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Jia X, Wang Y, Zhang H, Sun D. Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment. Quant Imaging Med Surg 2024; 14:6978-6989. [PMID: 39281123 PMCID: PMC11400702 DOI: 10.21037/qims-24-22] [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: 01/04/2024] [Accepted: 07/25/2024] [Indexed: 09/18/2024]
Abstract
Background Radiomics extracts specific quantitative data from medical images and explores the characteristics of tumors by analyzing these representations and making predictions. The purpose of this paper is to review computed tomography (CT)-based radiomics articles related to prognostic outcomes in non-small cell lung cancer (NSCLC), assess their scientificity and quality by the latest radiomics quality score (RQS) 2.0 scoring criteria, and provide references for subsequent related studies. Methods CT-based radiomics studies on NSCLC prognosis published from 1 November 2012 to 30 November 2022 in English were screened through the databases of the Cochrane Library, Embase, and PubMed. By excluding criteria such as non-original studies, small sample sizes studies, positron emission tomography (PET)/CT only, and methodological studies only, 17 studies in English were included. The RQS proposed in 2017 is a quality evaluation index specific to radiomics following the PRISMA guidelines, and the latest update of RQS 2.0 has improved the scientificity and completeness of the score. Each checkpoint either belongs to handcrafted radiomics (HCR), deep learning, or both. Results The 17 included studies covered most treatments for NSCLC, including radiotherapy, chemotherapy, surgery, radiofrequency ablation, immunotherapy, and targeted therapy, and predicted outcomes such as overall survival (OS), progression-free survival (PFS), distant metastases, and disease-free survival (DFS). The median score rate for the included studies was 28%, with a range of 12% to 44%. The quality of studies in HCR is not high, and only 4 studies have been validated with independent cohorts. Conclusions The value of radiomics studies needs to be increased, such that clinical application will be possible, and the field of radiomics still has much room for growth. To make prediction models more reliable and stable in forecasting the prognosis of NSCLC and advancing the individualized treatment of NSCLC patients, more clinicians must participate in their development and clinical testing.
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Affiliation(s)
- Xiaoteng Jia
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Yuhang Wang
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Han Zhang
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Daqiang Sun
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, Tianjin, China
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Oka H, Kawahara D, Murakami Y. Radiomics-based prediction of recurrence for head and neck cancer patients using data imbalanced correction. Comput Biol Med 2024; 180:108879. [PMID: 39067154 DOI: 10.1016/j.compbiomed.2024.108879] [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: 11/16/2023] [Revised: 06/21/2024] [Accepted: 07/10/2024] [Indexed: 07/30/2024]
Abstract
OBJECTIVES To propose a radiomics-based prediction model for head and neck squamous cell carcinoma (HSNCC) recurrence after radiation therapy using a novel data imbalance correction method known as Gaussian noise upsampling (GNUS). MATERIALS AND METHODS The dataset includes 97 HNSCC patients treated with definitive radiotherapy alone or concurrent chemoradiotherapy at two institutions. We performed radiomics analysis using nine segmentations created on pretreatment positron emission tomography and computed tomography images. Feature selection was performed by the least absolute shrinkage and selection operator analysis via five-fold cross-validation. The proposed GNUS was compared with seven conventional data-imbalance correction methods. Classification models of HNSCC recurrence were constructed on oversampled features using the machine learning algorithms of linear regression. Their predictive performance was evaluated based on accuracy, sensitivity, specificity, and the area under the curve (AUC) of the receiver operating performance characteristic curve via five-fold cross-validation using the same combinations as for feature selection. RESULT The prediction model without data imbalance correction shows sensitivity, specificity, accuracy, and AUC values of 83 %, 96 %, 92 %, and 0.96, respectively. The conventional model with the best performance is the random over-sampler model, which shows sensitivity, specificity, accuracy, and AUC values of 93 %, 91 %, 92 %, 0.97, respectively, whereas the GNUS model shows values of 93 %, 94 %, 94 %, 0.98, respectively. CONCLUSION Oversampling methods can reduce sensitivity and specificity bias. The proposed GNUS can improve accuracy as well as reduce sensitivity and specificity bias.
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Affiliation(s)
- Hiroki Oka
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
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Yu Y, Han C, Gan X, Tian W, Zhou C, Zhou Y, Xu X, Wen Z, Liu W. Predictive value of spectral computed tomography parameters for EGFR gene mutation in non-small-cell lung cancer. Clin Radiol 2024; 79:e1049-e1056. [PMID: 38797609 DOI: 10.1016/j.crad.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/25/2024] [Accepted: 04/27/2024] [Indexed: 05/29/2024]
Abstract
AIM To explore the predictive value of morphological signs and quantitative parameters from spectral CT for EGFR gene mutations in intermediate and advanced non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS This retrospective observational study included patients with intermediate or advanced NSCLC at Xinjiang Medical University Affiliated Tumor Hospital between January 2017 and December 2019. The patients were divided into the EGFR gene mutation-positive and -negative groups. RESULTS Seventy-nine patients aged 60.75 ± 9.66 years old were included: 32 were EGFR mutation-positive, and 47 were negative. There were significant differences in pathological stage (P<0.001), tumor diameter (P=0.019), lobulation sign, intrapulmonary metastasis, mediastinal lymph node metastasis, distant metastasis (P<0.001), bone metastasis (P<0.001), arterial phase normalized iodine concentration (NIC) (P=0.001), venous phase NIC (P=0.001), slope of the energy spectrum curve (λ) (P<0.001), and CT value at 70 keV in arterial phase (P=0.004) and venous phase (P=0.003) between the EGFR mutation-positive and -negative patients. The multivariable logistic regression analysis showed that intrapulmonary metastasis, distant metastasis, venous phase NIC, venous phase λ, and pathological stage were independent factors predicting EGFR gene mutations, with high diagnostic power (AUC = 0.975, 91.5% sensitivity, and 90.6% specificity). CONCLUSION The pathological stage and the spectral CT parameters of intrapulmonary metastasis, distant metastasis, venous phase NIC, and venous phase λ might pre-operatively predict EGFR gene mutations in intermediate and advanced NSCLC.
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Affiliation(s)
- Y Yu
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi 830011, China; Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - C Han
- Department of Laboratory, Traditional Chinese Medical Hospital of Xinjiang Uygur Autonomous Region, Urumchi 830011, China
| | - X Gan
- Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - W Tian
- Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - C Zhou
- Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - Y Zhou
- Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - X Xu
- Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - Z Wen
- Department of Radiology, Xinjiang Medical University Affiliated Tumor Hospital, Urumchi 830011, China
| | - W Liu
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi 830011, China.
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Zhi H, Xiang Y, Chen C, Zhang W, Lin J, Gao Z, Shen Q, Shao J, Yang X, Yang Y, Chen X, Zheng J, Lu M, Pan B, Dong Q, Shen X, Ma C. Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival. Cancer Imaging 2024; 24:99. [PMID: 39080806 PMCID: PMC11290137 DOI: 10.1186/s40644-024-00741-4] [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: 05/09/2024] [Accepted: 07/13/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC. METHODS We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness. RESULTS On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis. CONCLUSIONS Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.
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Affiliation(s)
- Huaiqing Zhi
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yilan Xiang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Chenbin Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Weiteng Zhang
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jie Lin
- Department of PET/CT, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Zekan Gao
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qingzheng Shen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jiancan Shao
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xinxin Yang
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yunjun Yang
- Department of PET/CT, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xiaodong Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jingwei Zheng
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Mingdong Lu
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Bujian Pan
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qiantong Dong
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - Xian Shen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - Chunxue Ma
- Department of Gastrointestinal Surgery Nursing Unit, Ward 443, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
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Kasap DNG, Mora NGN, Blömer DA, Akkurt BH, Heindel WL, Mannil M, Musigmann M. Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning. Biomedicines 2024; 12:725. [PMID: 38672080 PMCID: PMC11048271 DOI: 10.3390/biomedicines12040725] [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: 01/22/2024] [Revised: 02/24/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVES Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. MATERIAL AND METHODS In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. RESULTS Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. CONCLUSIONS Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.
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Li J, Cui N, Jiang Z, Li W, Liu W, Wang S, Wang K. Differentiating thymic epithelial tumors from mediastinal lymphomas: preoperative nomograms based on PET/CT radiomic features to minimize unnecessary anterior mediastinal surgery. J Cancer Res Clin Oncol 2023; 149:14101-14112. [PMID: 37552308 DOI: 10.1007/s00432-023-05054-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 06/28/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE Clinical feasibility nomograms were developed to facilitate the differentiation between thymic epithelial tumors (TETs) and mediastinal lymphomas (MLs), aiming to minimize the occurrence of non-therapeutic thymectomy. METHODS A total of 255 patients diagnosed with TETs or MLs underwent pre-treatment 18F-FDG PET/CT. Comprehensive clinical and imaging data were collected, including age, gender, lactate dehydrogenase (LDH) level, pathological results, presence of myasthenia gravis symptoms, B symptoms, mass size, location, morphology, margins, density, and metabolic parameters derived from PET/CT. Radiomic features were extracted from the region of interest (ROI) of the primary lesion. Feature selection techniques were employed to identify the most discriminative subset of features. Machine learning methods were utilized to build candidate models, which were subsequently evaluated based on their area under the curve (AUC). Finally, nomograms were constructed using the optimal model to provide a clinical tool for improved diagnostic accuracy. The performance of the radiomic models was evaluated by their calibration, discrimination, and clinical utility. RESULTS Several independent risk factors were identified for distinguishing TETs from MLs, including average standardized uptake value (SUVavg), LDH, age, mass size, and radiomic score (rad-score). Significance was observed in differentiating the two types of tumors based on these factors. The best clinical efficacy was demonstrated by the combined model, with an impressive AUC of 0.954. Decision curve analysis and calibration curves indicated that the combined model was clinically advantageous for discriminating TETs from MLs. Besides, the results of external validation showed a sensitivity of 0.8 and a specificity of 0.78. CONCLUSION Preoperatively, the differentiation of TETs from MLs can be facilitated by the utilization of the combined clinical information and radiomics model. This approach holds promise in minimizing the occurrence of unnecessary anterior mediastinal surgeries.
