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Li Y, Yu R, Chang H, Yan W, Wang D, Li F, Cui Y, Wang Y, Wang X, Yan Q, Liu X, Jia W, Zeng Q. Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:976-987. [PMID: 38347392 PMCID: PMC11169103 DOI: 10.1007/s10278-024-00988-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 06/13/2024]
Abstract
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.
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Affiliation(s)
- Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ruize Yu
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Dawei Wang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiao Wang
- Department of Radiology, Jining No. 1 People's Hospital, Jining, China
| | - Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China.
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Ghaderi S, Mohammadi S, Mohammadi M, Pashaki ZNA, Heidari M, Khatyal R, Zafari R. A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects. J Med Radiat Sci 2024; 71:269-289. [PMID: 38234262 PMCID: PMC11177032 DOI: 10.1002/jmrs.756] [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: 08/15/2023] [Accepted: 01/06/2024] [Indexed: 01/19/2024] Open
Abstract
INTRODUCTION Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. METHODS This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. RESULTS Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. CONCLUSION Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
| | - Sana Mohammadi
- Department of Medical Sciences, School of MedicineIran University of Medical SciencesTehranIran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran
| | | | - Mehrsa Heidari
- Department of Medical Science, School of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Rahim Khatyal
- Department of Radiology, Faculty of Allied Medical SciencesTabriz University of Medical SciencesTabrizIran
| | - Rasa Zafari
- School of MedicineTehran University of Medical SciencesTehranIran
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Li Y, Jin Y, Wang Y, Liu W, Jia W, Wang J. MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study. Cancer Imaging 2024; 24:65. [PMID: 38773634 PMCID: PMC11110398 DOI: 10.1186/s40644-024-00709-4] [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: 11/23/2023] [Accepted: 05/11/2024] [Indexed: 05/24/2024] Open
Abstract
OBJECTIVES Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence. METHODS A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort's support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set. RESULTS For contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set. CONCLUSIONS This study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans. CLINICAL RELEVANCE STATEMENT We propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.
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Affiliation(s)
- Yanran Li
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Yong Jin
- Department of Radiology, Changzhi People's Hospital, Changzhi, 046000, Shanxi Province, China
| | - Yunling Wang
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Wenya Liu
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Wenxiao Jia
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Jian Wang
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China.
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Gultekin MA, Peker AA, Oktay AB, Turk HM, Cesme DH, Shbair ATM, Yilmaz TF, Kaya A, Yasin AI, Seker M, Mayadagli A, Alkan A. Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1579-1586. [PMID: 37688435 DOI: 10.1002/jcu.23558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023]
Abstract
PURPOSE Metastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features. METHODS One hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre-trained CNN architectures and the texture-based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten-fold cross-validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance. RESULTS The texture-based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture-based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively. CONCLUSION CNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture-based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.
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Affiliation(s)
- Mehmet Ali Gultekin
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Abdusselim Adil Peker
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ayse Betul Oktay
- Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey
| | - Haci Mehmet Turk
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Dilek Hacer Cesme
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Abdallah T M Shbair
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Temel Fatih Yilmaz
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ahmet Kaya
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ayse Irem Yasin
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Mesut Seker
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Alpaslan Mayadagli
- Department of Radiation Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Alpay Alkan
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
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Chen M, Guo Y, Wang P, Chen Q, Bai L, Wang S, Su Y, Wang L, Gong G. An Effective Approach to Improve the Automatic Segmentation and Classification Accuracy of Brain Metastasis by Combining Multi-phase Delay Enhanced MR Images. J Digit Imaging 2023; 36:1782-1793. [PMID: 37259008 PMCID: PMC10406988 DOI: 10.1007/s10278-023-00856-3] [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: 02/12/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023] Open
Abstract
The objective of this study is to analyse the diffusion rule of the contrast media in multi-phase delayed enhanced magnetic resonance (MR) T1 images using radiomics and to construct an automatic classification and segmentation model of brain metastases (BM) based on support vector machine (SVM) and Dpn-UNet. A total of 189 BM patients with 1047 metastases were enrolled. Contrast-enhanced MR images were obtained at 1, 3, 5, 10, 18, and 20 min following contrast medium injection. The tumour target volume was delineated, and the radiomics features were extracted and analysed. BM segmentation and classification models in the MR images with different enhancement phases were constructed using Dpn-UNet and SVM, and differences in the BM segmentation and classification models with different enhancement times were compared. (1) The signal intensity for BM decreased with time delay and peaked at 3 min. (2) Among the 144 optimal radiomics features, 22 showed strong correlation with time (highest R-value = 0.82), while 41 showed strong correlation with volume (highest R-value = 0.99). (3) The average dice similarity coefficients of both the training and test sets were the highest at 10 min for the automatic segmentation of BM, reaching 0.92 and 0.82, respectively. (4) The areas under the curve (AUCs) for the classification of BM pathology type applying single-phase MRI was the highest at 10 min, reaching 0.674. The AUC for the classification of BM by applying the six-phase image combination was the highest, reaching 0.9596, and improved by 42.3% compared with that by applying single-phase images at 10 min. The dynamic changes of contrast media diffusion in BM can be reflected by multi-phase delayed enhancement based on radiomics, which can more objectively reflect the pathological types and significantly improve the accuracy of BM segmentation and classification.
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Affiliation(s)
- Mingming Chen
- Department of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, 250117, China
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Yujie Guo
- Department of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, 250117, China
| | - Pengcheng Wang
- College of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Qi Chen
- MedMind Technology Co., Ltd, 100084, Beijing, China
| | - Lu Bai
- MedMind Technology Co., Ltd, 100084, Beijing, China
| | - Shaobin Wang
- MedMind Technology Co., Ltd, 100084, Beijing, China
| | - Ya Su
- Department of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, 250117, China
| | - Lizhen Wang
- Department of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, 250117, China
| | - Guanzhong Gong
- Department of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, 250117, China.
- Department of Engineering Physics, Tsing Hua University, Beijing, 100084, China.
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Duan S, Cao G, Hua Y, Hu J, Zheng Y, Wu F, Xu S, Rong T, Liu B. Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods. World Neurosurg 2023; 175:e823-e831. [PMID: 37059360 DOI: 10.1016/j.wneu.2023.04.029] [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: 02/16/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods. METHODS We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features. RESULTS The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features. CONCLUSION The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.
