1
|
Wang H, Li J, Chen J, Li M, Liu J, Wei L, Zeng Q. Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01130-w. [PMID: 39020152 DOI: 10.1007/s10278-024-01130-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 07/19/2024]
Abstract
Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and validate a machine learning (ML) model for the non-invasive assessment of STA-MCA bypass surgery efficacy in MMD. This study enrolled 118 MMD patients undergoing STA-MCA bypass surgery. Clinical features were screened to construct a clinical model. MRI features were extracted from the middle cerebral artery supply area using 3D Slicer and employed to build five ML models using logistic regression algorithm. The combined model was developed by integrating the radiomics score (Rad-score) with the clinical features. Model performance validation was conducted using ROC curves. Platelet count (PLT) was identified as a significant clinical feature for constructing the clinical model. A total of 3404 features (851 × 4) were extracted, and 15 optimal features were selected from each MRI sequence as predictive factors. Multivariable logistic regression identified PLT and Rad-score as independent parameters used for constructing the combined model. In the testing set, the AUC of the T1WI ML model [0.84 (95% CI, 0.70-0.97)] was higher than that of the clinical model [0.66 (95% CI, 0.46-0.86)] and the combined model [0.80 (95% CI, 0.66-0.95)]. The T1WI ML model can be used to assess the postoperative efficacy of STA-MCA bypass surgery for MMD.
Collapse
Affiliation(s)
- Huaizhen Wang
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Jizhen Li
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, Shandong, China
| | - Jinming Chen
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong University, Jinan, Shandong, China
| | - Meilin Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jiahao Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Lingzhen Wei
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- School of Clinical Medicine, Jining Medical University, Jining, 27206, Shandong, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| |
Collapse
|
2
|
Zhang X, Sha Z, Feng D, Wu C, Tian Y, Wang D, Wang J, Jiang R. Establishment and validation of a CT-based prediction model for the good dissolution of mild chronic subdural hematoma with atorvastatin treatment. Neuroradiology 2024; 66:1113-1122. [PMID: 38587561 DOI: 10.1007/s00234-024-03340-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/19/2024] [Indexed: 04/09/2024]
Abstract
PURPOSE To develop and validate a prediction model based on imaging data for the prognosis of mild chronic subdural hematoma undergoing atorvastatin treatment. METHODS We developed the prediction model utilizing data from patients diagnosed with CSDH between February 2019 and November 2021. Demographic characteristics, medical history, and hematoma characteristics in non-contrast computed tomography (NCCT) were extracted upon admission to the hospital. To reduce data dimensionality, a backward stepwise regression model was implemented to build a prognostic prediction model. We calculated the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model by a tenfold cross-validation procedure. RESULTS Maximum thickness, volume, mean density, morphology, and kurtosis of the hematoma were identified as the most significant predictors of good hematoma dissolution in mild CSDH patients undergoing atorvastatin treatment. The prediction model exhibited good discrimination, with an area under the curve (AUC) of 0.82 (95% confidence interval [CI], 0.74-0.90) and good calibration (p = 0.613). The validation analysis showed the AUC of the final prognostic prediction model is 0.80 (95% CI 0.71-0.86) and it has good prediction performance. CONCLUSION The imaging data-based prediction model has demonstrated great prediction accuracy for good hematoma dissolution in mild CSDH patients undergoing atorvastatin treatment. The study results emphasize the importance of imaging data evaluation in the management of CSDH patients.
Collapse
Affiliation(s)
- Xinjie Zhang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Department of Pediatric Neurosurgery, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhuang Sha
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Dongyi Feng
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Chenrui Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Ye Tian
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Dong Wang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China
| | - Junping Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China.
| | - Rongcai Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China.
