, Turkey
| | - Mustafa Erol
- University of Health Sciences Turkey, Konya City Hospital, Clinic of Nuclear Medicine, Konya, Turkey
| | | |
Collapse
|
13
|
Liu Y, Zhou J, Wu J, Wang W, Wang X, Guo J, Wang Q, Zhang X, Li D, Xie J, Ding X, Xing Y, Hu D. Development and Validation of Machine Learning Models to Predict Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer: A Multi-Center Retrospective Radiomics Study. Cancer Control 2022; 29:10732748221092926. [PMID: 35417660 PMCID: PMC9016531 DOI: 10.1177/10732748221092926] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Objective To develop and validate a generalized prediction model that can classify
epidermal growth factor receptor (EGFR) mutation status in
non–small cell lung cancer patients. Methods A total of 346 patients (296 in the training cohort and 50 in the validation
cohort) from four centers were included in this retrospective study. First,
1085 features were extracted using IBEX from the computed tomography images.
The features were screened using the intraclass correlation coefficient,
hypothesis tests and least absolute shrinkage and selection operator.
Logistic regression (LR), decision tree (DT), random forest (RF), and
support vector machine (SVM) were used to build a radiomics model for
classification. The models were evaluated using the following metrics: area
under the curve (AUC), calibration curve (CAL), decision curve analysis
(DCA), concordance index (C-index), and Brier score. Results Sixteen features were selected, and models were built using LR, DT, RF, and
SVM. In the training cohort, the AUCs was .723, .842, .995, and .883; In the
validation cohort, the AUCs were .658, 0567, .88, and .765. RF model with
the best AUC, its CAL, C-index (training cohort=.998; validation
cohort=.883), and Brier score (training cohort=.007; validation
cohort=0.137) showed a satisfactory predictive accuracy; DCA indicated that
the RF model has better clinical application value. Conclusion Machine learning models based on computed tomography images can be used to
evaluate EGFR status in patients with non–small cell lung
cancer, and the RF model outperformed LR, DT, and SVM.
Collapse
Affiliation(s)
- Yafeng Liu
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Jiawei Zhou
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Jing Wu
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China.,Anhui Province Engineering Laboratory of Occupational Health and Safety, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Wenyang Wang
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Xueqin Wang
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Jianqiang Guo
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Qingsen Wang
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Xin Zhang
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Danting Li
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Jun Xie
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, 91594Anhui University of Science and Technology, Huainan, P.R. China
| | - Xuansheng Ding
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China.,Cancer Hospital of Anhui University of Science and Technology, Huainan, P.R. China.,School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yingru Xing
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China.,Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, P.R. China
| | - Dong Hu
- School of Medicine, 91594Anhui University of Science and Technology, Huainan, P.R. China.,Anhui Province Engineering Laboratory of Occupational Health and Safety, 91594Anhui University of Science and Technology, Huainan, P.R. China.,Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, 91594Anhui University of Science and Technology, Huainan, P.R. China
| |
Collapse
|
14
|
Huerga C, Morcillo A, Alejo L, Marín A, Obesso A, Travaglio D, Bayón J, Rodriguez D, Coronado M. Role of correlated noise in textural features extraction. Phys Med 2021; 91:87-98. [PMID: 34742098 DOI: 10.1016/j.ejmp.2021.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the assumption that an improvement in information quality can also enhance the predictive model. A heuristic approach was used that recognizes from the beginning that the noise has its own texture and it was analysed how it affects the quantitative signal data. A simple procedure to obtain noise image estimation is shown; one which makes it possible to extract the noise-texture features at each observation. The distance measured between the textural features in signal and estimated noise images allows us to determine the features affected in each observation by the noise and, for example, to exclude some of them from the model. A demonstration was carried out using synthetic images applying realistic noise models found in medical images. Drawn conclusions were applied to a public cohort of clinical images obtained using FDG-PET to show how the predictive model could be improved. A gain in the area under the receiver operating characteristic curve between 10 and 20% when noise texture information is used was shown. An improvement between 20 and 30% can be appreciated in the estimated model quality.
Collapse
Affiliation(s)
- Carlos Huerga
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain.
| | - Ana Morcillo
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain
| | - Luis Alejo
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain
| | - Alberto Marín
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain
| | - Alba Obesso
- Servicio de Radiofísica y Protección Radiológica, ESI/OSI Donostialdea, Donostia, Spain
| | - Daniela Travaglio
- Department of Nuclear Medicine, Hospital Universitario La Paz, Madrid, Spain
| | - Jose Bayón
- Servicio de Radiofísica y Protección Radiológica. Hospital Universitario Rey Juan Carlos, Madrid, Spain
| | | | - Monica Coronado
- Department of Nuclear Medicine, Hospital Universitario La Paz, Madrid, Spain
| |
Collapse
|
15
|
Jha AK, Myers KJ, Obuchowski NA, Liu Z, Rahman MA, Saboury B, Rahmim A, Siegel BA. Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician. PET Clin 2021; 16:493-511. [PMID: 34537127 DOI: 10.1016/j.cpet.2021.06.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-based evaluation of artificial intelligence methods. We provide a list of available tools to conduct this evaluation. We outline the important role of physicians in conducting these evaluation studies. The examples in this article are proposed in the context of PET scans with a focus on evaluating neural network-based methods. However, the framework is also applicable to evaluate other medical imaging modalities and other types of artificial intelligence methods.
Collapse
Affiliation(s)
- Abhinav K Jha
- Department of Biomedical Engineering, Mallinckrodt Institute of Radioly, Alvin J. Siteman Cancer Center, Washington University in St. Louis, 510 S Kingshighway Boulevard, St Louis, MO 63110, USA.
| | - Kyle J Myers
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration (FDA), Silver Spring, MD, USA
| | | | - Ziping Liu
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Drive, St Louis, MO 63130, USA
| | - Md Ashequr Rahman
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Drive, St Louis, MO 63130, USA
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Arman Rahmim
- Department of Radiology, Department of Physics, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada
| | - Barry A Siegel
- Division of Nuclear Medicine, Mallinckrodt Institute of Radiology, Alvin J. Siteman Cancer Center, Washington University School of Medicine, 510 S Kingshighway Boulevard #956, St Louis, MO 63110, USA
| |
Collapse
|
16
|
Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan MA, Boellaard R, Buvat I. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med 2021; 63:172-179. [PMID: 34531263 DOI: 10.2967/jnumed.121.262464] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/26/2021] [Indexed: 11/16/2022] Open
Abstract
The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is widely documented. This "scanner" effect is detrimental to the design and validation of predictive or prognostic models and limits the use of large multicenter cohorts. To reduce the impact of this scanner effect, the ComBat method has been proposed and is now used in various contexts. The purpose of this article is to explain and illustrate the use of ComBat based on practical examples. We also give examples in which the ComBat assumptions are not met; thus, ComBat should not be used.