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Affiliation(s)
- Jiatong Li
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Nan Cui
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Zhiyun Jiang
- Radiology Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Wei Li
- Interventional Vascular Surgery Department, The 4th Affiliated Hospital of Harbin Medical University, Harbin Medical University, 37 Yiyuan Road, Harbin, 150001, Heilongjiang, China
| | - Wei Liu
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Shuai Wang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, China.
| | - Kezheng Wang
- PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
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Zhou Y, Zhang B, Han J, Dai N, Jia T, Huang H, Deng S, Sang S. Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells. J Cancer Res Clin Oncol 2023; 149:11549-11560. [PMID: 37395846 DOI: 10.1007/s00432-023-05038-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 07/04/2023]
Abstract
BACKGROUND In our current work, an 18F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy. METHODS A total of 61 DLBCL cases receiving 18F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated. RESULTS The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models. CONCLUSIONS PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.
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Affiliation(s)
- Yeye Zhou
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Bin Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Jiangqin Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Na Dai
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Tongtong Jia
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Haiwen Huang
- Institute of Blood and Marrow Transplantation, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
| | - Shengming Deng
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China.
| | - Shibiao Sang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
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Hu D, Li X, Lin C, Wu Y, Jiang H. Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images. Diagnostics (Basel) 2023; 13:3107. [PMID: 37835850 PMCID: PMC10573026 DOI: 10.3390/diagnostics13193107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
(1) Background: Cell proliferation (Ki-67) has important clinical value in the treatment and prognosis of non-small cell lung cancer (NSCLC). However, current detection methods for Ki-67 are invasive and can lead to incorrect results. This study aimed to explore a deep learning classification model for the prediction of Ki-67 and the prognosis of NSCLC based on FDG-PET/CT images. (2) Methods: The FDG-PET/CT scan results of 159 patients with NSCLC confirmed via pathology were analyzed retrospectively, and the prediction models for the Ki-67 expression level based on PET images, CT images and PET/CT combined images were constructed using Densenet201. Based on a Ki-67 high expression score (HES) obtained from the prediction model, the survival rate of patients with NSCLC was analyzed using Kaplan-Meier and univariate Cox regression. (3) Results: The statistical analysis showed that Ki-67 expression was significantly correlated with clinical features of NSCLC, including age, gender, differentiation state and histopathological type. After a comparison of the three models (i.e., the PET model, the CT model, and the FDG-PET/CT combined model), the combined model was found to have the greatest advantage in Ki-67 prediction in terms of AUC (0.891), accuracy (0.822), precision (0.776) and specificity (0.902). Meanwhile, our results indicated that HES was a risk factor for prognosis and could be used for the survival prediction of NSCLC patients. (4) Conclusions: The deep-learning-based FDG-PET/CT radiomics classifier provided a novel non-invasive strategy with which to evaluate the malignancy and prognosis of NSCLC.
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Affiliation(s)
- Dehua Hu
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
| | - Xiang Li
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
| | - Chao Lin
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
| | - Yonggang Wu
- Department of Nuclear Medicine & PET Imaging Center, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Hao Jiang
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
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Shu Y, Xu W, Su R, Ran P, Liu L, Zhang Z, Zhao J, Chao Z, Fu G. Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis. Front Immunol 2023; 14:1251645. [PMID: 37799725 PMCID: PMC10547882 DOI: 10.3389/fimmu.2023.1251645] [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: 07/02/2023] [Accepted: 08/24/2023] [Indexed: 10/07/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) modulate the body's immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.
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Affiliation(s)
- Yang Shu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Wei Xu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Rui Su
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Pancen Ran
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Lei Liu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhizhao Zhang
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jing Zhao
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Guobin Fu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Department of Oncology, The Third Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China
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Li Y, Xu W, Fei Y, Wu M, Yuan J, Qiu L, Zhang Y, Chen G, Cheng Y, Cao Y, Zhou S. A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients. Discov Oncol 2023; 14:154. [PMID: 37612579 PMCID: PMC10447352 DOI: 10.1007/s12672-023-00751-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/08/2023] [Indexed: 08/25/2023] Open
Abstract
OBJECTIVE Anlotinib is a multitarget anti-angiogenic drug that combined with temozolomide (TMZ) can effectively prolongs the overall survival (OS) of recurrent malignant glioma(rMG),but some patients do not respond to anlotinib combined with TMZ. These patients were associated with a worse prognosis and lack effective identification methods. Therefore, it is necessary to differentiate patients who may have good response to anlotinb in combination with TMZ from those who are not, in order to provide personalized targeted therapies. METHODS Fifty three rMG patients (42 in training cohort and 11 in testing cohort) receiving anlotinib combined with TMZ were enrolled. A total of 3668 radiomics features were extracted from the recurrent MRI images. Radiomics features are reduced and filtered by hypothesis testing and Least Absolute Shrinkage And Selection (LASSO) regression. Eight machine learning models construct the radiomics model, and then screen out the optimal model. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with validation. RESULTS Fifty three patients with rMG were enrolled in our study. Thirty four patients displayed effective treatment response, showed a higher survival benefits than non-response group, the median progression-free survival(PFS) was 8.53 months versus 5.33 months (p = 0.06) and the median OS was 19.9 months and 7.33 months (p = 0.029), respectively. Three radiomics features were incorporated into the model construction as final variables after LASSO regression analysis. In testing cohort, Logistic Regression (LR) model has the best performance with an Area Under the Curve (AUC) of 0.93 compared with other models, which can effectively predict the response of rMG patients to anlotinib in combination with TMZ. The calibration curve confirmed the agreement between the observed actual and prediction probability. Within the reasonable threshold probability range (0.38-0.88), the radiomics model shows good clinical utility. CONCLUSIONS The above-described radiomics model performed well, which can serve as a clinical tool for individualized prediction of the response to anlotinb combined with TMZ in rMG patients.
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Affiliation(s)
- Yurong Li
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weilin Xu
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Yinjiao Fei
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Mengxing Wu
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Jinling Yuan
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Lei Qiu
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Yumeng Zhang
- Department of Radiation Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 201204, China
| | - Guanhua Chen
- Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yu Cheng
- Department of Oncology, The Second Hospital of Nanjing, Nanjing, China
| | - Yuandong Cao
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China.
| | - Shu Zhou
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China.
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Degtiarova G, Garefa C, Boehm R, Ciancone D, Sepulcri D, Gebhard C, Giannopoulos AA, Pazhenkottil AP, Kaufmann PA, Buechel RR. Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography. J Nucl Cardiol 2023; 30:1474-1483. [PMID: 36600174 PMCID: PMC10371953 DOI: 10.1007/s12350-022-03179-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 11/28/2022] [Indexed: 01/06/2023]
Abstract
AIM The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR). METHODS Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR. RESULTS A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion. CONCLUSION A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.
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Affiliation(s)
- Ganna Degtiarova
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Chrysoula Garefa
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Reto Boehm
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Domenico Ciancone
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Daniel Sepulcri
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Catherine Gebhard
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Andreas A. Giannopoulos
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Aju P. Pazhenkottil
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Philipp A. Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Ronny R. Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
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12
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Zheng YM, Che JY, Yuan MG, Wu ZJ, Pang J, Zhou RZ, Li XL, Dong C. A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma. Acad Radiol 2023; 30:1591-1599. [PMID: 36460582 DOI: 10.1016/j.acra.2022.11.007] [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: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/10/2022] [Indexed: 12/03/2022]
Abstract
RATIONALE AND OBJECTIVES Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC. MATERIALS AND METHODS A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA). RESULTS Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC. CONCLUSION A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jun-Yi Che
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui-Zhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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13
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Chen TY, Yang ZG, Li Y, Li MQ. Radiomic advances in the transarterial chemoembolization related therapy for hepatocellular carcinoma. World J Radiol 2023; 15:89-97. [PMID: 37181821 PMCID: PMC10167813 DOI: 10.4329/wjr.v15.i4.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/26/2023] Open
Abstract
Radiomics is a hot topic in the research on customized oncology treatment, efficacy evaluation, and tumor prognosis prediction. To achieve the goal of mining the heterogeneity information within the tumor tissue, the image features concealed within the tumoral images are turned into quantifiable data features. This article primarily describes the research progress of radiomics and clinical-radiomics combined model in the prediction of efficacy, the choice of treatment modality, and survival in transarterial chemoembolization (TACE) and TACE combination therapy for hepatocellular carcinoma.
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Affiliation(s)
- Tian-You Chen
- Department of Interventional Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Zong-Guo Yang
- Department of Integrative Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China
| | - Mao-Quan Li
- Department of Interventional & Vascular Surgery, Tenth People's Hospital of Tongji University, Tongji University, Shanghai 200433, China
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Sun MX, Zhao MJ, Zhao LH, Jiang HR, Duan YX, Li G. A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma. Radiat Oncol 2023; 18:67. [PMID: 37041545 PMCID: PMC10088158 DOI: 10.1186/s13014-023-02257-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 04/03/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA nasopharyngeal carcinoma (NPC) in South China. METHODS One hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method. RESULTS Integrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis. CONCLUSIONS MR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.
