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Laskov V, Rothbauer D, Malikova H. Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan. PLoS One 2024; 19:e0301978. [PMID: 38603674 PMCID: PMC11008844 DOI: 10.1371/journal.pone.0301978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson's disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p <0.01). Assessment of ICC and COV within CT attenuation correction (CTAC) and non-attenuation correction (NAC) groups and within particular feature classes showed an excellent reproducibility rate (ICC > 0.9, p < 0.01), except for an intensity-based NAC group, where radiomic features showed a good repeatability rate (ICC = 0.893, p <0.01). By our results, CTAC becomes the main threat to feature stability. However, many radiomic features were sensitive to the selected reconstruction algorithm irrespectively to the attenuation correction. Radiomic features extracted from DaT-SPECT showed moderate to excellent reproducibility rates. These results make them suitable for clinical practice and human studies, but awareness of feature selection should be held, as some radiomic features are more robust than others.
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Affiliation(s)
- Viktor Laskov
- Department of Radiology and Nuclear Medicine, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic
| | - David Rothbauer
- Department of Radiology and Nuclear Medicine, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic
| | - Hana Malikova
- Department of Radiology and Nuclear Medicine, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic
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Fooladi M, Soleymani Y, Rahmim A, Farzanefar S, Aghahosseini F, Seyyedi N, Sh Zadeh P. Impact of different reconstruction algorithms and setting parameters on radiomics features of PSMA PET images: A preliminary study. Eur J Radiol 2024; 172:111349. [PMID: 38310673 DOI: 10.1016/j.ejrad.2024.111349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/09/2024] [Accepted: 01/25/2024] [Indexed: 02/06/2024]
Abstract
PURPOSE Radiomics analysis of oncologic positron emission tomography (PET) images is an area of significant activity and potential. The reproducibility of radiomics features is an important consideration for routine clinical use. This preliminary study investigates the robustness of radiomics features in PSMA-PET images across penalized-likelihood (Q.Clear) and standard ordered subset expectation maximization (OSEM) reconstruction algorithms and their setting parameters in phantom and prostate cancer (PCa) patients. METHOD A NEMA image quality (IQ) phantom and 8 PCa patients were selected for phantom and patient analyses, respectively. PET images were reconstructed using Q.Clear (reconstruction β-value: 100-700, at intervals of 100 for both NEMA IQ phantom and patients) and OSEM (duration: 15sec, 30sec, 1 min, 2 min, 3 min, 4 min and 5 min for NEMA phantom and duration: 30 s, 1 min and 2 min for patients) reconstruction methods. Subsequently, 129 radiomic features were extracted from the reconstructed images. The coefficient of variation (COV) of each feature across reconstruction methods and their parameters was calculated to determine feature robustness. RESULTS The extracted radiomics features showed a different range of variability, depending on the reconstruction algorithms and setting parameters. Specifically, 23.0 % and 53.5 % of features were found as robust against β-value variations in Q.Clear and different durations in OSEM reconstruction algorithms, respectively. Taking into account the two algorithms and their parameters, eleven features (8.5 %) showed COV ≤ 5 % and eighteen (14 %) showed 5 % 20 %. The mean COVs of the extracted radiomics features were significantly different between the two reconstruction methods (p < 0.05) except for the phantom morphological features. CONCLUSIONS All radiomics features were affected by reconstruction methods and parameters, but features with small or very small variations are considered better candidates for reproducible quantification of either tumor or metastatic tissues in clinical trials. There is a need for standardization before the implementation of PET radiomics in clinical practice.
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Affiliation(s)
- Masoomeh Fooladi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Yunus Soleymani
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - Saeed Farzanefar
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Farahnaz Aghahosseini
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Negisa Seyyedi
- Nursing and Midwifery Care Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Peyman Sh Zadeh
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran; Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
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Choi JH, Choi JY, Woo SK, Moon JE, Lim CH, Park SB, Seo S, Ahn YC, Ahn MJ, Moon SH, Park JM. Prognostic Value of Radiomic Analysis Using Pre- and Post-Treatment 18F-FDG-PET/CT in Patients with Laryngeal Cancer and Hypopharyngeal Cancer. J Pers Med 2024; 14:71. [PMID: 38248772 PMCID: PMC10817325 DOI: 10.3390/jpm14010071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/20/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND The prognostic value of conducting 18F-FDG PET/CT imaging has yielded different results in patients with laryngeal cancer and hypopharyngeal cancer, but these results are controversial, and there is a lack of dedicated studies on each type of cancer. This study aimed to evaluate whether combining radiomic analysis of pre- and post-treatment 18F-FDG PET/CT imaging features and clinical parameters has additional prognostic value in patients with laryngeal cancer and hypopharyngeal cancer. METHODS From 2008 to 2016, data on patients diagnosed with cancer of the larynx and hypopharynx were retrospectively collected. The patients underwent pre- and post-treatment 18F-FDG PET/CT imaging. The values of ΔPre-Post PET were measured from the texture features. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most predictive features to formulate a Rad-score for both progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curve analysis and Cox regression were employed to assess PFS and OS. Then, the concordance index (C-index) and calibration plot were used to evaluate the performance of the radiomics nomogram. RESULTS Study data were collected for a total of 91 patients. The mean follow-up period was 71.5 mo. (8.4-147.3). The Rad-score was formulated based on the texture parameters and was significantly associated with both PFS (p = 0.024) and OS (p = 0.009). When predicting PFS, only the Rad-score demonstrated a significant association (HR 2.1509, 95% CI [1.100-4.207], p = 0.025). On the other hand, age (HR 1.116, 95% CI [1.041-1.197], p = 0.002) and Rad-score (HR 33.885, 95% CI [2.891-397.175], p = 0.005) exhibited associations with OS. The Rad-score value showed good discrimination when it was combined with clinical parameters in both PFS (C-index 0.802-0.889) and OS (C-index 0.860-0.958). The calibration plots also showed a good agreement between the observed and predicted survival probabilities. CONCLUSIONS Combining clinical parameters with radiomics analysis of pre- and post-treatment 18F-FDG PET/CT parameters in patients with laryngeal cancer and hypopharyngeal cancer might have additional prognostic value.
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Affiliation(s)
- Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Ji Eun Moon
- Department of Biostatistics, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Seongho Seo
- Department of Electronic Engineering, Pai Chai University, Daejeon 35345, Republic of Korea
| | - Yong Chan Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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Hou C, Liu XY, Du Y, Cheng LG, Liu LP, Nie F, Zhang W, He W. Radiomics in Carotid Plaque: A Systematic Review and Radiomics Quality Score Assessment. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2437-2445. [PMID: 37718124 DOI: 10.1016/j.ultrasmedbio.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/09/2023] [Accepted: 06/08/2023] [Indexed: 09/19/2023]
Abstract
Imaging modalities provide information on plaque morphology and vulnerability; however, they are operator dependent and miss a great deal of microscopic information. Recently, many radiomics models for carotid plaque that identify unstable plaques and predict cardiovascular outcomes have been proposed. This systematic review was aimed at assessing whether radiomics is a reliable and reproducible method for the clinical prediction of carotid plaque. A systematic search was conducted to identify studies published in PubMed and Cochrane library from January 1, 2001, to September 30, 2022. Both retrospective and prospective studies that developed and/or validated machine learning models based on radiomics data to classify or predict carotid plaques were included. The general characteristics of each included study were selected, and the methodological quality of radiomics reports and risk of bias were evaluated using the radiomics quality score (RQS) tool and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. Two investigators independently reviewed each study, and the consensus data were used for analysis. A total of 2429 patients from 16 studies were included. The mean area under the curve of radiomics models for diagnostic or predictive performance of the included studies was 0.88 ± 0.02, with a range of 0.741-0.989. The mean RQS was 9.25 (standard deviation: 6.04), representing 25.7% of the possible maximum value of 36, whereas the lowest point was -2, and the highest score was 22. Radiomics models have revealed additional information on patients with carotid plaque, but with respect to methodological quality, radiomics reports are still in their infancy, and many hurdles need to be overcome.
