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Tapias LF, Shen R, Cassivi SD, Reisenauer JS, Lunn BW, Lechtenberg BJ, Nichols FC, Wigle DA, Blackmon SH. Impact of FDG PET Standardized Uptake Value in Resected Clinical Stage IA Non-Small Cell Lung Cancer. Ann Thorac Surg 2024; 117:1017-1023. [PMID: 37080373 DOI: 10.1016/j.athoracsur.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/28/2023] [Accepted: 04/10/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND A significant proportion of patients with clinical stage IA non-small cell lung cancer (NSCLC) experience will recurrence and decreased survival after surgery. This study examined the impact of preoperative primary tumor positron emission tomography (PET) scan maximum standardized uptake value (SUVmax) on oncologic outcomes after surgery. METHODS This was a retrospective review of 251 patients who underwent surgical treatment of clinical stage IA NSCLC at an academic medical center (2005-2014). Patients were classified according to PET SUVmax level (low vs high) for analysis of upstaging, tumor recurrence, and overall survival. RESULTS Median SUVmax values were higher in squamous cell carcinoma than in adenocarcinoma (median 3.3 vs 7.2; P < .0001). There were 109 (43.4%) patients in the SUVmax low group and 142 (56.6%) in the SUVmax high group. Patients with SUVmax high had larger tumors. SUVmax high was associated with higher rates of nodal upstaging (16.2% vs 4.6% in SUVmax low; P = .004), particularly in N1 nodes. SUVmax high was independently associated with nodal upstaging (adjusted odds ratio, 3.95; 95% CI, 1.36-11.46; P = .011). SUVmax high was associated with time to recurrence (hazard ratio, 1.62; 95% CI, 1.03-2.54; P = .036), but this association was lost on multivariable analysis (hazard ratio, 1.52; 95% CI, 0.91-2.54; P = .106). SUVmax was not associated with overall survival. CONCLUSIONS Preoperative PET SUVmax level is strongly associated with nodal upstaging, particularly in N1 nodes, in patients with clinical stage IA NSCLC who undergo resection. PET SUVmax should be regarded as a risk factor when considering candidacy for sublobar resections and in future trials involving patients with stage I NSCLC.
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Affiliation(s)
- Luis F Tapias
- Division of Thoracic Surgery, Mayo Clinic, Rochester, Minnesota.
| | - Robert Shen
- Division of Thoracic Surgery, Mayo Clinic, Rochester, Minnesota
| | | | | | - Brendan W Lunn
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Dennis A Wigle
- Division of Thoracic Surgery, Mayo Clinic, Rochester, Minnesota
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Murillo AD, Kratz JR. PET Standardized Uptake Value May Influence Surgical Planning for Clinical Stage IA Non-Small Cell Lung Cancer. Ann Thorac Surg 2024; 117:1024. [PMID: 37369303 DOI: 10.1016/j.athoracsur.2023.05.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023]
Affiliation(s)
- Alyssa D Murillo
- Division of Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, Box 0118, San Francisco, CA 94143
| | - Johannes Ruediger Kratz
- Division of Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, Box 0118, San Francisco, CA 94143.
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Zhang L, Zhang J, Miao J, Zhu G, Su X, Wang H. Characteristics of whole-body dynamic 18F-FDG PET/CT Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters. Quant Imaging Med Surg 2024; 14:291-304. [PMID: 38223020 PMCID: PMC10784064 DOI: 10.21037/qims-23-862] [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: 06/13/2023] [Accepted: 10/17/2023] [Indexed: 01/16/2024]
Abstract
Background Dynamic course of flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) Patlak muti-parametric imaging spatial distribution in the targeted tissues may reveal highly useful clinical information about the tissue's metabolic properties. The characteristics of the Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters may provide reference for the clinical application of this new technology. Methods A total of 27 patients with pathologically diagnosed lung cancer underwent whole-body dynamic 18F-FDG PET/CT examination before treatment. Parametric images of metabolic rate of FDG (MRFDG) and Patlak intercept (or distribution volume; DV) were generated using Patlak reconstruction. The values of primary lung cancer lesions, target-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were investigated using contour delineation and boundary delineation. Statistical analysis was performed to analyze the relationship between multi-parametric images and clinicopathological features, and to compare the effects of contour delineation and boundary delineation on quantitative parameters. Results MRFDG images showed higher TBR and CNR than did standardized uptake value (SUV) images. There were significant differences in MRFDG-max, MRFDG-mean, and MRFDG-peak among groups with different tumor diameters and pathology types (P<0.05). Moreover, the metabolic parameters of MRFDG were higher in patients with tumor diameters ≥3 cm and squamous carcinoma. The differences of the maximum and peak values of MRFDG and DV were not statistically significant in the different outlining method subgroups (all P>0.05). However, the difference of the mean values of MRFDG and DV were statistically significant in the different outline method groupings (all P<0.05). Conclusions Dynamic 18F-FDG PET/CT Patlak multi-parametric imaging can obtain quantitative values for lung cancer with high TBR and CNR. Moreover, the multi-parameters are various from different pathology types to tumor size. Different delineation methods have a greater influence on the mean value of quantitative parameters.
