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Cilla S, Deodato F, Romano C, Macchia G, Buwenge M, Morganti AG. Radiomics-based discriminant analysis of principal components to stratify the treatment response of lung metastases following stereotactic body radiation therapy. Phys Med 2024; 121:103340. [PMID: 38593628 DOI: 10.1016/j.ejmp.2024.103340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 03/08/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE Discriminant analysis of principal components (DAPC) was introduced to describe the clusters of genetically related individuals focusing on the variation between the groups of individuals. Borrowing this approach, we evaluated the potential of DAPC for the evaluation of clusters in terms of treatment response to SBRT of lung lesions using radiomics analysis on pre-treatment CT images. MATERIALS AND METHODS 80 pulmonary metastases from 56 patients treated with SBRT were analyzed. Treatment response was stratified as complete, incomplete and null responses. For each lesion, 107 radiomics features were extracted using the PyRadiomics software. The concordance correlation coefficients (CCC) between the radiomics features obtained by two segmentations were calculated. DAPC analysis was performed to infer the structure of "radiomically" related lesions for treatment response assessment. The DAPC was performed using the "adegenet" package for the R software. RESULTS The overall mean CCC was 0.97 ± 0.14. The analysis yields 14 dimensions in order to explain 95 % of the variance. DAPC was able to group the 80 lesions into the 3 different clusters based on treatment response depending on the radiomics features characteristics. The first Linear Discriminant achieved the best discrimination of individuals into the three pre-defined groups. The greater radiomics loadings who contributed the most to the treatment response differentiation were associated with the "sphericity", "correlation" and "maximal correlation coefficient" features. CONCLUSION This study demonstrates that a DAPC analysis based on radiomics features obtained from pretreatment CT is able to provide a reliable stratification of complete, incomplete or null response of lung metastases following SBRT.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Responsible Research Hospital, Campobasso, Italy.
| | - Francesco Deodato
- Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Italy
| | - Carmela Romano
- Medical Physics Unit, Responsible Research Hospital, Campobasso, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Italy
| | - Milly Buwenge
- Radiation Oncology Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Alessio G Morganti
- Radiation Oncology Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; DIMEC, Alma Mater Studiorum, Bologna University, Bologna, Italy
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Zarei M, Abadi E, Vancoillie L, Samei E. Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions. J Med Imaging (Bellingham) 2024; 11:025501. [PMID: 38680209 PMCID: PMC11047768 DOI: 10.1117/1.jmi.11.2.025501] [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: 11/03/2023] [Revised: 02/28/2024] [Accepted: 04/03/2024] [Indexed: 05/01/2024] Open
Abstract
Background The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use. Purpose The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs. Method MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA). Results The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions. Conclusion The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.
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Affiliation(s)
- Mojtaba Zarei
- Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States
- Duke University, Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
- Duke University, Duke University School of Medicine, Durham, North Carolina, United States
| | - Ehsan Abadi
- Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States
- Duke University, Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
- Duke University, Duke University School of Medicine, Durham, North Carolina, United States
| | - Liesbeth Vancoillie
- Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States
- Duke University, Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
- Duke University, Duke University School of Medicine, Durham, North Carolina, United States
| | - Ehsan Samei
- Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States
- Duke University, Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
- Duke University, Duke University School of Medicine, Durham, North Carolina, United States
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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: 28] [Impact Index Per Article: 28.0] [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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Rinaldi L, Guerini Rocco E, Spitaleri G, Raimondi S, Attili I, Ranghiero A, Cammarata G, Minotti M, Lo Presti G, De Piano F, Bellerba F, Funicelli G, Volpe S, Mora S, Fodor C, Rampinelli C, Barberis M, De Marinis F, Jereczek-Fossa BA, Orecchia R, Rizzo S, Botta F. Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients. Cancers (Basel) 2023; 15:4553. [PMID: 37760521 PMCID: PMC10527057 DOI: 10.3390/cancers15184553] [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: 08/10/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.
