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Liu L, Ji X, Liang C, Zhu J, Huang L, Zhao Y, Xu X, Song Z, Shen W. An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer. Future Oncol 2024:1-15. [PMID: 39287151 DOI: 10.1080/14796694.2024.2398984] [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: 01/18/2024] [Accepted: 08/28/2024] [Indexed: 09/19/2024] Open
Abstract
Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.
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Affiliation(s)
- Ling Liu
- The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianiin, 300192, China
- Department of Radiology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, No. 354 North Road, Hongqiao District, Tianjin, 300120, China
| | - Xiaodong Ji
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Caihong Liang
- Department of Radiology, Tianjin Jinghai District Hospital, No. 14 Shengli South Road, Jinghai District, Tianjin, 301600, China
| | - Jinxia Zhu
- MR Research Collaboration, Siemens Healthineers Ltd., Beijing, 100102, China
| | - Lixiang Huang
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Yujiao Zhao
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Xiangfeng Xu
- Department of Radiology, Tianjin Central Hospital of Obstetrics & Gynecology, Nankai University Maternity Hospital, No. 156 Nankai Three Road, Nankai District, Tianjin, 301600, China
| | - Zhiyi Song
- Department of Radiology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, No. 354 North Road, Hongqiao District, Tianjin, 300120, China
| | - Wen Shen
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
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Young D, Khan N, Hobson SR, Sussman D. Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning. Comput Biol Med 2024; 178:108757. [PMID: 38878399 DOI: 10.1016/j.compbiomed.2024.108757] [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: 10/06/2023] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including postpartum hemorrhage. Despite its significance, PAS remains frequently underdiagnosed before delivery. This study delves into the realm of machine learning to enhance the precision of PAS classification. We introduce two distinct models for PAS classification employing ultrasound texture features. METHODS The first model leverages machine learning techniques, harnessing texture features extracted from ultrasound scans. The second model adopts a linear classifier, utilizing integrated features derived from 'weighted z-scores'. A novel aspect of our approach is the amalgamation of classical machine learning and statistical-based methods for feature selection. This, coupled with a more transparent classification model based on quantitative image features, results in superior performance compared to conventional machine learning approaches. RESULTS Our linear classifier and machine learning models attain test accuracies of 87 % and 92 %, and 5-fold cross validation accuracies of 88.7 (4.4) and 83.0 (5.0), respectively. CONCLUSIONS The proposed models illustrate the effectiveness of practical and robust tools for enhanced PAS detection, offering non-invasive and computationally-efficient diagnostic tools. As adjunct methods for prenatal diagnosis, these tools can assist clinicians by reducing the need for unnecessary interventions and enabling earlier planning of management strategies for delivery.
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Affiliation(s)
- Dylan Young
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Toronto Metropolitan University, Canada; St. Michael's Hospital, Toronto, Canada & Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Canada
| | - Naimul Khan
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada
| | - Sebastian R Hobson
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Toronto Metropolitan University, Canada; Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Toronto Metropolitan University, Canada; St. Michael's Hospital, Toronto, Canada & Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Canada; Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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Bomhals B, Cossement L, Maes A, Sathekge M, Mokoala KMG, Sathekge C, Ghysen K, Van de Wiele C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. J Clin Med 2023; 12:7731. [PMID: 38137800 PMCID: PMC10743692 DOI: 10.3390/jcm12247731] [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: 10/16/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.
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Affiliation(s)
- Birte Bomhals
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Lara Cossement
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Alex Maes
- Department of Morphology and Functional Imaging, University Hospital Leuven, 3000 Leuven, Belgium;
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
| | - Mike Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Kgomotso M. G. Mokoala
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Chabi Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Katrien Ghysen
- Department of Pneumology, AZ Groeninge, 8500 Kortrijk, Belgium
| | - Christophe Van de Wiele
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
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Yang W, Hao Y, Mu K, Li J, Tao Z, Ma D, Xu A. Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma. Bioengineering (Basel) 2023; 10:1423. [PMID: 38136014 PMCID: PMC10740639 DOI: 10.3390/bioengineering10121423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/23/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734-1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic-Radscore model had an AUC of 0.994 [95% CI, 0.978-1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma.
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Affiliation(s)
- Wenhua Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Yonghong Hao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Ketao Mu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Jianjun Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Zihui Tao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Delin Ma
- Department of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Anhui Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
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Chen X, Zhu X, Yan W, Wang L, Xue D, Zhu S, Pan J, Li Y, Zhao Q, Han D. Serum lncRNA THRIL predicts benign and malignant pulmonary nodules and promotes the progression of pulmonary malignancies. BMC Cancer 2023; 23:755. [PMID: 37582734 PMCID: PMC10426220 DOI: 10.1186/s12885-023-11264-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/06/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs. METHODS The study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL's significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene. RESULTS There were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a. CONCLUSION The detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.
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Affiliation(s)
- Xinyu Chen
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Xianji Zhu
- Department of Respiratory Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China
| | - Wenjun Yan
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Luan Wang
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Dongming Xue
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Shouying Zhu
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Jiajun Pan
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Yufeng Li
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Qixiang Zhao
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China
| | - Dong Han
- Department of Cardiothoracic Surgery, Xuzhou No.1 People's Hospital, Xuzhou Municipal Hospital Affiliated with Xuzhou Medical College, 269 Daxue Road, Xuzhou, 221000, China.
