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Haberl D, Spielvogel CP, Jiang Z, Orlhac F, Iommi D, Carrió I, Buvat I, Haug AR, Papp L. Multicenter PET image harmonization using generative adversarial networks. Eur J Nucl Med Mol Imaging 2024; 51:2532-2546. [PMID: 38696130 PMCID: PMC11224088 DOI: 10.1007/s00259-024-06708-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/25/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches. METHODS GAN-harmonization was developed to harmonize whole-body PET scans to perform image style and texture translation between different centers and scanners. GAN-harmonization was evaluated by application to two retrospectively collected open datasets and different tasks. First, GAN-harmonization was performed on a dual-center lung cancer cohort (127 female, 138 male) where the reproducibility of radiomic features in healthy liver tissue was evaluated. Second, GAN-harmonization was applied to a head and neck cancer cohort (43 female, 154 male) acquired from three centers. Here, the clinical impact of GAN-harmonization was analyzed by predicting the development of distant metastases using a logistic regression model incorporating first-order statistics and texture features from baseline 18F-FDG PET before and after harmonization. RESULTS Image quality remained high (structural similarity: left kidney ≥ 0.800, right kidney ≥ 0.806, liver ≥ 0.780, lung ≥ 0.838, spleen ≥ 0.793, whole-body ≥ 0.832) after image harmonization across all utilized datasets. Using GAN-harmonization, inter-site reproducibility of radiomic features in healthy liver tissue increased at least by ≥ 5 ± 14% (first-order), ≥ 16 ± 7% (GLCM), ≥ 19 ± 5% (GLRLM), ≥ 16 ± 8% (GLSZM), ≥ 17 ± 6% (GLDM), and ≥ 23 ± 14% (NGTDM). In the head and neck cancer cohort, the outcome prediction improved from AUC 0.68 (95% CI 0.66-0.71) to AUC 0.73 (0.71-0.75) by application of GAN-harmonization. CONCLUSIONS GANs are capable of performing image harmonization and increase reproducibility and predictive performance of radiomic features derived from different centers and scanners.
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Affiliation(s)
- David Haberl
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20/E4L, A-1090, Vienna, Austria
| | - Clemens P Spielvogel
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20/E4L, A-1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Zewen Jiang
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20/E4L, A-1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Fanny Orlhac
- LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France
| | - David Iommi
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20/E4L, A-1090, Vienna, Austria
| | - Ignasi Carrió
- Department of Nuclear Medicine, Hospital Sant Pau and Autonomous University of Barcelona, Barcelona, Spain
| | - Irène Buvat
- LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France
| | - Alexander R Haug
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20/E4L, A-1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
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Thor M, Lee C, Sun L, Patel P, Apte A, Grkovski M, Shepherd AF, Gelblum DY, Wu AJ, Simone CB, Chaft JE, Rimner A, Gomez DR, Deasy JO, Shaverdian N. An 18F-FDG PET/CT and Mean Lung Dose Model to Predict Early Radiation Pneumonitis in Stage III Non-Small Cell Lung Cancer Patients Treated with Chemoradiation and Immunotherapy. J Nucl Med 2024; 65:520-526. [PMID: 38485270 PMCID: PMC10995528 DOI: 10.2967/jnumed.123.266965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/11/2024] [Indexed: 04/04/2024] Open
Abstract
Radiation pneumonitis (RP) that develops early (i.e., within 3 mo) (RPEarly) after completion of concurrent chemoradiation (cCRT) leads to treatment discontinuation and poorer survival for patients with stage III non-small cell lung cancer. Since no RPEarly risk model exists, we explored whether published RP models and pretreatment 18F-FDG PET/CT-derived features predict RPEarly Methods: One hundred sixty patients with stage III non-small cell lung cancer treated with cCRT and consolidative immunotherapy were analyzed for RPEarly Three published RP models that included the mean lung dose (MLD) and patient characteristics were examined. Pretreatment 18F-FDG PET/CT normal-lung SUV featured included the following: 10th percentile of SUV (SUVP10), 90th percentile of SUV (SUVP90), SUVmax, SUVmean, minimum SUV, and SD. Associations between models/features and RPEarly were assessed using area under the receiver-operating characteristic curve (AUC), P values, and the Hosmer-Lemeshow test (pHL). The cohort was randomly split, with similar RPEarly rates, into a 70%/30% derivation/internal validation subset. Results: Twenty (13%) patients developed RPEarly Predictors for RPEarly were MLD alone (AUC, 0.72; P = 0.02; pHL, 0.87), SUVP10, SUVP90, and SUVmean (AUC, 0.70-0.74; P = 0.003-0.006; pHL, 0.67-0.70). The combined MLD and SUVP90 model generalized in the validation subset and was deemed the final RPEarly model (RPEarly risk = 1/[1+e(- x )]; x = -6.08 + [0.17 × MLD] + [1.63 × SUVP90]). The final model refitted in the 160 patients indicated improvement over the published MLD-alone model (AUC, 0.77 vs. 0.72; P = 0.0001 vs. 0.02; pHL, 0.65 vs. 0.87). Conclusion: Patients at risk for RPEarly can be detected with high certainty by combining the normal lung's MLD and pretreatment 18F-FDG PET/CT SUVP90 This refined model can be used to identify patients at an elevated risk for premature immunotherapy discontinuation due to RPEarly and could allow for interventions to improve treatment outcomes.
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Affiliation(s)
- Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York;
| | - Chen Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lian Sun
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Purvi Patel
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Milan Grkovski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Annemarie F Shepherd
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
| | - Daphna Y Gelblum
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
| | - Abraham J Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
| | - Charles B Simone
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
| | - Jamie E Chaft
- Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
| | - Daniel R Gomez
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Narek Shaverdian
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York; and
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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.
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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.
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Palomino-Fernández D, Seiffert AP, Gómez-Grande A, Jiménez López-Guarch C, Moreno G, Bueno H, Gómez EJ, Sánchez-González P. Robustness of [ 18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107981. [PMID: 38154326 DOI: 10.1016/j.cmpb.2023.107981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/01/2023] [Accepted: 12/12/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Standardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity. METHODS Image post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated. RESULTS The effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified. CONCLUSIONS Gray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.
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Affiliation(s)
- David Palomino-Fernández
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain.
| | - Alexander P Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain
| | - Adolfo Gómez-Grande
- Department of Nuclear Medicine, Hospital Universitario 12 de Octubre, Spain; Facultad de Medicina, Universidad Complutense de Madrid, Spain
| | - Carmen Jiménez López-Guarch
- Facultad de Medicina, Universidad Complutense de Madrid, Spain; Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Spain
| | - Guillermo Moreno
- Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Facultad de Enfermería, Fisioterapia y Podología, Universidad Complutense de Madrid, Spain
| | - Héctor Bueno
- Facultad de Medicina, Universidad Complutense de Madrid, Spain; Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Spain
| | - Enrique J Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain.
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Thor M, Fitzgerald K, Apte A, Oh JH, Iyer A, Odiase O, Nadeem S, Yorke ED, Chaft J, Wu AJ, Offin M, Simone Ii CB, Preeshagul I, Gelblum DY, Gomez D, Deasy JO, Rimner A. Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation. Radiother Oncol 2024; 190:109983. [PMID: 37926331 PMCID: PMC11233189 DOI: 10.1016/j.radonc.2023.109983] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE Disease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20-40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression. MATERIALS/METHODS Published CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis. RESULTS Tumor diameter and SUVmax were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUVmax presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CTspiculation1 and SUVentropy) had a c-index of 0.75 (log-rank p-value < 0.0001). CONCLUSIONS A re-fitted pre-treatment model using the two most frequently published features - tumor diameter and SUVmax - successfully stratified early-stage NSCLC patients by PFS after receiving SBRT.