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Affiliation(s)
| | | | - Jingxuan Miao
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Gan Zhu
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoyu Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Okumus Ö, Mardanzai K, Plönes T, Theegarten D, Darwiche K, Schuler M, Nensa F, Hautzel H, Hermann K, Stuschke M, Hegedus B, Aigner C. Preoperative PET-SUVmax and volume based PET parameters of the primary tumor fail to predict nodal upstaging in early-stage lung cancer. Lung Cancer 2023; 176:82-88. [PMID: 36623341 DOI: 10.1016/j.lungcan.2022.12.013] [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: 09/20/2022] [Revised: 12/17/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022]
Abstract
OBJECTIVES Accurate nodal staging is of utmost importance in patients with lung cancer. FDG-PET/CT imaging is now part of the routine staging. Despite thorough preoperative staging nodal upstaging still occurs in early-stage lung cancer. However, the predictive value of preoperative PET metrics of the primary tumor on nodal upstaging remains to be unexplored. Our aim was to assess the association of these preoperative PET-parameters with nodal upstaging in histologically confirmed lung adenocarcinoma and squamous cell carcinoma. METHODS From January 2016 to November 2018, 500 patients with pT1-T2/cN0 lung cancer received an anatomical resection with curative intent. 171 patients with adenocarcinoma and squamous cell carcinoma and available PET-CTs were retrospectively included. We analyzed the the association of nodal upstaging with preoperative PET-SUVmax and metabolic PET metrics including total lesion glycolysis (TLG) and metabolic tumor volume (MTV) with different defined thresholds. RESULTS High values of preoperative PET-SUVmax of the primary tumor were associated with squamous cell carcinoma (p < 0.0001) and with larger tumors (p < 0.0001). Increased preoperative C-reactive protein levels (<1mg/dL) correlated significantly with high preoperative PET-SUVmax values (p < 0.0001). No significant relationship between PET-SUVmax and lactate dehydrogenase activity (p = 0.6818), white blood cell count (p = 0.7681), gender (p = 0.1115) or age (p = 0.9284) was observed. Nodal upstaging rate was 14.0 % with 8.8 % N1 and 5.3 % N2 upstaging. Tumor size (p = 0.0468) and number of removed lymph nodes (p = 0.0461) were significant predictors of nodal upstaging but no significant association was found with histology or PET parameters. Of note, increased MTV - regardless of the threshold - tended to associate with nodal upstaging. CONCLUSION Early-stage lung cancer patients with squamous histology and T2 tumors presented increased preoperative PET-SUVmax values. Nevertheless, beyond tumor size and number of removed lymph nodes neither SUVmax nor metabolic PET parameters MTV and TLG were significant predictors of nodal upstaging.
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Affiliation(s)
- Özlem Okumus
- Department of Thoracic Surgery, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Khaled Mardanzai
- Department of Thoracic Surgery, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Till Plönes
- Department of Thoracic Surgery, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Dirk Theegarten
- Department of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kaid Darwiche
- Department of Pneumology, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Martin Schuler
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Thoracic Oncology, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Department of Radiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Hubertus Hautzel
- Department of Nuclear Medicine, Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Ken Hermann
- Department of Nuclear Medicine, Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Martin Stuschke
- German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany; Department of Radiation Oncology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Balazs Hegedus
- Department of Thoracic Surgery, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany
| | - Clemens Aigner
- Department of Thoracic Surgery, University Medicine Essen - Ruhrlandklinik, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany.
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FDG-PET/CT tumor to liver SUV ratio (TLR), tumor SUVmax, and tumor size: can this help in differentiating squamous cell carcinoma from adenocarcinoma of the lung? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00782-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
PET/CT plays an essential role in the diagnosis, staging, and follow-up of lung cancer. We aimed to assess the ability of PET/CT to differentiate between adenocarcinomas (AC) and squamous cell carcinomas (SCC) of the lung using tumor size, tumor maximum standardized uptake value (SUVmax), lymph nodes SUVmax, and tumor to liver SUV ratio (TLR).
