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Yang J, Bi Q, Jin Y, Yang Y, Du J, Zhang H, Wu K. Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study. Front Oncol 2024; 14:1392343. [PMID: 38939335 PMCID: PMC11208325 DOI: 10.3389/fonc.2024.1392343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Purpose To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA). Methods Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC). Results There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798). Conclusion Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.
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Affiliation(s)
- Jing Yang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Qiu Bi
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Yiren Jin
- Department of Radiation, The Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yong Yang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Ji Du
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Hongjiang Zhang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Kunhua Wu
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
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Chen X, Wang H, Xia Y, Shi F, He L, Liu E. The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma. Discov Oncol 2024; 15:201. [PMID: 38822860 PMCID: PMC11144178 DOI: 10.1007/s12672-024-01067-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/28/2024] [Indexed: 06/03/2024] Open
Abstract
OBJECTIVE Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China.
| | - Enmei Liu
- Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, 400014, China.
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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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Li A, Hu Y, Cui XW, Ye XH, Peng XJ, Lv WZ, Zhao CK. Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature. Acta Radiol 2024; 65:470-481. [PMID: 38321752 DOI: 10.1177/02841851231217227] [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] [Indexed: 02/08/2024]
Abstract
BACKGROUND Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.
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Affiliation(s)
- Ao Li
- Department of Medical Ultrasound, First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yu Hu
- Department of Medical Ultrasound, First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Xin-Hua Ye
- Department of Medical Ultrasound, First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xiao-Jing Peng
- Department of Medical Ultrasound, First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, PR China
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, PR China
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Mou Y, Han X, Li J, Yu P, Wang C, Song Z, Wang X, Zhang M, Zhang H, Mao N, Song X. Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma. Acad Radiol 2024; 31:1805-1817. [PMID: 38071100 DOI: 10.1016/j.acra.2023.11.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 05/12/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. METHODS This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. RESULTS A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. CONCLUSION This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.
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Affiliation(s)
- Yakui Mou
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Xiao Han
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Department of Otolaryngology-Head and Neck Surgery, BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing 210019, China (X.H.)
| | - Jingjing Li
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China (J.L.)
| | - Pengyi Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Cai Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Zheying Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); School of Clinical Medicine, Weifang Medical University, Weifang 261042, China (Z.S., X.W.)
| | - Xiaojie Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); School of Clinical Medicine, Weifang Medical University, Weifang 261042, China (Z.S., X.W.)
| | - Mingjun Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (H.Z., N.M.)
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (H.Z., N.M.)
| | - Xicheng Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.).
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Ferreira A, Li J, Pomykala KL, Kleesiek J, Alves V, Egger J. GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy. Med Image Anal 2024; 93:103100. [PMID: 38340545 DOI: 10.1016/j.media.2024.103100] [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: 12/05/2022] [Revised: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.
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Affiliation(s)
- André Ferreira
- Center Algoritmi/LASI, University of Minho, Braga, 4710-057, Portugal; Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, 52074 Aachen, Germany.
| | - Jianning Li
- Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany.
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, Essen, 45147, Germany; TU Dortmund University, Department of Physics, Otto-Hahn-Straße 4, 44227 Dortmund, Germany.
| | - Victor Alves
- Center Algoritmi/LASI, University of Minho, Braga, 4710-057, Portugal.
| | - Jan Egger
- Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 801, Austria.
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Duan C, Hao D, Cui J, Wang G, Xu W, Li N, Liu X. An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:510-519. [PMID: 38343220 PMCID: PMC11031553 DOI: 10.1007/s10278-023-00937-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 08/31/2023] [Accepted: 09/04/2023] [Indexed: 04/20/2024]
Abstract
The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643-0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.
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Affiliation(s)
- Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
| | - Jiufa Cui
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
| | - Nan Li
- Department of Information Management, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China.
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Moon SY, Park H, Lee W, Lee S, Lho SK, Kim M, Kim KW, Kwon JS. Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis. Mol Psychiatry 2023; 28:5309-5318. [PMID: 37500824 DOI: 10.1038/s41380-023-02163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/13/2023] [Accepted: 06/23/2023] [Indexed: 07/29/2023]
Abstract
Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix - informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) - were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.
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Affiliation(s)
- Sun Young Moon
- Department of Public Health Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Hyungyou Park
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Won Lee
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Subin Lee
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | | | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki Woong Kim
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Mao K, Wong LM, Zhang R, So TY, Shan Z, Hung KF, Ai QYH. Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review. Cancers (Basel) 2023; 15:4918. [PMID: 37894285 PMCID: PMC10605883 DOI: 10.3390/cancers15204918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.
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Affiliation(s)
- Kaijing Mao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Lun M. Wong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Rongli Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Zhiyi Shan
- Paediatric Dentistry & Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Kuo Feng Hung
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
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10
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Duan C, Li N, Li Y, Cui J, Xu W, Liu X. Prediction of progesterone receptor expression in high-grade meningioma by using radiomics based on enhanced T1WI. Clin Radiol 2023; 78:e752-e757. [PMID: 37487839 DOI: 10.1016/j.crad.2023.06.006] [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: 01/03/2023] [Revised: 04/13/2023] [Accepted: 06/03/2023] [Indexed: 07/26/2023]
Abstract
AIM To predict progesterone receptor (PR) expression of high-grade meningioma using radiomics based on enhanced T1-weighted imaging (WI). MATERIALS AND METHODS There were 157 cases of high-grade meningioma in the study. Seventy-eight cases had negative expression and 79 cases had positive expression. Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The models were developed by naive Bayes (NB), random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) and decision curve analysis (DCA) analysis were used to assess the models. RESULTS Nine features were selected as the valuable features using Spearman's analysis and LASSO regression. The RF and NB models achieved the same area under the ROC curve (AUC) of 0.75, which was higher than that of SVM (0.74). There was no significant difference among the AUCs of the three models (p>0.05). There was a larger net benefit in the RF model than the SVM and NB models across all threshold probabilities in the DCA analysis. CONCLUSION The RF model had good performance in predicting PR expression of high-grade meningioma. PR expression evaluation for high-grade meningioma would be helpful in clinical practice.
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Affiliation(s)
- C Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China
| | - N Li
- Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China
| | - Y Li
- Department of Radiology, Qingdao Women and Children's Hospital, Qingdao City, Shandong Province, China
| | - J Cui
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China
| | - W Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China
| | - X Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China.
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11
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Glogauer J, Kohanzadeh A, Feit A, Fournier JE, Zians A, Somogyi DZ. The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review. Cureus 2023; 15:e44476. [PMID: 37664330 PMCID: PMC10472720 DOI: 10.7759/cureus.44476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
Head and neck cancers represent a significant source of morbidity and mortality across the world. The individual genetic makeup of each tumor can help to determine the course of treatment and can help clinicians predict prognosis. Non-invasive tools to determine the genetic status of these tumors, particularly p16 (human papillomavirus (HPV)) status could prove extremely valuable to treating clinicians and surgeons. The field of radiomics is a burgeoning area of radiology practice that aims to provide quantitative biomarkers that can be derived from radiological images and could prove useful in determining p16 status non-invasively. In this review, we summarize the current evidence for the use of radiomics to determine the HPV status of head and neck tumors. .
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Affiliation(s)
- Judah Glogauer
- Department of Pathology and Molecular Medicine, McMaster University, Waterloo, CAN
| | | | - Avery Feit
- Medical School, Albert Einstein College of Medicine, Bronx, USA
| | - Jeffrey E Fournier
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, CAN
| | - Avraham Zians
- Department of Diagnostic and Interventional Radiology, Montefiore Medical Center, Wakefield Campus, Bronx, USA
| | - Dafna Z Somogyi
- Department of Internal Medicine, Westchester Medical Center, Valhalla, USA
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12
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Li M, Qin H, Yu X, Sun J, Xu X, You Y, Ma C, Yang L. Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map. Insights Imaging 2023; 14:125. [PMID: 37454355 DOI: 10.1186/s13244-023-01477-8] [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: 02/20/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
OBJECTIVE To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. RESULTS Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). CONCLUSION The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.