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Affiliation(s)
- Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA.
| | - Kelly Fitzgerald
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
| | - Aditi Iyer
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
| | - Otasowie Odiase
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
| | - Ellen D Yorke
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
| | - Jamie Chaft
- Department of Medicine, Memorial Sloan Kettering Cancer Center, USA
| | - Abraham J Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
| | - Michael Offin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, USA
| | - Charles B Simone Ii
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
| | | | - Daphna Y Gelblum
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
| | - Daniel Gomez
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, USA
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Cheng JA, Lin YC, Lin Y, Wu RC, Lu HY, Yang LY, Chiang HJ, Juan YH, Lai YC, Lin G. Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT. Diagnostics (Basel) 2023; 13:3632. [PMID: 38132216 PMCID: PMC10742777 DOI: 10.3390/diagnostics13243632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. METHODS We retrospectively analyzed CT studies from 139 patients (age range 26-93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. RESULTS Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. CONCLUSIONS CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
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Affiliation(s)
- Jih-An Cheng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Ren-Chin Wu
- Department of Pathology, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ying Lu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Lan-Yan Yang
- Clinical Trial Center, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ju Chiang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yu-Hsiang Juan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Ying-Chieh Lai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
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Fuchs T, Kaiser L, Müller D, Papp L, Fischer R, Tran-Gia J. Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data. Nuklearmedizin 2023; 62:389-398. [PMID: 37907246 PMCID: PMC10689089 DOI: 10.1055/a-2187-5701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/21/2023] [Indexed: 11/02/2023]
Abstract
Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.
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Affiliation(s)
- Timo Fuchs
- Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany
- Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Dominik Müller
- IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
- Medical Data Integration Center, University Hospital Augsburg, Augsburg, Germany
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Wien, Austria
| | - Regina Fischer
- Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany
- Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Wurzburg, Germany
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Wu H, Liu X, Peng L, Yang Y, Zhou Z, Du D, Xu H, Lv W, Lu L. Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer. Phys Med Biol 2023; 68:225014. [PMID: 37844604 DOI: 10.1088/1361-6560/ad03d1] [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: 05/06/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective. To determine the optimal approach for identifying and mitigating batch effects in PET/CT radiomics features, and further improve the prognosis of patients with head and neck cancer (HNC), this study investigated the performance of three batch harmonization methods.Approach. Unsupervised harmonization identified the batch labels by K-means clustering. Supervised harmonization regarding the image acquisition factors (center, manufacturer, scanner, filter kernel) as known/given batch labels, and Combat harmonization was then implemented separately and sequentially based on the batch labels, i.e. harmonizing features among batches determined by each factor individually or harmonizing features among batches determined by multiple factors successively. Extensive experiments were conducted to predict overall survival (OS) on public PET/CT datasets that contain 800 patients from 9 centers.Main results. In the external validation cohort, results show that compared to original models without harmonization, Combat harmonization would be beneficial in OS prediction with C-index of 0.687-0.740 versus 0.684-0.767. Supervised harmonization slightly outperformed unsupervised harmonization in all models (C-index: 0.692-0.767 versus 0.684-0.750). Separate harmonization outperformed sequential harmonization in CT_m+clinic and CT_cm+clinic models with C-index of 0.752 and 0.722, respectively, while sequential harmonization involved clinical features in PET_rs+clinic model further improving the performance and achieving the highest C-index of 0.767.Significance. Optimal batch determination especially sequential harmonization for Combat holds the potential to improve the prognostic power of radiomics model in multi-center HNC dataset with PET/CT imaging.
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Affiliation(s)
- Huiqin Wu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, 518037, People's Republic of China
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Xiaohui Liu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Lihong Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yuling Yang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Zidong Zhou
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Dongyang Du
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Wenbing Lv
- School of Information and Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, Yunnan, 650504, People's Republic of China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Pazhou Lab, Guangzhou 510330, People's Republic of China
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Lee H, Seo S, Won S, Park WY, Choi JY, Lee KH, Lee SH, Moon SH. Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients. Sci Rep 2023; 13:18247. [PMID: 37880322 PMCID: PMC10600181 DOI: 10.1038/s41598-023-45296-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/18/2023] [Indexed: 10/27/2023] Open
Abstract
In radiomics research, the issue of different instruments being used is significant. In this study, we compared three correction methods to reduce the batch effects in radiogenomic data from fluorodeoxyglucose (FDG) PET/CT images of lung cancer patients. Texture features of the FDG PET/CT images and genomic data were retrospectively obtained. The features were corrected with different methods: phantom correction, ComBat method, and Limma method. Batch effects were estimated using three analytic tools: principal component analysis (PCA), the k-nearest neighbor batch effect test (kBET), and the silhouette score. Finally, the associations of features and gene mutations were compared between each correction method. Although the kBET rejection rate and silhouette score were lower in the phantom-corrected data than in the uncorrected data, a PCA plot showed a similar variance. ComBat and Limma methods provided correction with low batch effects, and there was no significant difference in the results of the two methods. In ComBat- and Limma-corrected data, more texture features exhibited a significant association with the TP53 mutation than in those in the phantom-corrected data. This study suggests that correction with ComBat or Limma methods can be more effective or equally as effective as the phantom method in reducing batch effects.
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Affiliation(s)
- Hyunjong Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Sujin Seo
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Gwanak_1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Sungho Won
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Gwanak_1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Woong-Yang Park
- Department of Molecular Cell Biology, Samsung Medical Center, Samsung Genome Institute, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Kyung-Han Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Se-Hoon Lee
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107714. [PMID: 37473589 DOI: 10.1016/j.cmpb.2023.107714] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/19/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous features are commonly generated in radiomics applications as applied to medical imaging, and identification of robust radiomics features (RFs) can be an important step to derivation of reliable, reproducible solutions. In this work, we utilize a tensor radiomics (TR) framework, where numerous fusions are explored, to generate different flavours of RFs, and we aimed to identify RFs that are robust to fusion techniques in head and neck cancer. Overall, we aimed to predict progression-free survival (PFS) using Hybrid Machine Learning Systems (HMLS) and reproducible RFs. METHODS The study was performed on 408 patients with head and neck cancer from The Cancer Imaging Archive. After image preprocessing, 15 fusion techniques were employed to combine Positron Emission Tomography (PET) and Computed Tomography (CT) images. Subsequently, 215 RFs were extracted through a standardized radiomics software, with 17 'flavours' generated using PET-only, CT-only, and 15 fused PET&CT images. The variability of RFs across flavours was studied using the Intraclass Correlation Coefficient (ICC). Furthermore, the features were categorized into seven reliability groups, 106 reproducible RFs with ICC>0.75 were selected, highly correlated flavours were removed, Principal Component Analysis was used to convert 17 flavours to 1 attribute, the polynomial function was utilized to increase RFs, and Analysis of variance (ANOVA) was used to select the relevant attributes. Finally, 3 classifiers including Random Forest (RFC), Logistic regression (LR), and Multi-layer perceptron were applied to the preselected relevant attributes to predict binary PFS. In 5-fold cross-validation, 80% of 4 divisions were utilized to train the model, and the remaining 20% was utilized to evaluate the model. Further, the remaining fold was used for external nested testing. RESULTS Reliability analysis indicated that most morphological features belong to the high-reliability category. By contrast, local intensity and statistical features extracted from images belong to the low-reliability category. In the tensor framework, the highest 5-fold cross-validation accuracy of 76.7%±3.3% with an external nested testing of 70.6%±6.7% resulted from the reproducible TR+polynomial function+ANOVA+LR algorithm while the accuracy of 70.0%±4.2% with the external nested testing of 67.7%±4.9% was achieved through the PCA fusion+RFC (non-tensor paradigm). CONCLUSIONS This study demonstrated that using reproducible RFs as utilized within a tensor fusion radiomics framework, linked with ANOVA and LR, added value to prediction of progression-free survival outcome in head and neck cancer patients.
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Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada.
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada; Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran, Iran
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, 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
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Zhou Y, Zhang B, Han J, Dai N, Jia T, Huang H, Deng S, Sang S. Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells. J Cancer Res Clin Oncol 2023; 149:11549-11560. [PMID: 37395846 DOI: 10.1007/s00432-023-05038-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 07/04/2023]
Abstract
BACKGROUND In our current work, an 18F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy. METHODS A total of 61 DLBCL cases receiving 18F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated. RESULTS The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models. CONCLUSIONS PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.