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Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yali Zheng
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Fangfang Wu
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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Tulum G. Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets. Br J Radiol 2023; 96:20220841. [PMID: 37129296 PMCID: PMC10230391 DOI: 10.1259/bjr.20220841] [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: 09/10/2022] [Revised: 03/01/2023] [Accepted: 03/20/2023] [Indexed: 05/03/2023] Open
Abstract
OBJECTIVE Accurate diagnosis and early treatment are crucial for survival in patients with brain metastases. This study aims to expand the capability of radiomics-based classification algorithms with novel features and compare results with deep learning-based algorithms to differentiate the subtypes of lung cancer from MRI of metastatic lesions in the brain. METHODS This study includes 75 small cell lung carcinoma, 72 squamous cell carcinoma, and 75 adenocarcinoma segments. For the radiomics-based algorithm, novel features from the original Laplacian of Gaussian filtered and two-dimensional wavelet transformed images were extracted, and a new three-stage feature selection algorithm was proposed for feature selection. Two classification methods were applied to images to identify the subtypes of lung cancer. Additionally, EfficientNet and ResNet with transfer learning were used as classifiers to compare the results of the proposed algorithm. RESULTS The sensitivity and specificity values of the radiomics-based classifier are 94.44 and 95.33%, and for the second classifier are 87.67% and 92.62%, respectively. Besides, a one-vs-all approach comparison was made utilizing two deep learning-based classifiers; The sensitivity and specificity values of 94.29 and 94.08% were obtained from ResNet-50. Moreover, mentioned metrics for EfficientNet-b0 are 92.86 and 93.42%. Furthermore, the accuracies of two radiomics-based and two deep learning-based models were 84.68%, 78.37%, 92.34%, and 90.99%, respectively for one-vs-one approach. CONCLUSION The results suggest that the proposed radiomics-based algorithm is a helpful diagnostic assistant to improve decision-making for treating patients with brain metastases in small datasets. ADVANCES IN KNOWLEDGE Firstly, the proposed method of this study extracts novel features from transformations of the original images, such as wavelet and Laplacian of Gaussian filter for the first time in literature. Secondly, this is the first study that investigates the classification performance of the shallow and deep learning approaches to identify subtypes of lung cancer.
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Affiliation(s)
- Gökalp Tulum
- Department of Mechatronics Engineering, Engineering and Architecture Faculty, Nisantasi University, Istanbul, Turkey
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Zhao L, Shi L, Huang SG, Cai TN, Guo WL, Gao X, Wang J. Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children. BMC Pediatr 2023; 23:262. [PMID: 37226234 PMCID: PMC10207804 DOI: 10.1186/s12887-023-04057-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/03/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND To identify radiomic features that can predict the pathological type of neuroblastic tumor in children. METHODS Data on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance-minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous-phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma. RESULTS Based on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%. CONCLUSION Radiomic features can help predict the pathological type of neuroblastic tumors in children.
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Affiliation(s)
- Lian Zhao
- Radiology Department, Children's Hospital of Soochow University, Suzhou, Jiangsu, 215025, China
| | - Liting Shi
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, Anhui, 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, China
| | - Shun-Gen Huang
- Pediatric Surgery Department, Children's Hospital of Soochow University, Suzhou, Jiangsu, 215025, China
| | - Tian-Na Cai
- Radiology Department, Children's Hospital of Soochow University, Suzhou, Jiangsu, 215025, China
| | - Wan-Liang Guo
- Radiology Department, Children's Hospital of Soochow University, Suzhou, Jiangsu, 215025, China.
| | - Xin Gao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, China.
- Jinan Guoke Medical Engineering and Technology Development Co., Ltd, Jinan, Shandong, 250101, China.
| | - Jian Wang
- Pediatric Surgery Department, Children's Hospital of Soochow University, Suzhou, Jiangsu, 215025, China.
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Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning. Metabolites 2022; 12:metabo12121264. [PMID: 36557302 PMCID: PMC9781524 DOI: 10.3390/metabo12121264] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.
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Li Y, Lv X, Wang B, Xu Z, Wang Y, Gao S, Hou D. Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis. Eur J Radiol 2022; 155:110499. [PMID: 36049410 DOI: 10.1016/j.ejrad.2022.110499] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/29/2022] [Accepted: 08/20/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE More and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication. METHODS This retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts. RESULTS The AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts. CONCLUSION T2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.
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Affiliation(s)
- Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Bing Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Zexuan Xu
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Yichuan Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Shan Gao
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China.
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11
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Stadlbauer A, Marhold F, Oberndorfer S, Heinz G, Buchfelder M, Kinfe TM, Meyer-Bäse A. Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data. Cancers (Basel) 2022; 14:2363. [PMID: 35625967 PMCID: PMC9139355 DOI: 10.3390/cancers14102363] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/04/2022] [Accepted: 05/09/2022] [Indexed: 01/06/2023] Open
Abstract
The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.
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Affiliation(s)
- Andreas Stadlbauer
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany; (M.B.); (T.M.K.)
| | - Franz Marhold
- Department of Neurosurgery, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
| | - Stefan Oberndorfer
- Department of Neurology, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
| | - Gertraud Heinz
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
| | - Michael Buchfelder
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany; (M.B.); (T.M.K.)
| | - Thomas M. Kinfe
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany; (M.B.); (T.M.K.)
- Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany
| | - Anke Meyer-Bäse
- Department of Scientific Computing, Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306-4120, USA;
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12
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Park CJ, Park YW, Ahn SS, Kim D, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation. Korean J Radiol 2022; 23:77-88. [PMID: 34983096 PMCID: PMC8743155 DOI: 10.3348/kjr.2021.0421] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/05/2021] [Accepted: 08/05/2021] [Indexed: 12/14/2022] Open
Abstract
Objective Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.
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Affiliation(s)
- Chae Jung Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dain Kim
- Department of Psychology, Yonsei University, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
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13
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Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg 2021; 108:1243-1250. [PMID: 34423347 DOI: 10.1093/bjs/znab191] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/11/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiomics may be useful in rectal cancer management. The aim of this study was to assess and compare different radiomics approaches over qualitative evaluation to predict disease-free survival (DFS) in patients with locally advanced rectal cancer treated with neoadjuvant therapy. METHODS Patients from a phase II, multicentre, randomized study (GRECCAR4; NCT01333709) were included retrospectively as a training set. An independent cohort of patients comprised the independent test set. For both time points and both sets, radiomic features were extracted from two-dimensional manual segmentation (MS), three-dimensional (3D) MS, and from bounding boxes. Radiomics predictive models of DFS were built using a hyperparameters-tuned random forests classifier. Additionally, radiomics models were compared with qualitative parameters, including sphincter invasion, extramural vascular invasion as determined by MRI (mrEMVI) at baseline, and tumour regression grade evaluated by MRI (mrTRG) after chemoradiotherapy (CRT). RESULTS In the training cohort of 98 patients, all three models showed good performance with mean(s.d.) area under the curve (AUC) values ranging from 0.77(0.09) to 0.89(0.09) for prediction of DFS. The 3D radiomics model outperformed qualitative analysis based on mrEMVI and sphincter invasion at baseline (P = 0.038 and P = 0.027 respectively), and mrTRG after CRT (P = 0.017). In the independent test cohort of 48 patients, at baseline and after CRT the AUC ranged from 0.67(0.09) to 0.76(0.06). All three models showed no difference compared with qualitative analysis in the independent set. CONCLUSION Radiomics models can predict DFS in patients with locally advanced rectal cancer.
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Affiliation(s)
- H Tibermacine
- Radiology Department, Institut du Cancer de Montpellier, University of Montpellier, Montpellier, France.,Institut de Recherche en Cancérologie de Montpellier, INSERM, U1194, Montpellier, France
| | - P Rouanet
- Surgical Oncology Department, Institut du Cancer de Montpellier, University of Montpellier, Montpellier, France
| | - M Sbarra
- Departmental Faculty of Medicine and Surgery, Unit of Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - R Forghani
- Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - C Reinhold
- Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - S Nougaret
- Radiology Department, Institut du Cancer de Montpellier, University of Montpellier, Montpellier, France.,Institut de Recherche en Cancérologie de Montpellier, INSERM, U1194, Montpellier, France
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14
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Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results. Mol Imaging Biol 2021; 22:780-787. [PMID: 31463822 DOI: 10.1007/s11307-019-01423-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
PURPOSE To investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade. PROCEDURES This retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference. RESULTS Radiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI). CONCLUSIONS Radiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.