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Affiliation(s)
- Chao Hou
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China; Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin-Yao Liu
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yue Du
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ling-Gang Cheng
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lu-Ping Liu
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China
| | - Fang Nie
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China
| | - Wei Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wen He
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China; Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Ashrafinia S, Dalaie P, Schindler TH, Pomper MG, Rahmim A. Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification. Cureus 2023; 15:e43343. [PMID: 37700937 PMCID: PMC10493172 DOI: 10.7759/cureus.43343] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
PURPOSE Myocardial perfusion (MP) stress single-photon emission computed tomography (SPECT) is an established diagnostic test for patients suspected of coronary artery disease (CAD). Meanwhile, coronary artery calcification (CAC) scoring obtained from diagnostic CT is a highly sensitive test, offering incremental diagnostic information in identifying patients with significant CAD yet normal MP stress SPECT (MPSS) scans. However, after decades of wide utilization of MPSS, CAC is not commonly reimbursed (e.g. by the CMS), nor widely deployed in community settings. We studied the potential of complementary information deduced from the radiomics analysis of normal MPSS scans in predicting the CAC score. METHODS We collected data from 428 patients with normal (non-ischemic) MPSS (99mTc-sestamibi; consensus reading). A nuclear medicine physician verified iteratively reconstructed images (attenuation-corrected) to be free from fixed perfusion defects and artifactual attenuation. Three-dimensional images were automatically segmented into four regions of interest (ROIs), including myocardium and three vascular segments (left anterior descending [LAD]-left circumference [LCX]-right coronary artery [RCA]). We used our software package, standardized environment for radiomics analysis (SERA), to extract 487 radiomic features in compliance with the image biomarker standardization initiative (IBSI). Isotropic cubic voxels were discretized using fixed bin-number discretization (eight schemes). We first performed blind-to-outcome feature selection focusing on a priori usefulness, dynamic range, and redundancy of features. Subsequently, we performed univariate and multivariate machine learning analyses to predict CAC scores from i) selected radiomic features, ii) 10 clinical features, and iii) combined radiomics + clinical features. Univariate analysis invoked Spearman correlation with Benjamini-Hotchberg false-discovery correction. The multivariate analysis incorporated stepwise linear regression, where we randomly selected a 15% test set and divided the other 85% of data into 70% training and 30% validation sets. Training started from a constant (intercept) model, iteratively adding/removing features (stepwise regression), invoking the Akaike information criterion (AIC) to discourage overfitting. Validation was run similarly, except that the training output model was used as the initial model. We randomized training/validation sets 20 times, selecting the best model using log-likelihood for evaluation in the test set. Assessment in the test set was performed thoroughly by running the entire operation 50 times, subsequently employing Fisher's method to verify the significance of independent tests. RESULTS Unsupervised feature selection significantly reduced 8×487 features to 56. In univariate analysis, no feature survived the false-discovery rate (FDR) to directly correlate with CAC scores. Applying Fisher's method to the multivariate regression results demonstrated combining radiomics with the clinical features to enhance the significance of the prediction model across all cardiac segments. Conclusions: Our standardized and statistically robust multivariate analysis demonstrated significant prediction of the CAC score for all cardiac segments when combining MPSS radiomic features with clinical features, suggesting radiomics analysis can add diagnostic or prognostic value to standard MPSS for wide clinical usage.
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Affiliation(s)
- Saeed Ashrafinia
- Radiology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Pejman Dalaie
- Radiology, Johns Hopkins University School of Medicine, Baltimore, USA
| | | | - Martin G Pomper
- Radiology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Arman Rahmim
- Physics and Astronomy, University of British Columbia, Vancouver, CAN
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Yang Z, Gong J, Li J, Sun H, Pan Y, Zhao L. The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis. Int J Surg 2023; 109:2451-2466. [PMID: 37463039 PMCID: PMC10442126 DOI: 10.1097/js9.0000000000000441] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/01/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. MATERIALS AND METHODS PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. RESULTS Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. CONCLUSIONS Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.
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Affiliation(s)
- Zhi Yang
- Department of Radiation Oncology, Xijing Hospital
| | - Jie Gong
- Department of Radiation Oncology, Xijing Hospital
| | - Jie Li
- Department of Radiation Oncology, Xijing Hospital
| | - Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital
| | - Yanglin Pan
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Air Force Medical University, Xi’an, People’s Republic of China
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital
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Lim CH, Choi JY, Choi JH, Lee JH, Lee J, Lim CW, Kim Z, Woo SK, Park SB, Park JM. Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2023; 15:3842. [PMID: 37568658 PMCID: PMC10417050 DOI: 10.3390/cancers15153842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.
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Affiliation(s)
- Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Jun-Hee Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Jihyoun Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Cheol Wan Lim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Zisun Kim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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Chan KC, Perucho JAU, Subramaniam RM, Lee EYP. Utility of pre-treatment 18 F-fluorodeoxyglucose PET radiomic analysis in assessing nodal involvement in cervical cancer. Nucl Med Commun 2023; 44:375-380. [PMID: 36826394 DOI: 10.1097/mnm.0000000000001672] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
OBJECTIVE Intratumor heterogeneity has prognostic value in cervical cancer, which can be depicted on 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/computed tomography (PET/CT) and then quantitatively characterized by texture features. This study aimed to evaluate the discriminative performance and predictive ability of the texture features in determining lymph node involvement in cervical cancer. METHODS A total of 101 patients with newly diagnosed cervical cancer, who underwent pre-treatment whole-body 18 F-FDG PET/CT imaging were retrospectively recruited. Patients were categorized based on their nodal status. Thirty-five radiomic features together with the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary cervical tumors were extracted. Conventional indices were used to build logistic regression model and texture features were used to build random forest model. The performances for differentiating nodal status were assessed by receiver operating characteristic analysis. RESULTS Conventional PET indices were significantly higher in patients with nodal involvement compared to those without: SUVmax = 14.22 vs. 10.05; MTV = 57.02 vs. 28.73; TLG = 492.8 vs. 188.8 ( P < 0.05). Nineteen radiomic features describing regional heterogeneity were significantly different between nodal involvements. Area under the curves of the models with conventional indices and PET texture features for discriminating nodal status were 0.72 and 0.76, respectively. CONCLUSION PET-derived radiomic features had moderate performance in discriminating nodal involvement in cervical cancer; and they did not outperform model based on conventional indices.
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Affiliation(s)
- Kit Chi Chan
- Department of Radiotherapy, Hong Kong Sanatorium and Hospital
| | - Jose A U Perucho
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Rathan M Subramaniam
- Department of Medicine, University of Otago, Dunedin, New Zealand
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Elaine Y P Lee
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong
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Amrane K, Thuillier P, Bourhis D, Le Meur C, Quere C, Leclere JC, Ferec M, Jestin-Le Tallec V, Doucet L, Alemany P, Salaun PY, Metges JP, Schick U, Abgral R. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci Rep 2023; 13:5789. [PMID: 37031233 PMCID: PMC10082755 DOI: 10.1038/s41598-023-31587-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/14/2023] [Indexed: 04/10/2023] Open
Abstract
The main aim of this study was to evaluate the prognostic value of radiomic approach in pre-therapeutic 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET/CT) in a large cohort of patients with gastro-esophageal junction cancer (GEJC). This was a retrospective monocenter study including 97 consecutive patients with GEJC who underwent a pre-therapeutic FDG-PET and were followed up for 3 years. Standard first-order radiomic PET indices including SUVmax, SUVmean, SUVpeak, MTV and TLG and 32 textural features (TFs) were calculated using LIFEx software on PET imaging. Prognostic significance of these parameters was assessed in univariate and multivariate analysis. Relapse-free survival (RFS) and overall survival (OS) were respectively chosen as primary and secondary endpoints. An internal validation cohort was used by randomly drawing one-third of included patients. The main characteristics of this cohort were: median age of 65 years [41-88], sex ratio H/F = 83/14, 81.5% of patients with a histopathology of adenocarcinoma and 43.3% with a stage IV disease. The median follow-up was 28.5 months [4.2-108.5]. Seventy-seven (79.4%) patients had locoregional or distant progression or recurrence and 71 (73.2%) died. In univariate analysis, SUVmean, Histogram-Entropy and 2 TFs (GLCM-Homogeneity and GLCM-Energy) were significantly correlated with RFS and OS, as well as 2 others TFs (GLRLM-LRE and GLRLM-GLNU) with OS only. In multivariate analysis, Histogram-Entropy remained an independent prognostic factor of both RFS and OS whereas SUVmean was an independent prognostic factor of OS only. These results were partially confirmed in our internal validation cohort of 33 patients. Our results suggest that radiomic approach reveals independent prognostic factors for survival in patients with GEJC.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, France.
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | - Coline Le Meur
- Department of Oncology, University Hospital of Brest, Brest, France
| | - Chloe Quere
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Marc Ferec
- Department of Gastroenterology, Regional Hospital of Morlaix, Morlaix, France
| | | | - Laurent Doucet
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Pierre Alemany
- Department of Pathology, Ouestpathology Brest, Brest, France
| | - Pierre-Yves Salaun
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Ulrike Schick
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France.
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France.