Results
A total of 60 patients pathologically proved to have non-small cell lung cancer either AC or SCC were retrospectively evaluated. The mean tumor size, SUVmax of the tumor, and TLR were significantly higher in SCC lesions compared to AC lesions. The mean SCC tumoral size was 7.96 ± 2.18 cm compared to 5.66 ± 2.57 cm in AC lesions (P = 0.008). The mean tumor SUVmax in SCC lesions was 18.95 ± 8.3 compared to 12.4 ± 7.55 in AC lesions (P = 0.04). While the mean TLR of SCC lesions was 10.32 ± 4.03 compared to 7.36 ± 4.61 in AC lesions (P = 0.028). All three parameters showed the same sensitivity (75%), while TLR showed the highest specificity (77.78%) followed by tumor size (76.47%) and then SUVmax of the tumor (72.22%).
Conclusions
SCC of the lung has a higher mean tumor size, SUVmax of the tumor, and TLR as compared to AC which can be helpful tools in differentiation between them using PET/CT.
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Luo Y, Jiang H, Meng N, Huang Z, Li Z, Feng P, Fang T, Fu F, Yuan J, Wang Z, Yang Y, Wang M. A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types. Front Oncol 2022; 12:907860. [PMID: 35936757 PMCID: PMC9351313 DOI: 10.3389/fonc.2022.907860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression. Methods A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax. Results The ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively). Conclusion The parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.
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Affiliation(s)
- Yu Luo
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Han Jiang
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ziqiang Li
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ting Fang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
- *Correspondence: Meiyun Wang,
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Comparison of Lung Cancer Aggressiveness in Patients Who Never Smoked Compared to Those Who Smoked. Lung Cancer 2022; 171:90-96. [DOI: 10.1016/j.lungcan.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/27/2022] [Accepted: 07/02/2022] [Indexed: 11/22/2022]
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Liberini V, Pizzuto DA, Messerli M, Orita E, Grünig H, Maurer A, Mader C, Husmann L, Deandreis D, Kotasidis F, Trinckauf J, Curioni A, Opitz I, Winklhofer S, Huellner MW. BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients. J Digit Imaging 2022; 35:581-593. [PMID: 35212859 PMCID: PMC9156589 DOI: 10.1007/s10278-021-00570-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 07/10/2021] [Accepted: 12/13/2021] [Indexed: 12/15/2022] Open
Abstract
The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen's kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases' SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.
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Affiliation(s)
- Virginia Liberini
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
- Department of Medical Science, Unit of Nuclear Medicine, University of Turin, Turin, Italy.
- Nuclear Medicine Department, S. Croce E Carle Hospital, Cuneo, Italy.
| | - Daniele A Pizzuto
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Erika Orita
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
- Department of Radiology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Hannes Grünig
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Cäcilia Mader
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Lars Husmann
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Désirée Deandreis
- Department of Medical Science, Unit of Nuclear Medicine, University of Turin, Turin, Italy
| | | | - Josey Trinckauf
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Alessandra Curioni
- Department of Medical Oncology and Hematology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Isabelle Opitz
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Sebastian Winklhofer
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
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Schwaninger DR, Hüllner M, Bichsel D, Giacomelli-Hiestand B, Stutzmann NS, Balermpas P, Valdec S, Stadlinger B. FDG-PET/CT for oral focus assessment in head and neck cancer patients. Clin Oral Investig 2022; 26:4407-4418. [PMID: 35254526 PMCID: PMC9203386 DOI: 10.1007/s00784-022-04403-2] [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: 05/05/2021] [Accepted: 02/06/2022] [Indexed: 01/15/2023]
Abstract
Objectives To compare oral and maxillo-mandibular inflammatory foci on standard oral radiographs (OPT, periapical radiograph) with available fluorine-18-labelled fluoro-2-deoxy-D-glucose-positron emission tomography/computed tomography (FDG-PET/CT) data and to discuss whether additional metabolic information derived from FDG-PET/CT can support oral care specialists when performing oral focus examinations. Materials and methods Data from 23 patients with head and neck cancer who underwent FDG-PET/CT and panoramic and periapical radiography in close succession before first-line radiotherapy and/or chemotherapy were included in this exploratory retrospective study. Periapical lesions and marginal periodontal inflammation on FDG-PET/CT scans and standard oral radiographs were analysed and compared with regard to metabolic activity on FDG-PET/CT in comparison to recorded clinical symptoms and radiological scores. Additionally, inflammatory maxillo-mandibular pathologies were analysed using FDG-PET/CT. Results The maximum standardised uptake value (SUVmax) in FDG-avid marginal periodontal sites could not be conclusively associated with the radiologically recorded severity of marginal bone loss, but a potential positive correlation was identified. No association was found either between the metabolic activity of periapical lesions and their extent, as recorded on standard oral radiographs, or regarding clinical symptoms (percussion test). Most maxillo-mandibular pathologies did not show increased FDG uptake. Conclusions FDG-PET/CT provided additional metabolic information that can help clinicians identify lesions with increased inflammatory activity. The incorporation of available oral FDG-PET/CT findings into the primary oral focus assessment may allow for more accurate oral focus treatment. Clinical relevance FDG-PET/CT provides valuable metabolic information for oral care specialists. The detection of inflammatory oral processes using FDG-PET/CT facilitates treatment.
