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Kafkaletos A, Mix M, Sachpazidis I, Carles M, Rühle A, Ruf J, Grosu AL, Nicolay NH, Baltas D. The significance of partial volume effect on the estimation of hypoxic tumour volume with [ 18F]FMISO PET/CT. EJNMMI Phys 2024; 11:43. [PMID: 38722446 PMCID: PMC11082115 DOI: 10.1186/s40658-024-00643-1] [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: 01/16/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND The purpose of this study was to evaluate how a retrospective correction of the partial volume effect (PVE) in [18F]fluoromisonidazole (FMISO) PET imaging, affects the hypoxia discoverability within a gross tumour volume (GTV). This method is based on recovery coefficients (RC) and is tailored for low-contrast tracers such as FMISO. The first stage was the generation of the scanner's RC curves, using spheres with diameters from 10 to 37 mm, and the same homogeneous activity concentration, positioned in lower activity concentration background. Six sphere-to-background contrast ratios were used, from 10.0:1, down to 2.0:1, in order to investigate the dependence of RC on both the volume and the contrast ratio. The second stage was to validate the recovery-coefficient correction method in a more complex environment of non-spherical lesions of different volumes and inhomogeneous activity concentration. Finally, we applied the correction method to a clinical dataset derived from a prospective imaging trial (DRKS00003830): forty nine head and neck squamous cell carcinoma (HNSCC) cases who had undergone FMISO PET/CT scanning for the quantification of tumour hypoxia before (W0), 2 weeks (W2) and 5 weeks (W5) after the beginning of radiotherapy. Here, PVE was found to cause an underestimation of the activity in small volumes with high FMISO signal. RESULTS The application of the proposed correction method resulted in a statistically significant increase of both the hypoxic subvolume (171% at W0, 691% at W2 and 4.60 × 103% at W5 with p < 0.001) and the FMISO standardised uptake value (SUV) (27% at W0, 21% at W2 and by 25% at W5 with p < 0.001) within the primary GTV. CONCLUSIONS The proposed PVE-correction method resulted in a statistically significant increase of the hypoxic fraction (HF) with p < 0.001 and demonstrated results in better agreement with published HF data for HNSCC. To summarise, the proposed RC-based correction method can be a useful tool for a retrospective compensation against PVE.
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Affiliation(s)
- Athanasios Kafkaletos
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany.
- German Cancer Consortium (DKTK), Partner Site Freiburg, a partnership between DKFZ and Medical Center - University of Freiburg, Freiburg, Germany.
| | - Michael Mix
- Department of Nuclear Medicine, Medical Centre - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
| | - Ilias Sachpazidis
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
| | - Montserrat Carles
- Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Valencia, Spain
| | - Alexander Rühle
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
- Department of Radiation Oncology, University of Leipzig Medical Centre, Leipzig, Germany
| | - Juri Ruf
- Department of Nuclear Medicine, Medical Centre - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
| | - Anca L Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
| | - Nils H Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
- Department of Radiation Oncology, University of Leipzig Medical Centre, Leipzig, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, German Cancer Consortium (DKTK), partner site DKTK-Freiburg, University of Freiburg, Freiburg, Germany
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [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: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
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Saborido-Moral JD, Fernández-Patón M, Tejedor-Aguilar N, Cristian-Marín A, Torres-Espallardo I, Campayo-Esteban JM, Pérez-Calatayud J, Baltas D, Martí-Bonmatí L, Carles M. Free automatic software for quality assurance of computed tomography calibration, edges and radiomics metrics reproducibility. Phys Med 2023; 114:103153. [PMID: 37778209 DOI: 10.1016/j.ejmp.2023.103153] [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: 06/20/2023] [Revised: 09/16/2023] [Accepted: 09/22/2023] [Indexed: 10/03/2023] Open
Abstract
PURPOSE To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features. METHODS The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient). RESULTS Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%). CONCLUSIONS An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested.
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Affiliation(s)
- Juan D Saborido-Moral
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain.