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Hatt M. "Fuzzy" radiomics: the way forward for nuclear medicine imaging applications? Eur J Nucl Med Mol Imaging 2023; 50:1558-1559. [PMID: 36951992 DOI: 10.1007/s00259-023-06201-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Affiliation(s)
- Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
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11
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Takahashi N, Tanaka S, Umezawa R, Takanami K, Takeda K, Yamamoto T, Suzuki Y, Katsuta Y, Kadoya N, Jingu K. Development and validation of an [ 18F]FDG-PET/CT radiomic model for predicting progression-free survival for patients with stage II - III thoracic esophageal squamous cell carcinoma who are treated with definitive chemoradiotherapy. Acta Oncol 2023; 62:159-165. [PMID: 36794365 DOI: 10.1080/0284186x.2023.2178859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
BACKGROUND Radiomics is a method for extracting a large amount of information from images and used to predict treatment outcomes, side effects and diagnosis. In this study, we developed and validated a radiomic model of [18F]FDG-PET/CT for predicting progression-free survival (PFS) of definitive chemoradiotherapy (dCRT) for patients with esophageal cancer. MATERIAL AND METHODS Patients with stage II - III esophageal cancer who underwent [18F]FDG-PET/CT within 45 days before dCRT between 2005 and 2017 were included. Patients were randomly assigned to a training set (85 patients) and a validation set (45 patients). Radiomic parameters inside the area of standard uptake value ≥ 3 were calculated. The open-source software 3D slicer and Pyradiomics were used for segmentation and calculating radiomic parameters, respectively. Eight hundred sixty radiomic parameters and general information were investigated.In the training set, a radiomic model for PFS was made from the LASSO Cox regression model and Rad-score was calculated. In the validation set, the model was applied to Kaplan-Meier curves. The median value of Rad-score in the training set was used as a cutoff value in the validation set. JMP was used for statistical analysis. RStudio was used for the LASSO Cox regression model. p < 0.05 was defined as significant. RESULTS The median follow-up periods were 21.9 months for all patients and 63.4 months for survivors. The 5-year PFS rate was 24.0%. In the training set, the LASSO Cox regression model selects 6 parameters and made a model. The low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.019). In the validation set, the low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.040). CONCLUSIONS The [18F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.
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Affiliation(s)
- Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Kentaro Takanami
- Department of Radiology, Tohoku University Graduate School of Medicine
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Yu Suzuki
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
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12
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Mori M, Palumbo D, De Cobelli F, Fiorino C. Does radiomics play a role in the diagnosis, staging and re-staging of gastroesophageal junction adenocarcinoma? Updates Surg 2023; 75:273-279. [PMID: 36114920 DOI: 10.1007/s13304-022-01377-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/04/2022] [Indexed: 01/24/2023]
Abstract
Radiomics is an emerging field of investigation in medicine consisting in the extraction of quantitative features from conventional medical images and exploring their potentials in improving diagnosis, prognosis and outcome prediction after therapy. Clinical applications are still limited, mostly due to reproducibility and repeatability issues as well as to limited interpretability of predictive radiomic-based features/signatures. In the specific case of gastroesophageal junction (GEJ) adenocarcinoma, the expectancies are particularly high, mainly due to its increasing incidence and to the limited performance of conventional imaging techniques in assessing correct diagnosis and accurate pre-surgical tumor characterization. Accordingly, current literature was reviewed, emphasizing the methodological quality. In addition, papers were scored according to the Radiomic Quality Score (RQS), weighting more the clinical applicability and generalizability of the resulting models. According to the criteria of the search, only two papers were retained: the resulting technical quality was relatively high for both, while the corresponding RQS were 15 and 19 (on a scale of 31). Although the potentials of radiomics in the setting of GEJ adenocarcinoma are relevant, they remain largely unexplored, warranting an urgent need of high-quality, possibly prospective, multicenter studies.
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Affiliation(s)
- Martina Mori
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.,Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Diego Palumbo
- Department of Radiology, San Raffaele Scientific Institute, Milan, Italy.,School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology, San Raffaele Scientific Institute, Milan, Italy.,School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Fiorino
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy. .,Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers (Basel) 2023; 15:cancers15030932. [PMID: 36765889 PMCID: PMC9913076 DOI: 10.3390/cancers15030932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. METHODS 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. RESULTS There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. CONCLUSION Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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Yang L, Chang J, He X, Peng M, Zhang Y, Wu T, Xu P, Chu W, Gao C, Cao S, Kang S. PET/CT-based radiomics analysis may help to predict neoadjuvant chemotherapy outcomes in breast cancer. Front Oncol 2022; 12:849626. [DOI: 10.3389/fonc.2022.849626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
BackgroundThe aim of this study was to evaluate the clinical usefulness of radiomics signature-derived 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET-CT) for the early prediction of neoadjuvant chemotherapy (NAC) outcomes in patients with (BC).MethodsA total of 124 patients with BC who underwent pretreatment PET-CT scanning and received NAC between December 2016 and August 2019 were studied. The dataset was randomly assigned in a 7:3 ratio to either the training or validation cohort. Primary tumor segmentation was performed, and radiomics signatures were extracted from each PET-derived volume of interest (VOI) and CT-derived VOI. Radiomics signatures associated with pathological treatment response were selected from within a training cohort (n = 85), which were then applied to generate different classifiers to predict the probability of pathological complete response (pCR). Different models were then independently tested in the validation cohort (n = 39) regarding their accuracy, sensitivity, specificity, and area under the curve (AUC).ResultsThirty-five patients (28.2%) had pCR to NAC. Twelve features consisting of five PET-derived signatures, four CT-derived signatures, and three clinicopathological variables were candidates for the model’s development. The random forest (RF), k-nearest neighbors (KNN), and decision tree (DT) classifiers were established, which could be utilized to predict pCR to NAC with AUC ranging from 0.819 to 0.849 in the validation cohort.ConclusionsThe PET/CT-based radiomics analysis might provide efficient predictors of pCR in patients with BC, which could potentially be applied in clinical practice for individualized treatment strategy formulation.
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Payan N, Presles B, Truntzer C, Courcet E, Coutant C, Desmoulins I, Brunotte F, Vrigneaud JM, Cochet A. Critical analysis of the effect of various methodologies to compute breast cancer tumour blood flow-based texture features using first-pass 18F-FDG PET. Phys Med 2022; 103:98-107. [DOI: 10.1016/j.ejmp.2022.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 11/26/2022] Open
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Somasundaram A, Vállez García D, Pfaehler E, van Sluis J, Dierckx RAJO, de Vries EGE, Boellaard R. Mitigation of noise-induced bias of PET radiomic features. PLoS One 2022; 17:e0272643. [PMID: 36006959 PMCID: PMC9409510 DOI: 10.1371/journal.pone.0272643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/22/2022] [Indexed: 11/20/2022] Open
Abstract
Introduction One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias. Methods A phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A “difference image” created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria. Results Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected. Conclusion To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates.
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Affiliation(s)
- Ananthi Somasundaram
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
| | - David Vállez García
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC–Location VU University Medical Center, Amsterdam, The Netherlands
| | - Elisabeth Pfaehler
- Department of Nuclear Medicine, University Hospital Juelich, Aachen, Germany
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Rudi A. J. O. Dierckx
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Elisabeth G. E. de Vries
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC–Location VU University Medical Center, Amsterdam, The Netherlands
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Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
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Wang D, Zhang X, Liu H, Qiu B, Liu S, Zheng C, Fu J, Mo Y, Chen N, Zhou R, Chu C, Liu F, Guo J, Zhou Y, Zhou Y, Fan W, Liu H. Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [ 18F]FDG PET/CT imaging: quantitative analysis of [ 18F]FDG uptake in primary tumors and metastatic lymph nodes. Eur J Nucl Med Mol Imaging 2022; 49:4692-4704. [PMID: 35819498 DOI: 10.1007/s00259-022-05904-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/03/2022] [Indexed: 12/25/2022]
Abstract
PURPOSE This study aimed to quantitatively assess [18F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [18F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC. METHODS The 60-min dynamic total-body [18F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry. RESULTS A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUVmean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUVmean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3+/CD8+) and macrophages (CD68+/CD163+) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups. CONCLUSION The dynamic total-body [18F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUVmean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [18F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.
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Affiliation(s)
- DaQuan Wang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xu Zhang
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Hui Liu
- United Imaging Healthcare, Shanghai, China
| | - Bo Qiu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - SongRan Liu
- Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | | | - Jia Fu
- Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - YiWen Mo
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - NaiBin Chen
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Rui Zhou
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Chu Chu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - FangJie Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - JinYu Guo
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Yin Zhou
- SuZhou TongDiao Company, Suzhou, China
| | - Yun Zhou
- United Imaging Healthcare, Shanghai, China
| | - Wei Fan
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
| | - Hui Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
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Liang HY, Yang SF, Zou HM, Hou F, Duan LS, Huang CC, Xu JX, Liu SL, Hao DP, Wang HX. Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study. Front Oncol 2022; 12:897676. [PMID: 35814362 PMCID: PMC9265249 DOI: 10.3389/fonc.2022.897676] [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: 03/16/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives To build and evaluate a deep learning radiomics nomogram (DLRN) for preoperative prediction of lung metastasis (LM) status in patients with soft tissue sarcoma (STS). Methods In total, 242 patients with STS (training set, n=116; external validation set, n=126) who underwent magnetic resonance imaging were retrospectively enrolled in this study. We identified independent predictors for LM-status and evaluated their performance. The minimum redundancy maximum relevance (mRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm were adopted to screen radiomics features. Logistic regression, decision tree, random forest, support vector machine (SVM), and adaptive boosting classifiers were compared for their ability to predict LM. To overcome the imbalanced distribution of the LM data, we retrained each machine-learning classifier using the synthetic minority over-sampling technique (SMOTE). A DLRN combining the independent clinical predictors with the best performing radiomics prediction signature (mRMR+LASSO+SVM+SMOTE) was established. Area under the receiver operating characteristics curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the performance and clinical applicability of the models. Result Comparisons of the AUC values applied to the external validation set revealed that the DLRN model (AUC=0.833) showed better prediction performance than the clinical model (AUC=0.664) and radiomics model (AUC=0.799). The calibration curves indicated good calibration efficiency and the DCA showed the DLRN model to have greater clinical applicability than the other two models. Conclusion The DLRN was shown to be an accurate and efficient tool for LM-status prediction in STS.