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Affiliation(s)
- Min Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Hongtao Qin
- Department of Radiology and Nuclear Medicine, The First Hospital of Hebei Medical University, No. 89, Donggang Road, Shijiazhuang, 050031, Hebei Province, People's Republic of China
| | - Xianbo Yu
- Siemens Healthineers Ltd., 7, Wangjing Zhonghuan Nanlu, Beijing, 100102, People's Republic of China
| | - Junyi Sun
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Xiaosheng Xu
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Yang You
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Chongfei Ma
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Li Yang
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China.
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Ren J, Mao L, Zhao J, Li XL, Wang C, Liu XY, Jin ZY, He YL, Li Y, Xue HD. Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01666-x. [PMID: 37368228 DOI: 10.1007/s11547-023-01666-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/14/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVE To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. MATERIAL AND METHODS We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated. RESULTS The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000). CONCLUSION Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.
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Affiliation(s)
- Jing Ren
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Li Mao
- AI Lab, Deepwise Healthcare, Beijing, People's Republic of China
| | - Jia Zhao
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Xiu-Li Li
- AI Lab, Deepwise Healthcare, Beijing, People's Republic of China
| | - Chen Wang
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Beijing, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China.
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Zhang K, Lin J, Lin F, Wang Z, Zhang H, Zhang S, Mao N, Qiao G. Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023:XST221349. [PMID: 37066960 DOI: 10.3233/xst-221349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) has been regarded as one of the standard treatments for patients with locally advanced breast cancer. No previous study has investigated the feasibility of using a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict pathological complete response (pCR) after NAC. OBJECTIVE To develop and validate a CESM-based radiomics nomogram to predict pCR after NAC in breast cancer. METHODS A total of 118 patients were enrolled, which are divided into a training dataset including 82 patients (with 21 pCR and 61 non-pCR) and a testing dataset of 36 patients (with 9 pCR and 27 non-pCR). The tumor regions of interest (ROIs) were manually segmented by two radiologists on the low-energy and recombined images and radiomics features were extracted. Intraclass correlation coefficients (ICCs) were used to assess the intra- and inter-observer agreements of ROI features extraction. In the training set, the variance threshold, SelectKBest method, and least absolute shrinkage and selection operator regression were used to select the optimal radiomics features. Radiomics signature was calculated through a linear combination of selected features. A radiomics nomogram containing radiomics signature score (Rad-score) and clinical risk factors was developed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate prediction performance of the radiomics nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the radiomics nomogram. RESULTS The intra- and inter- observer ICCs were 0.769-0.815 and 0.786-0.853, respectively. Thirteen radiomics features were selected to calculate Rad-score. The radiomics nomogram containing Rad-score and clinical risk factor showed an encouraging calibration and discrimination performance with area under the ROC curves of 0.906 (95% confidence interval (CI): 0.840-0.966) in the training dataset and 0.790 (95% CI: 0.554-0.952) in the test dataset. CONCLUSIONS The CESM-based radiomics nomogram had good prediction performance for pCR after NAC in breast cancer; therefore, it has a good clinical application prospect.
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Affiliation(s)
- Kun Zhang
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Jun Lin
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Fan Lin
- Department of Radiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Zhongyi Wang
- Department of Radiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Shijie Zhang
- Department of Radiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Ning Mao
- Department of Radiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Guangdong Qiao
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
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Ruiz-España S, Ortiz-Ramón R, Pérez-Ramírez Ú, Díaz-Parra A, Ciccocioppo R, Bach P, Vollstädt-Klein S, Kiefer F, Sommer WH, Canals S, Moratal D. MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models. Comput Med Imaging Graph 2023; 104:102187. [PMID: 36696812 DOI: 10.1016/j.compmedimag.2023.102187] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 11/28/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023]
Abstract
Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction.
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Affiliation(s)
- Silvia Ruiz-España
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Rafael Ortiz-Ramón
- GRID Research Group, Universidad Internacional de Valencia - VIU, Valencia, Spain
| | - Úrsula Pérez-Ramírez
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Antonio Díaz-Parra
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | | | - Patrick Bach
- Department of Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Sabine Vollstädt-Klein
- Department of Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Falk Kiefer
- Department of Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Wolfgang H Sommer
- Department of Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Santiago Canals
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, Campus de San Juan, 03550 Sant Joan d'Alacant, Spain.
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
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Li S, Yu X, Shi R, Zhu B, Zhang R, Kang B, Liu F, Zhang S, Wang X. MRI-based radiomics nomogram for differentiation of solitary metastasis and solitary primary tumor in the spine. BMC Med Imaging 2023; 23:29. [PMID: 36755233 PMCID: PMC9909949 DOI: 10.1186/s12880-023-00978-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/27/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Differentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST. METHODS One hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer-Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness. RESULTS The age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer-Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. CONCLUSIONS A radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.
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Affiliation(s)
- Sha Li
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, No. 44, Wenhua West Road, Jinan, 250012 Shandong China
| | - Xinxin Yu
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, No. 44, Wenhua West Road, Jinan, 250012 Shandong China
| | - Rongchao Shi
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, No. 44, Wenhua West Road, Jinan, 250012 Shandong China
| | - Baosen Zhu
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, No. 44, Wenhua West Road, Jinan, 250012 Shandong China
| | - Ran Zhang
- Huiying Medical Technology Co. Ltd, Beijing, China
| | - Bing Kang
- grid.27255.370000 0004 1761 1174Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong University, No. 324, Jingwu Road, Jinan, 250021 Shandong China
| | - Fangyuan Liu
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, No. 44, Wenhua West Road, Jinan, 250012 Shandong China
| | - Shuai Zhang
- School of Medicine, Shandong First Medical University, No. 6699, Qingdao Road, Jinan, 250024, Shandong, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong University, No. 324, Jingwu Road, Jinan, 250021, Shandong, China.
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17
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Chen Y, Liu Y, Zuo X, Zhao Q, Sun M, Cui M, Zhao X, Du Y. Identification of significant imaging features for sensing oocyte viability. Microsc Res Tech 2023; 86:181-192. [PMID: 36278826 DOI: 10.1002/jemt.24248] [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: 07/11/2022] [Revised: 09/26/2022] [Accepted: 10/06/2022] [Indexed: 01/21/2023]
Abstract
The evaluation of oocyte viability in the laboratory is limited to the morphological assessment by naked eyes, but the realization that most normal-appearing oocytes may conceal abnormalities prompts the search for automated approaches that can detect the abnormalities imperceptible to naked eyes. In this study, we developed an image processing pipeline applicable to bright-field microscope images to quantify the causal relationship between the quantitative imaging features and the developmental potential of oocytes. We acquired 19 imaging features of approximately 700 oocytes and determined two imaging subtypes, namely viable and nonviable subtypes that correlated closely with a viability fluorescence indicator and cleavage rates. The causal relationship between these imaging features and oocyte viability was derived from a viability-oriented Bayesian network that was developed based on the Bayesian information criterion and Tabu search. Our experimental results revealed that entropy with mean Gray Level Co-Occurrence Matrix energy describing the uniformity and texture roughness of cytoplasm were salient features for the automated selection of promising oocytes that exhibited excellent developmental potential.