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Affiliation(s)
- Mi-Xue Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Meng-Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Li-Hao Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Hao-Ran Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Yu-Xia Duan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
| | - Gang Li
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
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15
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Zhang N, Zhang X, Li J, Ren J, Li L, Dong W, Liu Y. CT-derived radiomic analysis for predicting the survival rate of patients with non-small cell lung cancer receiving radiotherapy. Phys Med 2023; 107:102546. [PMID: 36796178 DOI: 10.1016/j.ejmp.2023.102546] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 12/09/2022] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Radiomics provides an opportunity to minimize adverse effects and optimize the efficacy of treatments noninvasively. This study aims to develop a computed tomography (CT) derived radiomic signature to predict radiological response for the patients with non-small cell lung cancer (NSCLC) receiving radiotherapy. METHODS Total 815 NSCLC patients receiving radiotherapy were sourced from public datasets. Using CT images of 281 NSCLC patients, we adopted genetic algorithm to establish a predictive radiomic signature for radiotherapy that had optimal C-index value by Cox model. Survival analysis and receiver operating characteristic curve were performed to estimate the predictive performance of the radiomic signature. Furthermore, radiogenomics analysis was performed in a dataset with matched images and transcriptome data. RESULTS Radiomic signature consisting of three features was established and then validated in the validation dataset (log-rank P = 0.0047) including 140 patient, and showed a significant predictive power in two independent datasets totaling 395 NSCLC patients with binary 2-year survival endpoint. Furthermore, the novel proposed radiomic nomogram significantly improved the prognostic performance (concordance index) of clinicopathological factors. Radiogenomics analysis linked our signature with important tumor biological processes (e.g. Mismatch repair, Cell adhesion molecules and DNA replication) associated with clinical outcomes. CONCLUSIONS The radiomic signature, reflecting tumor biological processes, could noninvasively predict therapeutic efficacy of NSCLC patients receiving radiotherapy and demonstrate unique advantage for clinical application.
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Affiliation(s)
- Nannan Zhang
- Modern Educational Technology and Experiment Center, Harbin Normal University, Harbin, China
| | - Xinxin Zhang
- College of Life Science and Technology, Harbin Normal University, Harbin, China
| | - Junheng Li
- Basic Medicine College, Harbin Medical University, Harbin, China
| | - Jie Ren
- Basic Medicine College, Harbin Medical University, Harbin, China
| | - Luyang Li
- Basic Medicine College, Harbin Medical University, Harbin, China
| | - Wenlei Dong
- Department of Radiotherapy Technology Center, Harbin Medical University Cancer Hospital, Harbin, China.
| | - Yixin Liu
- Basic Medicine College, Harbin Medical University, Harbin, China.
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Muntean DD, Lenghel LM, Ștefan PA, Fodor D, Bădărînză M, Csutak C, Dudea SM, Rusu GM. Radiomic Features Associated with Lymphoma Development in the Parotid Glands of Patients with Primary Sjögren's Syndrome. Cancers (Basel) 2023; 15:cancers15051380. [PMID: 36900173 PMCID: PMC10000076 DOI: 10.3390/cancers15051380] [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: 01/05/2023] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
Non-Hodgkin Lymphoma (NHL) represents a severe complication and the main cause of morbidity in patients with primary Sjögren's syndrome (pSS). This study aimed to assess the role of textural analysis (TA) in revealing lymphoma-associated imaging parameters in the parotid gland (PG) parenchyma of patients with pSS. This retrospective study included a total of 36 patients (54.93 ± 13.34 years old; 91.6% females) diagnosed with pSS according to the American College of Rheumatology and the European League Against Rheumatism criteria (24 subjects with pSS and no lymphomatous proliferation; 12 subjects with pSS and NHL development in the PG, confirmed by the histopathological analysis). All subjects underwent MR scanning between January 2018 and October 2022. The coronal STIR PROPELLER sequence was employed to segment PG and perform TA using the MaZda5 software. A total of 65 PGs underwent segmentation and texture feature extraction (48 PGs were included in the pSS control group, and 17 PGs were included in the pSS NHL group). Following parameter reduction techniques, univariate analysis, multivariate regression, and receiver operating characteristics (ROC) analysis, the following TA parameters proved to be independently associated with NHL development in pSS: CH4S6_Sum_Variance and CV4S6_Inverse_Difference_Moment, with an area under ROC of 0.800 and 0.875, respectively. The radiomic model (resulting by combining the two previously independent TA features), presented 94.12% sensitivity and 85.42% specificity in differentiating between the two studied groups, reaching the highest area under ROC of 0.931 for the chosen cutoff value of 1.556. This study suggests the potential role of radiomics in revealing new imaging biomarkers that might serve as useful predictors for lymphoma development in patients with pSS. Further research on multicentric cohorts is warranted to confirm the obtained results and the added benefit of TA in risk stratification for patients with pSS.
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Affiliation(s)
- Delia Doris Muntean
- Radiology Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Lavinia Manuela Lenghel
- Radiology Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Correspondence: (L.M.L.); (P.A.Ș.)
| | - Paul Andrei Ștefan
- Anatomy and Embryology, Morphological Sciences Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, General Hospital of Vienna (AKH), Waehringer Guertel 18-20, 1090 Vienna, Austria
- Correspondence: (L.M.L.); (P.A.Ș.)
| | - Daniela Fodor
- 2nd Internal Medicine Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Maria Bădărînză
- 2nd Internal Medicine Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Csaba Csutak
- Radiology Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Sorin Marian Dudea
- Radiology Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Georgeta Mihaela Rusu
- Radiology Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
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Yu Y, Bai Y, Zheng P, Wang N, Deng X, Ma H, Yu R, Ma C, Liu P, Xie Y, Wang C, Chen H. Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers. BMC Cancer 2022; 22:1241. [PMID: 36451109 PMCID: PMC9710011 DOI: 10.1186/s12885-022-10344-6] [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: 04/30/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) represent an approved treatment for various cancers; however, only a small proportion of the population is responsive to such treatment. We aimed to develop and validate a plain CT-based tool for predicting the response to ICI treatment among cancer patients. METHODS Data for patients with solid cancers treated with ICIs at two centers from October 2019 to October 2021 were randomly divided into training and validation sets. Radiomic features were extracted from pretreatment CT images of the tumor of interest. After feature selection, a radiomics signature was constructed based on the least absolute shrinkage and selection operator regression model, and the signature and clinical factors were incorporated into a radiomics nomogram. Model performance was evaluated using the training and validation sets. The Kaplan-Meier method was used to visualize associations with survival. RESULTS Data for 122 and 30 patients were included in the training and validation sets, respectively. Both the radiomics signature (radscore) and nomogram exhibited good discrimination of response status, with areas under the curve (AUC) of 0.790 and 0.814 for the training set and 0.831 and 0.847 for the validation set, respectively. The calibration evaluation indicated goodness-of-fit for both models, while the decision curves indicated that clinical application was favorable. Both models were associated with the overall survival of patients in the validation set. CONCLUSIONS We developed a radiomics model for early prediction of the response to ICI treatment. This model may aid in identifying the patients most likely to benefit from immunotherapy.
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Affiliation(s)
- Yang Yu
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Yuping Bai
- grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China ,grid.411294.b0000 0004 1798 9345Department of MR, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Peng Zheng
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Na Wang
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Xiaobo Deng
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Huanhuan Ma
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Rong Yu
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Chenhui Ma
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Peng Liu
- grid.461867.a0000 0004 1765 2646Department of Radiology, Gansu Provincial Cancer Hospital, Lanzhou, 730050 Gansu China
| | - Yijing Xie
- grid.411294.b0000 0004 1798 9345Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Chen Wang
- grid.411294.b0000 0004 1798 9345Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Hao Chen
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
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18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy. Acad Radiol 2022:S1076-6332(22)00548-7. [DOI: 10.1016/j.acra.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/22/2022] [Accepted: 10/11/2022] [Indexed: 11/27/2022]
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Xue T, Peng H, Chen Q, Li M, Duan S, Feng F. A CT-Based Radiomics Nomogram in Predicting the Postoperative Prognosis of Colorectal Cancer: A Two-center Study. Acad Radiol 2022; 29:1647-1660. [PMID: 35346564 DOI: 10.1016/j.acra.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/25/2022] [Accepted: 02/06/2022] [Indexed: 01/26/2023]
Abstract
RATIONALE AND OBJECTIVES This retrospective study aimed to develop a practical model to determine overall survival after surgery in patients with colorectal cancer according to radiomics signatures based on computed tomography (CT) images and clinical predictors. MATERIALS AND METHODS A total of 121 colorectal cancer (CRC) patients were selected to construct the model, and 51 patients and 114 patients were selected for internal validation and external testing. The radiomics features were extracted from each patient's CT images. Univariable Cox regression and least absolute shrinkage and selection operator regression were used to select radiomics features. The performance of the nomogram was evaluated by calibration curves and the c-index. Kaplan-Meier analysis was used to compare the overall survival between these subgroups. RESULTS The radiomics features of the CRC patients were significantly correlated with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of CRC patients' overall survival. The calibration curves showed that the predicted survival time was close to the actual survival time. According to Kaplan-Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group were higher than those of the high-risk group. CONCLUSION The nomogram combining the optimal radiomics signature and clinical predictors further improved the predicted accuracy of survival prognosis for CRC patients. These findings might affect treatment strategies and enable a step forward for precise medicine.
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Affiliation(s)
- Ting Xue
- Nantong University, Nantong, Jiangsu, PR China
| | - Hui Peng
- Nantong University, Nantong, Jiangsu, PR China
| | | | - Manman Li
- Nantong University, Nantong, Jiangsu, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu 226361, PR China.
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20
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Wu L, Li J, Ruan X, Ren J, Ping X, Chen B. Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images. Int J Gen Med 2022; 15:6725-6738. [PMID: 36039307 PMCID: PMC9419990 DOI: 10.2147/ijgm.s374002] [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: 05/20/2022] [Accepted: 08/03/2022] [Indexed: 12/02/2022] Open
Abstract
Background Energy spectrum CT is an effective method to evaluate the biological behavior of lung cancer. Radiomics is a non-invasive technology to obtain histological information related to lung cancer. Purpose To investigate the value of the radiomics models on the bases of enhanced spectral CT images of peripheral lung cancer to predict the expression of the vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR). Material and Methods This study retrospectively analyzed 73 patients with peripheral lung cancer confirmed by postoperative pathology. All patients underwent dual-phase enhanced spectral CT scans before surgery. Regions of interest (ROI) were delineated in the arterial phase and venous phase. Key radiomics features were extracted and models were established to predict the expression of VEGF and EGFR, respectively. All models were established based on the expression levels of VEGF and EGFR in tissues detected by immunohistochemical staining as reference standards. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of each model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the models. Results In predicting the expression level of VEGF, the combined (COMB) model composed of one spectral feature and two radiomics features achieved the best performance with area under ROC (AUC) 0.867 (95% CI: 0.767–0.966), accuracy of 0.812, sensitivity of 0.879, and specificity of 0.667. According to the expression level of EGFR, three importance radiomics features were retained in the arterial and venous phases to establish the multiphase phase model which has the best performance with AUC of 0.950 (95% confidence interval: 0.89–1.00), accuracy of 0.896, sensitivity of 0.868, and specificity of 1. Conclusion The radiomics model of enhanced spectral CT images of peripheral lung cancer can predict the expression of EGFR and VEGF.