Collapse
Affiliation(s)
- Fanny Orlhac
- Institut Curie, Universite PSL, Inserm, U1288 LITO, Universite Paris Saclay, France
| | - Jakoba J Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center, Netherlands
| | | | - Josee M Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center, Netherlands
| | - Catherine Thieblemont
- APHP, Hopital Saint-Louis, Service d'hemato-oncologie, DMU DHI, Universite de Paris, France
| | - Michel A Meignan
- AP-HP, Universite Paris-Est, Hopital Henri Mondor, Lysa Imaging, France
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center, Netherlands
| | - Irene Buvat
- Institut Curie, Universite PSL, Inserm, U1288 LITO, Universite Paris Saclay, France
| |
Collapse
|
17
|
Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med 2021; 11:842. [PMID: 34575619 PMCID: PMC8472571 DOI: 10.3390/jpm11090842] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.
Collapse
Affiliation(s)
- Shruti Atul Mali
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
- Department of Medical Physics, Division of Nuclear Medicine and Oncological Imaging, Hospital Center Universitaire de Liege, 4000 Liege, Belgium
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
| | - Vincent Andrearczyk
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland (HES-SO), rue du Technopole 3, 3960 Sierre, Switzerland; (V.A.); (H.M.)
| | - Henning Müller
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland (HES-SO), rue du Technopole 3, 3960 Sierre, Switzerland; (V.A.); (H.M.)
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
| |
Collapse
|
18
|
Oliveira C, Amstutz F, Vuong D, Bogowicz M, Hüllner M, Foerster R, Basler L, Schröder C, Eboulet EI, Pless M, Thierstein S, Peters S, Hillinger S, Tanadini-Lang S, Guckenberger M. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging. EJNMMI Res 2021; 11:79. [PMID: 34417899 PMCID: PMC8380219 DOI: 10.1186/s13550-021-00809-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/08/2021] [Indexed: 12/25/2022] Open
Abstract
Background Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). Methods A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). Results Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). Conclusions A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-021-00809-3.
Collapse
Affiliation(s)
- Carol Oliveira
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. .,Division of Radiation Oncology, Cancer Center of Southeastern Ontario, Queen's University, Kingston, ON, Canada.
| | - Florian Amstutz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Robert Foerster
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christina Schröder
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Eric I Eboulet
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Sandra Thierstein
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| |
Collapse
|
19
|
Da-ano R, Lucia F, Masson I, Abgral R, Alfieri J, Rousseau C, Mervoyer A, Reinhold C, Pradier O, Schick U, Visvikis D, Hatt M. A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets. PLoS One 2021; 16:e0253653. [PMID: 34197503 PMCID: PMC8248970 DOI: 10.1371/journal.pone.0253653] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center. MATERIAL AND METHODS The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines. RESULTS The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available. CONCLUSION The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.
Collapse
Affiliation(s)
- Ronrick Da-ano
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- * E-mail:
| | - François Lucia
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Radiation Oncology Department, University Hospital, Brest, France
| | - Ingrid Masson
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Department of Radiation Oncology, Institut de cancérologie de l’Ouest René-Gauducheau, Saint-Herblain, France
| | - Ronan Abgral
- Department of Nuclear Medicine, University of Brest, Brest, France
| | - Joanne Alfieri
- Department of Radiation Oncology, McGill University Health Centre, Montreal, Quebec
| | - Caroline Rousseau
- Department of Nuclear Medicine, Institut de cancérologie de l’Ouest René-Gauducheau, Saint-Herblain, France
| | - Augustin Mervoyer
- Department of Radiation Oncology, Institut de cancérologie de l’Ouest René-Gauducheau, Saint-Herblain, France
| | - Caroline Reinhold
- Department of Radiology, McGill University Health Centre, Montreal, Canada
- Augmented Intelligence & Precision Health Laboratory of the Research Institute of McGill University Health Centre, Montreal, Canada
| | - Olivier Pradier
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Radiation Oncology Department, University Hospital, Brest, France
| | - Ulrike Schick
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Radiation Oncology Department, University Hospital, Brest, France
| | | | - Mathieu Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| |
Collapse
|
20
|
Jaggi A, Mattonen SA, McNitt-Gray M, Napel S. Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. ACTA ACUST UNITED AC 2021; 6:111-117. [PMID: 32548287 PMCID: PMC7289253 DOI: 10.18383/j.tom.2019.00030] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.
Collapse
Affiliation(s)
- Akshay Jaggi
- Department of Radiology, Stanford University, Stanford, CA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; and
| | - Michael McNitt-Gray
- Department of Radiology, University of California, Los Angeles, Los Angeles, CA
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, CA
| |
Collapse
|
21
|
Xu QQ, Shan WL, Zhu Y, Huang CC, Bao SY, Guo LL. Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics. Eur J Radiol 2021; 139:109667. [PMID: 33867180 DOI: 10.1016/j.ejrad.2021.109667] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 02/27/2021] [Accepted: 03/13/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To investigate the relationship between CT radiomic features, pathological classification of pulmonary nodules, and evaluate the prediction effect of different stratified progressive radiomic models on the pathological classification of pulmonary nodules. METHODS Altogether, 189 patients pathologically confirmed with pulmonary nodules from July 2017 to August 2019 who had complete data were enrolled, including 71 patients with benign nodules, 51 with malignant non-invasive nodules, and 67 with invasive nodules. Three CT radiomic models were established respectively. Model 1 classified benign and malignant nodules (including malignant non-invasive and invasive nodules). Model 2 classified malignant non-invasive and invasive nodules. Model 3 classified benign, malignant non-invasive, and invasive nodules. High-throughput feature collection was carried out for all delineated regions of interest (ROIs), and the best models were established by screening features and classifiers using intelligent methods. ROC curves and areas under the curve (AUCs) were used to evaluate the prediction efficacy of the models by calculating the sensitivity, specificity, accuracies, positive predictive values, and negative predictive values. RESULTS Through Models 1, 2, and 3, we screened out 20, 2, and 20 radiomic features, respectively, and plotted the ROC curves. In the test group, the AUC values were 0.85, 0.89, and 0.84, respectively; the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 79.66 %, 70.42 %, 84.59 %, and 81.74 % and 67.57% for Model 1, 88.06 %, 74.51 %, 82.2 %, 81.94 %, and 82.61 % for Model 2, and 71.34 %, 85.05 %, 70.37 %, 83.2 %, and 76.3 % for Model 3. CONCLUSION The radiomic feature models based on CT images could well reflect the differences between benign nodules, malignant non-invasive nodules, and invasive nodules, and assist in their classification.