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Affiliation(s)
- Yizhe Chen
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Yaowei Liu
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Xiaoying Zuo
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Qili Zhao
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Mingzhu Sun
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Maosheng Cui
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China.,Innovation Team of Pig Feeding, Institute of Animal Science and Veterinary of Tianjin, Tianjin, China
| | - Xin Zhao
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Yue Du
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
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18
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Duan C, Li N, Liu X, Cui J, Wang G, Xu W. Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study. Front Oncol 2023; 13:1157379. [PMID: 37035216 PMCID: PMC10076744 DOI: 10.3389/fonc.2023.1157379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/10/2023] [Indexed: 04/11/2023] Open
Abstract
Objectives The objective of this study was to compare the predictive performance of 2D and 3D radiomics features in meningioma grade based on enhanced T1 WI images. Methods There were 170 high grade meningioma and 170 low grade meningioma were selected randomly. The 2D and 3D features were extracted from 2D and 3D ROI of each meningioma. The Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The 2D and 3D predictive models were constructed by naive Bayes (NB), gradient boosting decision tree (GBDT), and support vector machine (SVM). The ROC curve was drawn and AUC was calculated. The 2D and 3D models were compared by Delong test of AUCs and decision curve analysis (DCA) curve. Results There were 1143 features extracted from each ROI. Six and seven features were selected. The AUC of 2D and 3D model in NB, GBDT, and SVM was 0.773 and 0.771, 0.722 and 0.717, 0.733 and 0.743. There was no significant difference in two AUCs (p=0.960, 0.913, 0.830) between 2D and 3D model. The 2D features had a better performance than 3D features in NB models and the 3D features had a better performance than 2D features in GBDT models. The 2D features and 3D features had an equal performance in SVM models. Conclusions The 2D and 3D features had a comparable performance in predicting meningioma grade. Considering the issue of time and labor, 2D features could be selected for radiomics study in meningioma. Key points There was a comparable performance between 2D and 3D features in meningioma grade prediction. The 2D features was a proper selection in meningioma radiomics study because of its time and labor saving.
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Affiliation(s)
- Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Nan Li
- Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiufa Cui
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Wenjian Xu,
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19
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Shang H, Li J, Jiao T, Fang C, Li K, Yin D, Zeng Q. Differentiation of Lung Metastases Originated From Different Primary Tumors Using Radiomics Features Based on CT Imaging. Acad Radiol 2023; 30:40-46. [PMID: 35577699 DOI: 10.1016/j.acra.2022.04.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/26/2022] [Accepted: 04/09/2022] [Indexed: 01/02/2023]
Abstract
RATIONALE AND OBJECTIVES To explore the feasibility of differentiating three predominant metastatic tumor types using lung computed tomography (CT) radiomics features based on supervised machine learning. MATERIALS AND METHODS This retrospective analysis included 252 lung metastases (LM) (from 78 patients), which were divided into the training (n = 176) and test (n = 76) cohort randomly. The metastases originated from colorectal cancer (n = 97), breast cancer (n = 87), and renal carcinoma (n = 68). An additional 77 LM (from 35 patients) were used for external validation. All radiomics features were extracted from lung CT using an open-source software called 3D slicer. The least absolute shrinkage and selection operator (LASSO) method selected the optimal radiomics features to build the model. Random forest and support vector machine (SVM) were selected to build three-class and two-class models. The performance of the classification model was evaluated with the area under the receiver operating characteristic curve (AUC) by two strategies: one-versus-rest and one-versus-one. RESULTS Eight hundred and fifty-one quantitative radiomics features were extracted from lung CT. By LASSO, 23 optimal features were extracted in three-class, and 25, 29, and 35 features in two-class for differentiating every two of three LM (colorectal cancer vs. renal carcinoma, colorectal cancer vs. breast cancer, and breast cancer vs. renal carcinoma, respectively). The AUCs of the three-class model were 0.83 for colorectal cancer, 0.79 for breast cancer, and 0.91 for renal carcinoma in the test cohort. In the external validation cohort, the AUCs were 0.77, 0.83, and 0.81, respectively. Swarmplot shows the distribution of radiomics features among three different LM types. In the two-class model, high accuracy and AUC were obtained by SVM. The AUC of discriminating colorectal cancer LM from renal carcinoma LM was 0.84, and breast cancer LM from colorectal cancer LM and renal carcinoma LM were 0.80 and 0.94, respectively. The AUCs were 0.77, 0.78, and 0.84 in the external validation cohort. CONCLUSION Quantitative radiomics features based on Lung CT exhibited good discriminative performance in LM of primary colorectal cancer, breast cancer, and renal carcinoma.
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Affiliation(s)
- Hui Shang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jizhen Li
- Department of Radiology, Shandong Mental Health Center, Jinan, Shandong, China
| | - Tianyu Jiao
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Caiyun Fang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Kejian Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Di Yin
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China.
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20
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Bhattacharya S, Bennet L, Davidson JO, Unsworth CP. Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training. PLoS One 2022; 17:e0278874. [PMID: 36512546 PMCID: PMC9746996 DOI: 10.1371/journal.pone.0278874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.
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Affiliation(s)
- Saheli Bhattacharya
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
- * E-mail:
| | - Laura Bennet
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Joanne O. Davidson
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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21
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Su GY, Liu J, Xu XQ, Lu MP, Yin M, Wu FY. Texture analysis of conventional magnetic resonance imaging and diffusion-weighted imaging for distinguishing sinonasal non-Hodgkin's lymphoma from squamous cell carcinoma. Eur Arch Otorhinolaryngol 2022; 279:5715-5720. [PMID: 35731296 DOI: 10.1007/s00405-022-07493-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/06/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE To evaluate the value of texture analysis (TA) of conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) in the differential diagnosis between sinonasal non-Hodgkin's lymphoma (NHL) and squamous cell carcinoma (SCC). METHODS Forty-two patients with sinonasal SCC and 30 patients with NHL were retrospectively enrolled. TAs were performed on T2-weighted image (T2WI), apparent diffusion coefficient (ADC) and contrast-enhanced T1-weighted image (T1WI). Texture parameters, including mean value, skewness, kurtosis, entropy and uniformity were obtained and compared between sinonasal SCC and NHL groups. Receiver-operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic value and identify the independent TA parameters. RESULTS The mean value and entropy of ADC, and mean value of contrast-enhanced T1WI were significantly lower in the sinonasal NHL group than those in the SCC group (all P < 0.05). ROC analysis indicated that the entropy of ADC had the best diagnostic performance (AUC 0.832; Sensitivity 0.95; Specificity 0.67; Cutoff value 6.522). Logistic regression analysis showed that the entropy of ADC (P = 0.002, OR = 26.990) was the independent parameter for differentiating sinonasal NHL from SCC. CONCLUSION TA parameters of conventional MRI and DWI, particularly the entropy value of ADC, might be useful in the differentiating diagnosis between sinonasal NHL and SCC.
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Affiliation(s)
- Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Jun Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Mei-Ping Lu
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Min Yin
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China.
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22
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Wei H, Shao Z, Tai J, Fu F, Lv C, Guo Z, Wu Y, Chen L, Bai Y, Wu Q, Yu X, Mu X, Shao F, Wang M. Analysis of CT signs, radiomic features and clinical characteristics for delta variant COVID-19 patients with different vaccination status. BMC Med Imaging 2022; 22:209. [PMID: 36447133 PMCID: PMC9832212 DOI: 10.1186/s12880-022-00937-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To explore the characteristics of peripheral blood, high resolution computed tomography (HRCT) imaging and the radiomics signature (RadScore) in patients infected with delta variant virus under different coronavirus disease (COVID-19) vaccination status. METHODS 123 patients with delta variant virus infection collected from November 1, 2021 to March 1, 2022 were analyzed retrospectively. According to COVID-19 vaccination Status, they were divided into three groups: Unvaccinated group, partially vaccinated group and full vaccination group. The peripheral blood, chest HRCT manifestations and RadScore of each group were analyzed and compared. RESULTS The mean lymphocyte count 1.22 ± 0.49 × 10^9/L, CT score 7.29 ± 3.48, RadScore 0.75 ± 0.63 in the unvaccinated group; The mean lymphocyte count 1.55 ± 0.70 × 10^9/L, CT score 5.27 ± 2.72, RadScore 1.03 ± 0.46 in the partially vaccinated group; The mean lymphocyte count 1.87 ± 0.70 × 10^9/L, CT score 3.59 ± 3.14, RadScore 1.23 ± 0.29 in the fully vaccinated group. There were significant differences in lymphocyte count, CT score and RadScore among the three groups (all p < 0.05); Compared with the other two groups, the lung lesions in the unvaccinated group were more involved in multiple lobes, of which 26 cases involved the whole lung. CONCLUSIONS Through the analysis of clinical features, pulmonary imaging features and radiomics, we confirmed the positive effect of COVID-19 vaccine on pulmonary inflammatory symptoms and lymphocyte count (immune system) during delta mutant infection.