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Affiliation(s)
- Linhua Wu
- Department of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China
| | - Jian Li
- Department of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China
| | - Xiaowei Ruan
- Department of Radiology, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, People's Republic of China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, People's Republic of China
| | - Xuejun Ping
- Department of Clinical Medical Faculty, Medical University of Ningxia, Yinchuan, Ningxia Hui Autonomous Region, People's Republic of China
| | - Bing Chen
- Department of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China
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21
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Zheng YM, Yuan MG, Zhou RQ, Hou F, Zhan JF, Liu ND, Hao DP, Dong C. A computed tomography-based radiomics signature for predicting expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur Radiol 2022; 32:5362-5370. [PMID: 35298679 DOI: 10.1007/s00330-022-08651-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/02/2022] [Accepted: 02/13/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Accurate prediction of the expression of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) before immunotherapy is crucial. This study was performed to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to predict the expression of PD-L1 in HNSCC. METHODS In total, 157 patients with confirmed HNSCC who underwent CECT scans and immunohistochemical examination of tumor PD-L1 expression were enrolled in this study. The patients were divided into a training set (n = 104; 62 PD-L1-positive and 42 PD-L1-negative) and an external validation set (n = 53; 34 PD-L1-positive and 19 PD-L1-negative). A radiomics signature was constructed from radiomics features extracted from the CECT images, and a radiomics score was calculated. Performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS Nine features were finally selected to construct the radiomics signature. The performance of the radiomics signature to distinguish between a PD-L1-positive and PD-L1-negative status in both the training and validation sets was good, with an area under the receiver operating characteristics curve of 0.852 and 0.802 for the training and validation sets, respectively. CONCLUSIONS A CECT-based radiomics signature was constructed to predict the expression of PD-L1 in HNSCC. This model showed favorable predictive efficacy and might be useful for identifying patients with HNSCC who can benefit from anti-PD-L1 immunotherapy. KEY POINTS • Accurate prediction of the expression of PD-L1 in HNSCC before immunotherapy is crucial. • A CECT-based radiomics signature showed favorable predictive efficacy in estimation of the PD-L1 expression status in patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Rui-Qing Zhou
- Department of Radiology, Jiaozhou Hospital of Traditional Chinese Medicine, Jiaozhou, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Nai-Dong Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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22
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Chen LW, Yang SM, Chuang CC, Wang HJ, Chen YC, Lin MW, Hsieh MS, Antonoff MB, Chang YC, Wu CC, Pan T, Chen CM. Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography. Ann Surg Oncol 2022; 29:7473-7482. [PMID: 35789301 DOI: 10.1245/s10434-022-12055-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 06/08/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation component masks with deep learning in the prediction of high-grade components to optimize surgical strategy preoperatively. METHODS A total of 502 patients with pathologically confirmed high-grade adenocarcinomas were retrospectively enrolled between 2016 and 2020. The SACs attention DL model was developed to apply solid-attenuation-component-like subregion masks (tumor area ≥ - 190 HU) to guide the DL model for predicting high-grade subtypes. The SACA-DL was assessed using 5-fold cross-validation and external validation in the training and testing sets, respectively. The performance, which was evaluated using the area under the curve (AUC), was compared between SACA-DL and the DL model without SACs attention (DLwoSACs), the prior radiomics model, or the model based on the consolidation/tumor (C/T) diameter ratio. RESULTS We classified 313 and 189 patients into training and testing cohorts, respectively. The SACA-DL achieved an AUC of 0.91 for the cross-validation, which was significantly superior to those of the DLwoSACs (AUC = 0.88; P = 0.02), prior radiomics model (AUC = 0.85; P = 0.004), and C/T ratio (AUC = 0.84; P = 0.002). An AUC of 0.93 was achieved for external validation in the SACA-DL and was significantly better than those of the DLwoSACs (AUC = 0.89; P = 0.04), prior radiomics model (AUC = 0.85; P < 0.001), and C/T ratio (AUC = 0.85; P < 0.001). CONCLUSIONS The combination of solid-attenuation-component-like subregion masks with the DL model is a promising approach for the preoperative prediction of high-grade adenocarcinoma subtypes.
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Affiliation(s)
- Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.,Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shun-Mao Yang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.,Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, Zhubei City, Hsinchu County, Taiwan
| | - Ching-Chia Chuang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Hao-Jen Wang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Chang Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.,Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Carol C Wu
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tinsu Pan
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.
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23
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Xi Y, Ge X, Ji H, Wang L, Duan S, Chen H, Wang M, Hu H, Jiang F, Ding Z. Prediction of Response to Induction Chemotherapy Plus Concurrent Chemoradiotherapy for Nasopharyngeal Carcinoma Based on MRI Radiomics and Delta Radiomics: A Two-Center Retrospective Study. Front Oncol 2022; 12:824509. [PMID: 35530350 PMCID: PMC9074388 DOI: 10.3389/fonc.2022.824509] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/23/2022] [Indexed: 12/03/2022] Open
Abstract
Objective We aimed to establish an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) for primary nasopharyngeal carcinoma (NPC) in non-endemic areas and to validate its efficacy. Methods A total of 272 patients (155 in the training set, 66 in the internal validation set, and 51 in the external validation set) with biopsy pathologically confirmed primary NPC who were screened for pretreatment MRI were retrospectively collected. The NPC tumor was delineated as a region of interest in the two sequenced images of MRI before treatment and after IC, followed by radiomics feature extraction. With the use of maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, logistic regression was performed to establish pretreatment MRI radiomics and pre- and post-IC Delta radiomics models. The optimal Youden’s index was taken; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drawn to evaluate the predictive efficacy of different models. Results Seven optimal feature subsets were selected from the pretreatment MRI radiomics model, and twelve optimal subsets were selected from the Delta radiomics model. The area under the ROC curve, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the MRI radiomics model were 0.865, 0.827, 0.837, 0.813, 0.776, and 0.865, respectively; the corresponding indicators of the Delta radiomics model were 0.941, 0.883, 0.793, 0.968, 0.833, and 0.958, respectively. Conclusion The pretreatment MRI radiomics model and pre- and post-IC Delta radiomics models could predict the IC-CCRT response of NPC in non-epidemic areas.
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Affiliation(s)
- Yuzhen Xi
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, 903rd Hospital of PLA, Hangzhou, China
| | - Xiuhong Ge
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Haiming Ji
- Department of Radiology, Liangzhu Hospital, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Haonan Chen
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Mengze Wang
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital Affiliated to Medical College Zhejiang University, Hangzhou, China
| | - Feng Jiang
- Department of Head and Neck Radiotherapy, Zhejiang Cancer Hospital/Zhejiang Province Key Laboratory of Radiation Oncology, Hangzhou, China
- *Correspondence: Feng Jiang, ; Zhongxiang Ding,
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Feng Jiang, ; Zhongxiang Ding,
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Wang Q, Dong Y, Xiao T, Zhang S, Yu J, Li L, Zhang Q, Wang Y, Xiao Y, Wang W. Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps. Biomed Eng Online 2022; 21:24. [PMID: 35413926 PMCID: PMC9006564 DOI: 10.1186/s12938-021-00927-y] [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: 02/01/2021] [Accepted: 08/28/2021] [Indexed: 11/10/2022] Open
Abstract
Background This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC). Methods The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction. Results and conclusion Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC. Supplementary Information The online version contains supplementary material available at 10.1186/s12938-021-00927-y. We proposed RF-based radiomics analysis method by introducing three ultrasound features of direct energy attenuation (DEA), skewness of spectrum difference (SSD) and noncentrality parameter S of Rician distribution (NRD) as the feature extraction method from RF signals, investigated the effectiveness of RF-based radiomics analysis method in the immunocheckpoint prediction of programmed cell death protein 1 (PD-1), and validated the results with contrast testing of grayscale-based radiomics analysis method in this study. We also demonstrate a trend in prediction performance changes and its correlation with the number of ultrasound features. The results demonstrated that there were significant differences (p < 0.05) in radiomics scores between HCC patients with PD-1 and HCC patients without PD-1. RF-based radiomics analysis method performed well in PD-1 noninvasive preoperative prediction of HCC patients. In this study, the performance of RF-based radiomics analysis method was better than that of grayscale-based radiomics analysis method in the preoperative prediction of PD-1 in HCC patients. The AUC of DSNM, which was the RF-based radiomics analysis model with three ultrasound feature maps, reached 94.23% in the prediction of PD-1 cell protein in HCC patients.