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Affiliation(s)
- Yeye Zhou
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Bin Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Jiangqin Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Na Dai
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Tongtong Jia
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Haiwen Huang
- Institute of Blood and Marrow Transplantation, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
| | - Shengming Deng
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China.
| | - Shibiao Sang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
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12
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Vogel J, Seith F, Estler A, Nikolaou K, Schmidt H, la Fougère C, Küstner T. Impact of Tracer Dose Reduction in [18 F]-Labelled Fluorodeoxyglucose-Positron Emission Tomography ([18 F]-FDG)-PET) on Texture Features and Histogram Indices: A Study in Homogeneous Tissues of Phantom and Patient. Tomography 2023; 9:1799-1810. [PMID: 37888735 PMCID: PMC10611106 DOI: 10.3390/tomography9050143] [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: 09/07/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Histogram indices (HIs) and texture features (TFs) are considered to play an important role in future oncologic PET-imaging and it is unknown how these indices are affected by changes of tracer doses. A randomized undersampling of PET list mode data enables a simulation of tracer dose reduction. We performed a phantom study to compare HIs/TFs of simulated and measured tracer dose reductions and evaluated changes of HIs/TFs in the liver of patients with PETs from simulated reduced tracer doses. Overall, 42 HIs/TFs were evaluated in a NEMA phantom at measured and simulated doses (stepwise reduction of [18 F] from 100% to 25% of the measured dose). [18 F]-FDG-PET datasets of 15 patients were simulated from 3.0 down to 0.5 MBq/kgBW in intervals of 0.25 MBq/kgBW. HIs/TFs were calculated from two VOIs placed in physiological tissue of the right and left liver lobe and linear correlations and coefficients of variation analysis were performed. RESULTS All 42 TFs did not differ significantly in measured and simulated doses (p > 0.05). Also, 40 TFs showed the same behaviour over dose reduction regarding differences in the same group (measured or simulated), and for 26 TFs a linear behaviour over dose reduction for measured and simulated doses could be validated. Out of these, 13 TFs could be identified, which showed a linear change in TF value in both the NEMA phantom and patient data and therefore should maintain the same informative value when transferred in a dose reduction setting. Out of this Homogeneity 2, Entropy and Zone size non-uniformity are of special interest because they have been described as preferentially considerable for tumour heterogeneity characterization. CONCLUSIONS We could show that there was no significant difference of measured and simulated HIs/TFs in the phantom study and most TFs reveal a linear behaviour over dose reduction, when tested in homogeneous tissue. This indicates that texture analysis in PET might be robust to dose modulations.
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Affiliation(s)
- Jonas Vogel
- Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University Hospital of Tuebingen, Otfried-Mueller-Strasse 14, 72076 Tuebingen, Germany
- Diagnostic and Interventional Radiology, Department of Radiology, University Hospital of Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Ferdinand Seith
- Diagnostic and Interventional Radiology, Department of Radiology, University Hospital of Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Arne Estler
- Diagnostic and Interventional Radiology, Department of Radiology, University Hospital of Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Konstantin Nikolaou
- Diagnostic and Interventional Radiology, Department of Radiology, University Hospital of Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tuebingen, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Cluster of Excellence iFIT (EXC 2180): “Image-Guided and Functionally Instructed Tumor Therapies”, Eberhard Karls University, 72076 Tuebingen, Germany
| | - Holger Schmidt
- Medical Faculty, University of Tuebingen, Geschwister-Scholl-Platz, 72074 Tuebingen, Germany
| | - Christian la Fougère
- Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University Hospital of Tuebingen, Otfried-Mueller-Strasse 14, 72076 Tuebingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tuebingen, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Cluster of Excellence iFIT (EXC 2180): “Image-Guided and Functionally Instructed Tumor Therapies”, Eberhard Karls University, 72076 Tuebingen, Germany
| | - Thomas Küstner
- Medical Image and Data Analysis Lab (MIDAS.lab), Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
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Lim CH, Choi JY, Choi JH, Lee JH, Lee J, Lim CW, Kim Z, Woo SK, Park SB, Park JM. Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2023; 15:3842. [PMID: 37568658 PMCID: PMC10417050 DOI: 10.3390/cancers15153842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.
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Affiliation(s)
- Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Jun-Hee Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Jihyoun Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Cheol Wan Lim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Zisun Kim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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Jia T, Lv Q, Cai X, Ge S, Sang S, Zhang B, Yu C, Deng S. Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer. Front Oncol 2023; 13:1210125. [PMID: 37576897 PMCID: PMC10415070 DOI: 10.3389/fonc.2023.1210125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose The aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice. Methods We retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status. Results The Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application. Conclusion The combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.
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Affiliation(s)
- Tongtong Jia
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qingfu Lv
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaowei Cai
- Department of Nuclear Medicine, The Affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, China
| | - Shushan Ge
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shibiao Sang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chunjing Yu
- Department of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Shengming Deng
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
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Cheng MF, Guo YL, Yen RF, Wu YW, Wang HP. Pretherapy 18F-FDG PET/CT in Predicting Disease Relapse in Patients With Immunoglobulin G4-Related Disease: A Prospective Study. Korean J Radiol 2023; 24:590-598. [PMID: 37271212 DOI: 10.3348/kjr.2022.0576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 06/06/2023] Open
Abstract
OBJECTIVE To investigate whether the levels of inflammation detected by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) can predict disease relapse in immunoglobulin G4-related disease (IgG4-RD) patients receiving standard induction steroid therapy. MATERIALS AND METHODS This prospective study analyzed pretherapy FDG PET/CT images from 48 patients (mean age, 63 ± 12.9 years; 45 males and 3 females) diagnosed with IgG4-RD between September 2008 and February 2018, who subsequently received standard induction steroid therapy as the first-line treatment. Multivariable Cox proportional hazards models were used to identify the potential prognostic factors associated with relapse-free survival (RFS). RESULTS The median follow-up time for the entire cohort was 1913 days (interquartile range [IQR], 803-2929 days). Relapse occurred in 81.3% (39/48) patients during the follow-up period. The median time to relapse was 210 days (IQR, 140-308 days) after completion of standardized induction steroid therapy. Among the 17 parameters analyzed, Cox proportional hazard analysis identified whole-body total lesion glycolysis (WTLG) > 600 on FDG-PET as an independent risk factor for disease relapse (median RFS, 175 vs. 308 days; adjusted hazard ratio, 2.196 [95% confidence interval: 1.080-4.374]; P = 0.030). CONCLUSION WTLG on pretherapy FDG PET/CT was the only significant factor associated with RFS in IgG-RD patients receiving standard steroid induction therapy.
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Affiliation(s)
- Mei-Fang Cheng
- Department of Nuclear Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei, Taiwan
| | - Yue Leon Guo
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei, Taiwan
- Department of Environmental and Occupational Medicine, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan
| | - Ruoh-Fang Yen
- Department of Nuclear Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yen-Wen Wu
- Department of Nuclear Medicine, Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsiu-Po Wang
- Department of Internal Medicine, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan.
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Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
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Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
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17
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Cheng NM, Lin CY, Liao CT, Tsan DL, Ng SH, Yen TC. The added values of 18F-FDG PET/CT in differentiating cancer recurrence and osteoradionecrosis of mandible in patients with treated oral squamous cell carcinoma. EJNMMI Res 2023; 13:25. [PMID: 37010632 PMCID: PMC10070584 DOI: 10.1186/s13550-023-00965-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/07/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Osteoradionecrosis (ORN) of the jaw requires a differential diagnosis to exclude cancer recurrence. Here, we sought to develop a scoring system comprising 18F-FDG PET/CT parameters for distinguishing between the two conditions in patients with oral squamous cell carcinoma (OSCC). METHODS The study consisted of 103 OSCC patients with suspected ORN of the jaw. All participants underwent 18F-FDG PET/CT imaging within 6 months of diagnostic histopathology. Following extraction of PET parameters, we identified clinical and imaging predictors of mandibular recurrence-free survival (MRFS) using receiver operating characteristic curve analysis and multivariate Cox regression models. RESULTS The results of histopathology revealed mandibular cancer recurrence in 24 patients (23.3%). Multivariate Cox regression analyses identified an age at diagnosis ≤ 52 years (P = 0.013), a location of the SUVmax voxel with soft tissue predominance (P = 0.019), and mandibular total lesion glycolysis (TLG) > 62.68 g (P < 0.001) as independent risk factors for MRFS. A scoring system was devised with scores from 0 (no risk factor) to 3 (presence of all three risk factors). High-risk patients with a score of 2-3 compared with score of 0-1 had a significantly higher likelihood of mandibular cancer recurrence (hazard ratio: 32.50, 95% confidence interval: 8.51-124.18, P < 0.001). The scoring system had a sensitivity of 87.50%, a specificity of 82.28%, and an accuracy of 83.50% for identifying mandibular cancer recurrence. CONCLUSIONS The scoring system of our study is clinically useful for identifying mandibular cancer recurrence in patients with suspected ORN of the jaw.