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin, 300070, China.
| |
Collapse
|
3
|
Zhao W, Xu H, Zhao R, Zhou S, Mei S, Wang Z, Zhao F, Xiao T, Huang F, Qiu W, Tang J, Liu Q. MRI-based Radiomics Model for Preoperative Prediction of Lateral Pelvic Lymph Node Metastasis in Locally Advanced Rectal Cancer. Acad Radiol 2024; 31:2753-2772. [PMID: 37643928 DOI: 10.1016/j.acra.2023.07.016] [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: 06/06/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/31/2023]
Abstract
RATIONALE AND OBJECTIVES To develop a magnetic resonance imaging (MRI)-based radiomics model for preoperative prediction of lateral pelvic lymph node (LPLN) metastasis (LPLNM) in patients with locally advanced rectal cancer MATERIALS AND METHODS: We retrospectively enrolled 263 patients with rectal cancer who underwent total mesorectal excision and LPLN dissection. Radiomics features from the primary lesion and LPLNs on baseline MRI images were utilized to construct a radiomics model, and their radiomics scores were combined to develop a radiomics scoring system. A clinical prediction model was developed using logistic regression. A hybrid predicting model was created through multivariable logistic regression analysis, integrating the radiomics score with significant clinical risk factors (baseline Carcinoembryonic Antigen (CEA), clinical circumferential resection margin status, and the short axis diameter of LPLN). This hybrid model was presented with a hybrid clinical-radiomics nomogram, and its calibration, discrimination, and clinical usefulness were assessed. RESULTS A total of 148 patients were included in the analysis and randomly divided into a training cohort (n = 104) and an independent internal testing cohort (n = 44). The hybrid clinical-radiomics model exhibited the highest discrimination, with an area under the receiver operating characteristic (AUC) of 0.843 [95% confidence interval (CI), 0.706-0.968] in the testing cohort compared to the clinical model [AUC (95% CI) = 0.772 (0.589-0.856)] and radiomics model [AUC (95% CI) = 0.731 (0.613-0.849)]. The hybrid prediction model also demonstrated good calibration, and decision curve analysis confirmed its clinical usefulness. CONCLUSION This study developed a hybrid MRI-based radiomics model that incorporates a combination of radiomics score and significant clinical risk factors. The proposed model holds promise for individualized preoperative prediction of LPLNM in patients with locally advanced rectal cancer. DATA AVAILABILITY STATEMENT The data presented in this study are available on request from the corresponding author.
Collapse
Affiliation(s)
- Wei Zhao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China (H.X.)
| | - Rui Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (R.Z.)
| | - Sicheng Zhou
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Shiwen Mei
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Zhijie Wang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Fuqiang Zhao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Tixian Xiao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Fei Huang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Wenlong Qiu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Jianqiang Tang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Qian Liu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.).
| |
Collapse
|
4
|
Sun Z, Xia F, Lv W, Li J, Zou Y, Wu J. Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer. Am J Surg 2024; 232:59-67. [PMID: 38272767 DOI: 10.1016/j.amjsurg.2024.01.002] [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: 10/17/2023] [Revised: 12/17/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
AIM Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. MATERIALS AND METHODS A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n = 159) and a validation set (n = 40). The total incidence of TDs was 37.2 % (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI + ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. RESULTS The Rad-score (T2WI + ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI + ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 % in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 %, 92.0 %, 83.3 %, and 82.1 %, respectively. CONCLUSION The radiomics nomogram based on Rad-score (T2WI + ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.
Collapse
Affiliation(s)
- Zhen Sun
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Feng Xia
- Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China
| | - Jin Li
- Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - You Zou
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jianhong Wu
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| |
Collapse
|
5
|
Zhou L, Ji Q, Peng H, Chen F, Zheng Y, Jiao Z, Gong J, Li W. Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning. Eur Radiol 2024; 34:3644-3655. [PMID: 37994966 DOI: 10.1007/s00330-023-10316-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 08/19/2023] [Accepted: 08/26/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. METHODS Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. RESULTS Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. CONCLUSIONS A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. CLINICAL RELEVANCE STATEMENT A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. KEY POINTS • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.