Collapse
Affiliation(s)
- Qing-Qing Xu
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Wen-Li Shan
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Yan Zhu
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Chen-Cui Huang
- Department of research collaboration, Beijing Deepwise &League of PHD technology Co. LTD, R&D center, Beijing, 100089, China
| | - Si-Yu Bao
- Department of research collaboration, Beijing Deepwise &League of PHD technology Co. LTD, R&D center, Beijing, 100089, China
| | - Li-Li Guo
- Department of Radiology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China.
| |
Collapse
|
22
|
Delgadillo R, Spieler BO, Ford JC, Kwon D, Yang F, Studenski M, Padgett KR, Abramowitz MC, Dal Pra A, Stoyanova R, Pollack A, Dogan N. Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer. Med Phys 2021; 48:2386-2399. [PMID: 33598943 DOI: 10.1002/mp.14787] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/09/2020] [Accepted: 02/09/2021] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Radiomic features of cone-beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT-based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. METHODS The quality of 42 CBCT-based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. RESULTS Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray-level size zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and gray-level-run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top-repeatable iCBCT method (iCBCT-Sharp-VeryHigh) is more repeatable than the top-repeatable sCBCT method (sCBCT-Smooth) in 64% of the radiomic features. CONCLUSION Methods for reconstruction and preprocessing that improve CBCT radiomic feature repeatability often decrease reproducibility. The best approach may be to use methods that strike a balance repeatability and reproducibility such as iCBCT-Sharp-VeryLow-1-Lloyd-256 that has 17 repeatable and eight reproducible radiomic features. Previous radiomic studies that only used pCT radiomic features have generated prognostic models of prostate cancer outcome. Since our study indicates that CBCT radiomic features correlated well with a subset of pCT radiomic features, one may expect CBCT radiomics to also generate prognostic models for prostate cancer.
Collapse
Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Benjamin O Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Deukwoo Kwon
- Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.,Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew Studenski
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| |
Collapse
|
23
|
Comparison of three freeware software packages for 18F-FDG PET texture feature calculation. Jpn J Radiol 2021; 39:710-719. [PMID: 33595789 DOI: 10.1007/s11604-021-01100-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/01/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE To compare texture feature estimates obtained from 18F-FDG-PET images using three different software packages. METHODS PET images from 15 patients with head and neck cancer were processed with three different freeware software: CGITA, LIFEx, and Metavol. For each lesion, 38 texture features were extracted from each software package. To evaluate the statistical agreement among the features across packages a non-parametric Kruskal-Wallis test was used. Differences in the features between each couple of software were assessed using a subsequent Dunn test. Correlation between texture features was evaluated via the Spearman coefficient. RESULTS Twenty-three of 38 features showed a significant agreement across the three software (P < 0.05). The agreement was better between LIFEx vs. Metavol (36 of 38) and worse between CGITA and Metavol (24 of 38), and CGITA vs. LIFEx (23 of 38). All features resulted correlated (ρ > = 0.70, P < 0.001) in comparing LIFEx vs. Metavol. Seven of 38 features were found not in agreement and slightly or not correlated (ρ < 0.70, P < 0.001) in comparing CGITA vs. LIFEx, and CGITA vs. Metavol. CONCLUSION Some texture discrepancies across software packages exist. Our findings reinforce the need to continue the standardization process, and to succeed in building a reference dataset to be used for comparisons.
Collapse
|
24
|
Carles M, Fechter T, Radicioni G, Schimek-Jasch T, Adebahr S, Zamboglou C, Nicolay NH, Martí-Bonmatí L, Nestle U, Grosu AL, Baltas D, Mix M, Gkika E. FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy. Cancers (Basel) 2021; 13:cancers13040814. [PMID: 33672052 PMCID: PMC7919471 DOI: 10.3390/cancers13040814] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary In this study, we strive to identify clinically relevant image feature (IF) changes during chemoradiation in patients with non-small-cell lung cancer (NSCLC) to be able to predict tumor responses in an early stage of treatment. All patients underwent static (3D) and respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability such as noise, resolution and movement through breathing. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Abstract The aim of this study is to identify clinically relevant image feature (IF) changes during chemoradiation and evaluate their efficacy in predicting treatment response. Patients with non-small-cell lung cancer (NSCLC) were enrolled in two prospective trials (STRIPE, PET-Plan). We evaluated 48 patients who underwent static (3D) and retrospectively-respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Predictions of overall survival (OS), local recurrence (LR) and distant metastasis (DM) were evaluated. From 135 IFs, only 17 satisfied the required criteria of being normally distributed across 4D PET and robust between 3D and 4D images. Changes during treatment in the area-under-the-curve of the cumulative standard-uptake-value histogram (δAUCCSH) within primary tumor discriminated (AUC = 0.87, Specificity = 0.78) patients with and without LR. The resulted prognostic model was validated with a different segmentation method (AUC = 0.83) and in a different patient cohort (AUC = 0.63). The quantification of tumor FDG heterogeneity by δAUCCSH during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC.
Collapse
Affiliation(s)
- Montserrat Carles
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (T.F.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
- Correspondence:
| | - Tobias Fechter
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (T.F.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
| | - Gianluca Radicioni
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Tanja Schimek-Jasch
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Sonja Adebahr
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Constantinos Zamboglou
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Nils H. Nicolay
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Luis Martí-Bonmatí
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
| | - Ursula Nestle
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
- Department of Radiation Oncology, Kliniken Maria Hilf, GmbH Moenchengladbach, 41063 Moechengladbach, Germany
| | - Anca L. Grosu
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Dimos Baltas
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (T.F.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
| | - Michael Mix
- Department of Nuclear Medicine, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany;
| | - Eleni Gkika
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| |
Collapse
|
25
|
Shui L, Ren H, Yang X, Li J, Chen Z, Yi C, Zhu H, Shui P. The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front Oncol 2021; 10:570465. [PMID: 33575207 PMCID: PMC7870863 DOI: 10.3389/fonc.2020.570465] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/08/2020] [Indexed: 02/05/2023] Open
Abstract
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.
Collapse
Affiliation(s)
- Lin Shui
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haoyu Ren
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany
| | - Xi Yang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jian Li
- Department of Pharmacy, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Ziwei Chen
- Department of Nephrology, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, China
| | - Cheng Yi
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhu
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pixian Shui
- School of Pharmacy, Southwest Medical University, Luzhou, China
| |
Collapse
|
26
|
Shirvani SM, Huntzinger CJ, Melcher T, Olcott PD, Voronenko Y, Bartlett-Roberto J, Mazin S. Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer. Br J Radiol 2021; 94:20200873. [PMID: 33112685 PMCID: PMC7774706 DOI: 10.1259/bjr.20200873] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/30/2020] [Accepted: 10/05/2020] [Indexed: 12/14/2022] Open
Abstract
The emerging biological understanding of metastatic cancer and proof-of-concept clinical trials suggest that debulking all gross disease holds great promise for improving patient outcomes. However, ablation of multiple targets with conventional external beam radiotherapy systems is burdensome, which limits investigation and utilization of complete metastatic ablation in the majority of patients with advanced disease. To overcome this logistical hurdle, technical innovation is necessary. Biology-guided radiotherapy (BgRT) is a new external beam radiotherapy delivery modality combining positron emission tomography-computed tomography (PET-CT) with a 6 MV linear accelerator. The key innovation is continuous response of the linear accelerator to outgoing tumor PET emissions with beamlets of radiotherapy at subsecond latency. This allows the deposited dose to track tumors in real time. Multiple new hardware and algorithmic advances further facilitate this low-latency feedback process. By transforming tumors into their own fiducials after intravenous injection of a radiotracer, BgRT has the potential to enable complete metastatic ablation in a manner efficient for a single patient and scalable to entire populations with metastatic disease. Future trends may further enhance the utility of BgRT in the clinic as this technology dovetails with other innovations in radiotherapy, including novel dose painting and fractionation schemes, radiomics, and new radiotracers.