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Affiliation(s)
- Nai-Ming Cheng
- Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Linkou, Chang Gung University College of Medicine, No. 5, Fu-Shin St., Kueishan District, Taoyuan City, 333, Taiwan
- Department of Nuclear Medicine, Chang Gung Memorial Hospital, Keelung, Keelung, Taiwan
| | - Chien-Yu Lin
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chun-Ta Liao
- Department of Otolaryngology - Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Din-Li Tsan
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Shu-Hang Ng
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Linkou, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Tzu-Chen Yen
- Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Linkou, Chang Gung University College of Medicine, No. 5, Fu-Shin St., Kueishan District, Taoyuan City, 333, Taiwan.
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Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2023; 58:199-208. [PMID: 36070524 DOI: 10.1097/rli.0000000000000921] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. MATERIALS AND METHODS Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. RESULTS Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. CONCLUSION Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.
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Affiliation(s)
- Barbara D Wichtmann
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Felix N Harder
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Stefan O Schönberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Germany
| | - Ulrike I Attenberger
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers (Basel) 2023; 15:cancers15030932. [PMID: 36765889 PMCID: PMC9913076 DOI: 10.3390/cancers15030932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. METHODS 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. RESULTS There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. CONCLUSION Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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20
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Zounek AJ, Albert NL, Holzgreve A, Unterrainer M, Brosch-Lenz J, Lindner S, Bollenbacher A, Boening G, Rupprecht R, Brendel M, von Baumgarten L, Tonn JC, Bartenstein P, Ziegler S, Kaiser L. Feasibility of radiomic feature harmonization for pooling of [ 18F]FET or [ 18F]GE-180 PET images of gliomas. Z Med Phys 2023; 33:91-102. [PMID: 36710156 PMCID: PMC10068577 DOI: 10.1016/j.zemedi.2022.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 01/29/2023]
Abstract
INTRODUCTION Large datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated. METHODS [18F]FET and [18F]GE-180 PET data were acquired from 19 glioma patients. For each acquisition, 10 reconstruction settings and 9 segmentation methods were included to emulate multicentric data. Statistical robustness measures were calculated before and after ComBat harmonization. Differences between features due to setting variations were assessed using Friedman test, coefficient of variation (CV) and inter-rater reliability measures, including intraclass and Spearman's rank correlation coefficients and Fleiss' Kappa. RESULTS According to Friedman analyses, most features (>60%) showed significant differences. Yet, CV and inter-rater reliability measures indicated higher robustness. ComBat resulted in almost complete harmonization (>87%) according to Friedman test and little to no improvement according to CV and inter-rater reliability measures. [18F]GE-180 features were more sensitive to reconstruction settings than [18F]FET features. CONCLUSIONS According to Friedman test, feature distributions could be successfully aligned using ComBat. However, depending on settings, changes in patient ranks were observed for some features and could not be eliminated by harmonization. Thus, for clinical utilization it is recommended to exclude affected features.
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Affiliation(s)
- Adrian Jun Zounek
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Nathalie Lisa Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany.
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Marcus Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Julia Brosch-Lenz
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Simon Lindner
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Andreas Bollenbacher
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Guido Boening
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Rainer Rupprecht
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany.
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
| | - Louisa von Baumgarten
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany; Department of Neurosurgery, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Joerg-Christian Tonn
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
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21
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Xu H, Abdallah N, Marion JM, Chauvet P, Tauber C, Carlier T, Lu L, Hatt M. Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation. Eur J Nucl Med Mol Imaging 2023; 50:1720-1734. [PMID: 36690882 DOI: 10.1007/s00259-023-06118-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/16/2023] [Indexed: 01/25/2023]
Abstract
PURPOSE This study aimed to investigate the impact of several ComBat harmonization strategies, intra-tumoral sub-volume characterization, and automatic segmentations for progression-free survival (PFS) prediction through radiomics modeling for patients with head and neck cancer (HNC) in PET/CT images. METHODS The HECKTOR MICCAI 2021 challenge set containing PET/CT images and clinical data of 325 oropharynx HNC patients was exploited. A total of 346 IBSI-compliant radiomic features were extracted for each patient's primary tumor volume defined by the reference manual contours. Modeling relied on least absolute shrinkage Cox regression (Lasso-Cox) for feature selection (FS) and Cox proportional-hazards (CoxPH) models were built to predict PFS. Within this methodological framework, 8 different strategies for ComBat harmonization were compared, including before or after FS, in feature groups separately or all features directly, and with center or clustering-determined labels. Features extracted from tumor sub-volume clustering were also investigated for their prognostic additional value. Finally, 3 automatic segmentations (2 threshold-based and a 3D U-Net) were also compared. All results were evaluated with the concordance index (C-index). RESULTS Radiomics features without harmonization, combined with clinical factors, led to models with C-index values of 0.69 in the testing set. The best version of ComBat harmonization, i.e., after FS, for feature groups separately and relying on clustering-determined labels, achieved a C-index of 0.71. The use of features extracted from tumor sub-volumes further improved the C-index to 0.72. Models that relied on the automatic segmentations yielded close but slightly lower prognostic performance (0.67-0.70) compared to reference contours. CONCLUSION A standard radiomics pipeline allowed for prediction of PFS in a multicenter HNC cohort. Applying a specific strategy of ComBat harmonization improved the performance. The extraction of intra-tumoral sub-volume features and automatic segmentation could contribute to the improvement and automation of prognosis modeling, respectively.
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Affiliation(s)
- Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China.,LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | | | | | | | - Clovis Tauber
- INSERM U930, Université François Rabelais de Tours, Tours, France
| | - Thomas Carlier
- Nuclear Medicine Department, CHU and CRCINA, INSERM, CNRS, Univ Angers, Univ Nantes, Nantes, France
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China. .,Pazhou Lab, Guangzhou, 510330, China.
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
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Huang Y, Jiang X, Xu H, Zhang D, Liu LN, Xia YX, Xu DK, Wu HJ, Cheng G, Shi YH. Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET/CT radiomics. Clin Radiol 2023; 78:8-17. [PMID: 36192203 DOI: 10.1016/j.crad.2022.08.140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/14/2022] [Accepted: 08/26/2022] [Indexed: 01/07/2023]
Abstract
AIM To establish and verify a 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics nomogram to predict mediastinal lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) patients preoperatively. MATERIALS AND METHODS This retrospective study enrolled 155 NSCLC patients (primary cohort, n=93; validation cohort, n=62). For each patient, 2,704 radiomic features were extracted from the primary lung cancer regions. Four procedures including the Mann-Whitney U-test, Spearman's correlation analysis, minimum redundancy-maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were utilised for determining essential features and establishing a radiomics signature. After that, a nomogram was established. The nomogram's potential was assessed based on its discrimination, calibration, and clinical usefulness. The radiomics signature and nomogram predictive performances were evaluated with respect to the area under the receiver operating characteristic curve (AUC), specificity, accuracy, and sensitivity. RESULTS The radiomics signature composed of eight selected features had good discriminatory performance of LNM versus non-LNM groups an AUC of 0.851 and 0.826 in primary and validation cohorts, respectively. The nomogram also indicated good discrimination with an AUC of 0.869 and 0.847 in the primary and validation cohorts, respectively. Furthermore, good calibration was demonstrated utilising the nomogram. CONCLUSIONS An 18F-FDG PET/CT-based radiomics nomogram that integrates the radiomics signature and age was promoted to predict mediastinal LNM within NSCLC patients, which could potentially facilitate individualised therapy for mediastinal LNM before treatment. The nomogram was beneficial in clinical practice, as illustrated by decision curve analysis.