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Affiliation(s)
- Hao-yu Liang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shi-feng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Hong-mei Zou
- Department of Radiology, The Third People’s Hospital of Qingdao, Qingdao, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Li-sha Duan
- Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Chen-cui Huang
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Jing-xu Xu
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Shun-li Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Shun-li Liu, ; Da-peng Hao, ; He-xiang Wang,
| | - Da-peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Shun-li Liu, ; Da-peng Hao, ; He-xiang Wang,
| | - He-xiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Shun-li Liu, ; Da-peng Hao, ; He-xiang Wang,
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21
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O'Shea RJ, Rookyard C, Withey S, Cook GJR, Tsoka S, Goh V. Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT. Insights Imaging 2022; 13:104. [PMID: 35715706 PMCID: PMC9206060 DOI: 10.1186/s13244-022-01245-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/28/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies.
Methods This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. Results Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. Conclusions Future research must prioritise prospective validation of previously proposed models to further clinical translation. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01245-0.
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Affiliation(s)
- Robert J O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.
| | - Chris Rookyard
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
| | - Sam Withey
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Gary J R Cook
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.,King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK
| | - Sophia Tsoka
- Department of Informatics, School of Natural and Mathematical Sciences, King's College London, London, UK
| | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.,Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
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22
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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23
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Marr L, Haller B, Pyka T, Peeken JC, Jesinghaus M, Scheidhauer K, Friess H, Combs SE, Münch S. Predictive value of clinical and 18F-FDG-PET/CT derived imaging parameters in patients undergoing neoadjuvant chemoradiation for esophageal squamous cell carcinoma. Sci Rep 2022; 12:7148. [PMID: 35504955 PMCID: PMC9065158 DOI: 10.1038/s41598-022-11076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/12/2022] [Indexed: 12/24/2022] Open
Abstract
Aim of this study was to validate the prognostic impact of clinical parameters and baseline 18F-FDG-PET/CT derived textural features to predict histopathologic response and survival in patients with esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiation (nCRT) and surgery. Between 2005 and 2014, 38 ESCC were treated with nCRT and surgery. For all patients, the 18F-FDG-PET-derived parameters metabolic tumor volume (MTV), SUVmax, contrast and busyness were calculated for the primary tumor using a SUV-threshold of 3. The parameter uniformity was calculated using contrast-enhanced computed tomography. Based on histopathological response to nCRT, patients were classified as good responders (< 10% residual tumor) (R) or non-responders (≥ 10% residual tumor) (NR). Regression analyses were used to analyse the association of clinical parameters and imaging parameters with treatment response and overall survival (OS). Good response to nCRT was seen in 27 patients (71.1%) and non-response was seen in 11 patients (28.9%). Grading was the only parameter predicting response to nCRT (Odds Ratio (OR) = 0.188, 95% CI: 0.040–0.883; p = 0.034). No association with histopathologic treatment response was seen for any of the evaluated imaging parameters including SUVmax, MTV, busyness, contrast and uniformity. Using multivariate Cox-regression analysis, the heterogeneity parameters busyness (Hazard Ratio (HR) = 1.424, 95% CI: 1.044–1.943; p = 0.026) and contrast (HR = 6.678, 95% CI: 1.969–22.643;p = 0.002) were independently associated with OS, while no independent association with OS was seen for SUVmax and MTV. In patients with ESCC undergoing nCRT and surgery, baseline 18F-FDG-PET/CT derived parameters could not predict histopathologic response to nCRT. However, the PET/CT derived features busyness and contrast were independently associated with OS and should be further investigated.
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Affiliation(s)
- Lisa Marr
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Bernhard Haller
- Institute of Medical Informatics, Statistics and Epidemiology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Thomas Pyka
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,Department of Nuclear Medicine, Inselspital Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Moritz Jesinghaus
- Institute of Pathology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Klemens Scheidhauer
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Helmut Friess
- Department of Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany. .,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.
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24
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Beads phantom for evaluating heterogeneity of SUV on 18F-FDG PET images. Ann Nucl Med 2022; 36:495-503. [PMID: 35377093 DOI: 10.1007/s12149-022-01740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/21/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE This study aimed to develop a dedicated phantom using acrylic beads for texture analysis and to represent heterogeneous 18F-fluorodeoxyglucose (FDG) distributions in various acquisition periods. METHODS Images of acrylic spherical beads with or without diameters of 5- and 10-mm representing heterogeneous and homogeneous 18F-FDG distribution in phantoms, respectively, were collected for 20 min in list mode. Phantom data were reconstructed using three-dimensional ordered subset expectation maximization with attenuation and scatter corrections, and the time-of-flight algorithm. The beads phantom images were acquired twice to evaluate the robustness of texture features. Thirty-one texture features were extracted, and the robustness of texture feature values was evaluated by calculating the percentage of coefficient of variation (%COV) and intraclass coefficient of correlation (ICC). Cross-correlation coefficients among texture feature values were clustered to classify the characteristics of these features. RESULTS Heterogeneous 18F-FDG distribution was represented by the beads phantom images. The agreements of %COV between two measurements were acceptable (ICC ≥ 0.71). All texture features were classified into four groups. Among 31 texture features, 24 exhibited significant different values between phantoms with and without beads in 1-, 2-, 3-, 4-, 5-, 20-min image acquisitions. Whereas, the homogeneous and heterogeneous 18F-FDG distribution could not be discriminated by seven texture features: low gray-level run emphasis, high gray-level run emphasis, short-run low gray-level emphasis, low gray-level zone emphasis, high gray-level zone emphasis, short-zone low gray-level emphasis, and coarseness. CONCLUSIONS We have developed the acrylic beads phantom for texture analysis that could represent heterogeneous 18F-FDG distributions in various acquisition periods. Most texture features could discriminate homogeneous and heterogeneous 18F-FDG distributions in the beads phantom images.
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25
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Hosseini SA, Shiri I, Hajianfar G, Bahadorzade B, Ghafarian P, Zaidi H, Ay MR. Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: phantom and clinical studies. Med Phys 2022; 49:3783-3796. [PMID: 35338722 PMCID: PMC9322423 DOI: 10.1002/mp.15615] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18F‐FDG PET image radiomic features in non‐small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes. Methods An in‐house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full‐width at half‐maximum of post‐reconstruction smoothing filter and acquisition parameters, including injected activity and test–retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi‐automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions. Results Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty‐five percent and 76% of the features showed a COV ≤ 5% against the test–retest with and without motion in large lesions, respectively. Thirty‐three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p‐value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting). Conclusions Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non‐reproducibility.