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Affiliation(s)
- Huanhuan Wei
- grid.414011.10000 0004 1808 090XAcademy of Medical Sciences, The People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Zehua Shao
- grid.414011.10000 0004 1808 090XHeart Center of Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, 450003 Henan People’s Republic of China
| | - Jianqing Tai
- grid.417239.aThe First People’s Hospital of Zhengzhou, 56 East Street, Zhengzhou, 450004 Henan China
| | - Fangfang Fu
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Chuanjian Lv
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Zhiping Guo
- grid.417239.aThe First People’s Hospital of Zhengzhou, 56 East Street, Zhengzhou, 450004 Henan China
| | - Yaping Wu
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Lijuan Chen
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Yan Bai
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, 100089 China
| | - Xuan Yu
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Xinling Mu
- grid.417239.aThe First People’s Hospital of Zhengzhou, 56 East Street, Zhengzhou, 450004 Henan China
| | - Fengmin Shao
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Meiyun Wang
- grid.414011.10000 0004 1808 090XAcademy of Medical Sciences, The People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China ,grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
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23
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Gwo CY, Zhu DC, Zhang R. Brain white matter hyperintensity lesion characterization in 3D T 2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth. Front Neurosci 2022; 16:1028929. [PMID: 36507337 PMCID: PMC9731131 DOI: 10.3389/fnins.2022.1028929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10-3 for small lesions; P = 3.14 × 10-2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10-6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.
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Affiliation(s)
- Chih-Ying Gwo
- Department of Information Management, Chien Hsin University of Science and Technology, Taoyuan City, Taiwan
| | - David C. Zhu
- Department of Radiology, Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States
- Department of Psychology, Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States
| | - Rong Zhang
- Department of Neurology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, TX, United States
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24
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Väärälä A, Casula V, Peuna A, Panfilov E, Mobasheri A, Haapea M, Lammentausta E, Nieminen MT. Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative. J Orthop Res 2022; 40:2597-2608. [PMID: 35152476 PMCID: PMC9790756 DOI: 10.1002/jor.25293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 11/13/2021] [Accepted: 02/02/2022] [Indexed: 02/04/2023]
Abstract
In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.
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Affiliation(s)
- Ari Väärälä
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland
| | - Victor Casula
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland
| | - Arttu Peuna
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland,Department of Medical ImagingCentral Finland Central HospitalJyväskyläFinland
| | - Egor Panfilov
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland
| | - Ali Mobasheri
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Department of Regenerative MedicineState Research Institute Centre for Innovative MedicineVilniusLithuania,Departments of Orthopedics, Rheumatology and Clinical ImmunologyUniversity Medical Center UtrechtUtrechtThe Netherlands,Department of Joint SurgeryThe First Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Marianne Haapea
- Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland,Department of Diagnostic RadiologyOulu University HospitalOuluFinland
| | - Eveliina Lammentausta
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Department of Diagnostic RadiologyOulu University HospitalOuluFinland
| | - Miika T. Nieminen
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland,Department of Diagnostic RadiologyOulu University HospitalOuluFinland
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25
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Lu S, Ling H, Chen J, Tan L, Gao Y, Li H, Tan P, Huang D, Zhang X, Liu Y, Mao Y, Qiu Y. MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma. Front Oncol 2022; 12:936040. [PMID: 36212477 PMCID: PMC9539826 DOI: 10.3389/fonc.2022.936040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/06/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo investigate the role of pre-treatment magnetic resonance imaging (MRI) radiomics for the preoperative prediction of lymph node (LN) metastasis in patients with hypopharyngeal squamous cell carcinoma (HPSCC).MethodsA total of 155 patients with HPSCC were eligibly enrolled from single institution. Radiomics features were extracted from contrast-enhanced axial T-1 weighted (CE-T1WI) sequence. The most relevant features of LN metastasis were selected by the least absolute shrinkage and selection operator (LASSO) method. Univariate and multivariate logistic regression analysis was adopted to determine the independent clinical risk factors. Three models were constructed to predict the LN metastasis status: one using radiomics only, one using clinical factors only, and the other one combined radiomics and clinical factors. Receiver operating characteristic (ROC) curves and calibration curve were used to evaluate the discrimination and the accuracy of the models, respectively. The performances were tested by an internal validation cohort (n=47). The clinical utility of the models was assessed by decision curve analysis.ResultsThe nomogram consisted of radiomics scores and the MRI-reported LN status showed satisfactory discrimination in the training and validation cohorts with AUCs of 0.906 (95% CI, 0.840 to 0.972) and 0.853 (95% CI, 0.739 to 0.966), respectively. The nomogram, i.e., the combined model, outperformed the radiomics and MRI-reported LN status in both discrimination and clinical usefulness.ConclusionsThe MRI-based radiomics nomogram holds promise for individual and non-invasive prediction of LN metastasis in patients with HPSCC.
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Affiliation(s)
- Shanhong Lu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hang Ling
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
| | - Juan Chen
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Lei Tan
- College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, China
| | - Yan Gao
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Huayu Li
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Pingqing Tan
- Department of Head and Neck Surgery, Hunan Cancer Hospital, Xiangya Medical School, Central South University, Changsha, China
| | - Donghai Huang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Zhang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Liu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanzheng Qiu, ; Yitao Mao,
| | - Yuanzheng Qiu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanzheng Qiu, ; Yitao Mao,
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Ding H, Li J, Jiang K, Gao C, Lu L, Zhang H, Chen H, Gao X, Zhou K, Sun Z. Assessing the inflammatory severity of the terminal ileum in Crohn disease using radiomics based on MRI. BMC Med Imaging 2022; 22:118. [PMID: 35787255 PMCID: PMC9254684 DOI: 10.1186/s12880-022-00844-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 06/21/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Evaluating inflammatory severity using imaging is essential for Crohn's disease, but it is limited by potential interobserver variation and subjectivity. We compared the efficiency of magnetic resonance index of activity (MaRIA) collected by radiologists and a radiomics model in assessing the inflammatory severity of terminal ileum (TI). METHODS 121 patients were collected from two centers. Patients were divided into ulcerative group and mucosal remission group based on the TI Crohn's disease Endoscopic Severity Index. The consistency of bowel wall thickness (BWT), relative contrast enhancement (RCE), edema, ulcer, MaRIA and features of the region of interest between radiologists were described by weighted Kappa test and intraclass correlation coefficient (ICC), and developed receiver operating curve of MaRIA. The radiomics model was established using reproducible features of logistic regression based on arterial staging of T1WI sequences. Delong test was used to compare radiomics with MaRIA. RESULTS The consistency between radiologists were moderate in BWT (ICC = 0.638), fair in edema (κ = 0.541), RCE (ICC = 0.461), MaRIA (ICC = 0.579) and poor in ulcer (κ = 0.271). Radiomics model was developed by 6 reproducible features (ICC = 0.93-0.96) and equivalent to MaRIA which evaluated by the senior radiologist (0.872 vs 0.883 in training group, 0.824 vs 0.783 in validation group, P = 0.847, 0.471), both of which were significantly higher than MaRIA evaluated by junior radiologist (AUC: 0.621 in training group, 0.557 in validation group, all, P < 0.05). CONCLUSION The evaluation of inflammatory severity could be performed by radiomics objectively and reproducibly, and was comparable to MaRIA evaluated by the senior radiologist. Radiomics may be an important method to assist junior radiologists to assess the severity of inflammation objectively and accurately.