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Affiliation(s)
- Y Huang
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - X Jiang
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - H Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - D Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - L-N Liu
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - Y-X Xia
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - D-K Xu
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - H-J Wu
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - G Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Y-H Shi
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China.
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Choi YJ, Jeon KJ, Lee A, Han SS, Lee C. Harmonization of robust radiomic features in the submandibular gland using multi-ultrasound systems: a preliminary study. Dentomaxillofac Radiol 2023; 52:20220284. [PMID: 36341993 PMCID: PMC9974233 DOI: 10.1259/dmfr.20220284] [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: 08/30/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE This study aimed to identify robust radiomic features in multiultrasonography of the submandibular gland and normalize the interdevice discrepancies by applying a machine-learning-based harmonization method. METHODS Ultrasonographic images of normal submandibular gland of young healthy adults, aged between 20 and 40 years, were selected from two different devices. In a total of 30 images, the region of interest was determined along the border of gland parenchyma, and 103 radiomic features were extracted using A-VIEW. The coefficient of variation (CV) was obtained for individual features, and the features showing CV less than 10% were selected. For the selected features, the interdevice discrepancy was normalized using machine-learning method, called the ComBat harmonization. Median differences of the features between the two scanners, before and after harmonization, were compared using Mann-Whitney U-test; confidence interval of 95%. RESULTS Among total 103 radiomic features, 17 features were selected as robust, showing CV less than 10% in both scanners. All values of selected features, except two, showed a statistical difference between the two devices. After applying the ComBat harmonization method, the median and distribution of the 16 features were harmonized to show no significant difference between the two scanners (p > 0.05). One feature remained different (p ≤ 0.05). CONCLUSION On ultrasonographic examination, robust radiomic features for normal submandibular gland were obtained and interdevice normalization was efficiently conducted using ComBat harmonization. Our findings would be useful for multidevices or multicenter studies based on clinical ultrasonographic imaging data to improve the accuracy of the overall diagnostic model.
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Affiliation(s)
- Yoon Joo Choi
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
| | - Kug Jin Jeon
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
| | - Ari Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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25
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Payan N, Presles B, Truntzer C, Courcet E, Coutant C, Desmoulins I, Brunotte F, Vrigneaud JM, Cochet A. Critical analysis of the effect of various methodologies to compute breast cancer tumour blood flow-based texture features using first-pass 18F-FDG PET. Phys Med 2022; 103:98-107. [DOI: 10.1016/j.ejmp.2022.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 11/26/2022] Open
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Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
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Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022; 16:707-719. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [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] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. AREAS COVERED This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. EXPERT OPINION For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.
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Affiliation(s)
- Jiaying Bao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xiao Feng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Yan Ma
- Department of Ultrasound, Zibo Central Hospital, Zibo, P.R. China
| | - Yanyan Wang
- Departments of Emergency Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Jianni Qi
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Chengyong Qin
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xu Tan
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Yongmei Tian
- Department of Geriatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
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Ferrández MC, Eertink JJ, Golla SSV, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Zijlstra JM, Boellaard R. Combatting the effect of image reconstruction settings on lymphoma [ 18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res 2022; 12:44. [PMID: 35904645 PMCID: PMC9338209 DOI: 10.1186/s13550-022-00916-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background [18F]FDG PET-based metabolic tumor volume (MTV) is a promising prognostic marker for lymphoma patients. The aim of this study is to assess the sensitivity of several MTV segmentation methods to variations in image reconstruction methods and the ability of ComBat to improve MTV reproducibility. Methods Fifty-six lesions were segmented from baseline [18F]FDG PET scans of 19 lymphoma patients. For each scan, EARL1 and EARL2 standards and locally clinically preferred reconstruction protocols were applied. Lesions were delineated using 9 semiautomatic segmentation methods: fixed threshold based on standardized uptake value (SUV), (SUV = 4, SUV = 2.5), relative threshold (41% of SUVmax [41M], 50% of SUVpeak [A50P]), majority vote-based methods that select voxels detected by at least 2 (MV2) and 3 (MV3) out of the latter 4 methods, Nestle thresholding, and methods that identify the optimal method based on SUVmax (L2A, L2B). MTVs from EARL2 and locally clinically preferred reconstructions were compared to those from EARL1. Finally, different versions of ComBat were explored to harmonize the data.
Results MTVs from the SUV4.0 method were least sensitive to the use of different reconstructions (MTV ratio: median = 1.01, interquartile range = [0.96–1.10]). After ComBat harmonization, an improved agreement of MTVs among different reconstructions was found for most segmentation methods. The regular implementation of ComBat (‘Regular ComBat’) using non-transformed distributions resulted in less accurate and precise MTV alignments than a version using log-transformed datasets (‘Log-transformed ComBat’). Conclusion MTV depends on both segmentation method and reconstruction methods. ComBat reduces reconstruction dependent MTV variability, especially when log-transformation is used to account for the non-normal distribution of MTVs. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00916-9.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imaging (Bellingham) 2022; 9:044005. [PMID: 35992729 PMCID: PMC9386367 DOI: 10.1117/1.jmi.9.4.044005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/09/2022] [Indexed: 08/20/2023] Open
Abstract
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
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Affiliation(s)
- Robba Rai
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael B. Barton
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Phillip Chlap
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Gary Liney
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Carsten Brink
- Odense University Hospital, Laboratory of Radiation Physics, Department of Oncology, Odense, Denmark
- University of Southern Denmark, Department of Clinical Research, Odense, Denmark
| | - Shalini Vinod
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | | | - Yuvnik Trada
- Calvary Mater Newcastle, Department of Radiation Oncology, Newcastle, New South Wales, Australia
| | - Lois C. Holloway
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- University of Wollongong, Centre of Radiation Physics, Wollongong, New South Wales, Australia
- University of Sydney, Institute of Medical Physics, School of Physics, Sydney, New South Wales, Australia
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Puttanawarut C, Sirirutbunkajorn N, Tawong N, Khachonkham S, Pattaranutaporn P, Wongsawat Y. Impact of Interfractional Error on Dosiomic Features. Front Oncol 2022; 12:726896. [PMID: 35756677 PMCID: PMC9231355 DOI: 10.3389/fonc.2022.726896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives The purpose of this study was to investigate the stability of dosiomic features under random interfractional error. We investigated the differences in the values of features with different fractions and the error in the values of dosiomic features under interfractional error. Material and Methods The isocenters of the treatment plans of 15 lung cancer patients were translated by a maximum of ±3 mm in each axis with a mean of (0, 0, 0) and a standard deviation of (1.2, 1.2, 1.2) mm in the x, y, and z directions for each fraction. A total of 81 dose distributions for each patient were then calculated considering four fraction number groups (2, 10, 20, and 30). A total of 93 dosiomic features were extracted from each dose distribution in four different regions of interest (ROIs): gross tumor volume (GTV), planning target volume (PTV), heart, and both lungs. The stability of dosiomic features was analyzed for each fraction number group by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The agreements in the means of dosiomic features among the four fraction number groups were tested by ICC. The percent differences (PD) between the dosiomic features extracted from the original dose distribution and the dosiomic features extracted from the dose distribution with interfractional error were calculated. Results Eleven out of 93 dosiomic features demonstrated a large CV (CV ≥ 20%). Overall CV values were highest in GTV ROIs and lowest in lung ROIs. The stability of dosiomic features decreased as the total number of fractions decreased. The ICC results showed that five out of 93 dosiomic features had an ICC lower than 0.75, which indicates intermediate or poor stability under interfractional error. The mean dosiomic feature values were shown to be consistent with different numbers of fractions (ICC ≥ 0.9). Some of the dosiomic features had PD greater than 50% and showed different PD values with different numbers of fractions. Conclusion Some dosiomic features have low stability under interfractional error. The stability and values of the dosiomic features were affected by the total number of fractions. The effect of interfractional error on dosiomic features should be considered in further studies regarding dosiomics for reproducible results.