Collapse
Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639 Zhizaoju Road, Huangpu District, Shanghai, 20011, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Yi Zheng
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | | | - Jian Gong
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical Unversity, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
| |
Collapse
|
6
|
Wang R, Zhou R, Sun S, Yang Z, Chen H. Histograms of computed tomography values in differential diagnosis of benign and malignant osteogenic lesions. Acta Radiol 2024; 65:625-631. [PMID: 38213126 DOI: 10.1177/02841851231225418] [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: 01/13/2024]
Abstract
BACKGROUND The use of histogram analysis of computed tomography (CT) values is a potential method for differentiating between benign osteoblastic lesions (BOLs) and malignant osteoblastic lesions (MOLs). PURPOSE To explore the diagnostic efficacy of histogram analysis in accurately distinguishing between BOLs and MOLs based on CT values. MATERIAL AND METHODS A total of 25 BOLs and 25 MOLs, which were confirmed through pathology or imaging follow-up, were included in this study. FireVoxel software was used to process the lesions and obtain various histogram parameters, including mean value, standard deviation, variance, coefficient of variation, skewness, kurtosis, entropy value, and percentiles ranging from 1st to 99th. Statistical tests, such as two independent-sample t-tests and the Mann-Whitney U test with Bonferroni correction, were employed to compare the differences in histogram parameters between BOLs and MOLs. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy of each parameter. RESULTS Significant differences were observed in several histogram parameters between BOLs and MOLs, including the mean value, coefficient of variation, skewness, and various percentiles. Notably, the 25th percentile demonstrated the highest diagnostic efficacy, as indicated by the largest area under the curve in the ROC curve analysis. CONCLUSION Histogram analysis of CT values provides valuable diagnostic information for accurately differentiating between BOLs and MOLs. Among the different parameters, the 25th percentile parameter proves to be the most effective in this discrimination process.
Collapse
Affiliation(s)
- Ruiqing Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, PR China
| | - Ruizhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, PR China
| | - Shiqing Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, PR China
| | - Zhitao Yang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, PR China
| | - Haisong Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao City, PR China
| |
Collapse
|
7
|
Lee H, Chang W, Kim HY, Sung P, Cho J, Lee YJ, Kim YH. Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients. Eur Radiol 2024; 34:2036-2047. [PMID: 37656175 DOI: 10.1007/s00330-023-10135-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 07/06/2023] [Accepted: 07/14/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluate the effect of deep learning-based image conversion on CT reconstruction algorithms. METHODS This study included 78 hepatocellular carcinoma (HCC) patients who underwent four-phase liver CTs comprising non-contrast, late arterial (LAP), portal venous (PVP), and delayed phase (DP), reconstructed using both filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). PVP images were used to train a convolutional neural network (CNN) model to convert images from FBP to ADMIRE and vice versa. LAP, PVP, and DP images were used for validation and testing. Radiomic features were extracted for each patient with a semi-automatic segmentation tool. We used concordance correlation coefficients (CCCs) to evaluate the radiomics reproducibility for original FBP (oFBP) vs. original ADMIRE (oADMIRE), oFBP vs. converted FBP (cFBP), and oADMIRE vs. converted ADMIRE (cADMIRE). RESULTS In the test group including 30 patients, the CCC and proportion of reproducible features (CCC ≥ 0.85) for oFBP vs. oADMIRE were 0.65 and 32.9% (524/1595) for LAP, 0.65 and 35.9% (573/1595) for PVP, and 0.69 and 43.8% (699/1595) for DP. For oFBP vs. cFBP, the values increased to 0.92 and 83.9% (1339/1595) for LAP, 0.89 and 71.0% (1133/1595) for PVP, and 0.90 and 79.7% (1271/1595) for DP. Similarly, for oADMIRE vs. cADMIRE, the values increased to 0.87 and 68.1% (1086/1595) for LAP, 0.91 and 82.1% (1309/1595) for PVP, and 0.89 and 76.2% (1216/1595) for DP. CONCLUSIONS CNN-based image conversion between CT reconstruction algorithms improved the radiomics reproducibility of HCCs. CLINICAL RELEVANCE STATEMENT This study demonstrates that using a CNN-based image conversion technique significantly improves the reproducibility of radiomic features in HCCs, highlighting its potential for enhancing radiomics research in HCC patients. KEY POINTS Radiomics reproducibility of HCC was improved via CNN-based image conversion between two different CT reconstruction algorithms. This is the first clinical study to demonstrate improvements across a range of radiomic features in HCC patients. This study promotes the reproducibility and generalizability of different CT reconstruction algorithms in radiomics research.
Collapse
Affiliation(s)
- Heejin Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Hae Young Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Pamela Sung
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
8
|
Simpson AL, Peoples J, Creasy JM, Fichtinger G, Gangai N, Keshavamurthy KN, Lasso A, Shia J, D'Angelica MI, Do RKG. Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases. Sci Data 2024; 11:172. [PMID: 38321027 PMCID: PMC10847495 DOI: 10.1038/s41597-024-02981-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/17/2024] [Indexed: 02/08/2024] Open
Abstract
The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.