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Affiliation(s)
- Seyyed Ali Hosseini
- Department of Medical physics and biomedical engineering, Tehran University of medical sciences, Tehran, Iran.,Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Pardis Ghafarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,PET/CT and cyclotron center, Masih Daneshvari hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, Switzerland.,Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark
| | - Mohammad Reza Ay
- Department of Medical physics and biomedical engineering, Tehran University of medical sciences, Tehran, Iran.,Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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26
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Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021; 49:2462-2481. [PMID: 34939174 PMCID: PMC9206619 DOI: 10.1007/s00259-021-05658-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/12/2021] [Indexed: 10/24/2022]
Abstract
PURPOSE Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. METHODS A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. RESULTS Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. CONCLUSION A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
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27
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Diagnostic value of baseline 18FDG PET/CT skeletal textural features in follicular lymphoma. Sci Rep 2021; 11:23812. [PMID: 34893676 PMCID: PMC8664828 DOI: 10.1038/s41598-021-03278-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/17/2021] [Indexed: 01/06/2023] Open
Abstract
At present, 18F-fluorodesoxyglucose (18FDG) positron emission tomography (PET)/computed tomography (CT) cannot be used to omit a bone marrow biopsy (BMB) among initial staging procedures in follicular lymphoma (FL). The additional diagnostic value of skeletal textural features on baseline 18FDG-PET/CT in diffuse large B-cell lymphoma (DLBCL) patients has given promising results. The aim of this study is to evaluate the value of 18FDG-PET/CT radiomics for the diagnosis of bone marrow involvement (BMI) in FL patients. This retrospective bicentric study enrolled newly diagnosed FL patients addressed for baseline 18FDG PET/CT. For visual assessment, examinations were considered positive in cases of obvious bone focal uptakes. For textural analysis, the skeleton volumes of interest (VOIs) were automatically extracted from segmented CT images and analysed using LifeX software. BMB and visual assessment were taken as the gold standard: BMB −/PET − patients were considered as bone-NEGATIVE patients, whereas BMB +/PET −, BMB −/PET + and BMB +/PET + patients were considered bone-POSITIVE patients. A LASSO regression algorithm was used to select features of interest and to build a prediction model. Sixty-six consecutive patients were included: 36 bone-NEGATIVE (54.5%) and 30 bone-POSITIVE (45.5%). The LASSO regression found variance_GLCM, correlation_GLCM, joint entropy_GLCM and busyness_NGLDM to have nonzero regression coefficients. Based on ROC analysis, a cut-off equal to − 0.190 was found to be optimal for the diagnosis of BMI using PET pred.score. The corresponding sensitivity, specificity, PPV and NPV values were equal to 70.0%, 83.3%, 77.8% and 76.9%, respectively. When comparing the ROC AUCs with using BMB alone, visual PET assessment or PET pred.score, a significant difference was found between BMB versus visual PET assessments (p = 0.010) but not between BMB and PET pred.score assessments (p = 0.097). Skeleton texture analysis is worth exploring to improve the performance of 18FDG-PET/CT for the diagnosis of BMI at baseline in FL patients.
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28
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Aydos U, Ünal ER, Özçelik M, Akdemir D, Ekinci Ö, Taştepe Aİ, Memiş L, Atay LÖ, Akdemir ÜÖ. Texture features of primary tumor on 18F-FDG PET images in non-small cell lung cancer: The relationship between imaging and histopathological parameters. Rev Esp Med Nucl Imagen Mol 2021; 40:343-350. [PMID: 34752367 DOI: 10.1016/j.remnie.2020.09.012] [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: 05/04/2020] [Accepted: 06/19/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients. METHODS PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, MTV, TLG) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded. RESULTS Among the two histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis. CONCLUSIONS Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC.
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Affiliation(s)
- Uğuray Aydos
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turkey.
| | - Emel Rodoplu Ünal
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turkey
| | - Mahsun Özçelik
- Yüzüncü Yıl University, Faculty of Medicine, Department of Nuclear Medicine, Van, Turkey
| | - Deniz Akdemir
- Michigan State University, Department of Epidemiology and Biostatistics, East Lansing, MI, USA
| | - Özgür Ekinci
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turkey
| | - Abdullah İrfan Taştepe
- Gazi University, Faculty of Medicine, Department of Thoracic Surgery, Beşevler/Ankara, Turkey
| | - Leyla Memiş
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turkey
| | - Lütfiye Özlem Atay
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turkey
| | - Ümit Özgür Akdemir
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turkey
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29
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Sepehri S, Tankyevych O, Iantsen A, Visvikis D, Hatt M, Cheze Le Rest C. Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer. Front Oncol 2021; 11:726865. [PMID: 34733779 PMCID: PMC8560021 DOI: 10.3389/fonc.2021.726865] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022] Open
Abstract
Background The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (18F-FDG PET/CT) images based on a "rough" volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses. Methods A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively (n = 87) and prospectively (n = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer™) components. Both delineations were carried out within previously manually defined "rough" VOIs containing the tumor and the surrounding tissues, which were exploited for the second approach: the same features were extracted from this alternative VOI. Both sets for features were then combined with the clinical variables and processed through the same machine learning (ML) pipelines using the retrospectively recruited patients as the training set and the prospectively recruited patients as the testing set. Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). The resulting models were compared in terms of balanced accuracy, sensitivity, and specificity. Results Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 vs. 0.88 and 0.78 vs. 0.77). Conclusion Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.
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Affiliation(s)
| | - Olena Tankyevych
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,University Hospital Poitiers, Nuclear Medicine Department, Poitiers, France
| | | | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,University Hospital Poitiers, Nuclear Medicine Department, Poitiers, France
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30
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Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110170] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.
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Motwani M. Hiding beyond plain sight: Textural analysis of positron emission tomography to identify high-risk plaques in carotid atherosclerosis. J Nucl Cardiol 2021; 28:1872-1874. [PMID: 31832886 DOI: 10.1007/s12350-019-01981-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 11/26/2019] [Indexed: 10/25/2022]
Affiliation(s)
- Manish Motwani
- Department of Cardiology, Manchester Heart Centre, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Oxford Road, Manchester, UK.
- Institute of Cardiovascular Science, University of Manchester, Manchester, UK.
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Kafouris PP, Koutagiar IP, Georgakopoulos AT, Spyrou GM, Visvikis D, Anagnostopoulos CD. Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques. J Nucl Cardiol 2021; 28:1861-1871. [PMID: 31823329 DOI: 10.1007/s12350-019-01943-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 10/13/2019] [Indexed: 12/23/2022]
Abstract
BACKGROUND Texture analysis has been increasingly used in the field of positron emission tomography (PET)/computed tomography (CT) imaging with Fluorine-18 fluorodeoxyglucose (18F-FDG), aiming at assessing tumor heterogeneity. The purpose of the present study is to examine the feasibility of performing texture analysis in carotid arteries, investigate the value of textural features as predictors of potential plaque vulnerability using as reference standards histological and immunohistochemical data and compare their performance with conventional uptake measurements. METHODS 67 different 18F-FDG PET-based textural features were extracted from carotid images of 21 patients with high-grade carotid stenosis undergoing endarterectomy. To identify the more reliable predictors, univariate logistic regression analysis was performed. The accuracy was satisfactory in case of an Area Under the Receiver Operating Characteristic (ROC) curve (AUC) ≥ 0.80. RESULTS First measure of information correlation (AUC = 0.87, P < 0.001), large zone low gray level emphasis (AUC = 0.87, P < 0.001), and normalized run length non-uniformity (AUC = 0.84, P < 0.001) were the most optimal textural features for identifying characteristics of plaque vulnerability based on histological analysis. Addition of textural features to target-to-background ratio (TBR) (AUC = 0.74, P = 0.031) resulted in an AUC = 0.92 (P < 0.001), however, this did not reach statistical significance (Pdiff = 0.09). Intensity histogram standard deviation (AUC = 0.87, P < 0.001) and joint variance (AUC = 0.81, P = 0.001) were the most efficient features for signal differential in relation to immunohistochemical findings and provided incremental value compared to TBR (Pdiff = 0.02). CONCLUSION Texture analysis can be applied in 18F-FDG PET carotid imaging providing valuable information for plaque characterization.
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Affiliation(s)
- Pavlos P Kafouris
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
- Experimental Surgery, Clinical and Translational Research Centre, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou St., 11527, Athens, Greece
| | - Iosif P Koutagiar
- First Department of Cardiology, Hippokration Hospital, Athens, Greece
| | - Alexandros T Georgakopoulos
- Experimental Surgery, Clinical and Translational Research Centre, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou St., 11527, Athens, Greece
| | - George M Spyrou
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | | | - Constantinos D Anagnostopoulos
- Experimental Surgery, Clinical and Translational Research Centre, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou St., 11527, Athens, Greece.
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Repeatability of image features extracted from FET PET in application to post-surgical glioblastoma assessment. Phys Eng Sci Med 2021; 44:1131-1140. [PMID: 34436751 DOI: 10.1007/s13246-021-01049-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/18/2021] [Indexed: 11/27/2022]
Abstract
Positron emission tomography (PET) imaging using the amino acid tracer O-[2-(18F)fluoroethyl]-L-tyrosine (FET) has gained increased popularity within the past decade in the management of glioblastoma (GBM). Radiomics features extracted from FET PET images may be sensitive to variations when imaging at multiple time points. It is therefore necessary to assess feature robustness to test-retest imaging. Eight patients with histologically confirmed GBM that had undergone post-surgical test-retest FET PET imaging were recruited. In total, 1578 radiomic features were extracted from biological tumour volumes (BTVs) delineated using a semi-automatic contouring method. Feature repeatability was assessed using the intraclass correlation coefficient (ICC). The effect of both bin width and filter choice on feature repeatability was also investigated. 59/106 (55.7%) features from the original image and 843/1472 (57.3%) features from filtered images had an ICC ≥ 0.85. Shape and first order features were most stable. Choice of bin width showed minimal impact on features defined as stable. The Laplacian of Gaussian (LoG, σ = 5 mm) and Wavelet filters (HLL and LHL) significantly improved feature repeatability (p ≪ 0.0001, p = 0.003, p = 0.002, respectively). Correlation of textural features with tumour volume was reported for transparency. FET PET radiomic features extracted from post-surgical images of GBM patients that are robust to test-retest imaging were identified. An investigation with a larger dataset is warranted to validate the findings in this study.