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Affiliation(s)
- Dominic Raphael Schwaninger
- Clinic of Cranio-Maxillofacial and Oral Surgery, Center of Dental Medicine, University of Zurich, Plattenstrasse 11, 8032, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich/University of Zurich, Zurich, Switzerland
| | - Dominique Bichsel
- Clinic of Cranio-Maxillofacial and Oral Surgery, Center of Dental Medicine, University of Zurich, Plattenstrasse 11, 8032, Zurich, Switzerland
| | - Barbara Giacomelli-Hiestand
- Clinic of Cranio-Maxillofacial and Oral Surgery, Center of Dental Medicine, University of Zurich, Plattenstrasse 11, 8032, Zurich, Switzerland
| | | | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Silvio Valdec
- Clinic of Cranio-Maxillofacial and Oral Surgery, Center of Dental Medicine, University of Zurich, Plattenstrasse 11, 8032, Zurich, Switzerland
| | - Bernd Stadlinger
- Clinic of Cranio-Maxillofacial and Oral Surgery, Center of Dental Medicine, University of Zurich, Plattenstrasse 11, 8032, Zurich, Switzerland.
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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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Guo H, Xu K, Duan G, Wen L, He Y. Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine. Ann Nucl Med 2022; 36:1-14. [PMID: 34727331 DOI: 10.1007/s12149-021-01683-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/30/2021] [Indexed: 12/19/2022]
Abstract
With a 5-year overall survival of approximately 20%, lung cancer has always been the number one cancer-specific killer all over the world. As a fusion of positron emission computed tomography (PET) and computed tomography (CT), PET/CT has revolutionized cancer imaging over the past 20 years. In this review, we focused on the optimization of the function of 18F-flurodeoxyglucose (FDG)-PET/CT in diagnosis, prognostic prediction and therapy management of lung cancers by computer programs. FDG-PET/CT has demonstrated a surprising role in development of therapeutic biomarkers, prediction of therapeutic responses and long-term survival, which could be conducive to solving existing dilemmas. Meanwhile, novel tracers and optimized procedures are also developed to control the quality and improve the effect of PET/CT. With the continuous development of some new imaging agents and their clinical applications, application value of PET/CT has broad prospects in this area.
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Affiliation(s)
- Haoyue Guo
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
- School of Medicine, Tongji University, No. 1239 Siping Road, Shanghai, 200092, China
| | - Kandi Xu
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
- School of Medicine, Tongji University, No. 1239 Siping Road, Shanghai, 200092, China
| | - Guangxin Duan
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Ling Wen
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China.
- School of Medicine, Tongji University, No. 1239 Siping Road, Shanghai, 200092, China.