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Affiliation(s)
- Honglei Ding
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Jiaying Li
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Kefang Jiang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.,Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Liangji Lu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Huani Zhang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Haibo Chen
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Xuning Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Kefeng Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.
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Bi S, Li J, Wang T, Man F, Zhang P, Hou F, Wang H, Hao D. Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study. Eur Radiol 2022; 32:6933-6942. [PMID: 35687135 DOI: 10.1007/s00330-022-08780-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To assess the predictive ability of a multi-parametric MRI-based radiomics signature (RS) for the preoperative evaluation of Ki-67 proliferation status in sinonasal malignancies. METHODS A total of 128 patients with sinonasal malignancies that underwent multi-parametric MRIs at two medical centres were retrospectively analysed. Data from one medical centre (n = 77) were used to develop the predictive models and data from the other medical centre (n = 51) constitute the test dataset. Clinical data and conventional MRI findings were reviewed to identify significant predictors. Radiomics features were determined using maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms. Subsequently, RSs were established using a logistic regression (LR) algorithm. The predictive performance of RSs was assessed using calibration, decision curve analysis (DCA), accuracy, and AUC. RESULTS No independent predictors of high Ki-67 proliferation were observed based on clinical data and conventional MRI findings. RS-T1, RS-T2, and RS-T1c (contrast enhancement T1WI) were established based on a single-parametric MRI. RS-Combined (combining T1WI, FS-T2WI, and T1c features) was developed based on multi-parametric MRI and achieved an AUC and accuracy of 0.852 (0.733-0.971) and 86.3%, respectively, on the test dataset. The calibration curve and DCA demonstrated an improved fitness and benefits in clinical practice. CONCLUSIONS A multi-parametric MRI-based RS may be used as a non-invasive, dependable, and accurate tool for preoperative evaluation of the Ki-67 proliferation status to overcome the sampling bias in sinonasal malignancies. KEY POINTS • Multi-parametric MRI-based radiomics signatures (RSs) are used to preoperatively evaluate the proliferation status of Ki-67 in sinonasal malignancies. • Radiomics features are determined using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. • RSs are established using a logistic regression (LR) algorithm.
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Affiliation(s)
- Shucheng Bi
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China
| | - Jie Li
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China
| | - Tongyu Wang
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China
| | - Fengyuan Man
- The Department of Radiology, The PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Peng Zhang
- The Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Feng Hou
- The Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - Hexiang Wang
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China.
| | - Dapeng Hao
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China.
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Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study. Acad Radiol 2022; 29:806-816. [PMID: 34238656 DOI: 10.1016/j.acra.2021.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve. RESULTS A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical. CONCLUSION The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.
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Affiliation(s)
- Yancheng Song
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Bo Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Chentong Yuan
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Hao Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Ziwen Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Fanyi Min
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Yu Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong.
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Stanley BF, Wilfred Franklin S. Automated cerebral microbleed detection using selective 3D gradient co-occurance matrix and convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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30
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Wu Z, Bian T, Dong C, Duan S, Fei H, Hao D, Xu W. Spinal MRI-Based Radiomics Analysis to Predict Treatment Response in Multiple Myeloma. J Comput Assist Tomogr 2022; 46:447-454. [PMID: 35405690 DOI: 10.1097/rct.0000000000001298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE The aim of this study was to explore the clinical utility of spinal magnetic resonance imaging-based radiomics to predict treatment response (TR) in patients with multiple myeloma (MM). METHODS A total of 123 MM patients (85 in the training cohort and 38 in the test cohort) with complete response (CR) (n = 40) or non-CR (n = 83) were retrospectively enrolled in the study. Key feature selection and data dimension reduction were performed using the least absolute shrinkage and selection operator regression. A nomogram was built by combining radiomic signatures and independent clinical risk factors. The prediction performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Treatment response was assessed by determining the serum and urinary levels of M-proteins, serum-free light chain ratio, and the percentage of bone marrow plasma cells. RESULTS Thirteen features were selected to build a radiomic signature. The International Staging System (ISS) stage was selected as an independent clinical factor. The radiomic signature and nomogram showed better calibration and higher discriminatory capacity (AUC of 0.929 and 0.917 for the radiomics and nomogram in the training cohort, respectively, and 0.862 and 0.874 for the radiomics and nomogram in the test cohort, respectively) than the clinical model (AUC of 0.661 and 0.674 in the training and test cohort, respectively). Decision curve analysis confirmed the clinical utility of the radiomics model. CONCLUSIONS Nomograms incorporating a magnetic resonance imaging-based radiomic signature and ISS stage help predict the response to chemotherapy for MM and can be useful in clinical decision-making.
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Affiliation(s)
| | - Tiantian Bian
- Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao, Shandong
| | | | | | - Hairong Fei
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image. Int J Biomed Imaging 2022; 2022:5336373. [PMID: 35496640 PMCID: PMC9045982 DOI: 10.1155/2022/5336373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/18/2022] [Accepted: 03/25/2022] [Indexed: 11/18/2022] Open
Abstract
Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.
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Wang Q, Dong Y, Xiao T, Zhang S, Yu J, Li L, Zhang Q, Wang Y, Xiao Y, Wang W. Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps. Biomed Eng Online 2022; 21:24. [PMID: 35413926 PMCID: PMC9006564 DOI: 10.1186/s12938-021-00927-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/28/2021] [Indexed: 11/10/2022] Open
Abstract
Background This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC). Methods The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction. Results and conclusion Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC. Supplementary Information The online version contains supplementary material available at 10.1186/s12938-021-00927-y. We proposed RF-based radiomics analysis method by introducing three ultrasound features of direct energy attenuation (DEA), skewness of spectrum difference (SSD) and noncentrality parameter S of Rician distribution (NRD) as the feature extraction method from RF signals, investigated the effectiveness of RF-based radiomics analysis method in the immunocheckpoint prediction of programmed cell death protein 1 (PD-1), and validated the results with contrast testing of grayscale-based radiomics analysis method in this study. We also demonstrate a trend in prediction performance changes and its correlation with the number of ultrasound features. The results demonstrated that there were significant differences (p < 0.05) in radiomics scores between HCC patients with PD-1 and HCC patients without PD-1. RF-based radiomics analysis method performed well in PD-1 noninvasive preoperative prediction of HCC patients. In this study, the performance of RF-based radiomics analysis method was better than that of grayscale-based radiomics analysis method in the preoperative prediction of PD-1 in HCC patients. The AUC of DSNM, which was the RF-based radiomics analysis model with three ultrasound feature maps, reached 94.23% in the prediction of PD-1 cell protein in HCC patients.
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Affiliation(s)
- Qingmin Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Tianlei Xiao
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Shiquan Zhang
- Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, University Town, Shenzhen, 518055, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Leyin Li
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Qi Zhang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Yang Xiao
- Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, University Town, Shenzhen, 518055, China.
| | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Shi Z, Ma C, Huang X, Cao D. Magnetic Resonance Imaging Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study. J Magn Reson Imaging 2022; 55:823-839. [PMID: 34997795 DOI: 10.1002/jmri.28048] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM. PURPOSES To develop and validate a 3 T MRI primary tumor radiomics-based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients. STUDY TYPE Retrospective. SUBJECTS A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals. FIELD STRENGTH AND SEQUENCES A 3.0 T and fat-suppressed T1-weighted imaging. ASSESSMENT Quantitative imaging features were extracted from fat-suppressed T1-weighted (FS T1WI) images at the arterial phase. STATISTICAL TESTS Normally distributed data were compared by using t-tests, while the Mann-Whitney U test was used to evaluate non-normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant. RESULTS The AUCs of magnetic resonance imaging (MRI) Rad-score were 0.868 (95% confidence level [CI]: 0.613-0.852) and 0.772 (95% CI: 0.659-0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness. DATA CONCLUSION Pre-/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhenshan Shi
- Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350005, China
| | - Chengle Ma
- Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350005, China
| | - Xinming Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350005, China
| | - Dairong Cao
- Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350005, China
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Dong C, Zheng YM, Li J, Wu ZJ, Yang ZT, Li XL, Xu WJ, Hao DP. A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin's lymphoma of the palatine tonsil. Eur Radiol 2022; 32:243-253. [PMID: 34236464 DOI: 10.1007/s00330-021-08153-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/07/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Accurate preoperative differentiation between squamous cell carcinoma (SCC) and non-Hodgkin's lymphoma (NHL) in the palatine tonsil is crucial because of their different treatment. This study aimed to construct and validate a contrast-enhanced CT (CECT)-based radiomics nomogram for preoperative differentiation of SCC and NHL in the palatine tonsil. METHODS This study enrolled 135 patients with a pathological diagnosis of SCC or NHL from two clinical centers, who were divided into training (n = 94; SCC = 50, NHL = 44) and external validation sets (n = 41; SCC = 22, NHL = 19). A radiomics signature was constructed from radiomics features extracted from routine CECT images and a radiomics score (Rad-score) was calculated. A clinical model was established using demographic features and CT findings. The independent clinical factors and Rad-score were combined to construct a radiomics nomogram. Performance of the clinical model, radiomics signature, and nomogram was assessed using receiver operating characteristics analysis and decision curve analysis. RESULTS Eleven features were finally selected to construct the radiomics signature. The radiomics nomogram incorporating gender, mean CECT value, and radiomics signature showed better predictive value for differentiating SCC from NHL than the clinical model for training (AUC, 0.919 vs. 0.801, p = 0.004) and validation (AUC, 0.876 vs. 0.703, p = 0.029) sets. Decision curve analysis demonstrated that the radiomics nomogram was more clinically useful than the clinical model. CONCLUSIONS A CECT-based radiomics nomogram was constructed incorporating gender, mean CECT value, and radiomics signature. This nomogram showed favorable predictive efficacy for differentiating SCC from NHL in the palatine tonsil, and might be useful for clinical decision-making. KEY POINTS • Differential diagnosis between SCC and NHL in the palatine tonsil is difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, gender, and mean contrast-enhanced CT value facilitates differentiation of SCC from NHL with improved diagnostic efficacy.
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Affiliation(s)
- Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Jian Li
- Department of Radiology, The University of Hong Kong - Shenzhen Hospital, 1, Haiyuan Road, Futian District, Shenzhen, 518000, NO, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Zhi-Tao Yang
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Wen-Jian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China.
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Shu S, Wang C, Hong Z, Zhou X, Zhang T, Peng Q, Wang J, Zheng C. Prognostic Value of Late Enhanced Cardiac Magnetic Resonance Imaging Derived Texture Features in Dilated Cardiomyopathy Patients With Severely Reduced Ejection Fractions. Front Cardiovasc Med 2021; 8:766423. [PMID: 34977183 PMCID: PMC8718517 DOI: 10.3389/fcvm.2021.766423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/18/2021] [Indexed: 12/25/2022] Open
Abstract
Background: Late enhanced cardiac magnetic resonance (CMR) images of the left ventricular myocardium contain an enormous amount of information that could provide prognostic value beyond that of late gadolinium enhancements (LGEs). With computational postprocessing and analysis, the heterogeneities and variations of myocardial signal intensities can be interpreted and measured as texture features. This study aimed to evaluate the value of texture features extracted from late enhanced CMR images of the myocardium to predict adverse outcomes in patients with dilated cardiomyopathy (DCM) and severe systolic dysfunction.Methods: This single-center study retrospectively enrolled patients with DCM with severely reduced left ventricular ejection fractions (LVEFs < 35%). Texture features were extracted from enhanced late scanning images, and the presence and extent of LGEs were also measured. Patients were followed-up for clinical endpoints composed of all-cause deaths and cardiac transplantation. Cox proportional hazard regression and Kaplan–Meier analyses were used to evaluate the prognostic value of texture features and conventional CMR parameters with event-free survival.Results: A total of 114 patients (37 women, median age 47.5 years old) with severely impaired systolic function (median LVEF, 14.0%) were followed-up for a median of 504.5 days. Twenty-nine patients experienced endpoint events, 12 died, and 17 underwent cardiac transplantations. Three texture features from a gray-level co-occurrence matrix (GLCM) (GLCM_contrast, GLCM_difference average, and GLCM_difference entropy) showed good prognostic value for adverse events when analyzed using univariable Cox hazard ratio regression (p = 0.007, p = 0.011, and p = 0.007, retrospectively). When each of the three features was analyzed using a multivariable Cox regression model that included the clinical parameter (systolic blood pressure) and LGE extent, they were found to be independently associated with adverse outcomes.Conclusion: Texture features related LGE heterogeneities and variations (GLCM_contrast, GLCM_difference average, and GLCM_difference entropy) are novel markers for risk stratification toward adverse events in DCM patients with severe systolic dysfunction.
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Affiliation(s)
- Shenglei Shu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Cheng Wang
- Department of Cardiology, Institute of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziming Hong
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyue Zhou
- MR Collaboration, Siemens Healthineers, Shanghai, China
| | | | - Qinmu Peng
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- *Correspondence: Jing Wang
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Chuansheng Zheng
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Juras V, Szomolanyi P, Janáčová V, Kirner A, Angele P, Trattnig S. Differentiation of Cartilage Repair Techniques Using Texture Analysis from T 2 Maps. Cartilage 2021; 13:718S-728S. [PMID: 34269072 PMCID: PMC8808785 DOI: 10.1177/19476035211029698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE The aim of this study was to investigate texture features from T2 maps as a marker for distinguishing the maturation of repair tissue after 2 different cartilage repair procedures. DESIGN Seventy-nine patients, after either microfracture (MFX) or matrix-associated chondrocyte transplantation (MACT), were examined on a 3-T magnetic resonance (MR) scanner with morphological and quantitative (T2 mapping) MR sequences 2 years after surgery. Twenty-one texture features from a gray-level co-occurrence matrix (GLCM) were extracted. The texture feature difference between 2 repair types was assessed individually for the femoral condyle and trochlea/anterior condyle using linear regression models. The stability and reproducibility of texture features for focal cartilage were calculated using intra-observer variability and area under curve from receiver operating characteristics. RESULTS There was no statistical significance found between MFX and MACT for T2 values (P = 0.96). There was, however, found a statistical significance between MFX and MACT in femoral condyle in GLCM features autocorrelation (P < 0.001), sum of squares (P = 0.023), sum average (P = 0.005), sum variance (P = 0.0048), and sum entropy (P = 0.05); and in anterior condyle/trochlea homogeneity (P = 0.02) and dissimilarity (P < 0.001). CONCLUSION Texture analysis using GLCM provides a useful extension to T2 mapping for the characterization of cartilage repair tissue by increasing its sensitivity to tissue structure. Some texture features were able to distinguish between repair tissue after different cartilage repair procedures, as repair tissue texture (and hence, probably collagen organization) 24 months after MACT more closely resembled healthy cartilage than did MFX repair tissue.
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Affiliation(s)
- Vladimir Juras
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
| | - Pavol Szomolanyi
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
- Institute of Measurement Science,
Slovak Academy of Sciences, Bratislava, Slovakia
| | - Veronika Janáčová
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
| | | | | | - Siegfried Trattnig
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
- CD laboratory for Clinical Molecular MR
imaging, Vienna, Austria
- Austrian Cluster for Tissue
Regeneration, Vienna, Austria
- Institute for Clinical Molecular MRI in
the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
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Yang J, Angelini ED, Balte PP, Hoffman EA, Austin JHM, Smith BM, Barr RG, Laine AF. Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3652-3662. [PMID: 34224349 PMCID: PMC8715521 DOI: 10.1109/tmi.2021.3094660] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.