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Affiliation(s)
- Chanon Puttanawarut
- Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samutprakarn, Thailand.,Brain-Computer Interface Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhorn Pathom, Thailand
| | - Nat Sirirutbunkajorn
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Narisara Tawong
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Suphalak Khachonkham
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Poompis Pattaranutaporn
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Yodchanan Wongsawat
- Brain-Computer Interface Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhorn Pathom, Thailand
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Comte V, Schmutz H, Chardin D, Orlhac F, Darcourt J, Humbert O. Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. Eur J Nucl Med Mol Imaging 2022; 49:3787-3796. [PMID: 35567626 PMCID: PMC9399031 DOI: 10.1007/s00259-022-05816-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/23/2022] [Indexed: 11/30/2022]
Abstract
Purpose FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans. Methods We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale. Results The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model. Conclusion A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05816-7.
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Affiliation(s)
- Victor Comte
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.
| | - Hugo Schmutz
- Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - David Chardin
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Fanny Orlhac
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO) U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
| | - Jacques Darcourt
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Olivier Humbert
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
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Xue S, Guo R, Bohn KP, Matzke J, Viscione M, Alberts I, Meng H, Sun C, Zhang M, Zhang M, Sznitman R, El Fakhri G, Rominger A, Li B, Shi K. A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET. Eur J Nucl Med Mol Imaging 2022; 49:1843-1856. [PMID: 34950968 PMCID: PMC9015984 DOI: 10.1007/s00259-021-05644-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/30/2021] [Indexed: 11/12/2022]
Abstract
PURPOSE A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. METHODS Brain [18F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [18F]FDG PET images of 45 patients scanned with three different scanners, [18F]FET PET images of 18 patients scanned with two different scanners, as well as [18F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. RESULTS The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = -0.71, p < 0.05) and normalized dose acquisition (r = -0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). CONCLUSION The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.
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Affiliation(s)
- Song Xue
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Rui Guo
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Karl Peter Bohn
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Jared Matzke
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marco Viscione
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Chenwei Sun
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Min Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | | | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Axel Rominger
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
- Department of Informatics, Technical University of Munich, Munich, Germany
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Gallivanone F, D'Ambrosio D, Carne I, D'Arcangelo M, Montagna P, Giroletti E, Poggi P, Vellani C, Moro L, Castiglioni I. A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics. Phys Med 2022; 98:28-39. [PMID: 35489129 DOI: 10.1016/j.ejmp.2022.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 03/15/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Radiomics has emerged as an advanced image processing methodology to define quantitative imaging biomarkers for prognosis and prediction of treatment response and outcome. The development of quantitative imaging biomarkers requires careful analysis to define their accuracy, stability and reproducibility through phantom measurements. Few efforts were devoted to develop realistic anthropomorphic phantoms. In this work, we developed a multimodality image phantom suitable for PET, CT and multiparametric MRI imaging. METHODS A tissue-equivalent gel-based mixture was designed and tested for compatibility with different imaging modalities. Calibration measurements allowed to assess gel composition to simulate PET, CT and MRI contrasts of oncological lesions. The characterized gel mixture was used to create realistic synthetic lesions (e.g. lesions with irregular shape and non-uniform image contrast), to be inserted in a standard anthropomorphic phantom. In order to show phantom usefulness, issues related to accuracy, stability and reproducibility of radiomic biomarkers were addressed as proofs-of-concept. RESULTS The procedure for gel preparation was straightforward and the characterized gel mixture allowed to mime simultaneously oncological lesion contrast in CT, PET and MRI imaging. Proofs-of-concept studies suggested that phantom measurements can be customized for specific clinical situations and radiomic protocols. CONCLUSIONS We developed a strategy to manufacture an anthropomorphic, tissue-equivalent, multimodal phantom to be customized on specific radiomics protocols, for addressing specific methodological issues both in mono and multicentric studies.
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Affiliation(s)
- Francesca Gallivanone
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Milan, Italy.
| | - Daniela D'Ambrosio
- Istituti Clinici Scientifici Maugeri IRCCS, Medical Physics Unit of Pavia Institute, Italy.
| | - Irene Carne
- Istituti Clinici Scientifici Maugeri IRCCS, Medical Physics Unit of Pavia Institute, Italy.
| | | | - Paolo Montagna
- Istituti Clinici Scientifici Maugeri IRCCS, Nuclear Medicine Unit of Pavia Institute, Italy.
| | - Elio Giroletti
- Department of Physics, University of Pavia, Pavia, Italy; National Institute for Nuclear Physics (INFN), Pavia, Italy.
| | - Paolo Poggi
- Istituti Clinici Scientifici Maugeri IRCCS, Diagnostic Imaging Unit of Pavia Institute, Italy.
| | - Cecilia Vellani
- Istituti Clinici Scientifici Maugeri IRCCS, Nuclear Medicine Unit of Pavia Institute, Italy.
| | - Luca Moro
- Istituti Clinici Scientifici Maugeri IRCCS, Medical Physics Unit of Pavia Institute, Italy.
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Milan, Italy; Department of Physics "G. Occhialini", University of Milano - Bicocca, Italy.
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Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with 18F-FDG PET Radiomics Based Machine Learning Classification. Diagnostics (Basel) 2022; 12:diagnostics12051070. [PMID: 35626225 PMCID: PMC9139915 DOI: 10.3390/diagnostics12051070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/22/2022] Open
Abstract
Background: Approximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline 18F-FDG PET. Methods: Retrospectively, 143 18F-FDG PET radiomic features were extracted from 199 EC patients (T1N1-3M0/T2–4aN0-3M0) treated between 2009 and 2019. Non-response (n = 57; 29%) was defined as Mandard Tumor Regression Grade 4–5 (n = 44; 22%) or interval progression (n = 13; 7%). Randomly, 139 patients (70%) were allocated to explore all combinations of 24 feature selection strategies and 6 classification methods towards the cross-validated average precision (AP). The predictive value of the best-performing model, i.e AP and area under the ROC curve analysis (AUC), was evaluated on an independent test subset of 60 patients (30%). Results: The best performing model had an AP (mean ± SD) of 0.47 ± 0.06 on the training subset, achieved by a support vector machine classifier trained on five principal components of relevant clinical and radiomic features. The model was externally validated with an AP of 0.66 and an AUC of 0.67. Conclusion: In the present study, the best-performing model on pre-treatment 18F-FDG PET radiomics and clinical features had a small clinical benefit to identify non-responders to nCRT in EC.
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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.
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Hosseini SA, Shiri I, Hajianfar G, Bahadorzade B, Ghafarian P, Zaidi H, Ay MR. Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: phantom and clinical studies. Med Phys 2022; 49:3783-3796. [PMID: 35338722 PMCID: PMC9322423 DOI: 10.1002/mp.15615] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18F‐FDG PET image radiomic features in non‐small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes. Methods An in‐house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full‐width at half‐maximum of post‐reconstruction smoothing filter and acquisition parameters, including injected activity and test–retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi‐automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions. Results Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty‐five percent and 76% of the features showed a COV ≤ 5% against the test–retest with and without motion in large lesions, respectively. Thirty‐three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p‐value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting). Conclusions Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non‐reproducibility.
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Affiliation(s)
- Seyyed Ali Hosseini
- Department of Medical physics and biomedical engineering, Tehran University of medical sciences, Tehran, Iran.,Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Pardis Ghafarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,PET/CT and cyclotron center, Masih Daneshvari hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, Switzerland.,Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark
| | - Mohammad Reza Ay
- Department of Medical physics and biomedical engineering, Tehran University of medical sciences, Tehran, Iran.,Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Forouzannezhad P, Maes D, Hippe DS, Thammasorn P, Iranzad R, Han J, Duan C, Liu X, Wang S, Chaovalitwongse WA, Zeng J, Bowen SR. Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer. Cancers (Basel) 2022; 14:1228. [PMID: 35267535 PMCID: PMC8909466 DOI: 10.3390/cancers14051228] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022] Open
Abstract
Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.
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Affiliation(s)
- Parisa Forouzannezhad
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Dominic Maes
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Daniel S. Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA;
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Reza Iranzad
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Jie Han
- Department of Industrial, Manufacturing, and System Engineering, University of Texas, Arlington, TX 76019, USA; (J.H.); (S.W.)
| | - Chunyan Duan
- Department of Mechanical Engineering, Tongji University, Shanghai 200092, China;
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Shouyi Wang
- Department of Industrial, Manufacturing, and System Engineering, University of Texas, Arlington, TX 76019, USA; (J.H.); (S.W.)