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15
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Yang X, Yuan C, Zhang Y, Wang Z. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study. Medicine (Baltimore) 2021; 100:e25838. [PMID: 34106622 PMCID: PMC8133272 DOI: 10.1097/md.0000000000025838] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 04/16/2021] [Indexed: 12/21/2022] Open
Abstract
Radiomics contributes to the extraction of undetectable features with the naked eye from high-throughput quantitative images. In this study, 2 predictive models were constructed, which allowed recognition of poorly differentiated hepatocellular carcinoma (HCC). In addition, the effectiveness of the as-constructed signature was investigated in HCC patients.A retrospective study involving 188 patients (age, 29-85 years) enrolled from November 2010 to April 2018 was carried out. All patients were divided randomly into 2 cohorts, namely, the training cohort (n = 141) and the validation cohort (n = 47). The MRI images (DICOM) were collected from PACS before ablation; in addition, the radiomics features were extracted from the 3D tumor area on T1-weighted imaging (T1WI) scans, T2-weighted imaging (T2WI) scans, arterial images, portal images and delayed phase images. In total, 200 radiomics features were extracted. t test and Mann-Whitney U test were performed to exclude some radiomics signatures. Afterwards, a radiomics signature model was built through LASSO regression by RStudio Software. We constructed 2 support vector machine (SVM)-based models: 1 with a radiomics signature only (model 1) and 1 that integrated clinical and radiomics signatures (model 2). Then, the diagnostic performance of the radiomics signature was evaluated through receiver operating characteristic (ROC) analysis.The classification accuracy in the training and validation cohorts was 80.9% and 72.3%, respectively, for model 1. In the training cohort, the area under the ROC curve (AUC) was 0.623, while it was 0.576 in the validation cohort. The classification accuracy in the training and validation cohorts were 79.4% and 74.5%, respectively, for model 2. In the training cohort, the AUC was 0.721, while it was 0.681 in the validation cohort.The MRI-based radiomics signature and clinical model can distinguish HCC patients that belong in a low differentiation group from other patients, which helps in the performance of personal medical protocols.
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Affiliation(s)
- Xiaozhen Yang
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital
| | - Chunwang Yuan
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital
| | - Yinghua Zhang
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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16
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Sartoretti E, Sartoretti T, Wyss M, Reischauer C, van Smoorenburg L, Binkert CA, Sartoretti-Schefer S, Mannil M. Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases. Sci Rep 2021; 11:5506. [PMID: 33750899 PMCID: PMC7943598 DOI: 10.1038/s41598-021-85168-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
We sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.
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Affiliation(s)
- Elisabeth Sartoretti
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Faculty of Medicine, University of Zürich, Zürich, Switzerland
| | - Thomas Sartoretti
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Faculty of Medicine, University of Zürich, Zürich, Switzerland
| | - Michael Wyss
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Philips Healthsystems, Zürich, Switzerland
| | - Carolin Reischauer
- Department of Medicine, University of Fribourg, Fribourg, Switzerland.,Department of Radiology, HFR Fribourg-Hôpital Cantonal, Fribourg, Switzerland
| | | | | | | | - Manoj Mannil
- Department of Neuroradiology, Kantonsspital Aarau, Aarau, Switzerland. .,Institute of Clinical Radiology, University Hospital Münster, University of Münster, Albrecht-Schweitzer-Campus 1, E48149, Münster, Germany.
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17
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Chen BT, Jin T, Ye N, Mambetsariev I, Wang T, Wong CW, Chen Z, Rockne RC, Colen RR, Holodny AI, Sampath S, Salgia R. Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer. Front Oncol 2021; 11:621088. [PMID: 33747933 PMCID: PMC7973105 DOI: 10.3389/fonc.2021.621088] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 02/08/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration. Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration. Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22–85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group. Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.
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Affiliation(s)
- Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Taihao Jin
- 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
| | - Tao Wang
- Departments of Interventional Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chi Wah Wong
- Applied AI and Data Science, 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
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Rivka R Colen
- Department of Radiology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 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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18
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Leithner D, Bernard-Davila B, Martinez DF, Horvat JV, Jochelson MS, Marino MA, Avendano D, Ochoa-Albiztegui RE, Sutton EJ, Morris EA, Thakur SB, Pinker K. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Mol Imaging Biol 2021; 22:453-461. [PMID: 31209778 PMCID: PMC7062654 DOI: 10.1007/s11307-019-01383-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Purpose To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.
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Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Blanca Bernard-Davila
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Joao V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Nuevo Leon, Mexico
| | - R Elena Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth J Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA. .,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Wang G, Wang B, Wang Z, Li W, Xiu J, Liu Z, Han M. Radiomics signature of brain metastasis: prediction of EGFR mutation status. Eur Radiol 2021; 31:4538-4547. [PMID: 33439315 DOI: 10.1007/s00330-020-07614-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/05/2020] [Accepted: 12/07/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To predict epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma using MR-based radiomics signature of brain metastasis and explore the optimal MR sequence for prediction. METHODS Data from 52 patients with brain metastasis from lung adenocarcinoma (28 with mutant EGFR, 24 with wild-type EGFR) were retrospectively reviewed. Contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR), T2WI, and DWI sequences were selected for radiomics features extraction. A total of 438 radiomics features were extracted from each MR sequence. All sequences were randomly divided into training and validation cohorts. The least absolute shrinkage selection operator was used to select informative features, a radiomics signature was built with the logistic regression model of the training cohort, and the radiomics signature performance was evaluated using the validation cohort and an independent testing data set. RESULTS The radiomics signature built on 9 selected features showed good discrimination in both the training and validation cohorts for T2-FLAIR. The radiomics signature of T2-FLAIR yielded an AUC of 0.987, a classification accuracy of 0.991, sensitivity of 1.000, and specificity of 0.980 in the validation cohort. The AUC was 0.871 in the independent testing data set. The AUCs of our radiomics signature to differentiate exon 19 and exon 21 mutations were 0.529, 0.580, 0.645, and 0.406 for T1-CE, T2-FLAIR, T2WI, and DWI, respectively. CONCLUSIONS We developed a T2-FLAIR radiomics signature that can be used as a noninvasive auxiliary tool for predicting EGFR mutation status in lung adenocarcinoma, which is helpful to guide therapeutic strategies. KEY POINTS • MR-based radiomics signature of brain metastasis may help predict EGFR mutation status in lung adenocarcinoma, especially using T2-FLAIR. • Nine radiomics features extracted from T2-FLAIR sequence strongly correlate with EGFR mutation status. • Radiomics features reflect tumor heterogeneity through potential changes in tissue morphology caused by EGFR mutation.
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Affiliation(s)
- Guangyu Wang
- Cancer Therapy and Research Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, People's Republic of China
| | - Bomin Wang
- School of Information Science and Engineering, Shandong University, 27 Shanda South Road, Jinan, 250100, Shandong, People's Republic of China
| | - Zhou Wang
- Medical Imaging Department, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, People's Republic of China
| | - Wenchao Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University and Healthcare Big Data Institute of Shandong University, Jinan, 250012, People's Republic of China
| | - Jianjun Xiu
- Medical Imaging Department, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, People's Republic of China
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, 27 Shanda South Road, Jinan, 250100, Shandong, People's Republic of China.
| | - Mingyong Han
- Cancer Therapy and Research Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, People's Republic of China.