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Affiliation(s)
- Qingmin Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Tianlei Xiao
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Shiquan Zhang
- Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, University Town, Shenzhen, 518055, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Leyin Li
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Qi Zhang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Yang Xiao
- Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, University Town, Shenzhen, 518055, China.
| | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Jiang C, Li A, Teng Y, Huang X, Ding C, Chen J, Xu J, Zhou Z. Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging 2022; 49:2902-2916. [PMID: 35146578 DOI: 10.1007/s00259-022-05717-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 02/01/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop and externally validate models incorporating a PET radiomics signature (R-signature) obtained by the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL). METHODS A total of 383 patients with DLBCL from two medical centres between 2011 and 2019 were included. The cross-combination method was used on three types of PET radiomics features from the training cohort to generate 49 feature selection-classification candidates based on 7 different machine learning models. The R-signature was then built by selecting the optimal candidates based on their progression-free survival (PFS) and overall survival (OS). Cox regression analysis was used to develop the survival prediction models. The calibration, discrimination, and clinical utility of the models were assessed and externally validated. RESULTS The R-signatures determined by 12 and 31 radiomics features were significantly associated with PFS and OS, respectively (P<0.05). The combined models that incorporated R-signatures, metabolic metrics, and clinical risk factors exhibited significant prognostic superiority over the clinical models, PET-based models, and the National Comprehensive Cancer Network International Prognostic Index in terms of both PFS (C-index: 0.801 vs. 0.732 vs. 0.785 vs. 0.720, respectively) and OS (C-index: 0.807 vs. 0.740 vs. 0.773 vs. 0.726, respectively). For external validation, the C-indices were 0.758 vs. 0.621 vs. 0.732 vs. 0.673 and 0.794 vs. 0.696 vs. 0.781 vs. 0.708 in the PFS and OS analyses, respectively. The calibration curves showed good consistency, and the decision curve analysis supported the clinical utility of the combined model. CONCLUSION The R-signature could be used as a survival predictor for DLBCL, and its combination with clinical factors may allow for accurate risk stratification.
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Affiliation(s)
- Chong Jiang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Ang Li
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yue Teng
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Xiangjun Huang
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Chongyang Ding
- Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Jianxin Chen
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China.
| | - Jingyan Xu
- Department of Hematology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China.
| | - Zhengyang Zhou
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China.
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Lisson CS, Lisson CG, Achilles S, Mezger MF, Wolf D, Schmidt SA, Thaiss WM, Bloehdorn J, Beer AJ, Stilgenbauer S, Beer M, Götz M. Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). Cancers (Basel) 2022; 14:393. [PMID: 35053554 PMCID: PMC8773890 DOI: 10.3390/cancers14020393] [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: 11/25/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse (p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
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Affiliation(s)
- Catharina Silvia Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christoph Gerhard Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Sherin Achilles
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Marc Fabian Mezger
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Johannes Bloehdorn
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ambros J Beer
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stephan Stilgenbauer
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Götz
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- German Cancer Research Center (DKFZ), Division Medical Image Computing, 69120 Heidelberg, Germany
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Zheng YM, Zhan JF, Yuan MG, Hou F, Jiang G, Wu ZJ, Dong C. A CT-based radiomics signature for preoperative discrimination between high and low expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur J Radiol 2022; 146:110093. [PMID: 34890937 DOI: 10.1016/j.ejrad.2021.110093] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Accurate prediction of the expression level of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) is crucial before immunotherapy. The purpose of this study was to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to discriminate between high and low expression status of PD-L1. METHODS A total of 179 HNSCC patients who underwent immunohistochemical examination of tumor PD-L1 expression at one of two centers were enrolled in this study and divided into a training set (n = 122; 55 high PD-L1 expression and 67 low PD-L1 expression) and an external validation set (n = 57; 26 high PD-L1 expression and 31 low PD-L1 expression). The least absolute shrinkage and selection operator method was used to select the key features for a CECT-image-based radiomics signature. The performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS Six features were finally selected to construct the radiomics signature. The performance of the radiomics signature in the discrimination between high and low PD-L1 expression status was good in both the training and validation sets, with areas under the receiver operating characteristics curve of 0.889 and 0.834 for the training and validation sets, respectively. CONCLUSIONS The constructed CECT-based radiomics signature model showed favorable performance for discriminating between high and low PD-L1 expression status in HNSCC patients. It may be useful for screening out those patients with HNSCC who can best benefit from anti-PD-L1 immunotherapy.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Gang Jiang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Zheng YM, Chen J, Xu Q, Zhao WH, Wang XF, Yuan MG, Liu ZJ, Wu ZJ, Dong C. Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland. Dentomaxillofac Radiol 2021; 50:20210023. [PMID: 33950705 PMCID: PMC8474129 DOI: 10.1259/dmfr.20210023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE: Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA. METHODS AND MATERIALS A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model. RESULTS The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively. CONCLUSIONS The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.
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Affiliation(s)
- Ying-mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiao Chen
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Qi Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen-hui Zhao
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin-feng Wang
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao Universtity, Qingdao, China
| | - Zong-jing Liu
- Department of Pediatric Hematology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zeng-jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Chen LW, Lin MW, Hsieh MS, Yang SM, Wang HJ, Chen YC, Chen HY, Hu YH, Lee CE, Chen JS, Chang YC, Chen CM. Radiomic values from high-grade subtypes to predict spread through air spaces in lung adenocarcinoma. Ann Thorac Surg 2021; 114:999-1006. [PMID: 34454902 DOI: 10.1016/j.athoracsur.2021.07.075] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/01/2022]
Abstract
BACKGROUND We aimed to establish a radiomic prediction model for tumor spread through air spaces (STAS) in lung adenocarcinoma using radiomic values from high-grade subtypes (solid and micropapillary). METHODS We retrospectively reviewed 327 patients with lung adenocarcinoma from two institutes (Cohort 1: 227 patients; Cohort 2: 100 patients) between March 2017 and March 2019. STAS was identified in 113 (34.6%) patients. A high-grade likelihood prediction model was constructed based on a historical cohort of 82 patients with "near-pure" pathological subtype. The STAS prediction model based on the patch-wise mechanism identified the high-grade likelihood area for each voxel within the internal border of the tumor. STAS presence was indirectly predicted by a volume percentage threshold of the high-grade likelihood area. Performance was evaluated by receiver operating curve analysis with 10-repetition, 3-fold cross-validation in Cohort 1, and was individually tested in Cohort 2. RESULTS Overall, 227 patients (STAS-positive: 77 [33.9%]) were enrolled for cross-validation (Cohort 1) while 100 (STAS-positive: 36 [36.0%]) underwent individual testing (Cohort 2). The gray level co-occurrence matrix (variance) and histogram (75th percentile) features were selected to construct the high-grade likelihood prediction model, which was used as the STAS prediction model. The proposed model achieved good performance in Cohort 1 with an area under the curve, sensitivity, and specificity, of 81.44%, 86.75%, and 62.60%, respectively, and correspondingly, in Cohort 2, they were 83.16%, 83.33%, and 63.90%, respectively. CONCLUSIONS The proposed computed tomography-based radiomic prediction model could help guide preoperative prediction of STAS in early-stage lung adenocarcinoma and relevant surgeries.
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Affiliation(s)
- Li-Wei Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Shun-Mao Yang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan; Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, No. 2, Sec. 1, Shengyi Rd., Zhubei City, Hsinchu 302 Taiwan
| | - Hao-Jen Wang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Yi-Chang Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Hsin-Yi Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Yu-Hsuan Hu
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Chi-En Lee
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan; Department of Surgical Oncology, National Taiwan University Cancer Center, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Chung-Ming Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan.
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Wen Q, Yang Z, Dai H, Feng A, Li Q. Radiomics Study for Predicting the Expression of PD-L1 and Tumor Mutation Burden in Non-Small Cell Lung Cancer Based on CT Images and Clinicopathological Features. Front Oncol 2021; 11:620246. [PMID: 34422625 PMCID: PMC8377473 DOI: 10.3389/fonc.2021.620246] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 07/20/2021] [Indexed: 12/12/2022] Open
Abstract
Background The present study compared the predictive performance of pretreatment computed tomography (CT)-based radiomics signatures and clinicopathological and CT morphological factors for ligand programmed death-ligand 1 (PD-L1) expression level and tumor mutation burden (TMB) status and further explored predictive models in patients with advanced-stage non-small cell lung cancer (NSCLC). Methods A total of 120 patients with advanced-stage NSCLC were enrolled in this retrospective study and randomly assigned to a training dataset or validation dataset. Here, 462 radiomics features were extracted from region-of-interest (ROI) segmentation based on pretreatment CT images. The least absolute shrinkage and selection operator (LASSO) and logistic regression were applied to select radiomics features and develop combined models with clinical and morphological factors for PD-L1 expression and TMB status prediction. Ten-fold cross-validation was used to evaluate the accuracy, and the predictive performance of these models was assessed using receiver operating characteristic (ROC) and area under the curve (AUC) analyses. Results The PD-L1-positive expression level correlated with differentiation degree (p = 0.005), tumor shape (p = 0.006), and vascular convergence (p = 0.007). Stage (p = 0.023), differentiation degree (p = 0.017), and vacuole sign (p = 0.016) were associated with TMB status. Radiomics signatures showed good performance for predicting PD-L1 and TMB with AUCs of 0.730 and 0.759, respectively. Predictive models that combined radiomics signatures with clinical and morphological factors dramatically improved the predictive efficacy for PD-L1 (AUC = 0.839) and TMB (p = 0.818). The results were verified in the validation datasets. Conclusions Quantitative CT-based radiomics features have potential value in the classification of PD-L1 expression levels and TMB status. The combined model further improved the predictive performance and provided sufficient information for the guiding of immunotherapy in clinical practice, and it deserves further analysis.