Collapse
|
27
|
Simpson G, Ford JC, Llorente R, Portelance L, Yang F, Mellon EA, Dogan N. Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features. Phys Med 2020; 80:209-220. [PMID: 33190077 DOI: 10.1016/j.ejmp.2020.10.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images. METHODS Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design. RESULTS The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination. CONCLUSION Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.
Collapse
Affiliation(s)
- Garrett Simpson
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Ricardo Llorente
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Lorraine Portelance
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA.
| |
Collapse
|
28
|
Erdal BS, Demirer M, Little KJ, Amadi CC, Ibrahim GFM, O’Donnell TP, Grimmer R, Gupta V, Prevedello LM, White RD. Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters? PLoS One 2020; 15:e0240184. [PMID: 33057454 PMCID: PMC7561205 DOI: 10.1371/journal.pone.0240184] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/22/2020] [Indexed: 12/30/2022] Open
Abstract
Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as "compatible pairs" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.
Collapse
Affiliation(s)
- Barbaros S. Erdal
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Mutlu Demirer
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Kevin J. Little
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Chiemezie C. Amadi
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Gehan F. M. Ibrahim
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Thomas P. O’Donnell
- Siemens Healthineers, Malvern, Pennsylvania, United States of America and Erlangen, Germany
| | - Rainer Grimmer
- Siemens Healthineers, Malvern, Pennsylvania, United States of America and Erlangen, Germany
| | - Vikash Gupta
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Richard D. White
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| |
Collapse
|
29
|
Duan C, Chaovalitwongse WA, Bai F, Hippe DS, Wang S, Thammasorn P, Pierce LA, Liu X, You J, Miyaoka RS, Vesselle HJ, Kinahan PE, Rengan R, Zeng J, Bowen SR. Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer. Phys Med Biol 2020; 65:205007. [PMID: 33027064 PMCID: PMC7593986 DOI: 10.1088/1361-6560/abb0c7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.
Collapse
Affiliation(s)
- Chunyan Duan
- Department of Mechanical Engineering, Tongji University School of Mechanical Engineering, Shanghai China
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Fangyun Bai
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Daniel S. Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Shouyi Wang
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Larry A. Pierce
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Jianxin You
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
| | - Robert S. Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Hubert J. Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| |
Collapse
|
30
|
Abstract
PURPOSE OF REVIEW The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging. RECENT FINDINGS Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.
Collapse
|
31
|
Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
Collapse
Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| |
Collapse
|
32
|
van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020; 11:91. [PMID: 32785796 PMCID: PMC7423816 DOI: 10.1186/s13244-020-00887-2] [Citation(s) in RCA: 524] [Impact Index Per Article: 131.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
Collapse
Affiliation(s)
- Janita E van Timmeren
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Davide Cester
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
| |
Collapse
|
33
|
Forghani R. Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors. Radiol Imaging Cancer 2020; 2:e190047. [PMID: 33778721 DOI: 10.1148/rycan.2020190047] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 02/21/2020] [Accepted: 03/04/2020] [Indexed: 12/22/2022]
Abstract
Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as texture or radiomic analysis. Numerous studies have demonstrated applications for texture and radiomic characterization methods for assessing brain tumors to improve noninvasive predictions of tumor histologic characteristics, molecular profile, distinction of treatment-related changes, and prediction of patient survival. In this review, the current use and future potential of texture or radiomic-based approaches with machine learning for brain tumor image analysis and prediction algorithm construction will be discussed. This technology has the potential to advance the value of diagnostic imaging by extracting currently unused information on medical scans that enables more precise, personalized therapy; however, significant barriers must be overcome if this technology is to be successfully implemented on a wide scale for routine use in the clinical setting. Keywords: Adults and Pediatrics, Brain/Brain Stem, CNS, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Image Postprocessing, Informatics, Neural Networks, Neuro-Oncology, Oncology, Treatment Effects, Tumor Response Supplemental material is available for this article. © RSNA, 2020.
Collapse
Affiliation(s)
- Reza Forghani
- Department of Radiology, McGill University Health Centre, 1001 Decarie Blvd, Room C02.5821, Montreal, QC, Canada H4A 3J1; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada; and Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, Canada
| |
Collapse
|
34
|
Sollini M, Kirienko M, Cavinato L, Ricci F, Biroli M, Ieva F, Calderoni L, Tabacchi E, Nanni C, Zinzani PL, Fanti S, Guidetti A, Alessi A, Corradini P, Seregni E, Carlo-Stella C, Chiti A. Methodological framework for radiomics applications in Hodgkin's lymphoma. Eur J Hybrid Imaging 2020; 4:9. [PMID: 34191173 PMCID: PMC8218114 DOI: 10.1186/s41824-020-00078-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 05/06/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. PURPOSE The study aimed at setting up a methodological framework in radiomics applications in Hodgkin's lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions' similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. METHODS We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions' similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). RESULTS HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). CONCLUSIONS Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used.
Collapse
Affiliation(s)
- Martina Sollini
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| | - Margarita Kirienko
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
| | - Lara Cavinato
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
- MOX–Modelling and Scientific Computing lab., Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Francesca Ricci
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| | - Matteo Biroli
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
| | - Francesca Ieva
- MOX–Modelling and Scientific Computing lab., Department of Mathematics, Politecnico di Milano, Milano, Italy
- CADS–Center for Analysis, Decision, and Society, Human Technopole, Milano, Italy
| | | | | | | | - Pier Luigi Zinzani
- Institute of Hematology “Seràgnoli”, University of Bologna, Bologna, Italy
| | - Stefano Fanti
- Nuclear Medicine, AOU S.Orsola-Malpighi, Bologna, Italy
| | - Anna Guidetti
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- University of Milan, Milan, Italy
| | | | - Paolo Corradini
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- University of Milan, Milan, Italy
| | - Ettore Seregni
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Carmelo Carlo-Stella
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| | - Arturo Chiti
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| |
Collapse
|
35
|
Tixier F, Cheze-le-Rest C, Schick U, Simon B, Dufour X, Key S, Pradier O, Aubry M, Hatt M, Corcos L, Visvikis D. Transcriptomics in cancer revealed by Positron Emission Tomography radiomics. Sci Rep 2020; 10:5660. [PMID: 32221360 PMCID: PMC7101432 DOI: 10.1038/s41598-020-62414-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 03/13/2020] [Indexed: 11/09/2022] Open
Abstract
Metabolic images from Positron Emission Tomography (PET) are used routinely for diagnosis, follow-up or treatment planning purposes of cancer patients. In this study we aimed at determining if radiomic features extracted from 18F-Fluoro Deoxy Glucose (FDG) PET images could mirror tumor transcriptomics. In this study we analyzed 45 patients with locally advanced head and neck cancer (H&N) that underwent FDG-PET scans at the time of diagnosis and transcriptome analysis using RNAs from both cancer and healthy tissues on microarrays. Association between PET radiomics and transcriptomics was carried out with the Genomica software and a functional annotation was used to associate PET radiomics, gene expression and altered biological pathways. We identified relationships between PET radiomics and genes involved in cell-cycle, disease, DNA repair, extracellular matrix organization, immune system, metabolism or signal transduction pathways, according to the Reactome classification. Our results suggest that these FDG PET radiomic features could be used to infer tissue gene expression and cellular pathway activity in H&N cancers. These observations strengthen the value of radiomics as a promising approach to personalize treatments through targeting tumor-specific molecular processes.