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12
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Horn KP, Thomas HMT, Vesselle HJ, Kinahan PE, Miyaoka RS, Rengan R, Zeng J, Bowen SR. Reliability of Quantitative 18F-FDG PET/CT Imaging Biomarkers for Classifying Early Response to Chemoradiotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer. Clin Nucl Med 2021; 46:861-871. [PMID: 34172602 PMCID: PMC8490284 DOI: 10.1097/rlu.0000000000003774] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF THE REPORT We evaluated the reliability of 18F-FDG PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy for locally advanced non-small cell lung cancer. PATIENTS AND METHODS Thirty-one patients with unresectable locally advanced non-small cell lung cancer were prospectively enrolled on a phase 2 trial (NCT02773238) and underwent 18F-FDG PET on GE Discovery STE (DSTE) or GE Discovery MI (DMI) PET/CT systems at baseline and during the third week external beam radiation therapy regimens. All PET scans were reconstructed using OSEM; GE-DMI scans were also reconstructed with BSREM-TOF (block sequential regularized expectation maximization reconstruction algorithm incorporating time of flight). Primary tumors were contoured by 3 observers using semiautomatic gradient-based segmentation. SUVmax, SUVmean, SUVpeak, MTV (metabolic tumor volume), and total lesion glycolysis were correlated with midtherapy multidisciplinary clinical response assessment. Dice similarity of contours and response classification areas under the curve were evaluated across observers, scanners, and reconstruction algorithms. LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data. RESULTS Interobserver variability of PET contours was low for both OSEM and BSREM-TOF reconstructions; intraobserver variability between reconstructions was slightly higher. ΔSUVpeak was the most robust response predictor across observers and image reconstructions. LASSO models consistently selected ΔSUVpeak and ΔMTV as response predictors. Response classification models achieved high cross-validated performance on the DSTE cohort and more variable testing performance on the DMI cohort. CONCLUSIONS The variability FDG PET lesion contours and imaging biomarkers was relatively low across observers, scanners, and reconstructions. Objective midtreatment PET response assessment may lead to improved precision of biologically adaptive radiation therapy.
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Affiliation(s)
- Kevin P. Horn
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Hannah M. T. Thomas
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Hubert J. Vesselle
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Paul E. Kinahan
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Robert S. Miyaoka
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Jing Zeng
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Stephen R. Bowen
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
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13
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Fang T, Meng N, Feng P, Huang Z, Li Z, Fu F, Yuan J, Yang Y, Liu H, Roberts N, Wang M. A Comparative Study of Amide Proton Transfer Weighted Imaging and Intravoxel Incoherent Motion MRI Techniques Versus (18) F-FDG PET to Distinguish Solitary Pulmonary Lesions and Their Subtypes. J Magn Reson Imaging 2021; 55:1376-1390. [PMID: 34723413 DOI: 10.1002/jmri.27977] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Amide proton transfer weighted imaging (APTw), intravoxel incoherent motion (IVIM), and positron emission tomography (PET) imaging all have the potential to characterize solitary pulmonary lesions (SPLs). PURPOSE To compare APTw and IVIM with PET imaging for distinguishing between benign and malignant SPLs and their subtypes. STUDY TYPE Prospective. POPULATION Ninety-five patients, 78 with malignant SPLs (including 48 with adenocarcinoma [AC] and 17 with squamous cell carcinoma [SCC]), and 17 with benign SPLs. FIELD STRENGTH/SEQUENCE Fast spin-echo (FSE) T2WI, FSE APTw and echo-planar imaging IVIM, MR-base attenuation correction (MRAC), and PET imaging on a 3-T whole-body PET/MR system. ASSESSMENT The magnetization transfer ratio asymmetry (MTRasym) at 3.5 ppm, diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), and the maximum standardized uptake value (SUVmax) were analyzed. STATISTICAL TESTS Individual sample t-test, Delong test, Pearson's correlation analysis, and area under the receiver operating characteristic curve (AUC). P < 0.05 indicated statistical significance. RESULTS The MTRasym and SUVmax were significantly higher, and D was significantly lower in the malignant group (3.3 ± 2.6 [%], 7.8 ± 5, and 1.2 ± 0.3 [×10-3 mm2 /second]) compared to the benign group (-0.3 ± 1.6 [%], 3.1 ± 3.8, and 1.6 ± 0.3 [×10-3 mm2 /second]). The MTRasym and D were significantly lower, and SUVmax was significantly higher in the SCC group (0.8 ± 1.0 [%], 1.0 ± 0.2 [×10-3 mm2 /second] than in the AC group (4.1 ± 2.6 [%], 1.3 ± 0.3 [×10-3 mm2 /second], 6.7 ± 4.6). Besides, the combination (AUC = 0.964) of these three methods showed higher diagnostic efficacy than any individual method (AUC = 0.917, 0.851, 0.82, respectively) in identifying malignant and benign SPLs. However, APTw showed better diagnostic efficacy than the combination of three methods or PET imaging alone in distinguishing SCC and AC groups (AUC = 0.934, 0.781, 0.725, respectively). DATA CONCLUSION APTw, IVIM, and PET imaging are all effective methods to distinguish benign and malignant SPLs and their subtypes. APTw is potentially more capable than PET imaging of distinguishing lung SCC from AC. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ting Fang
- Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou, China.,Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou, China.,Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Pengyang Feng
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China.,Department of Medical Imaging, Henan University People's Hospital, Zhengzhou, China
| | - Zhun Huang