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Eertink JJ, Pfaehler EAG, Wiegers SE, van de Brug T, Lugtenburg PJ, Hoekstra OS, Zijlstra JM, de Vet HCW, Boellaard R. Quantitative radiomics features in diffuse large B-cell lymphoma: does segmentation method matter? J Nucl Med 2021; 63:389-395. [PMID: 34272315 DOI: 10.2967/jnumed.121.262117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/03/2021] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION Radiomics features may predict outcome in diffuse large B-cell lymphoma (DLBCL). Currently, multiple segmentation methods are used to calculate metabolic tumor volume (MTV). We assessed the influence of segmentation method on the discriminative power of radiomics features in DLBCL for patient level and for the largest lesion. Methods: 50 baseline 18F-fluorodeoxyglucose positron emission tomography computed tomography (PET/CT) scans of DLBCL patients who progressed or relapsed within 2 years after diagnosis were matched on uptake time and reconstruction method with 50 baseline PET/CT scans of DLBCL patients without progression. Scans were analysed using 6 semi-automatic segmentation methods (standardized uptake value (SUV)4.0, SUV2.5, 41% of the maximum SUV, 50% of the SUVpeak, majority vote (MV)2 and MV3, respectively). Based on these segmentations, 490 radiomics features were extracted at patient level and 486 features for the largest lesion. To quantify the agreement between features extracted from different segmentation methods, the intra-class correlation (ICC) agreement was calculated for each method compared to SUV4.0. The feature space was reduced by deleting features that had high Pearson correlations (≥0.7) with the previously established predictors MTV and/or SUVpeak. Model performance was assessed using stratified repeated cross-validation with 5 folds and 2000 repeats yielding the mean receiver-operating characteristics curve integral (CV-AUC) for all segmentation methods using logistic regression with backward feature selection. Results: The percentage of features yielding an ICC ≥0.75 compared to the SUV4.0 segmentation was lowest for A50P both at patient level and for the largest lesion, with 77.3% and 66.7% of the features yielding an ICC ≥0.75, respectively. Features were not highly correlated with MTV, with at least 435 features at patient level and 409 features for the largest lesion for all segmentation methods with a correlation coefficient <0.7. Features were highly correlated with SUVpeak (at least 190 and 134 were uncorrelated, respectively). CV-AUCs ranged between 0.69±0.11 and 0.84±0.09 for patient level, and between 0.69±0.11 and 0.73±0.10 for lesion level. Conclusion: Even though there are differences in the actual radiomics feature values derived and selected features between segmentation methods, there is no substantial difference in the discriminative power of radiomics features between segmentation methods.
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Affiliation(s)
- Jakoba J Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | | | - Sanne E Wiegers
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Tim van de Brug
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Epidemiology and Data Science, Amsterdam Public Health research institute, Netherlands
| | - Pieternella J Lugtenburg
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, department of Hematology, Netherlands
| | - Otto S Hoekstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Netherlands
| | - Josee M Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Henrica C W de Vet
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Epidemiology and Data Science, Amsterdam Public Health research institute, Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
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Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5566885. [PMID: 34337027 PMCID: PMC8289571 DOI: 10.1155/2021/5566885] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 05/24/2021] [Accepted: 07/02/2021] [Indexed: 12/24/2022]
Abstract
The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of patients with rectal cancer (RC). A total of 454 consecutive patients with pathologically confirmed RC who underwent preoperative MRI between January 2018 and December 2019 were retrospectively analyzed. RC patients were grouped according to whether the muscularis propria was penetrated. Two radiologists segmented lesions, respectively, by minimum delineation (Method 1) and maximum delineation (Method 2), after which radiomics features were extracted. Inter- and intraclass correlation coefficient (ICC) of all features was evaluated. After feature reduction, the support vector machine (SVM) was trained to build a prediction model. The diagnostic performances of models were determined by receiver operating characteristic (ROC) curves. Then, the areas under the curve (AUCs) were compared by the DeLong test. Decision curve analysis (DCA) was performed to evaluate clinical benefit. Finally, 317 patients were assessed, including 152 cases in the training set and 165 cases in the validation set. Moreover, 1288/1409 (91.4%) features of Method 1 and 1273/1409 (90.3%) features of Method 2 had good robustness (P < 0.05). The AUCs of Model 1 and Model 2 were 0.808 and 0.903 in the validation set, respectively (P = 0.035). DCA showed that the maximum delineation yielded more net benefit. MRI-based radiomics models derived from two segmentation methods demonstrated good performance in the preoperative T staging of RC. The minimum delineation had better stability in feature selection, while the maximum delineation method was more clinically beneficial.
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Carles M, Fechter T, Martí-Bonmatí L, Baltas D, Mix M. Experimental phantom evaluation to identify robust positron emission tomography (PET) radiomic features. EJNMMI Phys 2021; 8:46. [PMID: 34117929 PMCID: PMC8197692 DOI: 10.1186/s40658-021-00390-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/12/2021] [Indexed: 12/14/2022] Open
Abstract
Background Radiomics analysis usually involves, especially in multicenter and large hospital studies, different imaging protocols for acquisition, reconstruction, and processing of data. Differences in protocols can lead to differences in the quantification of the biomarker distribution, leading to radiomic feature variability. The aim of our study was to identify those radiomic features robust to the different degrading factors in positron emission tomography (PET) studies. We proposed the use of the standardized measurements of the European Association Research Ltd. (EARL) accreditation to retrospectively identify the radiomic features having low variability to the different systems and reconstruction protocols. In addition, we presented a reproducible procedure to identify PET radiomic features robust to PET/CT imaging metal artifacts. In 27 heterogeneous homemade phantoms for which ground truth was accurately defined by CT segmentation, we evaluated the segmentation accuracy and radiomic feature reliability given by the contrast-oriented algorithm (COA) and the 40% threshold PET segmentation. In the comparison of two data sets, robustness was defined by Wilcoxon rank tests, bias was quantified by Bland–Altman (BA) plot analysis, and strong correlations were identified by Spearman correlation test (r > 0.8 and p satisfied multiple test Bonferroni correction). Results Forty-eight radiomic features were robust to system, 22 to resolution, 102 to metal artifacts, and 42 to different PET segmentation tools. Overall, only 4 radiomic features were simultaneously robust to all degrading factors. Although both segmentation approaches significantly underestimated the volume with respect to the ground truth, with relative deviations of −62 ± 36% for COA and −50 ± 44% for 40%, radiomic features derived from the ground truth were strongly correlated and/or robust to 98 radiomic features derived from COA and to 102 from 40%. Conclusion In multicenter studies, we recommend the analysis of EARL accreditation measurements in order to retrospectively identify the robust PET radiomic features. Furthermore, 4 radiomic features (area under the curve of the cumulative SUV volume histogram, skewness, kurtosis, and gray-level variance derived from GLRLM after application of an equal probability quantization algorithm on the voxels within lesion) were robust to all degrading factors. In addition, the feasibility of 40% and COA segmentations for their use in radiomics analysis has been demonstrated. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-021-00390-7.
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Affiliation(s)
- Montserrat Carles
- 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, German Cancer Research Center (DKFZ), Heidelberg, Germany. .,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, Av. Fernando Abril Martorell, 106, 46026, Valencia, Spain.
| | - Tobias Fechter
- 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, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luis Martí-Bonmatí
- 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, Av. Fernando Abril Martorell, 106, 46026, 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, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Mix
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Department of Medical Imaging and Clinical Oncology, Nuclear Medicine Division, Faculty of Medicine and Health Science, Stellenbosch University, Stellenbosch, South Africa
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Taha B, Boley D, Sun J, Chen C. Potential and limitations of radiomics in neuro-oncology. J Clin Neurosci 2021; 90:206-211. [PMID: 34275550 DOI: 10.1016/j.jocn.2021.05.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/22/2021] [Accepted: 05/02/2021] [Indexed: 11/28/2022]
Abstract
Radiomics seeks to apply classical methods of image processing to obtain quantitative parameters from imaging. Derived features are subsequently fed into algorithmic models to aid clinical decision making. The application of radiomics and machine learning techniques to clinical medicine remains in its infancy. The great potential of radiomics lies in its objective, granular approach to investigating clinical imaging. In neuro-oncology, advanced machine learning techniques, particularly deep learning, are at the forefront of new discoveries in the field. However, despite the great promise of machine learning aided radiomic approaches, the current use remains confined to scholarly research, without real-world deployment in neuro-oncology. The paucity of data, inconsistencies in preprocessing, radiomic feature instability, and the rarity of the events of interest are critical barriers to clinical translation. In this article, we will outline the major steps in the process of radiomics, as well as review advances and challenges in the field as they pertain to neuro-oncology.
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Affiliation(s)
- Birra Taha
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN USA
| | - Daniel Boley
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ju Sun
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Clark Chen
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN USA.