Collapse
Affiliation(s)
- Amber L Simpson
- School of Computing, Queen's University, Kingston, Ontario, Canada.
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.
| | - Jacob Peoples
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | | | - Gabor Fichtinger
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Andras Lasso
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael I D'Angelica
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
9
|
Li J, Zhu J, Zou Y, Zhang G, Zhu P, Wang N, Xie P. Diagnostic CT of colorectal cancer with artificial intelligence iterative reconstruction: A clinical evaluation. Eur J Radiol 2024; 171:111301. [PMID: 38237522 DOI: 10.1016/j.ejrad.2024.111301] [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: 09/06/2023] [Revised: 12/26/2023] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVES To investigate the clinical value of a novel deep-learning based CT reconstruction algorithm, artificial intelligence iterative reconstruction (AIIR), in diagnostic imaging of colorectal cancer (CRC). METHODS This study retrospectively enrolled 217 patients with pathologically confirmed CRC. CT images were reconstructed with the AIIR algorithm and compared with those originally obtained with hybrid iterative reconstruction (HIR). Objective image quality was evaluated in terms of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality was graded on the conspicuity of tumor margin and enhancement pattern as well as the certainty in diagnosing organ invasion and regional lymphadenopathy. In patients with surgical pathology (n = 116), the performance of diagnosing visceral peritoneum invasion was characterized using receiver operating characteristic (ROC) analysis. Changes of diagnostic thinking in diagnosing hepatic metastases were assessed through lesion classification confidence. RESULTS The SNRs and CNRs on AIIR images were significantly higher than those on HIR images (all p < 0.001). The AIIR was scored higher for all subjective metrics (all p < 0.001) except for the certainty of diagnosing regional lymphadenopathy (p = 0.467). In diagnosing visceral peritoneum invasion, higher area under curve (AUC) of the ROC was found for AIIR than HIR (0.87 vs 0.77, p = 0.001). In assessing hepatic metastases, AIIR was found capable of correcting the misdiagnosis and improving the diagnostic confidence provided by HIR (p = 0.01). CONCLUSIONS Compared to HIR, AIIR offers better image quality, improves the diagnostic performance regarding CRC, and thus has the potential for application in routine abdominal CT.
Collapse
Affiliation(s)
- Jiao Li
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Junying Zhu
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Yixuan Zou
- United Imaging Healthcare, Shanghai 201800, China.
| | - Guozhi Zhang
- United Imaging Healthcare, Shanghai 201800, China.
| | - Pan Zhu
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Ning Wang
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Peiyi Xie
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| |
Collapse
|
10
|
Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15:304-314. [PMID: 38058604 PMCID: PMC10696186 DOI: 10.4329/wjr.v15.i11.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking. AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients. METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis. RESULTS 3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%. CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.
Collapse
Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Mayur Virarkar
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Hagar S Mahmoud
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Vincenzo K Wong
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Carlos Ignacio Gonzalez Baerga
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Miti Parikh
- Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States
| | - Sherif B Elsherif
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Priya R Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| |
Collapse
|
11
|
Grzywińska M, Karwecka M, Pomorska A, Irga-Jaworska N, Świętoń D. Textural Analysis of Magnetic Resonance Images as an Additional Evaluation Tool of Parotid Glands in Sjögren-Primarily Findings. Biomedicines 2023; 11:3132. [PMID: 38137353 PMCID: PMC10740887 DOI: 10.3390/biomedicines11123132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/18/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023] Open
Abstract
Magnetic Resonance Imaging (MRI) plays a leading role in diagnosing soft tissue pathologies, especially in the head and neck. It is increasingly popular for evaluating salivary gland issues like neoplasms and Sjogren's Syndrome. Advanced MRI methods, including MRI sialography and texture analysis, offer non-invasive alternatives, enhancing MRI's role. This study focused on the relationship between the apparent diffusion coefficient (ADC) and T2-weighted MRI sialography and texture analysis (TA) of parotid glands in children with and without Sjogren's Syndrome (SS). Using 3.0 Tesla MRI with DWI and T2-weighted imaging, expended texture analysis, first-order statistics (FSOs), second-order, and higher-order statistics were conducted. The results showed significant differences in parotid ADC values, with lower values in the SS group, particularly in cases of higher disease activity. Lower kurtosis values were associated with more severe Tonami Scale grades. FSO parameters correlated well with the texture analysis from T2-weighted images, indicating promise in grading parotid gland inflammation. However, further research is needed to understand the impact of variables like binning and region of interest (ROI) size. This study highlights the potential of texture analysis for assessing parotid gland inflammation and emphasizes the need for more investigations in this area.