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Affiliation(s)
- Lisa Rinaldi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| | - Elena Guerini Rocco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
| | - Gianluca Spitaleri
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Sara Raimondi
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Ilaria Attili
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Alberto Ranghiero
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
| | - Giulio Cammarata
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Marta Minotti
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Francesca De Piano
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Federica Bellerba
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Gianluigi Funicelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Stefania Volpe
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Serena Mora
- Data Management Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (S.M.); (C.F.)
| | - Cristiana Fodor
- Data Management Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (S.M.); (C.F.)
| | - Cristiano Rampinelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Massimo Barberis
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
| | - Filippo De Marinis
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Roberto Orecchia
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
- Scientific Direction, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), Via Tesserete 46, 6900 Lugano, Switzerland;
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Via G. Buffi 13, 6900 Lugano, Switzerland
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study. Eur Radiol Exp 2023; 7:48. [PMID: 37670193 PMCID: PMC10480134 DOI: 10.1186/s41747-023-00362-9] [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: 04/23/2023] [Accepted: 06/13/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). METHODS T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. RESULTS When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. CONCLUSIONS Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. RELEVANCE STATEMENT The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. KEY POINTS • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack.
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Affiliation(s)
- Laura Jacqueline Jensen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo Günter Steffen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian Niko Nagel
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
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Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
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Bicci E, Cozzi D, Cavigli E, Ruzga R, Bertelli E, Danti G, Bettarini S, Tortoli P, Mazzoni LN, Busoni S, Miele V. Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population. LA RADIOLOGIA MEDICA 2023; 128:203-211. [PMID: 36637739 PMCID: PMC9938819 DOI: 10.1007/s11547-023-01592-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
BACKGROUND The aim is to find a correlation between texture features extracted from neuroendocrine (NET) lung cancer subtypes, both Ki-67 index and the presence of lymph-nodal mediastinal metastases detected while using different computer tomography (CT) scanners. METHODS Sixty patients with a confirmed pulmonary NET histological diagnosis, a known Ki-67 status and metastases, were included. After subdivision of primary lesions in baseline acquisition and venous phase, 107 radiomic features of first and higher orders were extracted. Spearman's correlation matrix with Ward's hierarchical clustering was applied to confirm the absence of bias due to the database heterogeneity. Nonparametric tests were conducted to identify statistically significant features in the distinction between patient groups (Ki-67 < 3-Group 1; 3 ≤ Ki-67 ≤ 20-Group 2; and Ki-67 > 20-Group 3, and presence of metastases). RESULTS No bias arising from sample heterogeneity was found. Regarding Ki-67 groups statistical tests, seven statistically significant features (p value < 0.05) were found in post-contrast enhanced CT; three in baseline acquisitions. In metastasis classes distinction, three features (first-order class) were statistically significant in post-contrast acquisitions and 15 features (second-order class) in baseline acquisitions, including the three features distinguishing between Ki-67 groups in baseline images (MCC, ClusterProminence and Strength). CONCLUSIONS Some radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness.
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Affiliation(s)
- Eleonora Bicci
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Edoardo Cavigli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ron Ruzga
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Elena Bertelli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Silvia Bettarini
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Paolo Tortoli
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Lorenzo Nicola Mazzoni
- Department of Health Physics, AUSL Toscana Centro, Via Ciliegiole 97, 51100 Pistoia, Italy
| | - Simone Busoni
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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Carloni G, Garibaldi C, Marvaso G, Volpe S, Zaffaroni M, Pepa M, Isaksson LJ, Colombo F, Durante S, Lo Presti G, Raimondi S, Spaggiari L, de Marinis F, Piperno G, Vigorito S, Gandini S, Cremonesi M, Positano V, Jereczek-Fossa BA. Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms. Radiother Oncol 2023; 178:109424. [PMID: 36435336 DOI: 10.1016/j.radonc.2022.11.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 10/28/2022] [Accepted: 11/18/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Radiomics enables the mining of quantitative features from medical images. The influence of the radiomic feature extraction software on the final performance of models is still a poorly understood topic. This study aimed to investigate the ability of radiomic features extracted by two different radiomic platforms to predict clinical outcomes in patients treated with radiosurgery for brain metastases from non-small cell lung cancer. We developed models integrating pre-treatment magnetic resonance imaging (MRI)-derived radiomic features and clinical data. MATERIALS AND METHODS Pre-radiotherapy gadolinium enhanced axial T1-weighted MRI scans were used. MRI images were re-sampled, intensity-shifted, and histogram-matched before radiomic extraction by means of two different platforms (PyRadiomics and SOPHiA Radiomics). We adopted LASSO Cox regression models for multivariable analyses by creating radiomic, clinical, and combined models using three survival clinical endpoints (local control, distant progression, and overall survival). The statistical analysis was repeated 50 times with different random seeds and the median concordance index was used as performance metric of the models. RESULTS We analysed 276 metastases from 148 patients. The use of the two platforms resulted in differences in both the quality and the number of extractable features. That led to mismatches in terms of end-to-end performance, statistical significance of radiomic scores, and clinical covariates found significant in combined models. CONCLUSION This study shed new light on how extracting radiomic features from the same images using two different platforms could yield several discrepancies. That may lead to acute consequences on drawing conclusions, comparing results across the literature, and translating radiomics into clinical practice.