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Wang X, Wu K, Li X, Jin J, Yu Y, Sun H. Additional Value of PET/CT-Based Radiomics to Metabolic Parameters in Diagnosing Lynch Syndrome and Predicting PD1 Expression in Endometrial Carcinoma. Front Oncol 2021; 11:595430. [PMID: 34055595 PMCID: PMC8152935 DOI: 10.3389/fonc.2021.595430] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 04/12/2021] [Indexed: 01/13/2023] Open
Abstract
Purpose We aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features. Materials and Methods We conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data. Results There was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (rs =0.577; p < 0.001). Conclusions In patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Ke Wu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Xiaoran Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Junjie Jin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Yang Yu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
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Wang L, Gao Z, Li C, Sun L, Li J, Yu J, Meng X. Computed Tomography-Based Delta-Radiomics Analysis for Discriminating Radiation Pneumonitis in Patients With Esophageal Cancer After Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 111:443-455. [PMID: 33974887 DOI: 10.1016/j.ijrobp.2021.04.047] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 04/24/2021] [Accepted: 04/28/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Our purpose was to construct a computed tomography (CT)-based delta-radiomics nomogram and corresponding risk classification system for individualized and accurate estimation of severe acute radiation pneumonitis (SARP) in patients with esophageal cancer (EC) after radiation therapy. METHODS AND MATERIALS Four hundred patients with EC were enrolled from 2 independent institutions and were divided into the training (n = 200) and validation (n = 200) cohorts. Eight hundred fifty radiomics features of lung were extracted from treatment planning images, including the positioning CT before radiation therapy (CT1) and the resetting CT after receiving 40 to 45 Gy (CT2). The longitudinal net changes in radiomics features from CT1 to CT2 were calculated and defined as delta-radiomics features. Least absolute shrinkage and selection operator algorithm was performed to features selection and delta-radiomics signature building. Integrating the signature with multidimensional clinicopathologic, dosimetric, and hematological predictors of SARP, a novel CT-based delta-radiomics nomogram was established according to multivariate analysis. The clinical application values of nomogram were both evaluated in the training and validation cohorts by concordance index, calibration curves, and decision curve analysis. Recursive partitioning analysis was used to generate a risk classification system. RESULTS The delta-radiomics signature consisting of 24 features was significantly associated with SARP status (P < .001). Incorporating it with other high-risk factors, Subjective Global Assessment score, pulmonary fibrosis score, mean lung dose, and systemic immune inflammation index, the developed delta-radiomics nomogram showed increased improvement in SARP discrimination accuracy with concordance index of 0.975 and 0.921 in the training and validation cohorts, respectively. Calibration curves and decision curve analysis confirmed the satisfactory clinical feasibility and utility of nomogram. The risk classification system displayed excellent performance on identifying SARP occurrence (P < .001). CONCLUSIONS The delta-radiomics nomogram and risk classification system as low-cost and noninvasive means exhibited superior predictive accuracy and provided individualized probability of SARP stratification for patients with EC.
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Affiliation(s)
- Lu Wang
- Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhenhua Gao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chengming Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Liangchao Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jianing Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinming Yu
- Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xue Meng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
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Aydos U, Ünal ER, Özçelik M, Akdemir D, Ekinci Ö, Taştepe AI, Memiş L, Atay LÖ, Akdemir ÜÖ. Texture features of primary tumor on 18F-FDG PET images in non-small cell lung cancer: The relationship between imaging and histopathological parameters. Rev Esp Med Nucl Imagen Mol 2021; 40:S2253-654X(20)30134-7. [PMID: 33785321 DOI: 10.1016/j.remn.2020.06.025] [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: 05/04/2020] [Revised: 06/16/2020] [Accepted: 06/19/2020] [Indexed: 10/21/2022]
Abstract
OBJECTIVES The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients. MATERIAL AND METHODS PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, metabolic tumor volume [MTV] and total lesion glycolysis [TLG]) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded. RESULTS Among the 2histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis. CONCLUSIONS Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC.
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Affiliation(s)
- U Aydos
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turquía.
| | - E R Ünal
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turquía
| | - M Özçelik
- Yüzüncü Yıl University, Faculty of Medicine, Department of Nuclear Medicine, Van, Turquía
| | - D Akdemir
- Michigan State University, Department of Epidemiology and Biostatistics, East Lansing, MI, Estados Unidos
| | - Ö Ekinci
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turquía
| | - A I Taştepe
- Gazi University, Faculty of Medicine, Department of Thoracic Surgery, Beşevler/Ankara, Turquía
| | - L Memiş
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turquía
| | - L Ö Atay
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turquía
| | - Ü Ö Akdemir
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turquía
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Le EPV, Rundo L, Tarkin JM, Evans NR, Chowdhury MM, Coughlin PA, Pavey H, Wall C, Zaccagna F, Gallagher FA, Huang Y, Sriranjan R, Le A, Weir-McCall JR, Roberts M, Gilbert FJ, Warburton EA, Schönlieb CB, Sala E, Rudd JHF. Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events. Sci Rep 2021; 11:3499. [PMID: 33568735 PMCID: PMC7876096 DOI: 10.1038/s41598-021-82760-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/21/2021] [Indexed: 02/02/2023] Open
Abstract
Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.
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Affiliation(s)
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Jason M Tarkin
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Nicholas R Evans
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Mohammed M Chowdhury
- Division of Vascular Surgery, Department of Surgery, University of Cambridge, Cambridge, UK
| | - Patrick A Coughlin
- Division of Vascular Surgery, Department of Surgery, University of Cambridge, Cambridge, UK
| | - Holly Pavey
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, UK
| | - Chris Wall
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Fulvio Zaccagna
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Yuan Huang
- Department of Radiology, University of Cambridge, Cambridge, UK
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK
| | | | - Anthony Le
- School of Medicine, University of Leeds, Leeds, UK
| | | | - Michael Roberts
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK
- Oncology R&D, AstraZeneca, Cambridge, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - Carola-Bibiane Schönlieb
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - James H F Rudd
- Department of Medicine, University of Cambridge, Cambridge, UK.
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43
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Pfaehler E, Mesotten L, Zhovannik I, Pieplenbosch S, Thomeer M, Vanhove K, Adriaensens P, Boellaard R. Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer. Med Phys 2021; 48:1226-1238. [PMID: 33368399 PMCID: PMC7985880 DOI: 10.1002/mp.14684] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/21/2020] [Accepted: 12/21/2020] [Indexed: 01/06/2023] Open
Abstract
Background Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non‐small cell lung cancer (NSCLC) dataset. Materials and methods The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1‐yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume. Results The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4–10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%). Conclusion When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.
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Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Liesbet Mesotten
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan building D, Diepenbeek, B-3590, Belgium.,Department of Nuclear Medicine, Ziekenhuis Oost Limburg, Schiepse Bos 6, Genk, B-3600, Belgium
| | - Ivan Zhovannik
- Department of Radiation Oncology, Radboudumc, Nijmegen, The Netherlands.,Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht, The Netherlands
| | - Simone Pieplenbosch
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Michiel Thomeer
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan building D, Diepenbeek, B-3590, Belgium.,Department of Nuclear Medicine, Ziekenhuis Oost Limburg, Schiepse Bos 6, Genk, B-3600, Belgium
| | - Karolien Vanhove
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan building D, Diepenbeek, B-3590, Belgium.,Department of Respiratory Medicine, Ziekenhuis Oost Limburg, Schiepse Bos 6, Genk, B-3600, Belgium
| | - Peter Adriaensens
- Hasselt University, Institute for Materials Research (IMO) - Division Chemistry, Agoralaan Building D, Diepenbeek, B 3590, Belgium
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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Denzler S, Vuong D, Bogowicz M, Pavic M, Frauenfelder T, Thierstein S, Eboulet EI, Maurer B, Schniering J, Gabryś HS, Schmitt-Opitz I, Pless M, Foerster R, Guckenberger M, Tanadini-Lang S. Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types. Br J Radiol 2021; 94:20200947. [PMID: 33544646 DOI: 10.1259/bjr.20200947] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVES In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung. METHODS Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable. RESULTS We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (<10%) and lung radiomics the largest stability (26%). Robustness analysis done on the contralateral lung could to a large extent be transferred to the ipsilateral lung, and the overlap of stable lung features between different lung diseases was more than 50%. However, results of robustness studies cannot be transferred between tissue types, which was investigated in NSCLC and MPM patients; the overlap of stable features for lymph node and lung, as well as for primary tumor and lymph node was very small in both disease types. CONCLUSION The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type. ADVANCES IN KNOWLEDGE The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .