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Yaltırık Bilgin E, Ünal Ö, Törenek Ş, Çiledağ N. Computerized Tomography Texture Analysis in the Differential Diagnosis of Intracranial Epidermoid and Arachnoid Cysts. Cureus 2023; 15:e41945. [PMID: 37588326 PMCID: PMC10425918 DOI: 10.7759/cureus.41945] [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] [Accepted: 07/15/2023] [Indexed: 08/18/2023] Open
Abstract
PURPOSE This study evaluated the differences between arachnoid and epidermoid cysts in computerized tomography (CT) texture analysis (TA). MATERIAL AND METHODS The study included 12 patients with intracranial epidermoid cysts and 26 patients with intracranial arachnoid cysts who were diagnosed with diffusion-weighted magnetic resonance imaging (DW-MRI) and who had undergone an unenhanced CT examination before treatment. The LIFEx application software was used to obtain texture features. Eighty-two texture features from 38 lesions were automatically calculated for each lesion. The Shapiro-Wilk test was used to test the normality of the scores, and the Mann-Whitney U Test was used to test the difference between the groups. Receiver operating characteristic (ROC) curves and multivariate logistic regression modeling examined the parameters' diagnostic performances. RESULTS The median age of the patients was 53 years (range: 19-88 years). Eighty-two texture parameters were evaluated in the first order: gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), neighbor gray-tone difference matrix (NGTDM), and gray-level size zone matrix (GLSZM) groups. There was a statistically significant difference between the arachnoid cyst and the epidermoid cyst in the variables of compacity, compactness 1, compactness 2, sphericity, asphericity, sum average, coarseness, and low gray-level zone (p<0.05). According to the multiple logistic regression model, it was determined that the sum average in the GLCM group (B=-0.11; p=0.015), coarseness (B= 869.5; p=0.044) in the NGTDM group, and morphological sphericity (B=24.18; p=0.047) were the radiomics variables that increased the probability of epidermoid diagnosis. According to the classification table of the model, the sensitivity rate was found to be 83%, and the specificity rate was found to be 96%. Therefore, the probability of accurate model classification was 92%. CONCLUSION CT TA is a method that can be applied with high diagnostic accuracy in the differential diagnosis of intracranial epidermoid and arachnoid cysts, especially in patients who cannot undergo an MRI examination.
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Affiliation(s)
- Ezel Yaltırık Bilgin
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
| | - Özkan Ünal
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
| | - Şahap Törenek
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
| | - Nazan Çiledağ
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
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Qualitative and Semiquantitative Parameters of 18F-FDG-PET/CT as Predictors of Malignancy in Patients with Solitary Pulmonary Nodule. Cancers (Basel) 2023; 15:cancers15041000. [PMID: 36831344 PMCID: PMC9953844 DOI: 10.3390/cancers15041000] [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: 12/02/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 02/08/2023] Open
Abstract
This study aims to evaluate the reliability of qualitative and semiquantitative parameters of 18F-FDG PET-CT, and eventually a correlation between them, in predicting the risk of malignancy in patients with solitary pulmonary nodules (SPNs) before the diagnosis of lung cancer. A total of 146 patients were retrospectively studied according to their pre-test probability of malignancy (all patients were intermediate risk), based on radiological features and risk factors, and qualitative and semiquantitative parameters, such as SUVmax, SUVmean, TLG, and MTV, which were obtained from the FDG PET-CT scan of such patients before diagnosis. It has been observed that visual analysis correlates well with the risk of malignancy in patients with SPN; indeed, only 20% of SPNs in which FDG uptake was low or absent were found to be malignant at the cytopathological examination, while 45.45% of SPNs in which FDG uptake was moderate and 90.24% in which FDG uptake was intense were found to be malignant. The same trend was observed evaluating semiquantitative parameters, since increasing values of SUVmax, SUVmean, TLG, and MTV were observed in patients whose cytopathological examination of SPN showed the presence of lung cancer. In particular, in patients whose SPN was neoplastic, we observed a median (MAD) SUVmax of 7.89 (±2.24), median (MAD) SUVmean of 3.76 (±2.59), median (MAD) TLG of 16.36 (±15.87), and a median (MAD) MTV of 3.39 (±2.86). In contrast, in patients whose SPN was non-neoplastic, the SUVmax was 2.24 (±1.73), SUVmean 1.67 (±1.15), TLG 1.63 (±2.33), and MTV 1.20 (±1.20). Optimal cut-offs were drawn for semiquantitative parameters considered predictors of malignancy. Nodule size correlated significantly with FDG uptake intensity and with SUVmax. Finally, age and nodule size proved significant predictors of malignancy. In conclusion, considering the pre-test probability of malignancy, qualitative and semiquantitative parameters can be considered reliable tools in patients with SPN, since cut-offs for SUVmax, SUVmean, TLG, and MTV showed good sensitivity and specificity in predicting malignancy.