Collapse
Affiliation(s)
- Florent Tixier
- Department of Nuclear Medicine, Poitiers University Hospital, Poitiers, France.
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
| | - Catherine Cheze-le-Rest
- Department of Nuclear Medicine, Poitiers University Hospital, Poitiers, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
- Radiation Oncology Department, University Hospital, Brest, France
| | - Brigitte Simon
- INSERM, UMR 1078, Université de Brest, Génétique Génomique Fonctionnelle et Biotechnologies, Etablissement Français du Sang, Brest, France
| | - Xavier Dufour
- Head and Neck Department, Poitiers University Hospital, Poitiers, France
| | - Stéphane Key
- Radiation Oncology Department, University Hospital, Brest, France
| | - Olivier Pradier
- Radiation Oncology Department, University Hospital, Brest, France
| | - Marc Aubry
- CNRS, UMR 6290, IGDR, Université de Rennes 1, Rennes, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Laurent Corcos
- INSERM, UMR 1078, Université de Brest, Génétique Génomique Fonctionnelle et Biotechnologies, Etablissement Français du Sang, Brest, France
| | | |
Collapse
|
36
|
Prenosil GA, Weitzel T, Fürstner M, Hentschel M, Krause T, Cumming P, Rominger A, Klaeser B. Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics. PLoS One 2020; 15:e0229560. [PMID: 32176698 PMCID: PMC7075630 DOI: 10.1371/journal.pone.0229560] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 02/10/2020] [Indexed: 12/24/2022] Open
Abstract
Purpose Image texture is increasingly used to discriminate tissues and lesions in PET/CT. For quantification or in computer-aided diagnosis, textural feature analysis must produce robust and comparable values. Because statistical feature values depend on image count statistics, we investigated in depth the stability of Haralick features values as functions of acquisition duration, and for common image resolutions and reconstructions. Methods A homogeneous cylindrical phantom containing 9.6 kBq/ml Ge-68 was repeatedly imaged on a Siemens Biograph mCT, with acquisition durations ranging from three seconds to three hours. Images with 1.5, 2, and 4 mm isometrically spaced voxels were reconstructed with filtered back-projection (FBP), ordered subset expectation maximization (OSEM), and the Siemens TrueX algorithm. We analysed Haralick features derived from differently quantized (3 to 8-bit) grey level co-occurrence matrices (GLCMs) as functions of exposure E, which we defined as the product of activity concentration in a volume of interest (VOI) and acquisition duration. The VOI was a 50 mm wide cube at the centre of the phantom. Feature stability was defined for df/dE → 0. Results The most stable feature values occurred in low resolution FBPs, whereas some feature values from 1.5 mm TrueX reconstructions ranged over two orders of magnitude. Within the same reconstructions, most feature value-exposure curves reached stable plateaus at similar exposures, regardless of GLCM quantization. With 8-bit GLCM, median time to stability was 16 s and 22 s for FBPs, 18 s and 125 s for OSEM, and 23 s, 45 s, and 76 s for PSF reconstructions, with longer durations for higher resolutions. Stable exposures coincided in OSEM and TrueX reconstructions with image noise distributions converging to a Gaussian. In FBP, the occurrence of stable values coincided the disappearance of negatives image values in the VOI. Conclusions Haralick feature values depend strongly on exposure, but invariance exists within defined domains of exposure. Here, we present an easily replicable procedure to identify said stable exposure domains, where image noise does not substantially add to textural feature values. Only by imaging at predetermined feature-invariant exposure levels and by adjusting exposure to expected activity concentrations, can textural features have a quantitative use in PET/CT. The necessary exposure levels are attainable by modern PET/CT systems in clinical routine.
Collapse
Affiliation(s)
- George Amadeus Prenosil
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- * E-mail:
| | - Thilo Weitzel
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus Fürstner
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Michael Hentschel
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Thomas Krause
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paul Cumming
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- School of Psychology and Counselling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Bernd Klaeser
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Radiology and Nuclear Medicine, Cantonal Hospital Winterthur, Winterthur, Switzerland
| |
Collapse
|
37
|
Foy JJ, Armato SG, Al-Hallaq HA. Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis. J Med Imaging (Bellingham) 2020; 7:014504. [PMID: 32118090 DOI: 10.1117/1.jmi.7.1.014504] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/17/2020] [Indexed: 12/17/2022] Open
Abstract
Purpose: While radiomics feature values can differ when extracted using different radiomics software, the effects of these variations when applied to a particular clinical task are currently unknown. The goal of our study was to use various radiomics software packages to classify patients with radiation pneumonitis (RP) and to quantify the variation in classification ability among packages. Approach: A database of serial thoracic computed tomography scans was obtained from 105 patients with esophageal cancer. Patients were treated with radiation therapy (RT), resulting in 20 patients developing RP grade ≥ 2 . Regions of interest (ROIs) were randomly placed in the lung volume of the pre-RT scan within high-dose regions ( ≥ 30 Gy ), and corresponding ROIs were anatomically matched in the post-RT scan. Three radiomics packages were compared: A1 (in-house), IBEX v1.0 beta, and PyRadiomics v.2.0.0. Radiomics features robust to deformable registration and common among radiomics packages were calculated: four first-order and four gray-level co-occurrence matrix features. Differences in feature values between time points were calculated for each feature, and logistic regression was used in conjunction with analysis of variance to classify patients with and without RP ( p < 0.006 ). Classification ability for each package was assessed using receiver operating characteristic (ROC) analysis and compared using the area under the ROC curve (AUC). Results: Of the eight radiomics features, five were significantly correlated with RP status for all three packages, whereas one feature was not significantly correlated with RP for all three packages. The remaining two features differed in whether or not they were significantly associated with RP status among the packages. Seven of the eight features agreed among the packages in whether the AUC value was significantly > 0.5 . Conclusions: Radiomics features extracted using different software packages can result in differences in classification ability.
Collapse
Affiliation(s)
- Joseph J Foy
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Samuel G Armato
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Hania A Al-Hallaq
- The University of Chicago, Department of Radiation and Cellular Oncology, Chicago, Illinois, United States
| |
Collapse
|
38
|
Mattonen SA, Davidzon GA, Bakr S, Echegaray S, Leung ANC, Vasanawala M, Horng G, Napel S, Nair VS. [18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer. ACTA ACUST UNITED AC 2020; 5:145-153. [PMID: 30854452 PMCID: PMC6403030 DOI: 10.18383/j.tom.2018.00026] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non–small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56–2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66–0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67–0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.