Collapse
Affiliation(s)
- Małgorzata Grzywińska
- Neuroinformatics and Artificial Intelligence Lab, Department of Neurophysiology, Neuropsychology and Neuroinformatics, Medical University of Gdansk, 80-210 Gdansk, Poland
- Department of Radiology, University Clinical Center, 80-952 Gdansk, Poland (D.Ś.)
| | - Magdalena Karwecka
- Department of Radiology, University Clinical Center, 80-952 Gdansk, Poland (D.Ś.)
| | - Anna Pomorska
- 2nd Department of Pediatrics, Haemathology & Oncology, Medical University of Gdansk, 80-210 Gdansk, Poland
| | - Ninela Irga-Jaworska
- 2nd Department of Pediatrics, Haemathology & Oncology, Medical University of Gdansk, 80-210 Gdansk, Poland
| | - Dominik Świętoń
- Department of Radiology, University Clinical Center, 80-952 Gdansk, Poland (D.Ś.)
- 2nd Department of Radiology, Medical University of Gdansk, 80-210 Gdansk, Poland
| |
Collapse
|
12
|
Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study. Eur Radiol Exp 2023; 7:48. [PMID: 37670193 PMCID: PMC10480134 DOI: 10.1186/s41747-023-00362-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 06/13/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). METHODS T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. RESULTS When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. CONCLUSIONS Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. RELEVANCE STATEMENT The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. KEY POINTS • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack.
Collapse
Affiliation(s)
- Laura Jacqueline Jensen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo Günter Steffen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian Niko Nagel
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| |
Collapse
|
13
|
De Kock I, Bos S, Delrue L, Van Welden S, Bunyard P, Hindryckx P, De Vos M, Villeirs G, Laukens D. MRI texture analysis of T2-weighted images is preferred over magnetization transfer imaging for readily longitudinal quantification of gut fibrosis. Eur Radiol 2023; 33:5943-5952. [PMID: 37071162 DOI: 10.1007/s00330-023-09624-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVES To investigate the value of magnetization transfer (MT) MRI and texture analysis (TA) of T2-weighted MR images (T2WI) in the assessment of intestinal fibrosis in a mouse model. METHODS Chronic colitis was induced in mice by cyclic administration of dextran sodium sulphate (DSS) resulting in chronic inflammation and progressive bowel fibrosis. Mice underwent 7-T MR imaging at various time points. Bowel wall MT ratio (MTR) and textural features (skewness, kurtosis, entropy), extracted by a filtration histogram technique, were correlated with histopathology. Performance of both techniques were validated using antifibrotic therapy. Finally, a retrospective study was conducted in five patients with Crohn's disease (CD) who underwent bowel surgery. RESULTS MTR and texture entropy correlated with histopathological fibrosis (r = .85 and .81, respectively). Entropy was superior to MTR for monitoring bowel fibrosis in the presence of coexisting inflammation (linear regression R2 = .93 versus R2 = .01). Furthermore, texture entropy was able to assess antifibrotic therapy response (placebo mice versus treated mice at endpoint scan; Δmean = 0.128, p < .0001). An increase in entropy was indicative of fibrosis accumulation in human CD strictures (inflammation: 1.29; mixed strictures: 1.4 and 1.48; fibrosis: 1.73 and 1.9). CONCLUSION MT imaging and TA of T2WI can both noninvasively detect established intestinal fibrosis in a mouse model. However, TA is especially useful for the longitudinal quantification of fibrosis in mixed inflammatory-fibrotic tissue, as well as for antifibrotic treatment response evaluation. This accessible post-processing technique merits further validation as the benefits for clinical practice as well as antifibrotic trial design would be numerous. KEY POINTS • Magnetization transfer MRI and texture analysis of T2-weighted MR images can detect established bowel fibrosis in an animal model of gut fibrosis. • Texture entropy is able to identify and monitor bowel fibrosis progression in an inflammatory context and can assess the response to antifibrotic treatment. • A proof-of-concept study in five patients with Crohn's disease suggests that texture entropy can detect and grade fibrosis in human intestinal strictures.