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Chen Y, He Y, Jiang Z, Xie Y, Nie S. Ischemic stroke subtyping method combining convolutional neural network and radiomics. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:223-235. [PMID: 36591693 PMCID: PMC10041412 DOI: 10.3233/xst-221284] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 11/15/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics. METHODS Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model. RESULTS The experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People's Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively. CONCLUSION The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients.
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Affiliation(s)
- Yang Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yiwen He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhuoyun Jiang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuanzhong Xie
- Medical Imaging Center, Taian Central Hospital, Taian, Shandong, China
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Kim YJ. Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray. SENSORS (BASEL, SWITZERLAND) 2022; 22:6709. [PMID: 36081170 PMCID: PMC9460643 DOI: 10.3390/s22176709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning.
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Affiliation(s)
- Young Jae Kim
- Department of Biomedical Engineering, Gachon University, 21, Namdong-daero 774 beon-gil, Namdong-gu, Inchon 21936, Korea
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Salihoğlu YS, Uslu Erdemir R, Aydur Püren B, Özdemir S, Uyulan Ç, Ergüzel TT, Tekin HO. Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules. Mol Imaging Radionucl Ther 2022; 31:82-88. [PMID: 35770958 PMCID: PMC9246312 DOI: 10.4274/mirt.galenos.2021.43760] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Objectives This study aimed to evaluate the ability of 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN). Methods Data of 48 patients with SPN detected on 18F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC). Results The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%). Conclusion Machine learning based on 18F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.
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Affiliation(s)
- Yavuz Sami Salihoğlu
- Çanakkale Onsekiz Mart University Faculty of Medicine, Department of Nuclear Medicine, Çanakkale, Turkey
| | - Rabiye Uslu Erdemir
- Zonguldak Bülent Ecevit University Faculty of Medicine, Department of Nuclear Medicine, Zonguldak, Turkey
| | - Büşra Aydur Püren
- Çanakkale Onsekiz Mart University Faculty of Medicine, Department of Nuclear Medicine, Çanakkale, Turkey
| | - Semra Özdemir
- Çanakkale Onsekiz Mart University Faculty of Medicine, Department of Nuclear Medicine, Çanakkale, Turkey
| | - Çağlar Uyulan
- İzmir Katip Çelebi University Faculty of Engineering and Architecture, Department of Mechanical Engineering, İzmir, Turkey
| | - Türker Tekin Ergüzel
- Üsküdar University Faculty of Natural Sciences, Department of Software Engineering, İstanbul, Turkey
| | - Hüseyin Ozan Tekin
- University of Sharjah, College of Health Sciences, Department of Medical Diagnostic Imaging, Sharjah, United Arab Emirates
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11
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Speckter H, Radulovic M, Trivodaliev K, Vranes V, Joaquin J, Hernandez W, Mota A, Bido J, Hernandez G, Rivera D, Suazo L, Valenzuela S, Stoeter P. MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery. J Neurooncol 2022; 159:281-291. [PMID: 35715668 DOI: 10.1007/s11060-022-04063-y] [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: 05/08/2022] [Accepted: 06/07/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS). METHODS The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS. Analysis was performed on original 3D MR images and after their laplacian of gaussian (LoG), logarithm and exponential filtering. The prediction performance was evaluated by Pearson correlation, linear regression and ROC analysis, with meningioma volume change per month as the outcome. RESULTS Sixty calculated features significantly correlated with the outcome. The feature selection based on LASSO and multivariate regression started from all available 337 radiomic and 12 non-radiomic features. It selected LoG-sigma-1-0-mm-3D_firstorder_InterquartileRange and logarithm_ngtdm_Busyness as the predictively most robust and non-redundant features. The radiomic score based on these two features produced an AUC = 0.81. Adding the non-radiomic karnofsky performance status (KPS) to the score has increased the AUC to 0.88. Low values of the radiomic score defined a homogeneous subgroup of 50 patients with consistent absence (0%) of tumor progression. CONCLUSION This is the first report of a strong association between MRI radiomic features and volumetric meningioma response to radiosurgery. The clinical importance of the early and reliable prediction of meningioma responsiveness to radiosurgery is based on its potential to aid individualized therapy decision making.
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Affiliation(s)
- Herwin Speckter
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic.
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Pasterova 14, 11000, Belgrade, Serbia
| | | | - Velicko Vranes
- Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic
| | - Johanna Joaquin
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Wenceslao Hernandez
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Angel Mota
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Jose Bido
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Giancarlo Hernandez
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Diones Rivera
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Luis Suazo
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Santiago Valenzuela
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Peter Stoeter
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
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12
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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13
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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 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:1329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [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: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, 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
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, 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
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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14
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Lai YC, Wu KC, Tseng NC, Chen YJ, Chang CJ, Yen KY, Kao CH. Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography. Front Med (Lausanne) 2022; 9:773041. [PMID: 35372415 PMCID: PMC8971840 DOI: 10.3389/fmed.2022.773041] [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: 09/09/2021] [Accepted: 02/14/2022] [Indexed: 11/26/2022] Open
Abstract
Background The investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT. Methods In total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model. Results All pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively. Conclusion Our results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images.