Collapse
Affiliation(s)
| | | | - Shaimaa Bakr
- Electrical Engineering, Stanford University, Stanford, CA
| | | | | | | | - George Horng
- California Pacific Medical Center, San Francisco, CA
| | | | - Viswam S Nair
- Departments of Radiology.,Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute, Tampa, FL; and.,Morsani College of Medicine, University of South Florida, Tampa, FL
| |
Collapse
|
39
|
Martin-Isla C, Campello VM, Izquierdo C, Raisi-Estabragh Z, Baeßler B, Petersen SE, Lekadir K. Image-Based Cardiac Diagnosis With Machine Learning: A Review. Front Cardiovasc Med 2020; 7:1. [PMID: 32039241 PMCID: PMC6992607 DOI: 10.3389/fcvm.2020.00001] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/06/2020] [Indexed: 01/28/2023] Open
Abstract
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
Collapse
Affiliation(s)
- Carlos Martin-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Victor M Campello
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Cristian Izquierdo
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Bettina Baeßler
- Department of Diagnostic & Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Steffen E Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| |
Collapse
|
40
|
Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging: A Phantom Study. Invest Radiol 2019; 54:221-228. [PMID: 30433891 DOI: 10.1097/rli.0000000000000530] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVES The aim of this study was to investigate the robustness and reproducibility of radiomic features in different magnetic resonance imaging sequences. MATERIALS AND METHODS A phantom was scanned on a clinical 3 T system using fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1w), and T2-weighted (T2w) sequences with low and high matrix size. For retest data, scans were repeated after repositioning of the phantom. Test and retest datasets were segmented using a semiautomated approach. Intraobserver and interobserver comparison was performed. Radiomic features were extracted after standardized preprocessing of images. Test-retest robustness was assessed using concordance correlation coefficients, dynamic range, and Bland-Altman analyses. Reproducibility was assessed by intraclass correlation coefficients. RESULTS The number of robust features (concordance correlation coefficient and dynamic range ≥ 0.90) was higher for features calculated from FLAIR than from T1w and T2w images. High-resolution FLAIR images provided the highest percentage of robust features (n = 37/45, 81%). No considerable difference in the number of robust features was observed between low- and high-resolution T1w and T2w images (T1w low: n = 26/45, 56%; T1w high: n = 25/45, 54%; T2 low: n = 21/45, 46%; T2 high: n = 24/45, 52%). A total of 15 (33%) of 45 features showed excellent robustness across all sequences and demonstrated excellent intraobserver and interobserver reproducibility (intraclass correlation coefficient ≥ 0.75). CONCLUSIONS FLAIR delivers the most robust substrate for radiomic analyses. Only 15 of 45 features showed excellent robustness and reproducibility across all sequences. Care must be taken in the interpretation of clinical studies using nonrobust features.
Collapse
|
41
|
Ger RB, Meier JG, Pahlka RB, Gay S, Mumme R, Fuller CD, Li H, Howell RM, Layman RR, Stafford RJ, Zhou S, Mawlawi O, Court LE. Effects of alterations in positron emission tomography imaging parameters on radiomics features. PLoS One 2019; 14:e0221877. [PMID: 31487307 PMCID: PMC6728031 DOI: 10.1371/journal.pone.0221877] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 08/16/2019] [Indexed: 01/11/2023] Open
Abstract
Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners. A standard-protocol scan was acquired on each machine, and then each parameter that could be changed was altered individually. The phantom was contoured with 10 regions of interest (ROIs). Values for 45 features with 2 different preprocessing techniques were extracted for each image. To determine the impact of each parameter on the reliability of each radiomics feature, the intraclass correlation coefficient (ICC) was calculated with the ROIs as the subjects and the parameter values as the raters. For interscanner comparisons, we compared the standard deviation of each radiomics feature value from the standard-protocol images to the standard deviation of the same radiomics feature from PET scans of 224 patients with non-small cell lung cancer. When the pixel size was resampled prior to feature extraction, all features had good reliability (ICC > 0.75) for the field of view and matrix size. The time per bed position had excellent reliability (ICC > 0.9) on all features. When the filter cutoff was restricted to values below 6 mm, all features had good reliability. Similarly, when subsets and iterations were restricted to reasonable values used in clinics, almost all features had good reliability. The average ratio of the standard deviation of features on the phantom scans to that of the NSCLC patient scans was 0.73 using fixed-bin-width preprocessing and 0.92 using 64-level preprocessing. Most radiomics feature values had at least good reliability when imaging protocol parameters were within clinically used ranges. However, interscanner variability was about equal to interpatient variability; therefore, caution must be used when combining patients scanned on equipment from different vendors in radiomics data sets.
Collapse
Affiliation(s)
- Rachel B. Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- * E-mail:
| | - Joseph G. Meier
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Raymond B. Pahlka
- Department of Radiology, Texas Children’s Hospital, Houston, Texas, United States of America
| | - Skylar Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Clifton D. Fuller
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Rebecca M. Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Rick R. Layman
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - R. Jason Stafford
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Shouhao Zhou
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Osama Mawlawi
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| |
Collapse
|
42
|
Pfaehler E, van Sluis J, Merema BBJ, van Ooijen P, Berendsen RCM, van Velden FHP, Boellaard R. Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts. J Nucl Med 2019; 61:469-476. [PMID: 31420497 DOI: 10.2967/jnumed.119.229724] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 07/24/2019] [Indexed: 01/27/2023] Open
Abstract
The sensitivity of radiomic features to several confounding factors, such as reconstruction settings, makes clinical use challenging. To investigate the impact of harmonized image reconstructions on feature consistency, a multicenter phantom study was performed using 3-dimensionally printed phantom inserts reflecting realistic tumor shapes and heterogeneity uptakes. Methods: Tumors extracted from real PET/CT scans of patients with non-small cell lung cancer served as model for three 3-dimensionally printed inserts. Different heterogeneity pattern were realized by printing separate compartments that could be filled with different activity solutions. The inserts were placed in the National Electrical Manufacturers Association image-quality phantom and scanned various times. First, a list-mode scan was acquired and 5 statistically equal replicates were reconstructed. Second, the phantom was scanned 4 times on the same scanner. Third, the phantom was scanned on 6 PET/CT systems. All images were reconstructed using EANM Research Ltd. (EARL)-compliant and locally clinically preferred reconstructions. EARL-compliant reconstructions were performed without (EARL1) or with (EARL2) point-spread function. Images were analyzed with and without resampling to 2-mm cubic voxels. Images were discretized with a fixed bin width (FBW) of 0.25 and a fixed bin number (FBN) of 64. The intraclass correlation coefficient (ICC) of each scan setup was calculated and compared across reconstruction settings. An ICC above 0.75 was regarded as high. Results: The percentage of features yielding a high ICC was largest for the statistically equal replicates (70%-91% for FBN; 90%-96% for FBW discretization). For scans acquired on the same system, the percentage decreased, but most features still resulted in a high ICC (FBN, 52%-63%; FBW, 75%-85%). The percentage of features yielding a high ICC decreased more in the multicenter setting. In this case, the percentage of features yielding a high ICC was larger for images reconstructed with EARL-compliant reconstructions: for example, 40% for EARL1 and 60% for EARL2 versus 21% for the clinically preferred setting for FBW discretization. When discretized with FBW and resampled to isotropic voxels, this benefit was more pronounced. Conclusion: EARL-compliant reconstructions harmonize a wide range of radiomic features. FBW discretization and a sampling to isotropic voxels enhances the benefits of EARL-compliant reconstructions.