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20
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Park JH, Choi BS, Han JH, Kim CY, Cho J, Bae YJ, Sunwoo L, Kim JH. MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer. J Clin Med 2021; 10:jcm10020237. [PMID: 33440723 PMCID: PMC7827024 DOI: 10.3390/jcm10020237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 01/08/2021] [Accepted: 01/08/2021] [Indexed: 12/30/2022] Open
Abstract
This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.
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Affiliation(s)
- Jung Hyun Park
- Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Suwon 443-380, Korea;
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.C.); (Y.J.B.); (L.S.); (J.H.K.)
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.C.); (Y.J.B.); (L.S.); (J.H.K.)
- Correspondence: ; Tel.: +82-31-787-7625; Fax: +82-31-787-4011
| | - Jung Ho Han
- Department of Neurosurgery, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.H.H.); (C.-Y.K.)
| | - Chae-Yong Kim
- Department of Neurosurgery, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.H.H.); (C.-Y.K.)
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.C.); (Y.J.B.); (L.S.); (J.H.K.)
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.C.); (Y.J.B.); (L.S.); (J.H.K.)
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.C.); (Y.J.B.); (L.S.); (J.H.K.)
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea; (J.C.); (Y.J.B.); (L.S.); (J.H.K.)
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21
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Wei C, Chen YL, Li XX, Li NY, Wu YY, Lin TT, Wang CB, Zhang P, Dong JN, Yu YQ. Diagnostic Performance of MR Imaging-based Features and Texture Analysis in the Differential Diagnosis of Ovarian Thecomas/Fibrothecomas and Uterine Fibroids in the Adnexal Area. Acad Radiol 2020; 27:1406-1415. [PMID: 32035760 DOI: 10.1016/j.acra.2019.12.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 12/12/2019] [Accepted: 12/25/2019] [Indexed: 10/25/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of MRI-based features and texture analysis (TA) in the differential diagnosis between ovarian thecomas/fibrothecomas (OTCA/f-TCAs) and uterine fibroids in the adnexal area (UF-iaas). MATERIALS AND METHODS This retrospective study included 16 OTCA/f-TCA and 37 UF-iaa patients who underwent conventional MRI and DWI between August 2014 and September 2018. Three-dimensional TA was performed with T2-weighted MRI. The clinical, MRI-based and texture features were compared between OTCA/f-TCAs and UF-iaas. Multivariate logistic regression analysis was used for filtering the independent discriminative features and constructing the discriminating model. ROCs were generated to analyse MRI-based features, texture features and their combination for discriminating between the two diseases. RESULTS Six imaging-based features (ipsilateral ovary detection, arterial period enhancement, lesion components, peripheral cysts, "whorl signs", mean ADCs) and six texture features (Histogram-energy, Histogram-entropy, Histogram-kurtosis, GLCM-energy, GLCM-entropy, and Haralick correlation) were significantly different between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the MRI-based features revealed that arterial period enhancement (OR = 0.104), peripheral cysts (OR = 16.513), and whorl signs (OR = 0.029) were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the texture features showed that Histogram-energy and GLCM-energy were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). The area under the curve of imaging-based diagnosis was 0.85, and the combination of imaging-based diagnosis and TA improved the area under the curve to 0.87, with higher accuracy, specificity and sensitivity of 86%, 92%, and 84%, respectively (p < 0.05). CONCLUSIONS MRI-based features can be useful in differentiating OTCA/f-TCAs from UF-iaas. Furthermore, combining imaging-based diagnosis and TA can improve diagnostic performance.
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22
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Zhang J, Jin J, Ai Y, Zhu K, Xiao C, Xie C, Jin X. Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images. Eur Radiol 2020; 31:1022-1028. [PMID: 32822055 DOI: 10.1007/s00330-020-07183-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 06/29/2020] [Accepted: 08/11/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM). The purpose of this study is to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with BM based on radiomics from brain contrast-enhanced computer tomography (CECT) images. METHODS A total of 144 BM patients (94 male, 50 female) were enrolled in this study with 102 with primary lung AD and 42 with SCC, respectively. Radiomics features from manually contoured tumors were extracted using python. Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select relative radiomics features. Binary logistic regression and support vector machines (SVM) were applied to build models with radiomics features alone and with radiomics features plus age and sex. RESULTS Fourteen features were selected from a total of 105 radiomics features for the final model building. The area under the curves (AUCs) and accuracy of SVM and binary logistic regression models were 0.765 vs. 0.769, 0.795 vs.0.828, and 0.716 vs. 0.726, 0.768 vs. 0.758, respectively, for models with radiomics features alone and models with radiomics features plus sex and age. CONCLUSIONS Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC. KEY POINTS • It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM) to define the prognosis and treatment. • Few studies had investigated the feasibility and accuracy of differentiating the pathological subtypes of primary non-small-cell lung cancer between adenocarcinoma (AD) and squamous cell carcinoma (SCC) for patients with BM based on radiomics from brain contrast-enhanced CT (CECT) images, although CECT images are often the initial imaging modality to screen for metastases and are recommended on equal footing with MRI for the detection of cerebral metastases. • Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC with a highest area under the curve (AUC) of 0.828 and an accuracy of 0.758, respectively.
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Affiliation(s)
- Ji Zhang
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Juebin Jin
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yao Ai
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Kecheng Zhu
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Chengjian Xiao
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Congying Xie
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. .,Department of Radiation and Medical Oncology, The Second Affiliated Hospital of Wenzhou Medical University, No. 109 West Xueyuan Road, Wenzhou, 325000, China.
| | - Xiance Jin
- Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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23
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Vedantam A, Hassan I, Kotrotsou A, Hassan A, Zinn PO, Viswanathan A, Colen RR. Magnetic Resonance-Based Radiomic Analysis of Radiofrequency Lesion Predicts Outcomes After Percutaneous Cordotomy: A Feasibility Study. Oper Neurosurg (Hagerstown) 2020; 18:721-727. [PMID: 31665446 DOI: 10.1093/ons/opz288] [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: 01/10/2019] [Accepted: 07/19/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND To date, there is limited data on evaluation of the cordotomy lesion and predicting clinical outcome. OBJECTIVE To evaluate the utility of magnetic resonance (MR)-based radiomic analysis to quantify microstructural changes created by the cordotomy lesion and predict outcome in patients undergoing percutaneous cordotomy for medically refractory cancer pain. METHODS This is a retrospective interpretation of prospectively acquired data in 10 patients (5 males, age range 43-76 yr) who underwent percutaneous computed tomography-guided high cervical cordotomy for medically refractory cancer pain between 2015 and 2016. All patients underwent magnetic resonance imaging (MRI) of the cordotomy lesion on postoperative day 1. After segmentation of T2-weighted images, 310 radiomic features were extracted. Pain outcomes were recorded on postoperative day 1 and day 7 using the visual analog scale. R software was used to build statistical models based on MRI radiomic features for prediction of pain outcomes. RESULTS A total of 20 relevant radiomic features were identified using the maximum relevance minimum redundanc method. Radiomics predicted postoperative day 1 pain scores with an accuracy of 90% (P = .046), 100% sensitivity, 75% specificity, 85.7% positive predictive value, and 100% negative predictive value. The radiomics model also predicted if the postoperative day 1 pain score was sustained on postoperative day 7 with an accuracy of 100% (P = .028), 100% sensitivity, 100% specificity, and 100% positive and negative predictive value. CONCLUSION MR-based radiomic analysis of the cordotomy lesion was predictive of pain outcomes at 1 wk after percutaneous cordotomy for intractable cancer pain.