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Affiliation(s)
- Yung-Chi Lai
- Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan
| | - Kuo-Chen Wu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
| | - Neng-Chuan Tseng
- Division of Nuclear Medicine, Tungs’ Taichung MetroHarbor Hospital, Taichung, Taiwan
| | - Yi-Jin Chen
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
| | - Chao-Jen Chang
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
| | - Kuo-Yang Yen
- Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan
- Department of Biomedical Imaging and Radiological Science, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Chia-Hung Kao
- Department of Nuclear Medicine, PET Center, China Medical University Hospital, Taichung, Taiwan
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
- *Correspondence: Chia-Hung Kao, ,
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15
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Albano D, Gatta R, Marini M, Rodella C, Camoni L, Dondi F, Giubbini R, Bertagna F. Role of 18F-FDG PET/CT Radiomics Features in the Differential Diagnosis of Solitary Pulmonary Nodules: Diagnostic Accuracy and Comparison between Two Different PET/CT Scanners. J Clin Med 2021; 10:jcm10215064. [PMID: 34768584 PMCID: PMC8584460 DOI: 10.3390/jcm10215064] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/26/2021] [Accepted: 10/28/2021] [Indexed: 12/21/2022] Open
Abstract
The aim of this retrospective study was to investigate the ability of 18 fluorine-fluorodeoxyglucose positron emission tomography/CT (18F-FDG-PET/CT) metrics and radiomics features (RFs) in predicting the final diagnosis of solitary pulmonary nodules (SPN). We retrospectively recruited 202 patients who underwent a 18F-FDG-PET/CT before any treatment in two PET scanners. After volumetric segmentation of each lung nodule, 8 PET metrics and 42 RFs were extracted. All the features were tested for significant differences between the two PET scanners. The performances of all features in predicting the nature of SPN were analyzed by testing three classes of final logistic regression predictive models: two were built/trained through exploiting the separate data from the two scanners, and the other joined the data together. One hundred and twenty-seven patients had a final diagnosis of malignancy, while 64 were of a benign nature. Comparing the two PET scanners, we found that all metabolic features and most of RFs were significantly different, despite the cross correlation being quite similar. For scanner 1, a combination between grey level co-occurrence matrix (GLCM), histogram, and grey-level zone length matrix (GLZLM) related features presented the best performances to predict the diagnosis; for scanner 2, it was GLCM and histogram-related features and metabolic tumour volume (MTV); and for scanner 1 + 2, it was histogram features, standardized uptake value (SUV) metrics, and MTV. RFs had a significant role in predicting the diagnosis of SPN, but their accuracies were directly related to the scanner.
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Affiliation(s)
- Domenico Albano
- Nuclear Medicine, University of Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (L.C.); (F.D.); (R.G.); (F.B.)
- Correspondence:
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali dell’Università degli Studi di Brescia, 25128 Brescia, Italy;
| | | | - Carlo Rodella
- Health Physics Department, ASST-Spedali Civili, 25123 Brescia, Italy;
| | - Luca Camoni
- Nuclear Medicine, University of Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (L.C.); (F.D.); (R.G.); (F.B.)
| | - Francesco Dondi
- Nuclear Medicine, University of Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (L.C.); (F.D.); (R.G.); (F.B.)
| | - Raffaele Giubbini
- Nuclear Medicine, University of Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (L.C.); (F.D.); (R.G.); (F.B.)
| | - Francesco Bertagna
- Nuclear Medicine, University of Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (L.C.); (F.D.); (R.G.); (F.B.)
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Palumbo B, Bianconi F, Palumbo I. Solitary pulmonary nodule: Is positron emission tomography/computed tomography radiomics a valid diagnostic approach? Lung India 2021; 38:405-407. [PMID: 34472516 PMCID: PMC8509171 DOI: 10.4103/lungindia.lungindia_266_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Barbara Palumbo
- Department of Medicine and Surgery, Section of Nuclear Medicine and Health Physics, University of Perugia, Perugia, Italy
| | | | - Isabella Palumbo
- Department of Medicine and Surgery, Section of Radiotherapy, University of Perugia, Perugia, Italy
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17
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Kim YJ. Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18168710. [PMID: 34444459 PMCID: PMC8394435 DOI: 10.3390/ijerph18168710] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 12/12/2022]
Abstract
The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to manual muscle mass measurement through computed tomography (CT), artificial intelligence can be leveraged for the automation of these measurements. Although generally difficult to identify with the naked eye, the radiomic features in CT images are informative. In this study, the radiomic features were extracted from L3 CT images of the entire muscle area and partial areas of the erector spinae collected from non-small cell lung carcinoma (NSCLC) patients. The first-order statistics and gray-level co-occurrence, gray-level size zone, gray-level run length, neighboring gray-tone difference, and gray-level dependence matrices were the radiomic features analyzed. The identification performances of the following machine learning models were evaluated: logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting (XGB). Sex, coarseness, skewness, and cluster prominence were selected as the relevant features effectively identifying sarcopenia. The XGB model demonstrated the best performance for the entire muscle, whereas the SVM was the worst-performing model. Overall, the models demonstrated improved performance for the entire muscle compared to the erector spinae. Although further validation is required, the radiomic features presented here could become reliable indicators for quantifying the phenomena observed in the muscles of NSCLC patients, thus facilitating the diagnosis of sarcopenia.