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Affiliation(s)
- Sarah Denzler
- 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
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | | | - Britta Maurer
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Janine Schniering
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert Szymon Gabryś
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Isabelle Schmitt-Opitz
- Department of Thoracic Surgery, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Robert Foerster
- 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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45
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Peng L, Hong X, Yuan Q, Lu L, Wang Q, Chen W. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images. Ann Nucl Med 2021; 35:458-468. [PMID: 33543393 DOI: 10.1007/s12149-021-01585-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/11/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To develop a radiomics signature to predict locoregional recurrence (LR) and distant metastasis (DM), as extracted from pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/X-ray computed tomography (PET/CT) images in locally advanced nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Eighty-five patients with Stage III-IVB NPC underwent pretreatment [18F]FDG PET/CT scans and received radiotherapy or chemoradiotherapy. 53 of them achieved disease control, and 32 of them failed after treatment (15: LR, 17: DM). A total of 114 radiomic features were extracted from PET/CT images. For univariate analysis, Wilcoxon test and Chi-square test were used to compare median values of features between different treatment outcomes and predict the risk of treatment failure, respectively. For multivariate analysis, all features were grouped into clusters based on Pearson correlation using hierarchical clustering, and the representative feature of each cluster was chosen by the Relief algorithm. Then sequential floating forward selection (SFFS) coupled with a support vector machine (SVM) classifier were used to derive the optimized feature set in terms of the area under receiver operating characteristic (ROC) curve (AUC). The performance of the model was evaluated by leave-one-out-cross-validation, fivefold cross-validation, tenfold cross-validation. RESULTS Twenty features had significant differences between disease control and treatment failure. NPC patients with values of Compactness1, Compactness2, Coarseness_NGTDM or SGE_GLGLM above the median as well as patients with values of Irregularity, RLN_GLRLM or GLV_GLSZM below the median, showed a significant (p < 0.05) higher risk of treatment failure (about 50% vs. 25%). The derived radiomics signature consisted of 5 features with the highest AUC value of 0.8290 (sensitivity: 0.8438, specificity: 0.7736) using leave-one-out-cross-validation. CONCLUSION Locoregional recurrence (LR) and DM of locally advanced NPC can be predicted using radiomics analysis of pretreatment [18F]FDG PET/CT. The SFFS feature selection coupled with SVM classifier can derive the optimized feature set with correspondingly highest AUC value for pretreatment prediction of LR and/or DM of NPC.
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Affiliation(s)
- Lihong Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaotong Hong
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Wufan Chen
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.
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47
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Abstract
Carrying out large multicenter studies is one of the key goals to be achieved towards a faster transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics data analysis, hence the need for integrating radiomic features extracted from images acquired in different centers. This is challenging as radiomic features exhibit variable sensitivity to differences in scanner model, acquisition protocols and reconstruction settings, which is similar to the so-called 'batch-effects' in genomics studies. In this review we discuss existing methods to perform data integration with the aid of reducing the unwanted variation associated with batch effects. We also discuss the future potential role of deep learning methods in providing solutions for addressing radiomic multicentre studies.
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Affiliation(s)
- R Da-Ano
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - D Visvikis
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
- equally contributed
| | - M Hatt
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
- equally contributed
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48
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Wei L, Cui C, Xu J, Kaza R, El Naqa I, Dewaraja YK. Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics. EJNMMI Phys 2020; 7:74. [PMID: 33296050 PMCID: PMC7726084 DOI: 10.1186/s40658-020-00340-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 11/24/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90Y PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies Methods Given the noisy nature of 90Y PET, first, a liver phantom study with repeated acquisitions and varying reconstruction parameters was used to identify a subset of robust radiomics features for the patient analysis. In 36 radioembolization procedures, 90Y PET/CT was performed within a couple of hours to extract 46 radiomics features and estimate absorbed dose in 105 primary and metastatic liver lesions. Robust radiomics modeling was based on bootstrapped multivariate logistic regression with shrinkage regularization (LASSO) and Cox regression with LASSO. Nested cross-validation and bootstrap resampling were used for optimal parameter/feature selection and for guarding against overfitting risks. Spearman rank correlation was used to analyze feature associations. Area under the receiver-operating characteristics curve (AUC) was used for lesion response (at first follow-up) analysis while Kaplan-Meier plots and c-index were used to assess progression model performance. Models with absorbed dose only, radiomics only, and combined models were developed to predict lesion outcome. Results The phantom study identified 15/46 reproducible and robust radiomics features that were subsequently used in the patient models. A lesion response model with zone percentage (ZP) and mean absorbed dose achieved an AUC of 0.729 (95% CI 0.702–0.758), and a progression model with zone size nonuniformity (ZSN) and absorbed dose achieved a c-index of 0.803 (95% CI 0.790–0.815) on nested cross-validation (CV). Although the combined models outperformed the radiomics only and absorbed dose only models, statistical significance was not achieved with the current limited data set to establish expected superiority. Conclusion We have developed new lesion-level response and progression models using textural radiomics features, derived from 90Y PET combined with mean absorbed dose for predicting outcome in radioembolization. These encouraging, but limited results, will need further validation in independent and larger datasets prior to any clinical adoption. Supplementary Information Supplementary information accompanies this paper at 10.1186/s40658-020-00340-9.
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Affiliation(s)
- Lise Wei
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA
| | - Can Cui
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jiarui Xu
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ravi Kaza
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.,Machine Learning Department, Moffitt Cancer Center, Tampa, FL, USA
| | - Yuni K Dewaraja
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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49
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Rishi A, Zhang GG, Yuan Z, Sim AJ, Song EY, Moros EG, Tomaszewski MR, Latifi K, Pimiento JM, Fontaine JP, Mehta R, Harrison LB, Hoffe SE, Frakes JM. Pretreatment CT and 18 F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer. J Med Imaging Radiat Oncol 2020; 65:102-111. [PMID: 33258556 DOI: 10.1111/1754-9485.13128] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 10/21/2020] [Indexed: 01/12/2023]
Abstract
INTRODUCTION To develop a radiomic-based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer. METHODS We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F-fluorodeoxyglucose (18 F-FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in-house data-chjmirocterization algorithm was used to extract 3D-radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, Fc = α * FPET + (1 - α) * FCT , 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco-regional control (LRC), recurrence-free survival (RFS), metastasis-free survival (MFS) and overall survival (OS) were estimated by Kaplan-Meier analysis, and compared using log-rank test. RESULTS Median follow-up was 59 months. pCR was achieved in 34 (50%) patients. Five-year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0-1 indicative of complete response or minimal residual disease was significantly associated with improved 5-year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04-0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5-year LRC favouring low-score group (91.1% vs. 80%, 95% CI 0.09-1.77, P = 0.2). CONCLUSION The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes.
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Affiliation(s)
- Anupam Rishi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Geoffrey G Zhang
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Zhigang Yuan
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Austin J Sim
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Ethan Y Song
- Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Michal R Tomaszewski
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Jose M Pimiento
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Jacques-Pierre Fontaine
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Rutika Mehta
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Louis B Harrison
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Sjmiroh E Hoffe
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Jessica M Frakes
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
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50
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Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer? Eur Radiol 2020; 31:4156-4165. [PMID: 33247345 DOI: 10.1007/s00330-020-07507-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/04/2020] [Accepted: 11/11/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES We aimed at investigating the origin of the correlations between tumor volume and 18F-FDG-PET texture indices in lung cancer. METHODS Eighty-five consecutive patients with newly diagnosed non-small cell lung cancer (NSCLC) underwent a 18F-FDG-PET/CT scan before treatment. Seven phantom spheres uniformly filled with 18F-FDG, and covering a range of activities and volumes similar to that found in lung tumors, were also scanned. Established texture indices were computed for lung tumors and homogeneous spheres. The dependence between textural indices and volume in homogeneous spheres was modeled and then used to predict texture indices in lung tumors. Correlation analyses were carried out between predicted and texture features measured in lung tumors. Cox proportional hazards regression was used to investigate the associations between overall survival and volume-adjusted textural features. RESULTS All textural features showed strong, non-linear correlations with volume, both in tumors and homogeneous spheres. Correlations between predicted versus measured texture features were very high for contrast (r2 = 0.91), dissimilarity (r2 = 0.90), ZP (r2 = 0.90), GLNN (r2 = 0.86), and homogeneity (r2 = 0.82); high for entropy (r2 = 0.50) and HILAE (r2 = 0.53); and low for energy (r2 = 0.30). Cox regressions showed that among volume-adjusted features, only HILAE was associated with overall survival (b = - 0.35, p = 0.008). CONCLUSION We have shown that texture indices previously found to be correlated with a number of clinically relevant outcomes might not provide independent information apart from that driven by their correlation with tumor volume, suggesting that these metrics might not be suitable as intratumor heterogeneity markers. KEY POINTS • Associations between texture FDG-PET indices and overall survival have been widely reported in lung cancer, with tumor volume also being associated with overall survival, and therefore, it is still unclear whether the predictive power of textural indices is simply driven by this correlation. • Our results demonstrated strong non-linear correlations between textural indices and volume, showing an analogous behavior for lung tumors from patients and homogeneous spheres inserted in phantoms. • Our findings showed that texture FDG-PET indices might not provide independent information apart from that driven by their correlation with tumor volume.
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