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Affiliation(s)
- Aditya Vedantam
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Islam Hassan
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ahmed Hassan
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Pascal O Zinn
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Cancer Biology, Division of Basic Science Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Rivka R Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
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24
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Tong E, McCullagh KL, Iv M. Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response. Front Neurol 2020; 11:270. [PMID: 32351445 PMCID: PMC7174761 DOI: 10.3389/fneur.2020.00270] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/24/2020] [Indexed: 12/11/2022] Open
Abstract
Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize micro-structures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases-specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.
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Affiliation(s)
- Elizabeth Tong
- Stanford University Medical Center, Stanford, CA, United States
| | | | - Michael Iv
- Stanford University Medical Center, Stanford, CA, United States
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25
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Yuan G, Liu Y, Huang W, Hu B. Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:6913418. [PMID: 32328154 PMCID: PMC7166276 DOI: 10.1155/2020/6913418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 11/20/2019] [Accepted: 03/14/2020] [Indexed: 12/03/2022]
Abstract
PURPOSE The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC). Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic. METHODS Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and K-means algorithm is proposed. Secondly, TA was performed and the Gabor wavelet analysis is used to extract the texture feature of an MRI tumor. Then, according to the distance relationship between the features, key features are sorted and feature subsets are selected. Finally, the feature subset is classified by using a support vector machine and adjusted parameters to achieve the best classification effect. RESULTS By selecting key features for classification prediction, the classification accuracy of the classification model can reach 81.33%. 3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%. CONCLUSION The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors. This method can provide much valuable information for doctors' clinical diagnosis. With further development, the model demonstrates high potential for practical clinical use.
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MESH Headings
- Algorithms
- Breast Neoplasms/diagnostic imaging
- Breast Neoplasms/pathology
- Carcinoma, Ductal, Breast/diagnostic imaging
- Carcinoma, Ductal, Breast/pathology
- Computational Biology
- Diagnosis, Computer-Assisted/methods
- Diagnosis, Computer-Assisted/statistics & numerical data
- Female
- Humans
- Image Interpretation, Computer-Assisted/methods
- Image Interpretation, Computer-Assisted/statistics & numerical data
- Machine Learning
- Magnetic Resonance Imaging/methods
- Magnetic Resonance Imaging/statistics & numerical data
- Models, Statistical
- Neoplasm Grading/methods
- Neoplasm Grading/statistics & numerical data
- Neural Networks, Computer
- Support Vector Machine
- Wavelet Analysis
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Affiliation(s)
- Gaoteng Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yihui Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Wei Huang
- Department VI of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Bing Hu
- School of Medicine and Life Sciences, University of Jinan, Jinan 250022, China
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Chen BT, Jin T, Ye N, Mambetsariev I, Daniel E, Wang T, Wong CW, Rockne RC, Colen R, Holodny AI, Sampath S, Salgia R. Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases. Magn Reson Imaging 2020; 69:49-56. [PMID: 32179095 DOI: 10.1016/j.mri.2020.03.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 02/20/2020] [Accepted: 03/05/2020] [Indexed: 02/06/2023]
Abstract
Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.
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Affiliation(s)
- Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States.
| | - Taihao Jin
- 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 91010, CA, United States
| | - Ebenezer Daniel
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Tao Wang
- Departments of Interventional Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Chi Wah Wong
- Applied Al and Data Science, City of Hope National Medical Center, Duarte 91010, CA, United States
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Rivka 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
| | - 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 91010, CA, United States
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Jiang Z, Yin J. Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE-MRI in classifying benign from malignant lesions. J Surg Oncol 2020; 121:1181-1190. [PMID: 32167588 DOI: 10.1002/jso.25901] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/02/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND OBJECTIVES To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors. METHODS A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SIslope ), initial percentage of peak enhancement (Einitial ), percentage of peak enhancement (Epeak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out cross-validation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. RESULTS The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SIslope , 0.756 for Einitial , 0.923 for Epeak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between Einitial vs MSI, Einitial vs Epeak and Einitial vs SEP (P < .05). There were no significant differences in other pairwise comparisons. CONCLUSION Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis.
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Affiliation(s)
- Zejun Jiang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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28
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Ovarian cancer: An update on imaging in the era of radiomics. Diagn Interv Imaging 2019; 100:647-655. [DOI: 10.1016/j.diii.2018.11.007] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 11/23/2018] [Accepted: 11/26/2018] [Indexed: 12/13/2022]
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29
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Leithner D, Horvat JV, Marino MA, Bernard-Davila B, Jochelson MS, Ochoa-Albiztegui RE, Martinez DF, Morris EA, Thakur S, Pinker K. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results. Breast Cancer Res 2019; 21:106. [PMID: 31514736 PMCID: PMC6739929 DOI: 10.1186/s13058-019-1187-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 08/14/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. METHODS One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. RESULTS In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). CONCLUSIONS In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.
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Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Joao V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Blanca Bernard-Davila
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - R Elena Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA. .,Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria.
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Speckter H, Bido J, Hernandez G, Rivera D, Suazo L, Valenzuela S, Miches I, Oviedo J, Gonzalez C, Stoeter P. Pretreatment texture analysis of routine MR images and shape analysis of the diffusion tensor for prediction of volumetric response after radiosurgery for meningioma. J Neurosurg 2019; 129:31-37. [PMID: 30544300 DOI: 10.3171/2018.7.gks181327] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 07/19/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVEThe goal of this study was to identify parameters from routine T1- and T2-weighted MR sequences and diffusion tensor imaging (DTI) that best predict the volumetric changes in a meningioma after treatment with Gamma Knife radiosurgery (GKRS).METHODSIn 32 patients with meningioma, routine MRI and DTI data were measured before GKRS. A total of 78 parameters derived from first-level texture analysis of the pretreatment MR images, including calculation of the mean, SD, 2.5th and 97.5th percentiles, and kurtosis and skewness of data in histograms on a voxel-wise basis, were correlated with lesion volume change after a mean follow-up period of 3 years (range 19.5-63.3 months).RESULTSSeveral DTI-derived parameters correlated significantly with a meningioma volume change. The parameter that best predicted the results of GKRS was the 2.5th percentile value of the smallest eigenvalue (L3) of the diffusion tensor (correlation coefficient 0.739, p ≤ 0.001), whereas among the non-DTI parameters, only the SD of T2-weighted images correlated significantly with a tumor volume change (correlation coefficient 0.505, p ≤ 0.05, after correction for family-wise errors using false-detection-rate correction).CONCLUSIONSDTI-derived data had a higher correlation to shrinkage of meningioma volume after GKRS than data from T1- and T2-weighted image sequences. However, if only routine MR images are available, the SD of T2-weighted images can be used to predict control or possible progression of a meningioma after GKRS.