Collapse
Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Bram B J Merema
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, Groningen, The Netherlands
| | - Peter van Ooijen
- Department of Radiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Ralph C M Berendsen
- Department of Medical Physics, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Floris H P van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands; and
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| |
Collapse
|
43
|
Wolsztynski E, O'Sullivan J, Hughes NM, Mou T, Murphy P, O'Sullivan F, O'Regan K. Combining structural and textural assessments of volumetric FDG-PET uptake in NSCLC. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:421-433. [PMID: 33134652 PMCID: PMC7597463 DOI: 10.1109/trpms.2019.2912433] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Numerous studies have reported the prognostic utility of texture analyses and the effectiveness of radiomics in PET and PET/CT assessment of non-small cell lung cancer (NSCLC). Here we explore the potential, relative to this methodology, of an alternative model-based approach to tumour characterization, which was successfully applied to sarcoma in previous works. The spatial distribution of 3D FDG-PET uptake is evaluated in the spatial referential determined by the best-fitting ellipsoidal pattern, which provides a univariate uptake profile function of the radial position of intratumoral voxels. A group of structural features is extracted from this fit that include two heterogeneity variables and statistical summaries of local metabolic gradients. We demonstrate that these variables capture aspects of tumour metabolism that are separate to those described by conventional texture features. Prognostic model selection is performed in terms of a number of classifiers, including stepwise selection of logistic models, LASSO, random forests and neural networks with respect to two-year survival status. Our results for a cohort of 93 NSCLC patients show that structural variables have significant prognostic potential, and that they may be used in conjunction with texture features in a traditional radiomics sense, towards improved baseline multivariate models of patient overall survival. The statistical significance of these models also demonstrates the relevance of these machine learning classifiers for prognostic variable selection.
Collapse
Affiliation(s)
- Eric Wolsztynski
- Department of Statistics, School of Mathematical Sciences, University College Cork, T12 XY86, Ireland
| | - Janet O'Sullivan
- Department of Statistics, School of Mathematical Sciences, University College Cork, T12 XY86, Ireland
| | | | - Tian Mou
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Peter Murphy
- PET/CT Unit (Alliance Medical), Cork University Hospital, Cork, Ireland
| | - Finbarr O'Sullivan
- Department of Statistics, School of Mathematical Sciences, University College Cork, T12 XY86, Ireland
| | - Kevin O'Regan
- Department of Radiology, Cork University Hospital, Cork, Ireland
| |
Collapse
|
44
|
van Timmeren JE, Carvalho S, Leijenaar RTH, Troost EGC, van Elmpt W, de Ruysscher D, Muratet JP, Denis F, Schimek-Jasch T, Nestle U, Jochems A, Woodruff HC, Oberije C, Lambin P. Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics. PLoS One 2019; 14:e0217536. [PMID: 31158263 PMCID: PMC6546238 DOI: 10.1371/journal.pone.0217536] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 05/11/2019] [Indexed: 12/22/2022] Open
Abstract
Background Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment. Methods The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets. Results The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets. Conclusion The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets.
Collapse
Affiliation(s)
- Janna E. van Timmeren
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
- * E-mail:
| | - Sara Carvalho
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Esther G. C. Troost
- OncoRay–National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Cal Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT) Partner Site Dresden, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz Association / Helmholtz-Zentrum Dresden–Rossendorf (HZDR), Dresden, Germany
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Fabrice Denis
- Centre Jean Bernard, Clinique Victor Hugo, Le Mans, France
| | - Tanja Schimek-Jasch
- Department for Radiation Oncology, University Medical Center Freiburg, Freiburg, Germany
| | - Ursula Nestle
- Department for Radiation Oncology, University Medical Center Freiburg, Freiburg, Germany
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Henry C. Woodruff
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cary Oberije
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
45
|
Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019; 104:302-315. [PMID: 30711529 PMCID: PMC6499656 DOI: 10.1016/j.ijrobp.2019.01.087] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 02/06/2023]
Abstract
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.
Collapse
Affiliation(s)
- Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey.
| | - Hania Al-Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mi Huang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael V Knopp
- Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio
| | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - James Monroe
- Department of Radiation Oncology, St. Anthony's Cancer Center, St. Louis, Missouri
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Timothy Solberg
- Department of Radiation Oncology, University of California-San Francisco, San Francisco, California
| | - Jackie Wu
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Ying Xiao
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Issam El Naqa
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| |
Collapse
|
46
|
Kalendralis P, Traverso A, Shi Z, Zhovannik I, Monshouwer R, Starmans MPA, Klein S, Pfaehler E, Boellaard R, Dekker A, Wee L. Multicenter CT phantoms public dataset for radiomics reproducibility tests. Med Phys 2019; 46:1512-1518. [PMID: 30629299 PMCID: PMC6849778 DOI: 10.1002/mp.13385] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/15/2018] [Accepted: 12/06/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE The aim of this paper is to describe a public, open-access, computed tomography (CT) phantom image set acquired at three centers and collected especially for radiomics reproducibility research. The dataset is useful to test radiomic features reproducibility with respect to various parameters, such as acquisition settings, scanners, and reconstruction algorithms. ACQUISITION AND VALIDATION METHODS Three phantoms were scanned in three independent institutions. Images of the following phantoms were acquired: Catphan 700 and COPDGene Phantom II (Phantom Laboratory, Greenwich, NY, USA), and the Triple modality 3D Abdominal Phantom (CIRS, Norfolk, VA, USA). Data were collected at three Dutch medical centers: MAASTRO Clinic (Maastricht, NL), Radboud University Medical Center (Nijmegen, NL), and University Medical Center Groningen (Groningen, NL) with scanners from two different manufacturers Siemens Healthcare and Philips Healthcare. The following acquisition parameter were varied in the phantom scans: slice thickness, reconstruction kernels, and tube current. DATA FORMAT AND USAGE NOTES We made the dataset publically available on the Dutch instance of "Extensible Neuroimaging Archive Toolkit-XNAT" (https://xnat.bmia.nl). The dataset is freely available and reusable with attribution (Creative Commons 3.0 license). POTENTIAL APPLICATIONS Our goal was to provide a findable, open-access, annotated, and reusable CT phantom dataset for radiomics reproducibility studies. Reproducibility testing and harmonization are fundamental requirements for wide generalizability of radiomics-based clinical prediction models. It is highly desirable to include only reproducible features into models, to be more assured of external validity across hitherto unseen contexts. In this view, phantom data from different centers represent a valuable source of information to exclude CT radiomic features that may already be unstable with respect to simplified structures and tightly controlled scan settings. The intended extension of our shared dataset is to include other modalities and phantoms with more realistic lesion simulations.