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Jing Zeng
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Stephen R. Bowen
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
- Department of Radiology, School of Medicine, University of Washington, Seattle, WA 98195, USA
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Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT. Eur Radiol 2022; 32:5480-5490. [PMID: 35192011 PMCID: PMC9279234 DOI: 10.1007/s00330-022-08628-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 12/08/2021] [Accepted: 01/31/2022] [Indexed: 11/23/2022]
Abstract
Objectives To evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT). Methods A standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Results The test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively. Conclusions The majority of radiomics features were non-reproducible within and between SECT and DECT. Key Points • Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08628-3.
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Kalisvaart GM, van Velden FHP, Hernández-Girón I, Meijer KM, Ghesquiere-Dierickx LMH, Brink WM, Webb A, de Geus-Oei LF, Slump CH, Kuznetsov DV, Schaart DR, Grootjans W. Design and evaluation of a modular multimodality imaging phantom to simulate heterogeneous uptake and enhancement patterns for radiomic quantification in hybrid imaging; a feasibility study. Med Phys 2022; 49:3093-3106. [PMID: 35178781 PMCID: PMC9314050 DOI: 10.1002/mp.15537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 11/21/2022] Open
Abstract
Background Accuracy and precision assessment in radiomic features is important for the determination of their potential to characterize cancer lesions. In this regard, simulation of different imaging conditions using specialized phantoms is increasingly being investigated. In this study, the design and evaluation of a modular multimodality imaging phantom to simulate heterogeneous uptake and enhancement patterns for radiomics quantification in hybrid imaging is presented. Methods A modular multimodality imaging phantom was constructed that could simulate different patterns of heterogeneous uptake and enhancement patterns in positron emission tomography (PET), single‐photon emission computed tomography (SPECT), computed tomography (CT), and magnetic resonance (MR) imaging. The phantom was designed to be used as an insert in the standard NEMA‐NU2 IEC body phantom casing. The entire phantom insert is composed of three segments, each containing three separately fillable compartments. The fillable compartments between segments had different sizes in order to simulate heterogeneous patterns at different spatial scales. The compartments were separately filled with different ratios of 99mTc‐pertechnetate, 18F‐fluorodeoxyglucose ([18F]FDG), iodine‐ and gadolinium‐based contrast agents for SPECT, PET, CT, and T1‐weighted MR imaging respectively. Image acquisition was performed using standard oncological protocols on all modalities and repeated five times for repeatability assessment. A total of 93 radiomic features were calculated. Variability was assessed by determining the coefficient of quartile variation (CQV) of the features. Comparison of feature repeatability at different modalities and spatial scales was performed using Kruskal‐Wallis‐, Mann‐Whitney U‐, one‐way ANOVA‐ and independent t‐tests. Results Heterogeneous uptake and enhancement could be simulated on all four imaging modalities. Radiomic features in SPECT were significantly less stable than in all other modalities. Features in PET were significantly less stable than in MR and CT. A total of 20 features, particularly in the gray‐level co‐occurrence matrix (GLCM) and gray‐level run‐length matrix (GLRLM) class, were found to be relatively stable in all four modalities for all three spatial scales of heterogeneous patterns (with CQV < 10%). Conclusion The phantom was suitable for simulating heterogeneous uptake and enhancement patterns in [18F]FDG‐PET, 99mTc‐SPECT, CT, and T1‐weighted MR images. The results of this work indicate that the phantom might be useful for the further development and optimization of imaging protocols for radiomic quantification in hybrid imaging modalities.
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Affiliation(s)
| | | | | | - Karin M Meijer
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Laura M H Ghesquiere-Dierickx
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Mechanical, Maritime, and Materials Engineering, Delft University of Technology, Delft, The Netherlands.,Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wyger M Brink
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Andrew Webb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Cornelis H Slump
- Robotics and Mechatronics, University of Twente, Enschede, The Netherlands
| | - Dimitri V Kuznetsov
- Electronic and mechanical support division, Delft University of Technology, Delft, The Netherlands
| | - Dennis R Schaart
- Radiation Science and Technology, Delft University of Technology, Delft, The Netherlands.,Holland Proton Therapy Center (HollandPTC), Delft, The Netherlands
| | - Willem Grootjans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022; 13:22. [PMID: 35124733 PMCID: PMC8817778 DOI: 10.1186/s13244-021-01153-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
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Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation. Cancers (Basel) 2022; 14:cancers14030713. [PMID: 35158980 PMCID: PMC8833623 DOI: 10.3390/cancers14030713] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary In malignant lymphoma an early and accurate diagnosis is essential for therapy initiation and patient outcome. Within the diagnostic process, imaging plays a crucial role in disease staging. However, an invasive biopsy is required for subtype classification. Involvement of the spleen, a major lymphoid organ, is frequent in malignant lymphoma; this may be reactive or due to infiltration by malignant cells. Using radiomics features of the spleen in a machine learning approach, we investigated the possibility of distinguishing malignant lymphoma patients from other cancer patients and to classify lymphoma subtypes in the case of disease presence. Recent studies have proven the value of radiomics analysis in differentiating lymphoma from non-lymphoma groups on involved sites. Supported by machine learning, imaging could gain importance as a noninvasive diagnostic tool for future lymphoma classification, offering more precise radiological information for an interdisciplinary approach regarding treatment planning. Abstract The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence—in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/− 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 (p < 0.01); HL vs. NHL AUC = 0.75 (p < 0.01); DLBCL vs. other NHL AUC = 0.65 (p < 0.01); MCL vs. FL AUC = 0.67 (p < 0.01). Classifying malignant lymphoma vs. non-lymphoma was also possible using only shape features AUC = 0.77 (p < 0.01), with the most important feature being sphericity. Based on only shape features, a significant AUC could be achieved for all tasks, however, best results were achieved combining shape and textural features. This study demonstrates the value of splenic imaging and radiomic analysis in the diagnostic process in malignant lymphoma detection and subtype classification.
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Yi J, Lei X, Zhang L, Zheng Q, Jin J, Xie C, Jin X, Ai Y. The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technol Cancer Res Treat 2022; 21:15330338221118412. [PMID: 35971568 PMCID: PMC9386859 DOI: 10.1177/15330338221118412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Objective To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. Methods A total of 536 CC patients with confirmed histological characteristics and lymph node status after radical hysterectomy and pelvic lymphadenectomy were enrolled. Radiomics features were extracted and selected with US images acquired with ATL HDI5000, Voluson E8, MyLab classC, ACUSON S2000, and HI VISION Preirus to build radiomics models for LNM prediction using support vector machine (SVM) and logistic regression, respectively. Results There were 148 patients (training vs validation: 102:46) scanned in machine HDI5000, 75 patients (53:22) in machine Voluson E8, 100 patients (69:31) in machine MyLab classC, 110 patients (76:34) in machine ACUSON S2000, and 103 patients (73:30) in machine HI VISION Preirus, respectively. Few radiomics features were reproducible among different machines. The area under the curves (AUCs) ranged from 0.75 to 0.86, 0.73 to 0.86 in the training cohorts, and from 0.71 to 0.82, 0.70 to 0.80 in the validation cohorts for SVM and logistic regression models, respectively. The highest difference in AUCs for different machines reaches 17.8% and 15.5% in the training and validation cohorts, respectively. Conclusions The performance of radiomics model is dependent on the type of scanner. The problem of scanner dependency on radiomics features should be considered, and their effects should be minimized in future studies for US images.