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Affiliation(s)
- Young Jae Kim
- Department of Biomedical Engineering, Gachon University, Inchon 21936, Korea
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18
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Park YJ, Choi D, Choi JY, Hyun SH. Performance Evaluation of a Deep Learning System for Differential Diagnosis of Lung Cancer With Conventional CT and FDG PET/CT Using Transfer Learning and Metadata. Clin Nucl Med 2021; 46:635-640. [PMID: 33883488 DOI: 10.1097/rlu.0000000000003661] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE We aimed to evaluate the performance of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning (TL) and metadata. METHODS A total of 359 patients with a lung mass or nodule who underwent noncontrast chest CT and FDG PET/CT prior to treatment were enrolled retrospectively. All pulmonary lesions were classified by pathology (257 malignant, 102 benign). Deep learning classification models based on ResNet-18 were developed using the pretrained weights obtained from ImageNet data set. We propose a deep TL model for differential diagnosis of lung cancer using CT imaging data and metadata with SUVmax and lesion size derived from PET/CT. The area under the receiver operating characteristic curve (AUC) of the deep learning model was measured as a performance metric and verified by 5-fold cross-validation. RESULTS The performance metrics of the conventional CT model were generally better than those of the CT of PET/CT model. Introducing metadata with SUVmax and lesion size derived from PET/CT into baseline CT models improved the diagnostic performance of the CT of PET/CT model (AUC = 0.837 vs 0.762) and the conventional CT model (AUC = 0.877 vs 0.817). CONCLUSIONS Deep TL models with CT imaging data provide good diagnostic performance for lung cancer, and the conventional CT model showed overall better performance than the CT of PET/CT model. Metadata information derived from PET/CT can improve the performance of deep learning systems.
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Affiliation(s)
| | - Dongmin Choi
- Department of Computer Science, Yonsei University, Seoul, South Korea
| | - Joon Young Choi
- From the Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul
| | - Seung Hyup Hyun
- From the Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul
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Caruso D, Zerunian M, Daffina J, Polici M, Polidori T, Tipaldi MA, Ronconi E, Pucciarelli F, Lucertini E, Rossi M, Laghi A. Radiomics and functional imaging in lung cancer: the importance of radiological heterogeneity beyond FDG PET/CT and lung biopsy. Eur J Radiol 2021; 142:109874. [PMID: 34339955 DOI: 10.1016/j.ejrad.2021.109874] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/24/2020] [Accepted: 07/21/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET/CT) has a central role in the lung nodules' characterization even if, with SUV < 2.5, percutaneous CT-guided Lung Biopsy (CTLB) is needed to assess nodule nature. In that scenario, CT Texture Analysis (CTTA) could be a non-invasive imaging biomarker. Our purpose is to test CTTA ability in differentiating malignant from benign nodules. METHOD Patients that underwent FDG PET/CT followed by CTLB between January 2013 and December 2018 were retrospectively enrolled. Were included patients with lung nodule SUV < 2.5 and histological diagnosis. EXCLUSION CRITERIA nodules SUV > 2.5, patients who refused CTLB or received oncological treatment before CTLB, indeterminate pathology report, CT motion artifacts. Two radiologists in consensus performed CTTA, drawing a volumetric Region of Interest of nodule with a dedicated first order TA software with and without spatial scaling filters, on preliminary CT performed for CTLB. Statistics included a comparison between malignant and benign neoplasms distribution (2-tailed T-test or Mann-Whitney test according to normal/non-normal data distribution), P-values < 0.05 were considered statistically significant. CTTA accuracy was tested with Receiver Operating Characteristics (ROC) curve. RESULTS Form an initial population of 1178, 46 patients encountered inclusion criteria. Pathologist reported 27/46 (59%) malignant and 19/46 (41%) benign nodules. In malignant lesions CTTA showed lower Kurtosis' and higher Skewness' values (all P ≤ 0.0013 and all filtered TA P < 0.024, respectively). ROC curve showed significant Area Under the Curve for Kurtosis and Skewness (0.654 and 0.642, P < 0.001) at medium filtration. CONCLUSIONS CTTA is a promising radiological tool to characterize benign and malignant lung nodules, even in those cases without an altered glucose metabolism.
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Affiliation(s)
- Damiano Caruso
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Marta Zerunian
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Julia Daffina
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Michela Polici
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Tiziano Polidori
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Marcello Andrea Tipaldi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Edoardo Ronconi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Francesco Pucciarelli
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Elena Lucertini
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Michele Rossi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Andrea Laghi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
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20
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Chen X, Sheikh K, Nakajima E, Lin CT, Lee J, Hu C, Hales RK, Forde PM, Naidoo J, Voong KR. Radiation Versus Immune Checkpoint Inhibitor Associated Pneumonitis: Distinct Radiologic Morphologies. Oncologist 2021; 26:e1822-e1832. [PMID: 34251728 PMCID: PMC8488797 DOI: 10.1002/onco.13900] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Patients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies. MATERIALS AND METHODS We systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology. RESULTS Between 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus. CONCLUSION RT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making. IMPLICATIONS FOR PRACTICE Patients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.