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Affiliation(s)
- Herwin Speckter
- 1Centro Gamma Knife Dominicano and.,2Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | | | | | | | | | | | | | - Jairo Oviedo
- 2Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Cesar Gonzalez
- 2Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Peter Stoeter
- 1Centro Gamma Knife Dominicano and.,2Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
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Petrujkić K, Milošević N, Rajković N, Stanisavljević D, Gavrilović S, Dželebdžić D, Ilić R, Di Ieva A, Maksimović R. Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol 2019; 119:108634. [PMID: 31473463 DOI: 10.1016/j.ejrad.2019.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 07/28/2019] [Accepted: 08/05/2019] [Indexed: 01/31/2023]
Abstract
PURPOSE Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. METHOD In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. RESULTS All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. CONCLUSIONS Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.
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Affiliation(s)
- Katarina Petrujkić
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia.
| | - Nebojša Milošević
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Nemanja Rajković
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Dejana Stanisavljević
- Department for Medical Statistics, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
| | - Svetlana Gavrilović
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia
| | - Dragana Dželebdžić
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia
| | - Rosanda Ilić
- Department of Neurosurgery, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia; Clinical Centre of Serbia, Clinical for Neurosurgery, Dr Koste Todorovića 54, 11000 Belgrade, Serbia
| | - Antonio Di Ieva
- Department of Clinical Medicine, Faculty of Medicine and Health Science, Neurosurgery Unit, Macquarie University, 2 Technology Place, Macquarie University, Sydney, NSW 2109, Australia
| | - Ružica Maksimović
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia; Department of Radiology, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
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Abstract
PURPOSE OF REVIEW To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine. RECENT FINDINGS Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.
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Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas. Eur Radiol 2019; 29:2751-2759. [PMID: 30617484 DOI: 10.1007/s00330-018-5921-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 10/31/2018] [Accepted: 11/27/2018] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone. METHODS A total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data. RESULTS The ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters. CONCLUSION Texture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning. KEY POINTS • Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. • Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma. • Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.
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Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma? Clin Radiol 2018; 74:287-294. [PMID: 30554807 DOI: 10.1016/j.crad.2018.11.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 11/23/2018] [Indexed: 12/21/2022]
Abstract
AIM To investigate whether computed tomography (CT) texture analysis (TA) can be used to differentiate non-clear-cell renal cell carcinoma (non-ccRCC) from clear-cell RCC (ccRCC) and classify non-ccRCC subtypes. MATERIALS AND METHODS One hundred ccRCC and 27 non-ccRCC (12 papillary and 15 chromophobe) were analysed. Texture parameters quantified from multiphasic CT images were compared for the objectives. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. RESULTS Compared to ccRCC, non-ccRCC had significantly lower mean grey-level intensity (mean), standard deviation (SD), entropy, mean of positive pixels (MPP), and higher kurtosis (p<0.001). A model incorporating SD, entropy, MPP, and kurtosis produced an AUC of 0.94±0.03 with an accuracy of 87% (sensitivity=89%, specificity=92%) to identify non-ccRCC from ccRCC. Compared to chromophobe RCC, papillary RCC had significantly lower mean and MPP (p=0.002). A model incorporating SD, MPP, and skewness resulted in an AUC of 0.96±0.04 with an accuracy of 78% (sensitivity=87%, specificity=92%) to differentiate between papillary and chromophobe RCC. CONCLUSION CT TA could potentially be used as a less invasive tool to classify histological subtypes of RCC.
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Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018; 28:4514-4523. [PMID: 29761357 DOI: 10.1007/s00330-018-5463-6] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/07/2018] [Accepted: 04/05/2018] [Indexed: 01/17/2023]
Abstract
OBJECTIVE To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. METHODS Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. RESULTS In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). CONCLUSION Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. KEY POINTS • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.
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Affiliation(s)
- Rafael Ortiz-Ramón
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022, Valencia, Spain
| | - Andrés Larroza
- Department of Medicine, Universitat de València, Av. Blasco Ibáñez 15, 46010, Valencia, Spain
| | - Silvia Ruiz-España
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022, Valencia, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Calle Beltrán Báguena 8, 46009, Valencia, Spain
| | - David Moratal
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022, Valencia, Spain.
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Zhu J, Bai T, Gu J, Sun Z, Wei Y, Li B, Yin Y. Effects of megavoltage computed tomographic scan methodology on setup verification and adaptive dose calculation in helical TomoTherapy. Radiat Oncol 2018; 13:80. [PMID: 29699582 PMCID: PMC5921977 DOI: 10.1186/s13014-018-0989-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/02/2018] [Indexed: 11/26/2022] Open
Abstract
Background To evaluate the effect of pretreatment megavoltage computed tomographic (MVCT) scan methodology on setup verification and adaptive dose calculation in helical TomoTherapy. Methods Both anthropomorphic heterogeneous chest and pelvic phantoms were planned with virtual targets by TomoTherapy Physicist Station and were scanned with TomoTherapy megavoltage image-guided radiotherapy (IGRT) system consisted of six groups of options: three different acquisition pitches (APs) of ‘fine’, ‘normal’ and ‘coarse’ were implemented by multiplying 2 different corresponding reconstruction intervals (RIs). In order to mimic patient setup variations, each phantom was shifted 5 mm away manually in three orthogonal directions respectively. The effect of MVCT scan options was analyzed in image quality (CT number and noise), adaptive dose calculation deviations and positional correction variations. Results MVCT scanning time with pitch of ‘fine’ was approximately twice of ‘normal’ and 3 times more than ‘coarse’ setting, all which will not be affected by different RIs. MVCT with different APs delivered almost identical CT numbers and image noise inside 7 selected regions with various densities. DVH curves from adaptive dose calculation with serial MVCT images acquired by varied pitches overlapped together, where as there are no significant difference in all p values of intercept & slope of emulational spinal cord (p = 0.761 & 0.277), heart (p = 0.984 & 0.978), lungs (p = 0.992 & 0.980), soft tissue (p = 0.319 & 0.951) and bony structures (p = 0.960 & 0.929) between the most elaborated and the roughest serials of MVCT. Furthermore, gamma index analysis shown that, compared to the dose distribution calculated on MVCT of ‘fine’, only 0.2% or 1.1% of the points analyzed on MVCT of ‘normal’ or ‘coarse’ do not meet the defined gamma criterion. On chest phantom, all registration errors larger than 1 mm appeared at superior-inferior axis, which cannot be avoided with the smallest AP and RI. On pelvic phantom, craniocaudal errors are much smaller than chest, however, AP of ‘coarse’ presents larger registration errors which can be reduced from 2.90 mm to 0.22 mm by registration technique of ‘full image’. Conclusions AP of ‘coarse’ with RI of 6 mm is recommended in adaptive radiotherapy (ART) planning to provide craniocaudal longer and faster MVCT scan, while registration technique of ‘full image’ should be used to avoid large residual error. Considering the trade-off between IGRT and ART, AP of ‘normal’ with RI of 2 mm was highly recommended in daily practice. Electronic supplementary material The online version of this article (10.1186/s13014-018-0989-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jian Zhu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, 440# Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China.
| | - Tong Bai
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, 440# Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Jiabing Gu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, 440# Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Ziwen Sun
- Medical Department, Affiliated Hospital of Shandong Academy of Medical Sciences, Jinan, 250031, People's Republic of China
| | - Yumei Wei
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, 440# Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, 440# Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, 440# Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China.