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Affiliation(s)
- Gianluca Carloni
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy; Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Cristina Garibaldi
- Unit of Radiation Research, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Stefania Volpe
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy.
| | - Matteo Pepa
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Francesca Colombo
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Stefano Durante
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Department of Thoracic Surgery, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Filippo de Marinis
- Division of Thoracic Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Gaia Piperno
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Sabrina Vigorito
- Unit of Medical Physics, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Marta Cremonesi
- Unit of Radiation Research, IEO, European Institute of Oncology, IRCCS, Milan, Italy
| | - Vincenzo Positano
- Department of Information Engineering, University of Pisa, Pisa, Italy; Gabriele Monasterio Foundation, Pisa, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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9
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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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10
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Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer. J Pers Med 2022; 13:jpm13010083. [PMID: 36675744 PMCID: PMC9864775 DOI: 10.3390/jpm13010083] [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: 11/22/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Background: Radiomic features are increasingly used in CT of NSCLC. However, their robustness with respect to segmentation variability has not yet been demonstrated. The aim of this study was to assess radiomic features agreement across three kinds of segmentation. Methods: We retrospectively included 48 patients suffering from NSCLC who underwent pre-surgery CT. Two expert radiologists in consensus manually delineated three 3D-ROIs on each patient. To assess robustness for each feature, the intra-class correlation coefficient (ICC) across segmentations was evaluated. The ‘sensitivity’ of ICC upon some parameters affecting features computation (such as bin-width for first-order features and pixel-distances for second-order features) was also evaluated. Moreover, an assessment with respect to interpolator and isotropic resolution was also performed. Results: Our results indicate that ‘shape’ features tend to have excellent agreement (ICC > 0.9) across segmentations; moreover, they have approximately zero sensitivity to other parameters. ‘First-order’ features are in general sensitive to parameters variation; however, a few of them showed excellent agreement and low sensitivity (below 0.1) with respect to bin-width and pixel-distance. Similarly, a few second-order features showed excellent agreement and low sensitivity. Conclusions: Our results suggest that a limited number of radiomic features can achieve a high level of reproducibility in CT of NSCLC.