Collapse
Affiliation(s)
- Isabelle De Kock
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Simon Bos
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium
- VIB Center for Inflammation Research, Ghent, Belgium
| | - Louke Delrue
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Sophie Van Welden
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium
- VIB Center for Inflammation Research, Ghent, Belgium
| | | | - Pieter Hindryckx
- Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium
| | - Martine De Vos
- Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium
| | - Geert Villeirs
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Debby Laukens
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium.
- VIB Center for Inflammation Research, Ghent, Belgium.
| |
Collapse
|
14
|
Lee K, Goh J, Jang J, Hwang J, Kwak J, Kim J, Eom K. Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors. Front Vet Sci 2023; 10:1126165. [PMID: 37711438 PMCID: PMC10499047 DOI: 10.3389/fvets.2023.1126165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 08/01/2023] [Indexed: 09/16/2023] Open
Abstract
Objective This study aimed to investigate the feasibility of computed tomography (CT) texture analysis for distinguishing canine adrenal gland tumors and its usefulness in clinical decision-making. Materials and methods The medical records of 25 dogs with primary adrenal masses who underwent contrast CT and a histopathological examination were retrospectively reviewed, of which 12 had adenomas (AAs), 7 had adenocarcinomas (ACCs), and 6 had pheochromocytomas (PHEOs). Conventional CT evaluation of each adrenal gland tumor included the mean, maximum, and minimum attenuation values in Hounsfield units (HU), heterogeneity of the tumor parenchyma, and contrast enhancement (type, pattern, and degree), respectively, in each phase. In CT texture analysis, precontrast and delayed-phase images of 18 adrenal gland tumors, which could be applied for ComBat harmonization were used, and 93 radiomic features (18 first-order and 75 second-order statistics) were extracted. Then, ComBat harmonization was applied to compensate for the batch effect created by the different CT protocols. The area under the receiver operating characteristic curve (AUC) for each significant feature was used to evaluate the diagnostic performance of CT texture analysis. Results Among the conventional features, PHEO showed significantly higher mean and maximum precontrast HU values than ACC (p < 0.05). Eight second-order features on the precontrast images showed significant differences between the adrenal gland tumors (p < 0.05). However, none of them were significantly different between AA and PHEO, or between precontrast images and delayed-phase images. This result indicates that ACC exhibited more heterogeneous and complex textures and more variable intensities with lower gray-level values than AA and PHEO. The correlation, maximal correlation coefficient, and gray level non-uniformity normalized were significantly different between AA and ACC, and between ACC and PHEO. These features showed high AUCs in discriminating ACC and PHEO, which were comparable or higher than the precontrast mean and maximum HU (AUC = 0.865 and 0.860, respectively). Conclusion Canine primary adrenal gland tumor differentiation can be achieved with CT texture analysis on precontrast images and may have a potential role in clinical decision-making. Further prospective studies with larger populations and cross-validation are warranted.
Collapse
Affiliation(s)
- Kyungsoo Lee
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Jinhyong Goh
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Jaeyoung Jang
- Jang Jae Young Veterinary Surgery Center, Seong-nam, Gyunggi-do, Republic of Korea
| | | | - Jungmin Kwak
- Saram and Animal Medical Center, Yongin-si, Gyunggi-do, Republic of Korea
| | - Jaehwan Kim
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Kidong Eom
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| |
Collapse
|
15
|
Zhu H, Hu M, Ma Y, Yao X, Lin X, Li M, Li Y, Wu Z, Shi D, Tong T, Chen H. Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients. Cancer Imaging 2023; 23:74. [PMID: 37537659 PMCID: PMC10401876 DOI: 10.1186/s40644-023-00588-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/02/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). METHODS A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). RESULTS A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). CONCLUSION The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. TRIAL REGISTRATION Retrospectively registered.