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Affiliation(s)
- Xuguang Chen
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Erica Nakajima
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cheng Ting Lin
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chen Hu
- Division of Biostatistics, Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Russell K Hales
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick M Forde
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jarushka Naidoo
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Khinh Ranh Voong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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21
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Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by 18F-FDG PET/CT texture analysis. Sci Rep 2021; 11:11509. [PMID: 34075072 PMCID: PMC8169739 DOI: 10.1038/s41598-021-90674-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 05/10/2021] [Indexed: 12/27/2022] Open
Abstract
The differentiation of non-small cell lung cancer (NSCLC) and radiation pneumonitis (RP) is critically essential for selecting optimal clinical therapeutic strategies to manage post carbon-ion radiotherapy (CIRT) in patients with NSCLC. The aim of this study was to assess the ability of 18F-FDG PET/CT metabolic parameters and its textural image features to differentiate NSCLC from RP after CIRT to develop a differential diagnosis of malignancy and benign lesion. We retrospectively analyzed 18F-FDG PET/CT image data from 32 patients with histopathologically proven NSCLC who were scheduled to undergo CIRT and 31 patients diagnosed with RP after CIRT. The SUV parameters, metabolic tumor volume (MTV), total lesion glycolysis (TLG) as well as fifty-six texture parameters derived from seven matrices were determined using PETSTAT image-analysis software. Data were statistically compared between NSCLC and RP using Wilcoxon rank-sum tests. Diagnostic accuracy was assessed using receiver operating characteristics (ROC) curves. Several texture parameters significantly differed between NSCLC and RP (p < 0.05). The parameters that were high in areas under the ROC curves (AUC) were as follows: SUVmax, 0.64; GLRLM run percentage, 0.83 and NGTDM coarseness, 0.82. Diagnostic accuracy was improved using GLRLM run percentage or NGTDM coarseness compared with SUVmax (p < 0.01). The texture parameters of 18F-FDG uptake yielded excellent outcomes for differentiating NSCLC from radiation pneumonitis after CIRT, which outperformed SUV-based evaluation. In particular, GLRLM run percentage and NGTDM coarseness of 18F-FDG PET/CT images would be appropriate parameters that can offer high diagnostic accuracy.
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22
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Tipaldi MA, Ronconi E, Lucertini E, Krokidis M, Zerunian M, Polidori T, Begini P, Marignani M, Mazzuca F, Caruso D, Rossi M, Laghi A. Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features. Diagnostics (Basel) 2021; 11:diagnostics11060956. [PMID: 34073545 PMCID: PMC8226518 DOI: 10.3390/diagnostics11060956] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/22/2021] [Accepted: 05/24/2021] [Indexed: 02/08/2023] Open
Abstract
(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03-2.35) using texture features, of 1.7 (95% CI: 1.54-1.9) using clinical data and of 4.61 (95% CI: 4.24-5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.
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Affiliation(s)
- Marcello Andrea Tipaldi
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza-University of Rome, 00189 Rome, Italy; (M.Z.); (M.R.); (A.L.)
- Department of Radiology, Sant’Andrea University of Hospital La Sapienza, 00189 Rome, Italy; (E.R.); (E.L.); (T.P.)
- Correspondence: ; Tel.: +39-06-33775391 (ext. 5893)
| | - Edoardo Ronconi
- Department of Radiology, Sant’Andrea University of Hospital La Sapienza, 00189 Rome, Italy; (E.R.); (E.L.); (T.P.)
| | - Elena Lucertini
- Department of Radiology, Sant’Andrea University of Hospital La Sapienza, 00189 Rome, Italy; (E.R.); (E.L.); (T.P.)
| | - Miltiadis Krokidis
- Department of Radiology, Areteion Hospital, National and Kapodistrian University of Athens, 11528 Athens, Greece;
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Marta Zerunian
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza-University of Rome, 00189 Rome, Italy; (M.Z.); (M.R.); (A.L.)
| | - Tiziano Polidori
- Department of Radiology, Sant’Andrea University of Hospital La Sapienza, 00189 Rome, Italy; (E.R.); (E.L.); (T.P.)
| | - Paola Begini
- Department of Liver Diseases Section, AOU Sant’Andrea Hospital, University of Hospital La Sapienza, 00189 Rome, Italy; (P.B.); (M.M.)
| | - Massimo Marignani
- Department of Liver Diseases Section, AOU Sant’Andrea Hospital, University of Hospital La Sapienza, 00189 Rome, Italy; (P.B.); (M.M.)
| | - Federica Mazzuca
- Department of Clinical and Molecular Oncology-Sapienza, University of Rome, Sant’Andrea University Hospital, via di Grottarossa 1035, 00189 Rome, Italy;
| | - Damiano Caruso
- Department of Radiological Sciences, Oncological and Pathological Sciences, University of Rome Sapienza, Sant’Andrea University Hospital, 00189 Rome, Italy;
| | - Michele Rossi
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza-University of Rome, 00189 Rome, Italy; (M.Z.); (M.R.); (A.L.)