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China.,Department of Medical Imaging, Henan University People's Hospital, Zhengzhou, China
| | - Ziqiang Li
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China.,Department of Medical Imaging, Xinxiang Medical University, Xinxiang, China
| | - Fangfang Fu
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, UIH Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, UIH Group, Beijing, China
| | - Hui Liu
- UIH America, Inc, Houston, Texas, USA
| | - Neil Roberts
- Clinical Research Imaging Centre, School of Clinical Sciences and Community Health, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou, China.,Department of Medical Imaging, Henan University People's Hospital, Zhengzhou, China.,Department of Medical Imaging, Xinxiang Medical University, Xinxiang, China
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14
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Tatsumi M, Soeda F, Kamiya T, Ueda J, Katayama D, Matsunaga K, Watabe T, Kato H, Tomiyama N. Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations. Front Oncol 2021; 11:707023. [PMID: 34485143 PMCID: PMC8415497 DOI: 10.3389/fonc.2021.707023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 07/29/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose This study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximization (OSEM) reconstruction algorithm. Metabolic tumor volume (MTV) and texture features (TFs), as well as SUV-related metrics, were evaluated to clarify the BPL effects on quantification. Materials and Methods A total of 153 upper abdominal lesions (82 liver metastatic and 71 pancreatic cancers) were included in this study. FDG PET/CT images were acquired with a GE Discovery 710 scanner equipped with a time-of-flight system. Images were reconstructed using OSEM and BPL (beta 700) algorithms. In 58 lesions <1.5 cm in greatest diameter (small-lesion group), visual image quality of each lesion was evaluated using a four-point scale. SUVmax was obtained for quantitative metrics. Visual scores and SUVmax were compared between OSEM and BPL images. In 95 lesions >2.0 cm in greatest diameter (larger-lesion group), SUVmax, SUVpeak, MTV, and six TFs were compared between OSEM and BPL images. In addition to the size-based analyses, an increase of SUVmax with BPL was evaluated according to the original SUVmax in OSEM images. Results In the small-lesion group, both visual score and SUVmax were significantly higher in the BPL than OSEM images. The increase in visual score was observed in 20 (34%) of all 58 lesions. In the larger-lesion group, no statistical difference was observed in SUVmax, SUVpeak, or MTV between OSEM and BPL images. BPL increased high gray-level zone emphasis and decreased low gray-level zone emphasis among six TFs compared to OSEM with statistical significance. No statistical differences were observed in other TFs. SUVmax-based analysis demonstrated that BPL increased and decreased SUVmax in lesions with low (<5) and high (>10) SUVmax in original OSEM images, respectively. Conclusion This study demonstrated that BPL improved conspicuity of small or low-count upper abdominal malignant lesions in clinical FDG PET/CT examinations. Only two TFs represented significant differences between OSEM and BPL images of all quantitative metrics in larger lesions.
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Affiliation(s)
- Mitsuaki Tatsumi
- Department of Radiology, Osaka University Hospital, Suita, Japan.,Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Fumihiko Soeda
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Takashi Kamiya
- Department of Medical Technology, Osaka University Hospital, Suita, Japan
| | - Junpei Ueda
- Department of Medical Technology, Osaka University Hospital, Suita, Japan
| | - Daisuke Katayama
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Keiko Matsunaga
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tadashi Watabe
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Hiroki Kato
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Japan
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15
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Wong JM, Puri T, Siddique MM, Frost ML, Moore AEB, Blake GM, Fogelman I. Comparison of ordered-subset expectation maximization and filtered back projection reconstruction based on quantitative outcome from dynamic [18F]NaF PET images. Nucl Med Commun 2021; 42:699-706. [PMID: 33625180 DOI: 10.1097/mnm.0000000000001393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
[18F]NaF PET imaging is a useful tool for measuring regional bone metabolism. However, due to tracer in urine, [18F]NaF PET images of the hip reconstructed using filtered back projection (FBP) frequently show streaking artifacts in slices through the bladder leading to noisy time-activity curves unsuitable for quantification. This study compares differences between quantitative outcomes at the hip derived from images reconstructed using the FBP and ordered-subset expectation maximization (OSEM) methods. Dynamic [18F]NaF PET data at the hip for four postmenopausal women were reconstructed using FBP and nine variations of the OSEM algorithm (all combinations of 1, 5, 15 iterations and 10, 15, 21 subsets). Seven volumes of interest were placed in the hip. Bone metabolism was measured using standardized uptake values, Patlak analysis (Ki-PAT) and Hawkins model Ki-4k. Percentage differences between the standardized uptake values and Ki values from FBP and OSEM images were assessed. OSEM images appeared visually smoother and without the streaking artifacts seen with FBP. However, due to loss of counts, they failed to recover the quantitative values in VOIs close to the bladder, including the femoral head and femoral neck. This was consistent for all quantification methods. Volumes of interest farther from the bladder or larger and receiving greater counts showed good convergence with 5 iterations and 21 subsets. For VOIs close to the bladder, including the femoral neck and femoral head, 15 iterations and 10, 15 or 21 subsets were not enough to obtain OSEM images suitable for measuring bone metabolism and showed no improvement compared to FBP.