Collapse
Affiliation(s)
- Petros Kalendralis
- MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands
| | - Alberto Traverso
- MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands
| | - Zhenwei Shi
- MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands
| | - Ivan Zhovannik
- MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands.,Department of Radiation Oncology, Radboud University Medical Center, 6525 GC, Nijmegen, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, 6525 GC, Nijmegen, The Netherlands
| | - Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus Medical Centre, 3015 GD, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus Medical Centre, 3015 GD, Rotterdam, The Netherlands
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus Medical Centre, 3015 GD, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus Medical Centre, 3015 GD, Rotterdam, The Netherlands
| | - Elisabeth Pfaehler
- University Medical Center Groningen, 9713 GZ, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, VU Medical Center, 1081 HV, Amsterdam, The Netherlands
| | - Andre Dekker
- MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands
| | - Leonard Wee
- MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands
| |
Collapse
|
47
|
Chatterjee A, Vallieres M, Dohan A, Levesque IR, Ueno Y, Saif S, Reinhold C, Seuntjens J. Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2019.2893860] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
48
|
Spraker MB, Wootton LS, Hippe DS, Ball KC, Peeken JC, Macomber MW, Chapman TR, Hoff MN, Kim EY, Pollack SM, Combs SE, Nyflot MJ. MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma. Adv Radiat Oncol 2019; 4:413-421. [PMID: 31011687 PMCID: PMC6460235 DOI: 10.1016/j.adro.2019.02.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 02/12/2019] [Indexed: 11/21/2022] Open
Abstract
Purpose Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index. Results In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; P = .009). Conclusions This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.
Collapse
Affiliation(s)
- Matthew B. Spraker
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri
- Corresponding author. 4921 Parkview Place, CAM LL, CB 8224, St. Louis, MO 63110.
| | - Landon S. Wootton
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, Washington
| | - Kevin C. Ball
- Aurora St. Luke's Medical Center, Department of Diagnostic Radiology, Milwaukee, Wisconsin
| | - Jan C. Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Innovative Radiation therapy, Department of Radiation Sciences, Helmholtz Zentrum München, Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung, Munich, Germany
| | - Meghan W. Macomber
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Tobias R. Chapman
- Beth Israel Deaconess Medical Center, Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts
| | - Michael N. Hoff
- Department of Radiology, University of Washington, Seattle, Washington
| | - Edward Y. Kim
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Seth M. Pollack
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Division of Medical Oncology, University of Washington, Seattle, Washington
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, Washington
- Department of Radiology, University of Washington, Seattle, Washington
| |
Collapse
|
49
|
Paoletti M, Pichiecchio A, Cotti Piccinelli S, Tasca G, Berardinelli AL, Padovani A, Filosto M. Advances in Quantitative Imaging of Genetic and Acquired Myopathies: Clinical Applications and Perspectives. Front Neurol 2019; 10:78. [PMID: 30804884 PMCID: PMC6378279 DOI: 10.3389/fneur.2019.00078] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 01/21/2019] [Indexed: 12/11/2022] Open
Abstract
In the last years, magnetic resonance imaging (MRI) has become fundamental for the diagnosis and monitoring of myopathies given its ability to show the severity and distribution of pathology, to identify specific patterns of damage distribution and to properly interpret a number of genetic variants. The advances in MR techniques and post-processing software solutions have greatly expanded the potential to assess pathological changes in muscle diseases, and more specifically of myopathies; a number of features can be studied and quantified, ranging from composition, architecture, mechanical properties, perfusion, and function, leading to what is known as quantitative MRI (qMRI). Such techniques can effectively provide a variety of information beyond what can be seen and assessed by conventional MR imaging; their development and application in clinical practice can play an important role in the diagnostic process and in assessing disease course and treatment response. In this review, we briefly discuss the current role of muscle MRI in diagnosing muscle diseases and describe in detail the potential and perspectives of the application of advanced qMRI techniques in this field.
Collapse
Affiliation(s)
- Matteo Paoletti
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Anna Pichiecchio
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Stefano Cotti Piccinelli
- Unit of Neurology, Center for Neuromuscular Diseases, ASST Spedali Civili and University of Brescia, Brescia, Italy
| | - Giorgio Tasca
- Neurology Department, Dipartimento di Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Alessandro Padovani
- Unit of Neurology, Center for Neuromuscular Diseases, ASST Spedali Civili and University of Brescia, Brescia, Italy
| | - Massimiliano Filosto
- Unit of Neurology, Center for Neuromuscular Diseases, ASST Spedali Civili and University of Brescia, Brescia, Italy
| |
Collapse
|
50
|
Forgacs A, Kallos-Balogh P, Nagy F, Krizsan AK, Garai I, Tron L, Dahlbom M, Balkay L. Activity painting: PET images of freely defined activity distributions applying a novel phantom technique. PLoS One 2019; 14:e0207658. [PMID: 30682024 PMCID: PMC6347296 DOI: 10.1371/journal.pone.0207658] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 11/04/2018] [Indexed: 12/18/2022] Open
Abstract
The aim of this work was to develop a novel phantom that supports the construction of highly reproducible phantoms with arbitrary activity distributions for PET imaging. It could offer a methodology for answering questions related to texture measurements in PET imaging. The basic idea is to move a point source on a 3-D trajectory in the field of view, while continuously acquiring data. The reconstruction results in a 3-D activity concentration map according to the pathway of the point source. A 22Na calibration point source was attached to a high precision robotic arm system, where the 3-D movement was software controlled. 3-D activity distributions of a homogeneous cube, a sphere, a spherical shell and a heart shape were simulated. These distributions were used to measure uniformity and to characterize reproducibility. Two potential applications using the lesion simulation method are presented: evaluation in changes of textural properties related to the position in the PET field of view; scanner comparison based on visual and quantitative evaluation of texture features. A lesion with volume of 50x50x50 mm3 can be simulated during approximately 1 hour. The reproducibility of the movement was found to be >99%. The coefficients of variation of the voxels within a simulated homogeneous cube was 2.34%. Based on 5 consecutive and independent measurements of a 36 mm diameter hot sphere, the coefficient of variation of the mean activity concentration was 0.68%. We obtained up to 18% differences within the values of investigated textural indexes, when measuring a lesion in different radial positions of the PET field of view. In comparison of two different human PET scanners the percentage differences between heterogeneity parameters were in the range of 5-55%. After harmonizing the voxel sizes this range reduced to 2-16%. The general activity distributions provided by the two different vendor show high similarity visually. For the demonstration of the flexibility of this method, the same pattern was also simulated on a small animal PET scanner giving similar results, both quantitatively and visually. 3-D motion of a point source in the PET field of view is capable to create an irregular shaped activity distribution with high reproducibility.
Collapse
Affiliation(s)
- Attila Forgacs
- Scanomed Nuclear Medicine Center, Debrecen, Hungary
- Division of Nuclear Medicine, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Piroska Kallos-Balogh
- Division of Nuclear Medicine, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ferenc Nagy
- Scanomed Nuclear Medicine Center, Debrecen, Hungary
| | | | - Ildiko Garai
- Scanomed Nuclear Medicine Center, Debrecen, Hungary
- Division of Nuclear Medicine, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Lajos Tron
- Division of Nuclear Medicine, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Magnus Dahlbom
- Ahmanson Translational Imaging Division, University of California at Los Angeles, United States of America
| | - Laszlo Balkay
- Division of Nuclear Medicine, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| |
Collapse
|