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Affiliation(s)
- Qiang Wen
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhe Yang
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Honghai Dai
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Alei Feng
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Qiang Li
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Spadarella G, Calareso G, Garanzini E, Ugga L, Cuocolo A, Cuocolo R. MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol 2021; 140:109744. [PMID: 33962253 DOI: 10.1016/j.ejrad.2021.109744] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND MRI based radiomics has the potential to better define tumor biology compared to qualitative MRI assessment and support decisions in patients affected by nasopharyngeal carcinoma. Aim of this review was to systematically evaluate the methodological quality of studies using MRI- radiomics for nasopharyngeal cancer patient evaluation. METHODS A systematic search was performed in PUBMED, WEB OF SCIENCE and SCOPUS using "MRI, magnetic resonance imaging, radiomic, texture analysis, nasopharyngeal carcinoma, nasopharyngeal cancer" in all possible combinations. The methodological quality of study included ( = 24) was evaluated according to the RQS (Radiomic quality score). Subgroup, for journal type (imaging/clinical) and biomarker (prognostic/predictive), and correlation, between RQS and journal Impact Factor, analyses were performed. Mann-Whitney U test and Spearman's correlation were performed. P value < .05 were defined as statistically significant. RESULTS Overall, no studies reported a phantom study or a test re-test for assessing stability in image, biological correlation or open science data. Only 8% of them included external validation. Almost half of articles (45 %) performed multivariable analysis with non-radiomics features. Only 1 study was prospective (4%). The mean RQS was 7.5 ± 5.4. No significant differences were detected between articles published in clinical/imaging journal and between studies with a predictive or prognostic biomarker. No significant correlation was found between total RQS and Impact Factor of the year of publication (p always > 0.05). CONCLUSIONS Radiomic articles in nasopharyngeal cancer are mostly of low methodological quality. The greatest limitations are the lack of external validation, biological correlates, prospective design and open science.
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Affiliation(s)
- Gaia Spadarella
- Department of Translational Medical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Giuseppina Calareso
- Department of Radiology, Fondazione IRCCS, Istituto Nazionale Dei Tumori, Milan, Italy
| | - Enrico Garanzini
- Department of Radiology, Fondazione IRCCS, Istituto Nazionale Dei Tumori, Milan, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Fan S, Cui X, Liu C, Li X, Zheng L, Song Q, Qi J, Ma W, Ye Z. CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer. Front Oncol 2021; 11:644933. [PMID: 33816297 PMCID: PMC8017337 DOI: 10.3389/fonc.2021.644933] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 02/24/2021] [Indexed: 12/27/2022] Open
Abstract
Objective: To evaluate whether a radiomics signature could improve stratification of postoperative risk and prediction of chemotherapy benefit in stage II colorectal cancer (CRC) patients. Material and Methods: This retrospective study enrolled 299 stage II CRC patients from January 2010 to December 2015. Based on preoperative portal venous-phase CT scans, radiomics features were generated and selected to build a radiomics score (Rad-score) using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The minority group was balanced by the synthetic minority over-sampling technique (SMOTE). Predictive models were built with the Rad-score and clinicopathological factors, and the area under the curve (AUC) was used to evaluate their performance. A nomogram was also constructed for predicting 3-year disease-free survival (DFS). The performance of the nomogram was assessed with a concordance index (C-index) and calibration plots. Results: Overall, 114 features were selected to construct the Rad-score, which was significantly associated with the 3-year DFS. Multivariate analysis demonstrated that the Rad-score, CA724 level, mismatch repair status, and perineural invasion were independent predictors of recurrence. Results showed that the Rad-score can classify patients into high-risk and low-risk groups in the training cohort (AUC 0.886) and the validation cohort (AUC 0.874). On this basis, a nomogram that integrated the Rad-score and clinical variables demonstrated superior performance (AUC 0.954, 0.906) than the clinical model alone (AUC 0.765, 0.705) in the training and validation cohorts, respectively. The C-index of the nomogram was 0.872, and the performance was acceptable. Conclusion: Our radiomics-based model can reliably predict recurrence risk in stage II CRC patients and potentially provide complementary prognostic value to the traditional clinicopathological risk factors for better identification of patients who are most likely to benefit from adjuvant therapy. The proposed nomogram promises to be an effective tool for personalized postoperative surveillance for stage II CRC patients.
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Affiliation(s)
- Shuxuan Fan
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiaonan Cui
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Chunli Liu
- School of Electronics and Information Engineering, TianGong University, Tianjin, China
| | - Xubin Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Lei Zheng
- Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Qian Song
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jin Qi
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Wenjuan Ma
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Chen LW, Yang SM, Wang HJ, Chen YC, Lin MW, Hsieh MS, Song HL, Ko HJ, Chen CM, Chang YC. Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes. Eur Radiol 2021; 31:5127-5138. [PMID: 33389033 DOI: 10.1007/s00330-020-07570-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/01/2020] [Accepted: 11/26/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values. METHODS Overall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the "near-pure" pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction. RESULTS In cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the "near-pure"-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness. CONCLUSION Using near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting. KEY POINTS • The radiomic values extracted from lung adenocarcinoma with "near-pure" histological subtypes provide useful information for high-grade (micropapillary and solid) components detection. • Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity. • Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection.
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Affiliation(s)
- Li-Wei Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan
| | - Shun-Mao Yang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.,Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, No. 2, Sec.1, Shengyi Rd., Zhubei City, Hsinchu County, 302, Taiwan
| | - Hao-Jen Wang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan
| | - Yi-Chang Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.,Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan
| | - Hsiang-Lin Song
- Department of Pathology, National Taiwan University Hospital, Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu, 300, Taiwan
| | - Huan-Jang Ko
- Department of Surgery, National Taiwan University Hospital, Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu, 300, Taiwan
| | - Chung-Ming Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei, Taiwan.
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Lee S, Choe EK, Kim SY, Kim HS, Park KJ, Kim D. Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan. BMC Bioinformatics 2020; 21:382. [PMID: 32938394 PMCID: PMC7495853 DOI: 10.1186/s12859-020-03686-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related entity, but not on the clinical implication of the feature itself. Here we analyzed liver imaging features of abdominal CT images collected from 2019 patients with stage I – III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clinical implication in oncological perspectives. Results CNN generated imaging features from the liver parenchyma. Dimension reduction was done for the features by principal component analysis. We designed multiple prediction models for 5-year metachronous liver metastasis (5YLM) using combinations of clinical variables (age, sex, T stage, N stage) and top principal components (PCs), with logistic regression classification. The model using “1st PC (PC1) + clinical information” had the highest performance (mean AUC = 0.747) to predict 5YLM, compared to the model with clinical features alone (mean AUC = 0.709). The PC1 was independently associated with 5YLM in multivariate analysis (beta = − 3.831, P < 0.001). For the 5-year mortality rate, PC1 did not contribute to an improvement to the model with clinical features alone. For the PC1, Kaplan-Meier plots showed a significant difference between PC1 low vs. high group. The 5YLM-free survival of low PC1 was 89.6% and the high PC1 was 95.9%. In addition, PC1 had a significant correlation with sex, body mass index, alcohol consumption, and fatty liver status. Conclusion The imaging features combined with clinical information improved the performance compared to the standardized prediction model using only clinical information. The liver imaging features generated by CNN may have the potential to predict liver metastasis. These results suggest that even though there were no liver metastasis during the primary colectomy, the features of liver imaging can impose characteristics that could be predictive for metachronous liver metastasis.
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Affiliation(s)
- Sangwoo Lee
- Division of Future Convergent, The Cyber University of Korea, Seoul, 03051, South Korea
| | - Eun Kyung Choe
- Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea.,Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
| | - So Yeon Kim
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.,Department of Software and Computer Engineering, Ajou University, Suwon, 16499, South Korea
| | - Hua Sun Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, 03080, South Korea
| | - Kyu Joo Park
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, South Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA. .,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Mayerhoefer ME, Umutlu L, Schöder H. Functional imaging using radiomic features in assessment of lymphoma. Methods 2020; 188:105-111. [PMID: 32634555 DOI: 10.1016/j.ymeth.2020.06.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023] Open
Abstract
Lymphomas are typically large, well-defined, and relatively homogeneous tumors, and therefore represent ideal targets for the use of radiomics. Of the available functional imaging tests, [18F]FDG-PET for body lymphoma and diffusion-weighted MRI (DWI) for central nervous system (CNS) lymphoma are of particular interest. The current literature suggests that two main applications for radiomics in lymphoma show promise: differentiation of lymphomas from other tumors, and lymphoma treatment response and outcome prognostication. In particular, encouraging results reported in the limited number of presently available studies that utilize functional imaging suggest that (1) MRI-based radiomics enables differentiation of CNS lymphoma from glioblastoma, and (2) baseline [18F]FDG-PET radiomics could be useful for survival prognostication, adding to or even replacing commonly used metrics such as standardized uptake values and metabolic tumor volume. However, due to differences in biological and clinical characteristics of different lymphoma subtypes and an increasing number of treatment options, more data are required to support these findings. Furthermore, a consensus on several critical steps in the radiomics workflow -most importantly, image reconstruction and post processing, lesion segmentation, and choice of classification algorithm- is desirable to ensure comparability of results between research institutions.
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Affiliation(s)
- Marius E Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, NY, USA; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria.
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, NY, USA
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Liu Q, Huang Y, Chen H, Liu Y, Liang R, Zeng Q. The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma. BMC Cancer 2020; 20:533. [PMID: 32513144 PMCID: PMC7278188 DOI: 10.1186/s12885-020-07017-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 05/28/2020] [Indexed: 12/12/2022] Open
Abstract
Background Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN. Methods This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated. Results The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735–0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707–0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723–0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power. Conclusion The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.
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Affiliation(s)
- Qin Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Yan Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Huai Chen
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Yanwen Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Ruihong Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Qingsi Zeng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China.
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Zhou Y, Su GY, Hu H, Ge YQ, Si Y, Shen MP, Xu XQ, Wu FY. Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer. Eur Radiol 2020; 30:6251-6262. [PMID: 32500193 DOI: 10.1007/s00330-020-06866-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/14/2020] [Accepted: 04/06/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To investigate the value of radiomics analysis of dual-energy computed tomography (DECT)-derived iodine maps for preoperative diagnosing cervical lymph nodes (LNs) metastasis in patients with papillary thyroid cancer (PTC). METHODS Two hundred and fifty-five LNs (143 non-metastatic and 112 metastatic) were enrolled and allocated to training and validation sets (7:3 ratio). Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 10-fold cross-validation. Logistic regression modeling was employed to build models based on CT image features (model 1), radiomics signature (model 2), and the combined (model 3). A nomogram was plotted for the combined model and decision curve analysis was applied for clinical use. Diagnostic performance was assessed and compared. Internal validation was performed on an independent set containing 78 LNs. RESULTS Model 3 showed optimal diagnostic performance in both training (AUC = 0.933) and validation set (AUC = 0.895), followed by model 2 (training set, AUC = 0.910; validation set, AUC = 0.847). Both these two models outperformed model 1 in both training (AUC = 0.763) (p < 0.05) and validation set (AUC = 0.728) (p < 0.05). CONCLUSION Radiomics analysis of DECT-derived iodine maps showed better diagnostic performance than qualitative evaluation of CT image features in preoperative diagnosing cervical LN metastasis in PTC patients. Radiomics signature integrated with CT image features can serve as a promising imaging biomarker for the differentiation. KEY POINTS • Conventional CT image features have limited value for the diagnosis of metastatic LNs in PTC patients. • Radiomics analysis of dual-energy CT-derived iodine maps significantly outperformed qualitative CT image features in differentiating metastatic from non-metastatic LNs. • Radiomics signature integrated with qualitative CT image features can serve as a useful tool in judging LNs status, thus aiding clinical decision-making.