| | - Matías Fernández-Patón
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain
| | - Natalia Tejedor-Aguilar
- Department of Radiation Oncology, La Fe Polytechnic and University Hospital, Valencia, Spain
| | - Andrei Cristian-Marín
- Department of Radiation Protection, La Fe Polytechnic and University Hospital, Valencia, Spain
| | | | - Juan M Campayo-Esteban
- Department of Radiation Protection, La Fe Polytechnic and University Hospital, Valencia, Spain
| | - José Pérez-Calatayud
- Department of Radiation Oncology, La Fe Polytechnic and University Hospital, Valencia, Spain
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany
| | - Luis Martí-Bonmatí
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain
| | - Montserrat Carles
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain
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Ventura D, Schindler P, Masthoff M, Görlich D, Dittmann M, Heindel W, Schäfers M, Lenz G, Wardelmann E, Mohr M, Kies P, Bleckmann A, Roll W, Evers G. Radiomics of Tumor Heterogeneity in 18F-FDG-PET-CT for Predicting Response to Immune Checkpoint Inhibition in Therapy-Naïve Patients with Advanced Non-Small-Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15082297. [PMID: 37190228 DOI: 10.3390/cancers15082297] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
We aimed to evaluate the predictive and prognostic value of baseline 18F-FDG-PET-CT (PET-CT) radiomic features (RFs) for immune checkpoint-inhibitor (CKI)-based first-line therapy in advanced non-small-cell lung cancer (NSCLC) patients. In this retrospective study 44 patients were included. Patients were treated with either CKI-monotherapy or combined CKI-based immunotherapy-chemotherapy as first-line treatment. Treatment response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST). After a median follow-up of 6.4 months patients were stratified into "responder" (n = 33) and "non-responder" (n = 11). RFs were extracted from baseline PET and CT data after segmenting PET-positive tumor volume of all lesions. A Radiomics-based model was developed based on a Radiomics signature consisting of reliable RFs that allow classification of response and overall progression using multivariate logistic regression. These RF were additionally tested for their prognostic value in all patients by applying a model-derived threshold. Two independent PET-based RFs differentiated well between responders and non-responders. For predicting response, the area under the curve (AUC) was 0.69 for "PET-Skewness" and 0.75 predicting overall progression for "PET-Median". In terms of progression-free survival analysis, patients with a lower value of PET-Skewness (threshold < 0.2014; hazard ratio (HR) 0.17, 95% CI 0.06-0.46; p < 0.001) and higher value of PET-Median (threshold > 0.5233; HR 0.23, 95% CI 0.11-0.49; p < 0.001) had a significantly lower probability of disease progression or death. Our Radiomics-based model might be able to predict response in advanced NSCLC patients treated with CKI-based first-line therapy.
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Affiliation(s)
- David Ventura
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Philipp Schindler
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Max Masthoff
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Muenster, 48149 Muenster, Germany
| | - Matthias Dittmann
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Walter Heindel
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Michael Schäfers
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Georg Lenz
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Eva Wardelmann
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Gerhard-Domagk-Institute of Pathology, University Hospital Muenster, 48149 Muenster, Germany
| | - Michael Mohr
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Peter Kies
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Annalen Bleckmann
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Wolfgang Roll
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Georg Evers
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
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Ciarmiello A, Giovannini E, Pastorino S, Ferrando O, Foppiano F, Mannironi A, Tartaglione A, Giovacchini G. Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET. Clin Nucl Med 2023; 48:1-7. [PMID: 36240660 DOI: 10.1097/rlu.0000000000004433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET. PATIENTS AND METHODS Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model. RESULTS The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score). CONCLUSIONS Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.
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Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022; 68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [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: 06/09/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022]
Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
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Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
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Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study. Tomography 2022; 8:1113-1128. [PMID: 35448725 PMCID: PMC9025788 DOI: 10.3390/tomography8020091] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/04/2022] [Accepted: 04/08/2022] [Indexed: 01/12/2023] Open
Abstract
For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a "harmonized" alongside a "standard" dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.
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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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Miwa K, Yamao T, Kamitaka Y. [[Nuclear Medicine] 1. Review of Phantoms for Nuclear Medicine Imaging]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:207-212. [PMID: 35185100 DOI: 10.6009/jjrt.780216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Kenta Miwa
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology
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Carles M, Fechter T, Grosu AL, Sörensen A, Thomann B, Stoian RG, Wiedenmann N, Rühle A, Zamboglou C, Ruf J, Martí-Bonmatí L, Baltas D, Mix M, Nicolay NH. 18F-FMISO-PET Hypoxia Monitoring for Head-and-Neck Cancer Patients: Radiomics Analyses Predict the Outcome of Chemo-Radiotherapy. Cancers (Basel) 2021; 13:3449. [PMID: 34298663 PMCID: PMC8303992 DOI: 10.3390/cancers13143449] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022] Open
Abstract
Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by 18F-fluoromisonidazole (18F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of 18F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal 18F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two 18F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUCW2 = 0.82 and AUCW5 = 0.81 in initial cohort (N = 25) and AUCW2 = 0.79 and AUCW5 = 0.80 in validation cohort. Radiomics analysis of 18F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT.
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Affiliation(s)
- Montserrat Carles
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
| | - Tobias Fechter
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
| | - Anca L. Grosu
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Arnd Sörensen
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Benedikt Thomann
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
| | - Raluca G. Stoian
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Nicole Wiedenmann
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Alexander Rühle
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Constantinos Zamboglou
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Juri Ruf
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Luis Martí-Bonmatí
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
| | - Dimos Baltas
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
| | - Michael Mix
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Nuclear Medicine, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Nils H. Nicolay
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
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