- Department of Radiology, Sant’Andrea University of Hospital La Sapienza, 00189 Rome, Italy; (E.R.); (E.L.); (T.P.)
| | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza-University of Rome, 00189 Rome, Italy; (M.Z.); (M.R.); (A.L.)
- Department of Radiology, Sant’Andrea University of Hospital La Sapienza, 00189 Rome, Italy; (E.R.); (E.L.); (T.P.)
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Ripani D, Caldarella C, Za T, Rossi E, De Stefano V, Giordano A. Progression to Symptomatic Multiple Myeloma Predicted by Texture Analysis-Derived Parameters in Patients Without Focal Disease at 18F-FDG PET/CT. CLINICAL LYMPHOMA MYELOMA & LEUKEMIA 2021; 21:536-544. [PMID: 33985932 DOI: 10.1016/j.clml.2021.03.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/25/2021] [Accepted: 03/30/2021] [Indexed: 11/26/2022]
Abstract
This retrospective study is focused on the possible clinical implications of texture analysis-derived PET parameters in patients with smoldering multiple myeloma. Several texture features are significantly associated with progression to symptomatic multiple myeloma and with a shorter time to progression. The results of this study may lead to early identification of patients who could benefit from specific therapies. BACKGROUND The aim of the study was to determine whether positron emission tomography parameters derived from texture analysis of axial and peripheral skeleton predict progression to symptomatic multiple myeloma (MM) in patients undergoing 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) without evidence of focal sites of 18F-FDG uptake. PATIENTS AND METHODS Patients with smoldering MM who underwent 18F-FDG PET/CT from May 2014 to June 2018 were retrospectively reviewed. Volumes of interest (VOIs) were placed on T5-T7 and L2-L4, iliac crests, and femoral diaphyses. Dedicated software (LIFEx) allowed us to obtain PET-derived first-, second-, and higher order texture features. Possible associations between PET parameters and progression to symptomatic MM were determined. Kaplan-Meier curves allowed to assess time to progression (TTP) based on the PET parameters. RESULTS Forty-five patients were included: 26 patients (58%) did not meet the criteria for symptomatic MM, but 19 patients (42%) progressed to symptomatic MM. Several texture features extracted from VOIs placed on iliac crests and femoral diaphyses were significantly associated with progression to symptomatic MM and with a shorter TTP (P < .05); conversely, the above-mentioned parameters extracted from VOIs placed on T5-T7 and L2-L4 did not significantly differ among the patients with regard to their progression to symptomatic MM and length of TTP, except for the gray-level zone length matrix-short-zone low-gray-level emphasis and gray-level zone length matrix-low gray-level zone emphasis. Particularly, second- and higher order texture features showed a significant association with the above-mentioned outcomes. CONCLUSION Texture features derived from PET may be an expression of subtle disease distribution in the axial and peripheral bone marrow.
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Affiliation(s)
- Daria Ripani
- Dipartimento di Scienze Radiologiche ed Ematologiche, Istituto di Medicina Nucleare, Università Cattolica del Sacro Cuore, Rome, Italy; Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Medicina Nucleare, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Carmelo Caldarella
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Medicina Nucleare, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Tommaso Za
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Servizio e Day Hospital di Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo. A. Gemelli, 8, 00168 Rome, Italy
| | - Elena Rossi
- Dipartimento di Scienze Radiologiche ed Ematologiche, Istituto di Ematologia, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy; Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Servizio e Day Hospital di Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo. A. Gemelli, 8, 00168 Rome, Italy
| | - Valerio De Stefano
- Dipartimento di Scienze Radiologiche ed Ematologiche, Istituto di Ematologia, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy; Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Servizio e Day Hospital di Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo. A. Gemelli, 8, 00168 Rome, Italy
| | - Alessandro Giordano
- Dipartimento di Scienze Radiologiche ed Ematologiche, Istituto di Medicina Nucleare, Università Cattolica del Sacro Cuore, Rome, Italy; Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Medicina Nucleare, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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24
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Niu R, Wang Y, Shao X, Jiang Z, Wang J, Shao X. Association Between 18F-FDG PET/CT-Based SUV Index and Malignant Status of Persistent Ground-Glass Nodules. Front Oncol 2021; 11:594693. [PMID: 33842310 PMCID: PMC8024639 DOI: 10.3389/fonc.2021.594693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 03/02/2021] [Indexed: 11/14/2022] Open
Abstract
To explore the association between 18F-FDG PET/CT-based SUV index and malignant risk of persistent ground-glass nodules (GGNs). We retrospectively analyzed a total of 166 patients with GGN who underwent PET/CT examination from January 2012 to October 2019. There were 113 women and 53 men, with an average age of 60.8 ± 9.1 years old. A total of 192 GGNs were resected and confirmed by pathology, including 22 in benign group and 170 in adenocarcinoma group. They were divided into three groups according to SUV index tertiles: Tertile 1 (0.14–0.54), Tertile 2 (0.55–1.17), and Tertile 3 (1.19–6.78), with 64 GGNs in each group. The clinical and imaging data of all patients were collected and analyzed. After adjusting for the potential confounding factors, we found that the malignancy risk of GGN significantly decreased as the SUV index increased (OR, 0.245; 95%CI, 0.119–0.504; P <0.001), the average probability of malignant GGN was 89.1% (95% CI, 53.1–98.3%), 80.5% (95% CI, 36.7–96.7%), and 34.3% (95%CI, 9.5–72.2%) for Tertile 1 to Tertile 3. And the increasing trend of SUV index was significantly correlated with the reduction of malignant risk (OR, 0.099; 95%CI, 0.025–0.394; P = 0.001), especially between Tertile 3 versus Tertile 1 (OR, 0.064; 95%CI, 0.012–0.356; P = 0.002). Curve fitting showed that the SUV index was linearly and negatively correlated with the malignant risk of GGN. SUV index is an independent correlation factor for malignancy risk of GGN, the higher the SUV index, the lower the probability of GGN malignancy.