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Affiliation(s)
- James M Wong
- Department of Anaesthesia, Royal Berkshire Hospitals NHS Foundation Trust, Reading
| | - Tanuj Puri
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, St Thomas' Hospital, London
| | | | - Michelle L Frost
- Clinical Trials & Statistics Unit (ICR-CTSU), Institute of Cancer Research, Sutton
| | - Amelia E B Moore
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, St Thomas' Hospital, London
| | - Glen M Blake
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, St Thomas' Hospital, London
| | - Ignac Fogelman
- Osteoporosis Research Unit, Guy's & St Thomas' Hospital, King's College London, London, UK
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16
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Liberini V, Messerli M, Husmann L, Kudura K, Grünig H, Maurer A, Skawran S, Orita E, Pizzuto DA, Deandreis D, Dummer R, Mangana J, Mihic-Probst D, Rupp N, Huellner MW. Improved detection of in-transit metastases of malignant melanoma with BSREM reconstruction in digital [ 18F]FDG PET/CT. Eur Radiol 2021; 31:8011-8020. [PMID: 33768288 PMCID: PMC8452544 DOI: 10.1007/s00330-021-07852-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/01/2021] [Accepted: 03/04/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To compare block sequential regularized expectation maximization (BSREM) and ordered subset expectation maximization (OSEM) for the detection of in-transit metastasis (ITM) of malignant melanoma in digital [18F]FDG PET/CT. METHODS We retrospectively analyzed a cohort of 100 [18F]FDG PET/CT scans of melanoma patients with ITM, performed between May 2017 and January 2020. PET images were reconstructed with both OSEM and BSREM algorithms. SUVmax, target-to-background ratio (TBR), and metabolic tumor volume (MTV) were recorded for each ITM. Differences in PET parameters were analyzed with the Wilcoxon signed-rank test. Differences in image quality for different reconstructions were tested using the Man-Whitney U test. RESULTS BSREM reconstruction led to the detection of 287 ITM (39% more than OSEM). PET parameters of ITM were significantly different between BSREM and OSEM reconstructions (p < 0.001). SUVmax and TBR were higher (76.5% and 77.7%, respectively) and MTV lower (49.5%) on BSREM. ITM missed with OSEM had significantly lower SUVmax (mean 2.03 vs. 3.84) and TBR (mean 1.18 vs. 2.22) and higher MTV (mean 2.92 vs. 1.01) on OSEM compared to BSREM (all p < 0.001). CONCLUSIONS BSREM detects significantly more ITM than OSEM, owing to higher SUVmax, higher TBR, and less blurring. BSREM is particularly helpful in small and less avid lesions, which are more often missed with OSEM. KEY POINTS • In melanoma patients, [18F]FDG PET/CT helps to detect in-transit metastases (ITM), and their detection is improved by using BSREM instead of OSEM reconstruction. • BSREM is particularly useful in small lesions.
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Affiliation(s)
- Virginia Liberini
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland.
- Department of Nuclear Medicine, Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy.
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
| | - Lars Husmann
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
| | - Ken Kudura
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
| | - Hannes Grünig
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
| | - Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
| | - Erika Orita
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
- Department of Radiology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Daniele A Pizzuto
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - Désirée Deandreis
- Department of Nuclear Medicine, Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Reinhard Dummer
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Joanna Mangana
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniela Mihic-Probst
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Niels Rupp
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, CH-8091, Zürich, Switzerland
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17
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Wu Z, Guo B, Huang B, Zhao B, Qin Z, Hao X, Liang M, Xie J, Li S. Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography? J Appl Clin Med Phys 2021; 22:224-233. [PMID: 33683004 PMCID: PMC7984479 DOI: 10.1002/acm2.13129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 09/13/2020] [Accepted: 11/24/2020] [Indexed: 11/27/2022] Open
Abstract
Purpose This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)‐based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules. Methods A National Electrical Manufacturers Association (NEMA) image‐quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub‐centimeter (<10 mm, n = 7) group and medium size (10–30 mm, n = 16) group. Signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers. Results With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub‐centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion‐size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub‐centimeter group was no significantly different among different beta values (P = 0.830). Conclusion Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.