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Guo J, Liu Z, Shen C, Li Z, Yan F, Tian J, Xian J. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol 2018; 28:3872-3881. [PMID: 29632999 DOI: 10.1007/s00330-018-5381-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 02/06/2018] [Accepted: 02/08/2018] [Indexed: 01/08/2023]
Abstract
OBJECTIVES To assess the value of the MR-based radiomics signature in differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI). METHODS One hundred fifty-seven patients with pathology-proven OAL (84 patients) and IOI (73 patients) were divided into primary and validation cohorts. Eight hundred six radiomics features were extracted from morphological MR images. The least absolute shrinkage and selection operator (LASSO) procedure and linear combination were used to select features and build radiomics signature for discriminating OAL from IOI. Discriminating performance was assessed by the area under the receiver-operating characteristic curve (AUC). The predictive results were compared with the assessment of radiologists by chi-square test. RESULTS Five radiomics features were included in the radiomics signature, which differentiated OAL from IOI with an AUC of 0.74 and 0.73 in the primary and validation cohorts respectively. There was a significant difference between the classification results of the radiomics signature and those of a radiology resident (p < 0.05), although there was no significant difference between the results of the radiomics signature and those of a more experienced radiologist (p > 0.05). CONCLUSIONS Radiomics features have the potential to differentiate OAL from IOI. KEY POINTS • Clinical and imaging findings of OAL and IOI often overlap, which makes diagnosis difficult. • Radiomics features can potentially differentiate OAL from IOI non invasively. • The radiomics signature discriminates OAL from IOI at the same level as an experienced radiologist.
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Affiliation(s)
- Jian Guo
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 of Dongjiaominxiang, Dongcheng District, Beijing, 100730, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Chen Shen
- School of Life Science and Technology, Xidian University, Xi'an, Shanxi, 710126, China
| | - Zheng Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 of Dongjiaominxiang, Dongcheng District, Beijing, 100730, China
| | - Fei Yan
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 of Dongjiaominxiang, Dongcheng District, Beijing, 100730, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 of Dongjiaominxiang, Dongcheng District, Beijing, 100730, China.
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Hou Z, Li S, Ren W, Liu J, Yan J, Wan S. Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis 2018; 10:2256-2267. [PMID: 29850130 DOI: 10.21037/jtd.2018.03.123] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To investigate the capability of radiomic analysis using T2-weighted (T2W) and spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI) for predicting the therapeutic response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Methods Pretreatment T2W- and SPAIR T2W-MRI of 68 ESCC patients (37 responders, 31 nonresponders) were analyzed. A number of 138 radiomic features were extracted from each image sequence respectively. Kruskal-Wallis test were performed to evaluate the capability of each feature on treatment response classification. Sensitivity and specificity for each of the studied features were derived using receiver operating characteristic (ROC) analysis. Support vector machine (SVM) and artificial neural network (ANN) models were constructed based on the training set (23 responders, 20 nonresponders) for the prediction of treatment response, and then the testing set (14 responders, 11 nonresponders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test. Results Radiomic analysis showed significance in the prediction of treatment response. The analyses showed that complete responses (CRs) versus stable diseases (SDs), partial responses (PRs) versus SDs, and responders (CRs and PRs) versus nonresponders (SDs) could be differentiated by 26, 17, and 33 features (T2W: 11/11/15, SPAIR T2W: 15/6/18), respectively. The prediction models (ANN and SVM) based on features extracted from SPAIR T2W sequence (SVM: 0.929, ANN: 0.883) showed higher accuracy than those derived from T2W (SVM: 0.893, ANN: 0.861). No statistical difference was observed in the performance of the two classifiers (P=0.999). Conclusions Radiomic analysis based on pretreatment T2W- and SPAIR T2W-MRI can be served as imaging biomarkers to predict treatment response to CRT in ESCC patients.
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Affiliation(s)
- Zhen Hou
- State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shuangshuang Li
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Wei Ren
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Juan Liu
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Jing Yan
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Suiren Wan
- State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
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Reischauer C, Patzwahl R, Koh DM, Froehlich JM, Gutzeit A. Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases-A pilot study. Eur J Radiol 2018; 101:184-190. [PMID: 29571795 DOI: 10.1016/j.ejrad.2018.02.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/16/2018] [Accepted: 02/17/2018] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To evaluate whole-lesion volumetric texture analysis of apparent diffusion coefficient (ADC) maps for assessing treatment response in prostate cancer bone metastases. MATERIALS AND METHODS Texture analysis is performed in 12 treatment-naïve patients with 34 metastases before treatment and at one, two, and three months after the initiation of androgen deprivation therapy. Four first-order and 19 second-order statistical texture features are computed on the ADC maps in each lesion at every time point. Repeatability, inter-patient variability, and changes in the feature values under therapy are investigated. Spearman rank's correlation coefficients are calculated across time to demonstrate the relationship between the texture features and the serum prostate specific antigen (PSA) levels. RESULTS With few exceptions, the texture features exhibited moderate to high precision. At the same time, Friedman's tests revealed that all first-order and second-order statistical texture features changed significantly in response to therapy. Thereby, the majority of texture features showed significant changes in their values at all post-treatment time points relative to baseline. Bivariate analysis detected significant correlations between the great majority of texture features and the serum PSA levels. Thereby, three first-order and six second-order statistical features showed strong correlations with the serum PSA levels across time. CONCLUSION The findings in the present work indicate that whole-tumor volumetric texture analysis may be utilized for response assessment in prostate cancer bone metastases. The approach may be used as a complementary measure for treatment monitoring in conjunction with averaged ADC values.
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Affiliation(s)
- Carolin Reischauer
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland; Institute for Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland.
| | - René Patzwahl
- Department of Radiology, Cantonal Hospital Winterthur, Winterthur, Switzerland
| | - Dow-Mu Koh
- Academic Department of Radiology, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK; CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Sutton, Surrey, UK
| | - Johannes M Froehlich
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland
| | - Andreas Gutzeit
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland; Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland; Department of Radiology, Paracelsus Medical University Salzburg, Salzburg, Austria
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Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma. Oncotarget 2017; 8:104444-104454. [PMID: 29262652 PMCID: PMC5732818 DOI: 10.18632/oncotarget.22304] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 10/05/2017] [Indexed: 01/04/2023] Open
Abstract
Objectives To investigate the capability of computed-tomography (CT) radiomic features to predict the therapeutic response of Esophageal Carcinoma (EC) to chemoradiotherapy (CRT). Methods Pretreatment contrast-enhanced CT images of 49 EC patients (33 responders, 16 nonresponders) who received with CRT were retrospectively analyzed. The region of tumor was contoured by two radiologists. A total of 214 features were extracted from the tumor region. Kruskal-Wallis test and receiver operating characteristic (ROC) analysis were performed to evaluate the capability of each feature on treatment response classification. Support vector machine (SVM) and artificial neural network (ANN) algorithms were used to build models for prediction of the treatment response. The statistical difference between the performances of the models was assessed using McNemar's test. Results Radiomic-based classification showed significance in differentiating responders from nonresponders. Five features were found to discriminate nonresponders from responders (AUCs from 0.686 to 0.727). Considering these features, two features (Histogram2D_skewness: P = 0.015. Histogram2D_kurtosis: P = 0.039) were significant for differentiating SDs (stable disease) from PRs (partial response) and one feature (Histogram2D_skewness: P = 0.027) for differentiating SDs from CRs (complete response). Both classifiers showed potential in predicting the treatment response with higher accuracy (ANN: 0.972, SVM: 0.891). No statistically significant difference was observed in the performance of the two classifiers (P = 0.250). Conclusions CT-based radiomic features can be used as imaging biomarkers to predict tumor response to CRT in EC patients.