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11
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Volpe S, Isaksson LJ, Zaffaroni M, Pepa M, Raimondi S, Botta F, Lo Presti G, Vincini MG, Rampinelli C, Cremonesi M, de Marinis F, Spaggiari L, Gandini S, Guckenberger M, Orecchia R, Jereczek-Fossa BA. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl Lung Cancer Res 2022; 11:2452-2463. [PMID: 36636424 PMCID: PMC9830263 DOI: 10.21037/tlcr-22-248] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022]
Abstract
Background No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. Methods Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. Results Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). Conclusions Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Giulia Vincini
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Cristiano Rampinelli
- Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Filippo de Marinis
- Division of Thoracic Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;,Division of Thoracic Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roberto Orecchia
- Scientific Direction, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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12
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Demircioğlu A. The effect of preprocessing filters on predictive performance in radiomics. Eur Radiol Exp 2022; 6:40. [PMID: 36045274 PMCID: PMC9433552 DOI: 10.1186/s41747-022-00294-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/30/2022] [Indexed: 11/16/2022] Open
Abstract
Background Radiomics is a noninvasive method using machine learning to support personalised medicine. Preprocessing filters such as wavelet and Laplacian-of-Gaussian filters are commonly used being thought to increase predictive performance. However, the use of preprocessing filters increases the number of features by up to an order of magnitude and can produce many correlated features. Both substantially increase the dataset complexity, which in turn makes modeling with machine learning techniques more challenging, possibly leading to poorer performance. We investigated the impact of these filters on predictive performance. Methods Using seven publicly available radiomic datasets, we measured the impact of adding features preprocessed with eight different preprocessing filters to the unprocessed features on the predictive performance of radiomic models. Modeling was performed using five feature selection methods and five classifiers, while predictive performance was measured using area-under-the-curve at receiver operating characteristics analysis (AUC-ROC) with nested, stratified 10-fold cross-validation. Results Significant improvements of up to 0.08 in AUC-ROC were observed when all image preprocessing filters were applied compared to using only the original features (up to p = 0.024). Decreases of -0.04 and -0.10 were observed on some data sets, but these were not statistically significant (p > 0.179). Tuning of the image preprocessing filters did not result in decreases in AUC-ROC but further improved results by up to 0.1; however, these improvements were not statistically significant (p > 0.086) except for one data set (p = 0.023). Conclusions Preprocessing filters can have a significant impact on the predictive performance and should be used in radiomic studies. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00294-w.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
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13
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study. Eur Radiol Exp 2022; 6:43. [PMID: 36104519 PMCID: PMC9474978 DOI: 10.1186/s41747-022-00297-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In radiomics studies, differences in the volume of interest (VOI) are often inevitable and may confound the extracted features. We aimed to correct this confounding effect of VOI variability by applying parametric maps with a fixed voxel size. METHODS Ten scans of a cup filled with sodium chloride solution were scanned using a multislice computed tomography (CT) unit. Sphere-shaped VOIs with different diameters (4, 8, or 16 mm) were drawn centrally into the phantom. A total of 93 features were extracted conventionally from the original images using PyRadiomics. Using a self-designed and pretested software tool, parametric maps for the same 93 features with a fixed voxel size of 4 mm3 were created. To retrieve the feature values from the maps, VOIs were copied from the original images to preserve the position. Differences in feature quantities between the VOI sizes were tested with the Mann-Whitney U-test and agreement with overall concordance correlation coefficients (OCCC). RESULTS Fifty-five conventionally extracted features were significantly different between the VOI sizes, and none of the features showed excellent agreement in terms of OCCCs. When read from the parametric maps, only 8 features showed significant differences, and 3 features showed an excellent OCCC (≥ 0.85). The OCCCs for 89 features substantially increased using the parametric maps. CONCLUSIONS This phantom study shows that converting CT images into parametric maps resolves the confounding effect of VOI variability and increases feature reproducibility across VOI sizes.
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Affiliation(s)
- Laura J Jensen
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo G Steffen
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian N Nagel
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
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14
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Rinaldi L, Pezzotta F, Santaniello T, De Marco P, Bianchini L, Origgi D, Cremonesi M, Milani P, Mariani M, Botta F. HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images. Phys Med 2022; 97:13-24. [PMID: 35334407 DOI: 10.1016/j.ejmp.2022.03.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 02/01/2022] [Accepted: 03/14/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions. METHODS We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10. RESULTS The best similarity with the patients was obtained with the polyacrylate inserts (55.6-90.2%), the worst with Catphan (15.7-19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3-100% at 120kVp, 75.7-97.9% at 100kVp), and observed a texture dependency in repeatability. CONCLUSIONS Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies.
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Affiliation(s)
- Lisa Rinaldi
- Department of Physics, Università degli Studi di Pavia and INFN, via Bassi 6, 27100 Pavia, Italy; Radiation Research Unit, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy.
| | - Federico Pezzotta
- CIMaINa, Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Tommaso Santaniello
- CIMaINa, Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Linda Bianchini
- Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Paolo Milani
- CIMaINa, Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Manuel Mariani
- Department of Physics, Università degli Studi di Pavia and INFN, via Bassi 6, 27100 Pavia, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
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