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Affiliation(s)
- Jinling Yi
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiyao Lei
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lei Zhang
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiao Zheng
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, 89657The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Congying Xie
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Radiation and Medical Oncology, 26452The 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiance Jin
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,School of Basic Medical Science, 26453Wenzhou Medical University, Wenzhou, China
| | - Yao Ai
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Kolinger GD, Vállez García D, Kramer GM, Frings V, Zwezerijnen GJC, Smit EF, De Langen AJ, Buvat I, Boellaard R. Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics. J Nucl Med 2021; 63:919-924. [PMID: 34933890 DOI: 10.2967/jnumed.121.262660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/21/2021] [Indexed: 11/16/2022] Open
Abstract
Positron emission tomography (PET) radiomics applied to oncology allows the measurement of intra-tumoral heterogeneity. This quantification can be affected by image protocols hence there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that, this study explores how radiomic features are affected by changes in 18F-FDG uptake time, image reconstruction, lesion delineation, and radiomics binning settings. Methods: Ten non-small cell lung cancer (NSCLC) patients underwent 18F-FDG PET scans on two consecutive days. On each day, scans were obtained at 60min and 90min post-injection and reconstructed following EARL version 1 (EARL1) and with point-spread-function resolution modelling (PSF-EARL2). Lesions were delineated using thresholds at SUV=4.0, 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both fixed bin width (FBW) and fixed bin number (FBN) before the calculation of the radiomic features. Repeatability of features was measured with intraclass correlation (ICC), and the change in feature value over time was calculated as a function of its repeatability. Features were then classified on use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (ICC>0.9), 39% being classified for dual-time-point use-case for being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with unclear dependency on time, 20% classified for cross-sectional use while being robust to tracer uptake time changes, and 6% were discarded for poor repeatability. EARL1 images had one less repeatable feature than PSF-EARL2 (Neighborhood Gray-Level Different Matrix Coarseness), the contrast-based delineation had the poorest repeatability of the delineation methods with 45% features being discarded, and FBN resulted in lower repeatability than FBW (45% and 6% features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. Based on their susceptibility to tracer uptake time, radiomic features were classified into specific NSCLC PET radiomics use-cases.
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Affiliation(s)
| | - David Vállez García
- Medical Imaging Center, University Medical Center Groningen, University of Groningen, Netherlands
| | - Gerbrand Maria Kramer
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VU Medical Center, Netherlands
| | - Virginie Frings
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VU Medical Center, Netherlands
| | | | - Egbert F Smit
- Department of Pulmonology, Amsterdam University Medical Center, location VU Medical Center, Netherlands
| | | | - Irène Buvat
- Laboratoire d'Imagerie Translationnelle en Oncologie, INSERM, Institut Curie, Université Paris-Saclay, France
| | - Ronald Boellaard
- Medical Imaging Center, University Medical Center Groningen, University of Groningen, Netherlands
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Edalat-Javid M, Shiri I, Hajianfar G, Abdollahi H, Arabi H, Oveisi N, Javadian M, Shamsaei Zafarghandi M, Malek H, Bitarafan-Rajabi A, Oveisi M, Zaidi H. Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study. J Nucl Cardiol 2021; 28:2730-2744. [PMID: 32333282 DOI: 10.1007/s12350-020-02109-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/12/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND The aim of this work was to assess the robustness of cardiac SPECT radiomic features against changes in imaging settings, including acquisition, and reconstruction parameters. METHODS Four commercial SPECT and SPECT/CT cameras were used to acquire images of a static cardiac phantom mimicking typical myorcardial perfusion imaging using 185 MBq of 99mTc. The effects of different image acquisition and reconstruction parameters, including number of views, view matrix size, attenuation correction, as well as image reconstruction related parameters (algorithm, number of iterations, number of subsets, type of post-reconstruction filter, and its associated parameters, including filter order and cut-off frequency) were studied. In total, 5,063 transverse views were reconstructed by varying the aforementioned factors. Eighty-seven radiomic features including first-, second-, and high-order textures were extracted from these images. To assess reproducibility and repeatability, the coefficient of variation (COV), as a widely adopted metric, was measured for each of the radiomic features over the different imaging settings. RESULTS The Inverse Difference Moment Normalized (IDMN) and Inverse Difference Normalized (IDN) features from the Gray Level Co-occurrence Matrix (GLCM), Run Percentage (RP) from the Gray Level Co-occurrence Matrix (GLRLM), Zone Entropy (ZE) from the Gray Level Size Zone Matrix (GLSZM), and Dependence Entropy (DE) from the Gray Level Dependence Matrix (GLDM) feature sets were the only features that exhibited high reproducibility (COV ≤ 5%) against changes in all imaging settings. In addition, Large Area Low Gray Level Emphasis (LALGLE), Small Area Low Gray Level Emphasis (SALGLE) and Low Gray Level Zone Emphasis (LGLZE) from GLSZM, and Small Dependence Low Gray Level Emphasis (SDLGLE) from GLDM feature sets turned out to be less reproducible (COV > 20%) against changes in imaging settings. The GLRLM (31.88%) and GLDM feature set (54.2%) had the highest (COV < 5%) and lowest (COV > 20%) number of the reproducible features, respectively. Matrix size had the largest impact on feature variability as most of the features were not repeatable when matrix size was modified with 82.8% of them having a COV > 20%. CONCLUSION The repeatability and reproducibility of SPECT/CT cardiac radiomic features under different imaging settings is feature-dependent. Different image acquisition and reconstruction protocols have variable effects on radiomic features. The radiomic features exhibiting low COV are potential candidates for future clinical studies.
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Affiliation(s)
- Mohammad Edalat-Javid
- Department of Energy Engineering and Physics, Amir Kabir University of Technology, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Niki Oveisi
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Mohammad Javadian
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | | | - Hadi Malek
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, 1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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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.
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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
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Orlhac F, Nioche C, Klyuzhin I, Rahmim A, Buvat I. Radiomics in PET Imaging:: A Practical Guide for Newcomers. PET Clin 2021; 16:597-612. [PMID: 34537132 DOI: 10.1016/j.cpet.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critical reading of the content of articles. The advantages and limitations of handcrafted radiomics compared with deep-learning approaches for the characterization of PET images are also discussed.
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Affiliation(s)
- Fanny Orlhac
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France.
| | - Christophe Nioche
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
| | - Ivan Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Irène Buvat
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
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47
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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: 89] [Impact Index Per Article: 29.7] [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.
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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
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48
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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.
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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
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Du D, Gu J, Chen X, Lv W, Feng Q, Rahmim A, Wu H, Lu L. Reply to Letter to Editor RE: "Integration of PET/CT Radiomics and Semantic Features for Differentiation Between Active Pulmonary Tuberculosis and Lung Cancer". Mol Imaging Biol 2021; 23:975-977. [PMID: 34312805 DOI: 10.1007/s11307-021-01634-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Dongyang Du
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Jiamei Gu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaohui Chen
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qianjin Feng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada.,Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, V5Z 1L3, Canada
| | - Hubing Wu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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Zeydanli T, Kilic HK. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol 2021; 40:56-65. [PMID: 34304383 DOI: 10.1007/s11604-021-01181-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 07/18/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To examine the computed tomography (CT) images of patients with a diagnosis of gastric tumor by texture analysis and to investigate its place in differential diagnosis. MATERIALS AND METHODS Contrast enhanced venous phase CT images of 163 patients with pathological diagnosis of gastric adenocarcinoma (n = 125), gastric lymphoma (n = 12) and gastrointestinal stromal tumors (n = 26) were retrospectively analyzed. Pixel size adjustment, gray-level discretization and gray-level normalization procedures were applied as pre-processing steps. Region of interest (ROI) was determined from the axial slice that represented the largest lesion area and a total of 40 texture features were calculated for each patient. Texture features were compared between the tumor subtypes and between adenocarcinoma grades. Statistically significant texture features were combined into a single parameter by logistic regression analysis. The sensitivity and specificity of these features and the combined parameter were measured to differentiate tumor subtypes by receiver-operating characteristic curve (ROC) analysis. RESULTS Classifications between adenocarcinoma versus lymphoma, adenocarcinoma vs. gastrointestinal stromal tumor (GIST) and well-differentiated adenocarcinoma versus poorly differentiated adenocarcinoma using texture features yielded successful results with high sensitivity (98, 91, 96%, respectively) and specificity (75, 77, 80%, respectively). CONCLUSIONS CT texture analysis is a non-invasive promising method for classifying gastric tumors and predicting gastric adenocarcinoma differentiation.
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Affiliation(s)
- Tolga Zeydanli
- Radiology Department, Ardahan Devlet Hastanesi, 75000, Ardahan, Turkey.
| | - Huseyin Koray Kilic
- Radiology Department, Gazi University School of Medicine, 06500, Ankara, Turkey
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