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Wang SH, Han XJ, Du J, Wang ZC, Yuan C, Chen Y, Zhu Y, Dou X, Xu XW, Xu H, Yang ZH. Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI. Insights Imaging 2021; 12:173. [PMID: 34817732 PMCID: PMC8613326 DOI: 10.1186/s13244-021-01117-z] [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: 09/19/2021] [Accepted: 10/26/2021] [Indexed: 12/12/2022] Open
Abstract
Background The imaging features of focal liver lesions (FLLs) are diverse and complex. Diagnosing FLLs with imaging alone remains challenging. We developed and validated an interpretable deep learning model for the classification of seven categories of FLLs on multisequence MRI and compared the differential diagnosis between the proposed model and radiologists. Methods In all, 557 lesions examined by multisequence MRI were utilised in this retrospective study and divided into training–validation (n = 444) and test (n = 113) datasets. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the model. The accuracy and confusion matrix of the model and individual radiologists were compared. Saliency maps were generated to highlight the activation region based on the model perspective. Results The AUC of the two- and seven-way classifications of the model were 0.969 (95% CI 0.944–0.994) and from 0.919 (95% CI 0.857–0.980) to 0.999 (95% CI 0.996–1.000), respectively. The model accuracy (79.6%) of the seven-way classification was higher than that of the radiology residents (66.4%, p = 0.035) and general radiologists (73.5%, p = 0.346) but lower than that of the academic radiologists (85.4%, p = 0.291). Confusion matrices showed the sources of diagnostic errors for the model and individual radiologists for each disease. Saliency maps detected the activation regions associated with each predicted class. Conclusion This interpretable deep learning model showed high diagnostic performance in the differentiation of FLLs on multisequence MRI. The analysis principle contributing to the predictions can be explained via saliency maps. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01117-z.
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Affiliation(s)
- Shu-Hui Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.,Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong Province, People's Republic of China
| | - Xin-Jun Han
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Jing Du
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Zhen-Chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Chunwang Yuan
- Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yinan Chen
- SenseTime Research, SenseTime, Shanghai, People's Republic of China.,WCH-SenseTime Joint Lab, SenseTime, Shanghai, Sichuan, People's Republic of China
| | - Yajing Zhu
- SenseTime Research, SenseTime, Shanghai, People's Republic of China
| | - Xin Dou
- SenseBrain Technology, SenseTime, Princeton, NJ, 08540, USA
| | - Xiao-Wei Xu
- SenseTime Research, SenseTime, Shanghai, People's Republic of China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.
| | - Zheng-Han Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.
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Mao N, Yin P, Zhang H, Zhang K, Song X, Xing D, Chu T. Mammography-based radiomics for predicting the risk of breast cancer recurrence: a multicenter study. Br J Radiol 2021; 94:20210348. [PMID: 34520235 DOI: 10.1259/bjr.20210348] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE This study aimed to establish a mammography-based radiomics model for predicting the risk of estrogen receptor (ER)-positive, lymph node (LN)-negative invasive breast cancer recurrence based on Oncotype DX and validated it by using multicenter data. METHODS A total of 304 potentially eligible patients with pre-operative mammography images and available Oncotype DX score were retrospectively enrolled from two hospitals. The patients were grouped as training set (168 patients), internal test set (72 patients), and external test set (64 patients). Radiomics features were extracted from the mammography images of each patient. Spearman correlation analysis, analysis of variance, and least absolute shrinkage and selection operator regression were performed to reduce the redundant features in the training set, and the least absolute shrinkage and selection operator algorithm was used to construct the radiomics signature based on selected features. Multivariate logistic regression was utilized to construct classification models that included radiomics signature and clinical risk factors to predict low vs intermediate and high recurrence risk of ER-positive, LN-negative invasive breast cancer in the training set. The models were evaluated with the receiver operating characteristic curve in the training set. The internal and external test sets were used to confirm the discriminatory power of the models. The clinical usefulness was evaluated by using decision curve analysis. RESULTS The radiomics signature consisting of three radiomics features achieved favorable prediction performance. The multivariate logistic regression model including radiomics signature and clinical risk factors (tumor grade and HER 2) showed good performance with areas under the curve of 0.92 (95% confidence interval [CI] 0.86 to 0.97), 0.88 (95% CI 0.75 to 1.00), and 0.84 (95% CI 0.69 to 0.99) in the training, internal and external test sets, respectively. The DCA indicated that when the threshold probability is ranges from 0.1 to 1.0, the radiomics model adds more net benefit than the "treat all" or "treat none" scheme in internal and external test sets. CONCLUSION As a non-invasive pre-operative prediction tool, the mammography-based radiomics model incorporating radiomics and clinical factors show favorable predictive performance for predicting the risk of ER-positive, LN-negative invasive breast cancer recurrence based on Oncotype DX. ADVANCES IN KNOWLEDGE The mammography-based radiomics model incorporating radiomics and clinical factors shows favorable predictive performance for predicting the risk of ER-positive, LN-negative invasive breast cancer recurrence.
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Affiliation(s)
- Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.,Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xicheng Song
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.,Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
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Zheng YM, Chen J, Xu Q, Zhao WH, Wang XF, Yuan MG, Liu ZJ, Wu ZJ, Dong C. Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland. Dentomaxillofac Radiol 2021; 50:20210023. [PMID: 33950705 PMCID: PMC8474129 DOI: 10.1259/dmfr.20210023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE: Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA. METHODS AND MATERIALS A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model. RESULTS The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively. CONCLUSIONS The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.
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Affiliation(s)
- Ying-mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiao Chen
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Qi Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen-hui Zhao
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin-feng Wang
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao Universtity, Qingdao, China
| | - Zong-jing Liu
- Department of Pediatric Hematology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zeng-jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Ghalati MK, Nunes A, Ferreira H, Serranho P, Bernardes R. Texture Analysis and its Applications in Biomedical Imaging: A Survey. IEEE Rev Biomed Eng 2021; 15:222-246. [PMID: 34570709 DOI: 10.1109/rbme.2021.3115703] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.
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Assignment Flow for Order-Constrained OCT Segmentation. Int J Comput Vis 2021. [DOI: 10.1007/s11263-021-01520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractAt the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.
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Kolios C, Sannachi L, Dasgupta A, Suraweera H, DiCenzo D, Stanisz G, Sahgal A, Wright F, Look-Hong N, Curpen B, Sadeghi-Naini A, Trudeau M, Gandhi S, Kolios MC, Czarnota GJ. MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Oncotarget 2021; 12:1354-1365. [PMID: 34262646 PMCID: PMC8274727 DOI: 10.18632/oncotarget.28002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/11/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND METHODS MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance. RESULTS 7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively. CONCLUSIONS Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.
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Affiliation(s)
- Christopher Kolios
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Archya Dasgupta
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Harini Suraweera
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Gregory Stanisz
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Frances Wright
- Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Surgery, University of Toronto, Toronto, Canada
| | - Nicole Look-Hong
- Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Surgery, University of Toronto, Toronto, Canada
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Ali Sadeghi-Naini
- Department of Electrical and Computer Engineering, York University, North York, Canada
| | - Maureen Trudeau
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | | | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Department of Physics, Ryerson University, Toronto, Canada
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44
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Su GY, Xu XQ, Zhou Y, Zhang H, Si Y, Shen MP, Wu FY. Texture analysis of dual-phase contrast-enhanced CT in the diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer. Acta Radiol 2021; 62:890-896. [PMID: 32757639 DOI: 10.1177/0284185120946711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Computed tomography texture analysis (CTTA) provides objective and quantitative information regarding tumor heterogeneity beyond visual inspection. However, no study has yet used CTTA to differentiate metastatic from non-metastatic cervical lymph node in patients with papillary thyroid cancer (PTC). PURPOSE To evaluate the value of texture analysis of dual-phase contrast-enhanced CT images in diagnosing cervical lymph node metastasis in patients with PTC. MATERIAL AND METHODS Metastatic (n = 27) and non-metastatic (n = 32) cervical lymph nodes were analyzed retrospectively. Texture analyses were performed on both arterial (A) and venous (V) phase CT images. Texture parameters, including mean gray-level intensity, skewness, kurtosis, entropy, and uniformity, were obtained and compared between groups. Receiver operating characteristic (ROC) curves analyses and multivariate logistic regression analysis were used in our study. RESULTS Metastatic lymph nodes showed significantly higher A-mean gray-level intensity, A-entropy, and lower A-kurtosis and V-kurtosis (all P < 0.001) than non-metastatic mimics. The ROC curve analyses indicated that A-kurtosis demonstrated an optimal diagnostic area under the curve (AUC; 0.884) and specificity (92.59%), while the A-mean gray-level intensity showed optimal diagnostic sensitivity (90.62%). Multivariate logistic regression analysis showed that A-mean gray-level intensity (P = 0.006, odds ratio [OR] = 24.297) and V-kurtosis (P = 0.014, OR = 19.651) were the independent predictor for metastatic cervical lymph node. CONCLUSION Dual-phase contrast-enhanced CCTA-especially A-mean gray-level intensity and V-kurtosis-may have the potential to diagnose metastatic cervical lymph node in patients with PTC.