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Affiliation(s)
- Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Zhenxing Jiang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Jianfeng Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, The Third Affiliated Hospital of Soochow University, Changzhou, China
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25
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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: 34] [Impact Index Per Article: 11.3] [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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26
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Ren C, Li M, Zhang Y, Zhang S. Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Cancer Imaging 2020; 20:86. [PMID: 33308325 PMCID: PMC7731456 DOI: 10.1186/s40644-020-00364-5] [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: 02/26/2020] [Accepted: 11/26/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. METHODS Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots. RESULTS Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications. CONCLUSION A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.
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Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China. .,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.
| | - Mingli Li
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China
| | - Yunyan Zhang
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.,Department of Radiology, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
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Correlation of texture feature analysis with bone marrow infiltration in initial staging of patients with lymphoma using 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography. Pol J Radiol 2020; 85:e586-e594. [PMID: 33204373 PMCID: PMC7654316 DOI: 10.5114/pjr.2020.99833] [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: 05/07/2020] [Accepted: 08/29/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose To explore whether radiomic features of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomo-graphy-computed tomography (PET/CT) has association with bone marrow infiltration (BMI) in comparison to other conventional PET metrics. Material and methods Forty-four patients (with pathologically proven lymphoma disease) underwent staging 18F-FDG PET/CT scan. Primary tumour was semi-automatically or manually segmented with a threshold standardised uptake value (SUV) of 3. A total of 73 features were extracted from eight different textures. Spearman correlation was used to test the correlation of features with conventional quantitative metrics such as SUV, metabolic tumour volume, and total lesion glycolysis. Specificity and sensitivity (including 95% confidence intervals [CI]) for each of the studied parameters were derived using receiver operative characteristic (ROC) curves. Univariate and multivariate analyses were used to identify independent predictors associated with BMI. Results Correlation between conventional PET metrics and features ranged between 0.50 and 0.97 for positive correlation (33 significant association features) and ranged from -0.52 to -0.97 for inverse correlation (three significant association features) for both strong and moderate correlations. Analysis of ROC curves showed that high-intensity long-run emphasis 4 bin, high-intensity large zone emphasis 64 bin, long-run emphasis (LRE) 64 bin, large-zone emphasis 64 bin, max spectrum 8 bin, busyness 64 bin, and code similarity 32 and 64 bin were significant discriminators of BMI among other features (area under curve > 0.682, p < 0.05). Univariate analyses of texture features showed that code similarity and long-run emphasis (both 64 bin) were significant predictors of bone marrow involvement. Multivariate analyses revealed that LRE (64 bin, p = 0.031) with an odds ratio of 1.022 and 95% CI of (1.002-1.043) were independent variables for bone marrow involvement. Conclusions 18F-FDG PET/CT radiomic features are synergistic to visual assessment of BMI in patients diagnosed with lymphoma using 18F-FDG PET/CT. Further assessment of long-run emphasis is highly warranted.
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MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting. Int J Mol Sci 2020; 21:ijms21218004. [PMID: 33121211 PMCID: PMC7662499 DOI: 10.3390/ijms21218004] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/23/2020] [Accepted: 10/25/2020] [Indexed: 12/25/2022] Open
Abstract
Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffused Weighted Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.
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Palumbo B, Bianconi F, Palumbo I, Fravolini ML, Minestrini M, Nuvoli S, Stazza ML, Rondini M, Spanu A. Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation. Diagnostics (Basel) 2020; 10:E696. [PMID: 32942729 PMCID: PMC7555302 DOI: 10.3390/diagnostics10090696] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/10/2020] [Accepted: 09/10/2020] [Indexed: 12/12/2022] Open
Abstract
In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4-11.2 pp and 2.2-10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.
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Affiliation(s)
- Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (B.P.); (M.M.)
| | - Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06135 Perugia, Italy;
| | - Isabella Palumbo
- Section of Radiation Oncology, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy;
| | - Mario Luca Fravolini
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06135 Perugia, Italy;
| | - Matteo Minestrini
- Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (B.P.); (M.M.)
| | - Susanna Nuvoli
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (S.N.); (M.L.S.); (M.R.); (A.S.)
| | - Maria Lina Stazza
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (S.N.); (M.L.S.); (M.R.); (A.S.)
| | - Maria Rondini
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (S.N.); (M.L.S.); (M.R.); (A.S.)
| | - Angela Spanu
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (S.N.); (M.L.S.); (M.R.); (A.S.)
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2-[ 18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2020; 188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
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