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Affiliation(s)
- Yan Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd, Gulou District, Nanjing, 210029, People's Republic of China
| | - Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd, Gulou District, Nanjing, 210029, People's Republic of China
| | - Hao Hu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd, Gulou District, Nanjing, 210029, People's Republic of China
| | - Ying-Qian Ge
- Siemens Healthineers, Shanghai, People's Republic of China
| | - Yan Si
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Mei-Ping Shen
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd, Gulou District, Nanjing, 210029, People's Republic of China.
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd, Gulou District, Nanjing, 210029, People's Republic of China.
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Mayerhoefer ME, Riedl CC, Kumar A, Dogan A, Gibbs P, Weber M, Staber PB, Huicochea Castellanos S, Schöder H. [18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients. Cancers (Basel) 2020; 12:cancers12051138. [PMID: 32370121 PMCID: PMC7281173 DOI: 10.3390/cancers12051138] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/22/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023] Open
Abstract
Biopsy is the standard for assessment of bone marrow involvement in mantle cell lymphoma (MCL). We investigated whether [18F]FDG-PET radiomic texture features can improve prediction of bone marrow involvement in MCL, compared to standardized uptake values (SUV), and whether combination with laboratory data improves results. Ninety-seven MCL patients were retrospectively included. SUVmax, SUVmean, SUVpeak and 16 co-occurrence matrix texture features were extracted from pelvic bones on [18F]FDG-PET/CT. A multi-layer perceptron neural network was used to compare three combinations for prediction of bone marrow involvement—the SUVs, a radiomic signature based on SUVs and texture features, and the radiomic signature combined with laboratory parameters. This step was repeated using two cut-off values for relative bone marrow involvement: REL > 5% (>5% of red/cellular bone marrow); and REL > 10%. Biopsy demonstrated bone marrow involvement in 67/97 patients (69.1%). SUVs, the radiomic signature, and the radiomic signature with laboratory data showed AUCs of up to 0.66, 0.73, and 0.81 for involved vs. uninvolved bone marrow; 0.68, 0.84, and 0.84 for REL ≤ 5% vs. REL > 5%; and 0.69, 0.85, and 0.87 for REL ≤ 10% vs. REL > 10%. In conclusion, [18F]FDG-PET texture features improve SUV-based prediction of bone marrow involvement in MCL. The results may be further improved by combination with laboratory parameters.
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Affiliation(s)
- Marius E. Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.R.); (P.G.); (S.H.C.); (H.S.)
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090 Vienna, Austria;
- Correspondence: ; Tel.: +1-646-961-5030
| | - Christopher C. Riedl
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.R.); (P.G.); (S.H.C.); (H.S.)
| | - Anita Kumar
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Ahmet Dogan
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.R.); (P.G.); (S.H.C.); (H.S.)
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090 Vienna, Austria;
| | - Philipp B. Staber
- Department of Medicine, Medical University of Vienna, 1090 Vienna, Austria;
| | - Sandra Huicochea Castellanos
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.R.); (P.G.); (S.H.C.); (H.S.)
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.R.); (P.G.); (S.H.C.); (H.S.)
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Chen BT, Chen Z, Ye N, Mambetsariev I, Fricke J, Daniel E, Wang G, Wong CW, Rockne RC, Colen RR, Nasser MW, Batra SK, Holodny AI, Sampath S, Salgia R. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach. Front Oncol 2020; 10:593. [PMID: 32391274 PMCID: PMC7188953 DOI: 10.3389/fonc.2020.00593] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 03/31/2020] [Indexed: 01/06/2023] Open
Abstract
Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.
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Affiliation(s)
- Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Zikuan Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Ningrong Ye
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Isa Mambetsariev
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
| | - Jeremy Fricke
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
| | - Ebenezer Daniel
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - George Wang
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chi Wah Wong
- Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Rivka R Colen
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.,Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Mohd W Nasser
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Sagus Sampath
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
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Krarup MMK, Nygård L, Vogelius IR, Andersen FL, Cook G, Goh V, Fischer BM. Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool. Radiother Oncol 2020; 144:72-78. [PMID: 31733491 DOI: 10.1016/j.radonc.2019.10.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/01/2019] [Accepted: 10/17/2019] [Indexed: 02/06/2023]
Abstract
AIM The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. METHODS 18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant. RESULTS Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively). CONCLUSION The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.
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Affiliation(s)
- Marie Manon Krebs Krarup
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Lotte Nygård
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Ivan Richter Vogelius
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark; Faculty of Health and Medical Sciences, Copenhagen University, Denmark.
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Gary Cook
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Vicky Goh
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark; PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
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Han Y, Wang W, Yang Y, Sun YZ, Xiao G, Tian Q, Zhang J, Cui GB, Yan LF. Amide Proton Transfer Imaging in Predicting Isocitrate Dehydrogenase 1 Mutation Status of Grade II/III Gliomas Based on Support Vector Machine. Front Neurosci 2020; 14:144. [PMID: 32153362 PMCID: PMC7047712 DOI: 10.3389/fnins.2020.00144] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 02/05/2020] [Indexed: 12/12/2022] Open
Abstract
Background To compare the efficacies of univariate and radiomics analyses of amide proton transfer weighted (APTW) imaging in predicting isocitrate dehydrogenase 1 (IDH1) mutation of grade II/III gliomas. Methods Fifty-nine grade II/III glioma patients with known IDH1 mutation status were prospectively included (IDH1 wild type, 16; IDH1 mutation, 43). A total of 1044 quantitative radiomics features were extracted from APTW images. The efficacies of univariate and radiomics analyses in predicting IDH1 mutation were compared. Feature values were compared between two groups with independent t-test and receiver operating characteristic (ROC) analysis was applied to evaluate the predicting efficacy of each feature. Cases were randomly assigned to either the training (n = 49) or test cohort (n = 10) for the radiomics analysis. Support vector machine with recursive feature elimination (SVM-RFE) was adopted to select the optimal feature subset. The adverse impact of the imbalance dataset in the training cohort was solved by synthetic minority oversampling technique (SMOTE). Subsequently, the performance of SVM model was assessed on both training and test cohort. Results As for univariate analysis, 18 features were significantly different between IDH1 wild-type and mutant groups (P < 0.05). Among these parameters, High Gray Level Run Emphasis All Direction offset 8 SD achieved the biggest area under the curve (AUC) (0.769) with the accuracy of 0.799. As for radiomics analysis, SVM model was established using 19 features selected with SVM-RFE. The AUC and accuracy for IDH1 mutation on training set were 0.892 and 0.952, while on the testing set were 0.7 and 0.84, respectively. Conclusion Radiomics strategy based on APT image features is potentially useful for preoperative estimating IDH1 mutation status.
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Affiliation(s)
- Yu Han
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Ying-Zhi Sun
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Gang Xiao
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Qiang Tian
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Jin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
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Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G. Introduction to Radiomics. J Nucl Med 2020; 61:488-495. [PMID: 32060219 DOI: 10.2967/jnumed.118.222893] [Citation(s) in RCA: 726] [Impact Index Per Article: 181.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 01/28/2020] [Indexed: 12/11/2022] Open
Abstract
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.
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Affiliation(s)
- Marius E Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York .,Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Andrzej Materka
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ida Häggström
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Piotr Szczypiński
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gary Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; and.,King's College London and Guy's and St. Thomas' PET Centre, St. Thomas' Hospital, London, United Kingdom
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Computed Tomography-Based Radiomic Features for Diagnosis of Indeterminate Small Pulmonary Nodules. J Comput Assist Tomogr 2020; 44:90-94. [PMID: 31939888 DOI: 10.1097/rct.0000000000000976] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE This study aimed to determine the potential of radiomic features extracted from preoperative computed tomography to discriminate malignant from benign indeterminate small (≤10 mm) pulmonary nodules. METHODS A total of 197 patients with 210 nodules who underwent surgical resections between January 2011 and March 2017 were analyzed. Three hundred eighty-five radiomic features were extracted from the computed tomographic images. Feature selection and data dimension reduction were performed using the Kruskal-Wallis test, Spearman correlation analysis, and principal component analysis. The random forest was used for radiomic signature building. The receiver operating characteristic curve analysis was used to evaluate the model performance. RESULTS Fifteen principal component features were selected for modeling. The area under the curve, sensitivity, specificity, and accuracy of the prediction model were 0.877 (95% confidence interval [CI], 0.795-0.959), 81.8% (95% CI, 72.0%-90.9%), 77.4% (95% CI, 63.9%-89.3%), and 80.0% (95% CI, 72.0%-86.7%) in the validation cohort, respectively. CONCLUSIONS Computed tomography-based radiomic features showed good discriminative power for benign and malignant indeterminate small pulmonary nodules.