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Affiliation(s)
- Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yan Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Hao Zhang
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yan Si
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Mei-Ping Shen
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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45
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Li Q, Xiao Q, Li J, Wang Z, Wang H, Gu Y. Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer. Cancer Manag Res 2021; 13:5053-5062. [PMID: 34234550 PMCID: PMC8253937 DOI: 10.2147/cmar.s304547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/04/2021] [Indexed: 12/15/2022] Open
Abstract
Background To assess the value of radiomics based on multiphases contrast-enhanced magnetic resonance imaging (CE-MRI) for early prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with human epithelial growth factor receptor 2 (HER2) positive invasive breast cancer. Methods A total of 127 patients with newly diagnosed primary HER2 positive invasive breast cancer underwent CE-MRI before NAT and performed surgery after NAT. Radiomic features were extracted from the 1st postcontrast CE-MRI phase (CE1) and multi-phases CE-MRI (CEm),respectively. With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE1 and CEm were constructed respectively for differentiating pCR and non-pCR patients. The performances of classifiers were assessed and compared by their accuracy, sensitivity, specificity and AUC (area under curve). The optimal machine learning classification was used to discriminate pCR vs non-pCR in mass and non-mass groups, uni-focal and unilateral multi-focal groups, respectively. Results For the task of pCR classification, 6 radiomic features from CE1 and 6 from CEm were selected for the construction of machine learning models, respectively. The linear SVM based on CEm outperformed the logistic regression model using CE1 with an AUC of 0.84 versus 0.69. In mass and non-mass enhancement groups, the accuracy of linear SVM achieved 84% and 76%. Whereas in unifocal and unilateral multifocal cases, 79% and 75% accuracy were achieved by linear SVM. Conclusion Multiphases CE-MRI imaging may offer more heterogeneity information in the tumor and provide a non-invasive approach for early prediction of pCR to NAT in patients with HER2-positive invasive breast cancer.
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Affiliation(s)
- Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qin Xiao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Jianwei Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Zhe Wang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China.,Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - He Wang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China.,Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
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46
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Zheng YM, Li J, Liu S, Cui JF, Zhan JF, Pang J, Zhou RZ, Li XL, Dong C. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. Eur Radiol 2021; 31:4042-4052. [PMID: 33211145 DOI: 10.1007/s00330-020-07483-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/31/2020] [Accepted: 11/05/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT. METHODS A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets. RESULTS Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness. CONCLUSIONS The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process. KEY POINTS • Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Jian Li
- Department of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan Road, Futian District, Shenzhen, 518000, China
| | - Song Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Jiu-Fa Cui
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Rui-Zhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China.
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Deciphering the glioblastoma phenotype by computed tomography radiomics. Radiother Oncol 2021; 160:132-139. [PMID: 33984349 DOI: 10.1016/j.radonc.2021.05.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 04/19/2021] [Accepted: 05/03/2021] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. MATERIALS AND METHODS Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated. RESULTS Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71. CONCLUSION In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.
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Zhang R, Xu Z, Hao J, Yu J, Liu Z, Liu S, Chen W, Zhou J, Li H, Lin Z, Zheng W. Label-free identification of human coronary atherosclerotic plaque based on a three-dimensional quantitative assessment of multiphoton microscopy images. BIOMEDICAL OPTICS EXPRESS 2021; 12:2979-2995. [PMID: 34168910 PMCID: PMC8194630 DOI: 10.1364/boe.422525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/12/2021] [Accepted: 04/15/2021] [Indexed: 06/13/2023]
Abstract
The rupture of coronary atherosclerotic plaque (CAP) and the resulting intracoronary thrombosis account for most acute coronary syndromes. Thus, the early identification and risk assessment of CAP is crucial for timely medical intervention. In this study, we propose a quantitative and label-free method for human CAP identification using multiphoton microscopy (MPM) and three-dimensional (3D) image analysis techniques. By detecting the intrinsic MPM signals, the microstructures of collagen and elastin fibers within normal and CAP-lesioned human coronary artery walls were imaged. Using a 3D gray level co-occurrence matrix method and 3D weighted vector summation algorithm, quantitative indicators that characterize the spatial texture and orientation features of the fibers were extracted. We demonstrate that these indicators show superior accuracy and repeatability over 2D texture features in CAP discrimination. Furthermore, by combining the 3D microstructural indicators, a support vector machine model that classifies CAP from the normal arterial wall with an accuracy of >97% was established. In conjunction with advances in multiphoton endoscopy, the proposed method shows great potential in providing a quantitative, label-free, and real-time tool for the early identification and risk assessment of CAP in the future.
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Affiliation(s)
- Rongli Zhang
- Guangdong Provincial Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
- Department of Cardiology, Guangdong Provincial Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
- Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhongbiao Xu
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Junhai Hao
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Jia Yu
- Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhiyi Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Shun Liu
- Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710021, China
| | - Wanwen Chen
- Department of Cardiology, Guangdong Provincial Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Jiahui Zhou
- Department of Cardiology, Guangdong Provincial Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Hui Li
- Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhanyi Lin
- Guangdong Provincial Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
- Department of Cardiology, Guangdong Provincial Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Wei Zheng
- Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Kalinin AA, Hou X, Ade AS, Fon GV, Meixner W, Higgins GA, Sexton JZ, Wan X, Dinov ID, O'Meara MJ, Athey BD. Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells. Mol Biol Cell 2021; 32:1624-1633. [PMID: 33909457 PMCID: PMC8684733 DOI: 10.1091/mbc.e20-08-0502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.
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Affiliation(s)
- Alexandr A Kalinin
- Shenzhen Research Institute of Big Data, Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, Guangdong, China.,Department of Computational Medicine and Bioinformatics.,Statistics Online Computational Resource (SOCR), Health Behavior and Biological Sciences
| | - Xinhai Hou
- Shenzhen Research Institute of Big Data, Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, Guangdong, China.,School of Science and Engineering, Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, Guangdong, China.,Department of Computational Medicine and Bioinformatics
| | - Alex S Ade
- Department of Computational Medicine and Bioinformatics
| | | | | | | | - Jonathan Z Sexton
- Department of Internal Medicine, Gastroenterology, Michigan Medicine.,Department of Medicinal Chemistry, College of Pharmacy.,Center for Drug Repurposing
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, Guangdong, China
| | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics.,Statistics Online Computational Resource (SOCR), Health Behavior and Biological Sciences.,Michigan Institute for Data Science (MIDAS), and
| | | | - Brian D Athey
- Department of Computational Medicine and Bioinformatics.,Michigan Institute for Data Science (MIDAS), and.,Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109
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50
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Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021; 11:633176. [PMID: 33854969 PMCID: PMC8039446 DOI: 10.3389/fonc.2021.633176] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.
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Affiliation(s)
- Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States
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