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Affiliation(s)
- Zhifang Wu
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
- Molecular Imaging Precision Medical Collaborative Innovation CenterShanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Binwei Guo
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Bin Huang
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Bin Zhao
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Zhixing Qin
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Xinzhong Hao
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Meng Liang
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Jun Xie
- Department of Biochemistry and Molecular BiologyShanxi Medical UniversityTaiyuanShanxiP.R. China
| | - Sijin Li
- Department of Nuclear MedicineFirst Hospital of Shanxi Medical UniversityTaiyuanShanxiP.R. China
- Molecular Imaging Precision Medical Collaborative Innovation CenterShanxi Medical UniversityTaiyuanShanxiP.R. China
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18
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Liberini V, De Santi B, Rampado O, Gallio E, Dionisi B, Ceci F, Polverari G, Thuillier P, Molinari F, Deandreis D. Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor. EJNMMI Phys 2021; 8:21. [PMID: 33638729 PMCID: PMC7914329 DOI: 10.1186/s40658-021-00367-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 02/09/2021] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. METHODS Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs' correlation with volume and SUVmax was analyzed by calculating Pearson's correlation coefficients. RESULTS DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax. CONCLUSIONS RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.
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Affiliation(s)
- Virginia Liberini
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy.
| | - Bruno De Santi
- Biolab, Department of Electronics and Telecomunications, Politecnico di Torino, Turin, Italy
| | - Osvaldo Rampado
- Medical Physics Unit, AOU Città della Salute e della Scienza, Turin, Italy
| | - Elena Gallio
- Medical Physics Unit, AOU Città della Salute e della Scienza, Turin, Italy
| | - Beatrice Dionisi
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
| | - Francesco Ceci
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
| | - Giulia Polverari
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
| | - Philippe Thuillier
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
- Department of Endocrinology, University Hospital of Brest, Politecnico di Torino Brest, Turin, France
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecomunications, Politecnico di Torino, Turin, Italy
| | - Désirée Deandreis
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
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Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS). Sci Rep 2021; 11:2273. [PMID: 33500455 PMCID: PMC7838183 DOI: 10.1038/s41598-020-80855-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/29/2020] [Indexed: 12/17/2022] Open
Abstract
To evaluate whether quantitative PET parameters of motion-corrected 68Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent 68Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM1000). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM1000 reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM1000 reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM1000/OSEM + DDG/BSREM1000 + DDG, respectively. BSREM1000 + DDG can accurately differentiate pNET from accessory spleen. Both BSREM1000 and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.
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Caribé PRRV, Koole M, D’Asseler Y, Van Den Broeck B, Vandenberghe S. Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner. EJNMMI Phys 2019; 6:22. [PMID: 31823084 PMCID: PMC6904688 DOI: 10.1186/s40658-019-0264-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 11/25/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT. METHODS The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300-3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region. RESULTS Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2-4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast. CONCLUSION The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2-4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.
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Affiliation(s)
- Paulo R. R. V. Caribé
- Medical Image and Signal Processing – MEDISIP, Ghent University, Corneel Heymanslaan 10, 9000 Gent, Belgium
| | - M. Koole
- Division of Nuclear Medicine and Molecular Imaging, UZ/KU, Herestraat 49, B-3000 Leuven, Belgium
| | - Yves D’Asseler
- Department of Nuclear Medicine, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Gent, Belgium
| | - B. Van Den Broeck
- Department of Nuclear Medicine, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Gent, Belgium
| | - S. Vandenberghe
- Medical Image and Signal Processing – MEDISIP, Ghent University, Corneel Heymanslaan 10, 9000 Gent, Belgium
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Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S. CT radiomics and PET radiomics: ready for clinical implementation? THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:355-370. [PMID: 31527578 DOI: 10.23736/s1824-4785.19.03192-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest. EVIDENCE ACQUISITION Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications. EVIDENCE SYNTHESIS We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling. CONCLUSIONS Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland -
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert S Gabrys
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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