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Jokar N, Velez E, Shooli H, Dadgar H, Sadathosseini SA, Assadi M, Gholamrezanezhad A. Advanced modalities of molecular imaging in precision medicine for musculoskeletal malignancies. World J Nucl Med 2019; 18:345-350. [PMID: 31933549 PMCID: PMC6945365 DOI: 10.4103/wjnm.wjnm_119_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Accepted: 05/18/2019] [Indexed: 12/20/2022] Open
Abstract
Musculoskeletal malignancies consist of a heterogenous group of mesenchymal tumors, often with high inter- and intratumoral heterogeneity. The early and accurate diagnosis of these malignancies can have a substantial impact on optimal treatment and quality of life for these patients. Several new applications and techniques have emerged in molecular imaging, including advances in multimodality imaging, the development of novel radiotracers, and advances in image analysis with radiomics and artificial intelligence. This review highlights the recent advances in molecular imaging modalities and the role of non-invasive imaging in evaluating tumor biology in the era of precision medicine.
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Affiliation(s)
- Narges Jokar
- The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Erik Velez
- Department of Diagnostic Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hossein Shooli
- The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Habibollah Dadgar
- Cancer Research Center, RAZAVI Hospital, Imam Reza International University, Mashhad, Iran
| | - Seyed Abbas Sadathosseini
- Department of Medical Ethics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Majid Assadi
- Department of Molecular Imaging and Radionuclide Therapy (MIRT), The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr Medical University Hospital, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Ali Gholamrezanezhad
- Department of Diagnostic Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Abdollahi H. Radiotherapy dose painting by circadian rhythm based radiomics. Med Hypotheses 2019; 133:109415. [PMID: 31586813 DOI: 10.1016/j.mehy.2019.109415] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 09/29/2019] [Indexed: 12/12/2022]
Abstract
Radiotherapy dose painting is a new dose delivery technique to achieve higher treatment outcome. In this approach, does is escalated to high progressive regions which are heterogeneous and determined by advanced medical imaging. Radiomics is issued as a feasible image quantification method to reveal tumor heterogeneity by extraction of high throughput mineable texture features. On the other hand, circadian rhythm is a given biological process that studied as a critical factor to obtain more effective treatment outcome. In this study, we hypothesized that radiotherapy dose painting could be enhanced by using circadian rhythm that is determined on the radiomics maps obtained from medical images. This hypothesis is based on the idea which circadian rhythm could change the tumor heterogeneity and therefore image features.
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Affiliation(s)
- Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran; Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran.
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Wang H, Chen H, Duan S, Hao D, Liu J. Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas. J Magn Reson Imaging 2019; 51:791-797. [PMID: 31486565 DOI: 10.1002/jmri.26901] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 07/30/2019] [Accepted: 08/01/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning. PURPOSE To assess the value of radiomics features in distinguishing histological grades of STSs. STUDY TYPE Retrospective. POPULATION In all, 113 patients with pathology-confirmed low-grade (grade I), intermediate-grade (grade II), or high-grade (grade III) soft tissue sarcoma were collected. FIELD STRENGTH/SEQUENCE The 3.0T axial T1 -weighted imaging (T1 WI) with 550 msec repetition time (TR); 18 msec echo time (TE), 312 × 312 matrix, fat-suppressed fast spin-echo T2 WI with 4291 msec TR, 85 msec TE, 312 × 312 matrix. ASSESSMENT Multiple machine-learning methods were trained to establish classification models for predicting STS grades. Eighty STS patients (18 low-grade [grade I]; 62 high-grade [grades II-III]) were enrolled in the primary set and we tested the model with a validation set with 33 patients (7 low-grade, 26 high-grade). STATISTICAL TESTS 1) Student's t-tests were applied for continuous variables and the χ2 test were applied for categorical variables between low-grade STS and high-grade STS groups. 2) For feature subset selection, either no subset selection or recursive feature elimination was performed. This technology was combined with random forest and support vector machine-learning methods. Finally, to overcome the disparity in the frequencies of the STS grades, each machine-learning model was trained i) without subsampling, ii) with the synthetic minority oversampling technique, and iii) with random oversampling examples, for a total of 12 combinations of machine-learning algorithms that were assessed, trained, and tested in the validation cohort. RESULTS The best classification model for the prediction of STS grade was a combination of features selected by recursive feature elimination and random forest classification algorithms with a synthetic minority oversampling technique, which had an area under the curve of 0.9615 (95% confidence interval 0.8944-1.0) in the validation set. DATA CONCLUSION Radiomics feature-based machine-learning methods are useful for distinguishing STS grades. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:791-797.
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Affiliation(s)
- Hexiang Wang
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Haisong Chen
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | | | - Dapeng Hao
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jihua Liu
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. Acad Radiol 2019; 26:1245-1252. [PMID: 30502076 DOI: 10.1016/j.acra.2018.10.013] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 09/27/2018] [Accepted: 10/04/2018] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images. MATERIALS AND METHODS 278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC) and 97 small cell lung cancers (SCLC) patients. Among the NSCLC patients, 88 patients were adenocarcinomas (AD) and 93 patients were squamous cell carcinomas (SCC). In total, 1695 quantitative radiomic features (QRF) were calculated from the primary lung cancer tumor in each patient. To build radiomic classification model based on the extracted QRFs, several machine-learning algorithms were applied sequentially. First, unsupervised hierarchical clustering was used to exclude highly correlated QRFs; second, the minimum Redundancy Maximum Relevance feature selection algorithm was employed to select informative and nonredundant QRFs; finally, the Incremental Forward Search and Support Vector Machine classification algorithms were used to combine the selected QRFs and build the model. In our work, to study the phenotypic differences among lung cancer histological subtypes, four classification models were built. They were models of SCLC vs NSCLC, SCLC vs AD, SCLC vs SCC, and AD vs SCC. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC) estimated by three-fold cross-validation. RESULTS The AUC (95% confidence interval) for the model of SCLC vs NSCLC was 0.741(0.678, 0.795). For the models of SCLC vs AD and SCLC vs SCC, the AUCs were 0.822(0.755, 0.875) and 0.665(0.583, 0.738), respectively. The AUC for the model of AD vs SCC was 0.655(0.570, 0.731). Several QRFs ("Law_15," "LoG_Uniformity," "GLCM_Contrast," and "Compactness Factor") that characterize tumor heterogeneity and shape were selected as the significant features to build the models. CONCLUSION Our results show that phenotypic differences exist among different lung cancer histological subtypes on nonenhanced computed tomography image.
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Affiliation(s)
- Linning E
- Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA.
| | - Li Li
- Department of Pathology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China
| | - Hao Yang
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA
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Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma. Eur J Nucl Med Mol Imaging 2019; 46:2760-2769. [PMID: 31286200 PMCID: PMC6879438 DOI: 10.1007/s00259-019-04420-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 06/11/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To determine whether [18F]FDG PET/CT-derived radiomic features alone or in combination with clinical, laboratory and biological parameters are predictive of 2-year progression-free survival (PFS) in patients with mantle cell lymphoma (MCL), and whether they enable outcome prognostication. METHODS Included in this retrospective study were 107 treatment-naive MCL patients scheduled to receive CD20 antibody-based immuno(chemo)therapy. Standardized uptake values (SUV), total lesion glycolysis, and 16 co-occurrence matrix radiomic features were extracted from metabolic tumour volumes on pretherapy [18F]FDG PET/CT scans. A multilayer perceptron neural network in combination with logistic regression analyses for feature selection was used for prediction of 2-year PFS. International prognostic indices for MCL (MIPI and MIPI-b) were calculated and combined with the radiomic data. Kaplan-Meier estimates with log-rank tests were used for PFS prognostication. RESULTS SUVmean (OR 1.272, P = 0.013) and Entropy (heterogeneity of glucose metabolism; OR 1.131, P = 0.027) were significantly predictive of 2-year PFS: median areas under the curve were 0.72 based on the two radiomic features alone, and 0.82 with the addition of clinical/laboratory/biological data. Higher SUVmean in combination with higher Entropy (SUVmean >3.55 and entropy >3.5), reflecting high "metabolic risk", was associated with a poorer prognosis (median PFS 20.3 vs. 39.4 months, HR 2.285, P = 0.005). The best PFS prognostication was achieved using the MIPI-bm (MIPI-b and metabolic risk combined): median PFS 43.2, 38.2 and 20.3 months in the low-risk, intermediate-risk and high-risk groups respectively (P = 0.005). CONCLUSION In MCL, the [18F]FDG PET/CT-derived radiomic features SUVmean and Entropy may improve prediction of 2-year PFS and PFS prognostication. The best results may be achieved using a combination of metabolic, clinical, laboratory and biological parameters.
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Bak SH, Park H, Sohn I, Lee SH, Ahn MJ, Lee HY. Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer. Sci Rep 2019; 9:8730. [PMID: 31217441 PMCID: PMC6584670 DOI: 10.1038/s41598-019-45117-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 05/31/2019] [Indexed: 01/10/2023] Open
Abstract
Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.
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Affiliation(s)
- So Hyeon Bak
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon, Korea
| | - Insuk Sohn
- Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
| | - Seung Hak Lee
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
| | - Myung-Ju Ahn
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
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Yu L, Tao G, Zhu L, Wang G, Li Z, Ye J, Chen Q. Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis. BMC Cancer 2019; 19:464. [PMID: 31101024 PMCID: PMC6525347 DOI: 10.1186/s12885-019-5646-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 04/26/2019] [Indexed: 12/17/2022] Open
Abstract
PURPOSE To explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature analysis. METHODS Patients with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity > 0.005). The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision-recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA database. RESULTS The prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in LUSC. CONCLUSIONS The pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.
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Affiliation(s)
- Lingming Yu
- Department of Radiology, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, No. 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, No. 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
| | - Lei Zhu
- Department of Pathology, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, Shanghai, 200030, China
| | - Gang Wang
- Center for Statistics, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, Shanghai, 200030, China
| | - Ziming Li
- Center for Lung Tumor Clinical Medical, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, Shanghai, 200030, China
| | - Jianding Ye
- Department of Radiology, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, No. 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China.
| | - Qunhui Chen
- Department of Radiology, Shanghai Chest Hospital, The Affiliated Chest Hospital of Shanghai Jiaotong University, No. 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China.
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