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Béresová M, Larroza A, Arana E, Varga J, Balkay L, Moratal D. 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2017; 31:285-294. [DOI: 10.1007/s10334-017-0653-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 08/24/2017] [Accepted: 09/11/2017] [Indexed: 11/25/2022]
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Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, Li B. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging 2017; 17:42. [PMID: 28705145 PMCID: PMC5508617 DOI: 10.1186/s12880-017-0212-x] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 06/19/2017] [Indexed: 12/14/2022] Open
Abstract
Background To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). Methods The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients). The TA was performed and textual parameters derived from the gray level co-occurrence matrix (GLCM), gray level gradient co-occurrence matrix (GLGCM), gray-level run-length matrix (GLRLM), Gabor wavelet transform (GWTF), intensity-size-zone matrix (ISZM), and histogram features were calculated. The capacity of each parameter to classify three types of single liver lesions was assessed using the Kruskal-Wallis test. Specificity and sensitivity for each of the studied parameters were derived using ROC curves. Four supervised classification algorithms were trained with the most influential textural features in the classification of tumor types. The test datasets validated the reliability of the models. Results The texture analyses showed that the HH versus HM, HM versus HCC, and HH versus HCC could be differentiated by 9, 16 and 10 feature parameters, respectively. The model’s misclassification rates were 11.7, 9.6 and 9.7% respectively. No texture feature was able to adequately distinguish among the three types of single liver lesions at the same time. The BP-ANN model had better predictive ability. Conclusion Texture features of SPAIR T2W-MRI can classify the three types of single liver lesions (HH, HM and HCC) and may serve as an adjunct tool for accurate diagnosis of these diseases. Electronic supplementary material The online version of this article (doi:10.1186/s12880-017-0212-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhenjiang Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yu Mao
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Wei Huang
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Hongsheng Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jian Zhu
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Wanhu Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Baosheng Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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Verma RK, Wiest R, Locher C, Heldner MR, Schucht P, Raabe A, Gralla J, Kamm CP, Slotboom J, Kellner‐Weldon F. Differentiating enhancing multiple sclerosis lesions, glioblastoma, and lymphoma with dynamic texture parameters analysis (
DTPA
): A feasibility study. Med Phys 2017; 44:4000-4008. [DOI: 10.1002/mp.12356] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 03/13/2017] [Accepted: 05/13/2017] [Indexed: 12/11/2022] Open
Affiliation(s)
- Rajeev Kumar Verma
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
- Institute of Radiology and Neuroradiology Tiefenau Hospital Bern 3004 Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | - Cäcilia Locher
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | | | - Phillip Schucht
- Department of Neurosurgery Inselspital University of Bern Bern 3010 Switzerland
| | - Andreas Raabe
- Department of Neurosurgery Inselspital University of Bern Bern 3010 Switzerland
| | - Jan Gralla
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | | | - Johannes Slotboom
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | - Frauke Kellner‐Weldon
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
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Kunimatsu A, Kunimatsu N, Kamiya K, Watadani T, Mori H, Abe O. Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis. Magn Reson Med Sci 2017. [PMID: 28638001 PMCID: PMC5760233 DOI: 10.2463/mrms.mp.2017-0044] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
PURPOSE To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features. METHODS This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T1-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL. RESULTS Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM. CONCLUSIONS Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.
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Affiliation(s)
- Akira Kunimatsu
- Department of Radiology, Graduate School of Medicine, The University of Tokyo.,Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo
| | - Natsuko Kunimatsu
- Department of Radiology, International University of Health and Welfare, Mita Hospital
| | - Kouhei Kamiya
- Department of Radiology, The University of Tokyo Hospital
| | | | - Harushi Mori
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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Abrol S, Kotrotsou A, Salem A, Zinn PO, Colen RR. Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images. Top Magn Reson Imaging 2017; 26:43-53. [PMID: 28079714 DOI: 10.1097/rmr.0000000000000117] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.
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Affiliation(s)
- Srishti Abrol
- *Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center †Department of Neurosurgery, Baylor College of Medicine ‡Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
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Nardone V, Tini P, Biondi M, Sebaste L, Vanzi E, De Otto G, Rubino G, Carfagno T, Battaglia G, Pastina P, Cerase A, Mazzoni LN, Banci Buonamici F, Pirtoli L. Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation. Cureus 2016; 8:e584. [PMID: 27226944 PMCID: PMC4876005 DOI: 10.7759/cureus.584] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
UNLABELLED BACKGROUND : Stereotactic irradiation is widely used in brain oligo-metastases treatment. The aim of this study is to evaluate the prognostic value of magnetic resonance imaging (MRI) texture analysis (TA) of brain metastases (BM) of non-small cell lung cancer (NSCLC). MATERIALS AND METHODS : This study included thirty-eight consecutive patients undergoing stereotactic irradiation, that is, stereotactic fractionated radiotherapy (SRT) or radiosurgery (SRS), from January 2011 to December 2014 for 1-2 brain BM from NSCLC. Whole-brain radiotherapy (WBRT) was not delivered. The diagnostic MRI DICOM (Digital Imaging and Communications in Medicine) images were collected and analyzed with a homemade ImageJ macro, and typical TA parameters (mean, standard deviation, skewness, kurtosis, entropy, and uniformity) were evaluated for: brain progression-free survival; modality of brain metastatic progression (local progression or/and new metastases); and overall survival, after SRT/SRS. RESULTS After SRT/SRS 14 patients (36.8%) experienced recurrence in the brain, with a recurrence in the irradiated site (five patients, 13.2%), new metastases (11 patients, 28.9%), local recurrence and new metastases (two patients, 5.25%). Nineteen patients (50%) died of tumor progression or other causes. Entropy and uniformity were significantly associated with local progression, whereas kurtosis was significantly associated with both local progression and new brain metastases. CONCLUSIONS : These results appear promising, since the knowledge of factors correlated with the modality of brain progression after stereotactic irradiation of brain oligo-metastatic foci of NSCLC might help in driving the best treatment in these patients (association of SRT/SRS with WBRT? Increase of SRT/SRS dose?). Our preliminary data needs confirmation in large patient series.
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Affiliation(s)
- Valerio Nardone
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
| | - Paolo Tini
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
| | | | - Lucio Sebaste
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
| | - Eleonora Vanzi
- Department of Medical Physics, University Hospital of Siena, Siena, Italy
| | - Gianmarco De Otto
- Department of Medical Physics, University Hospital of Siena, Siena, Italy
| | - Giovanni Rubino
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
| | - Tommaso Carfagno
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
| | | | - Pierpaolo Pastina
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
| | - Alfonso Cerase
- Unit of Neuro Radiology, University Hospital of Siena, Siena, Italy
| | | | | | - Luigi Pirtoli
- Unit of Radiation Oncology, University Hospital of Siena, Siena, Italy
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