Collapse
Affiliation(s)
- Hui Zhu
- Department of Diagnostic Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, 270 DongAn Road, Shanghai, 200032, China
| | - Muni Hu
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Yanru Ma
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Xun Yao
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China
| | - Xiaozhu Lin
- Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin Er Rd, Shanghai, 200025, China
| | - Menglei Li
- Department of Diagnostic Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, 270 DongAn Road, Shanghai, 200032, China
| | - Yue Li
- Department of Diagnostic Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, 270 DongAn Road, Shanghai, 200032, China
| | - Zhiyuan Wu
- Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin Er Rd, Shanghai, 200025, China.
| | - Debing Shi
- Department of Colorectal Surgery, Department of Oncology, Fudan University Shanghai Cancer Center; Shanghai Medical College, Fudan University, 270 DongAn Road, Shanghai, 200032, China.
| | - Tong Tong
- Department of Diagnostic Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, 270 DongAn Road, Shanghai, 200032, China.
| | - Haoyan Chen
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China.
| |
Collapse
|
16
|
Anichini M, Galluzzo A, Danti G, Grazzini G, Pradella S, Treballi F, Bicci E. Focal Lesions of the Liver and Radiomics: What Do We Know? Diagnostics (Basel) 2023; 13:2591. [PMID: 37568954 PMCID: PMC10417608 DOI: 10.3390/diagnostics13152591] [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: 06/22/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Despite differences in pathological analysis, focal liver lesions are not always distinguishable in contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), and positron emission tomography (PET). This issue can cause problems of differential diagnosis, treatment, and follow-up, especially in patients affected by HBV/HCV chronic liver disease or fatty liver disease. Radiomics is an innovative imaging approach that extracts and analyzes non-visible quantitative imaging features, supporting the radiologist in the most challenging differential diagnosis when the best-known methods are not conclusive. The purpose of this review is to evaluate the most significant CT and MRI texture features, which can discriminate between the main benign and malignant focal liver lesions and can be helpful to predict the response to pharmacological or surgical therapy and the patient's prognosis.
Collapse
Affiliation(s)
| | | | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy; (M.A.); (A.G.); (G.G.); (S.P.); (F.T.); (E.B.)
| | | | | | | | | |
Collapse
|
17
|
Wang Y, Zhou M, Ding Y, Li X, Zhou Z, Xie T, Shi Z, Fu W. A radiomics model for predicting the outcome of endovascular abdominal aortic aneurysm repair based on machine learning. Vascular 2023; 31:654-663. [PMID: 35440250 DOI: 10.1177/17085381221091061] [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: 11/15/2022]
Abstract
OBJECTIVE This study aimed to develop a radiomics model to predict the outcome of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA), based on machine learning (ML) algorithms. METHODS We retrospectively reviewed 711 patients with infra-renal AAA who underwent elective EVAR procedures between January 2016 and December 2019 at our single center. The radiomics features of AAA were extracted using Pyradiomics. Pearson correlation analysis, analysis of variance (ANOVA), least absolute shrinkage, and selection operator (LASSO) regression were applied to determine the predictors for EVAR-related severe adverse events (SAEs). Eighty percent of patients were classified as the training set and the remaining 20 percent of patients were classified as the test set. The selected features were used to build a radiomics model in training set using different ML algorithms. The performance of each model was assessed using the area under the curve (AUC) from the receiver operating characteristic (ROC) curve in the test set. RESULTS A total of 493 patients were enrolled in this study, the mean follow-up time was 32 months. During the follow-up, 156 (31.6%) patients experienced EVAR-related SAEs. A total of 1223 radiomics features were extracted from each patient, of which 30 radiomics features were finally identified. The quantitative performance assessment and the ROC curves indicated that the logistics regression (LR) model had better predictive value than others, with accuracy, 0.86; AUC, 0.93; and F1 score, 0.91. The Rad-score waterfall plot showed that the overall amount of error was small both in the training set and in the test set. Calibration curve showed that the calibration degree of the training set and the test set were good (p > 0.05). Decision curve analysis (threshold 0.32) demonstrated that the model had good clinical applicability. CONCLUSION Our radiomics model could be used as an efficient and adjunctive tool to predict the outcome after EVAR.
Collapse
Affiliation(s)
- Yonggang Wang
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Min Zhou
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong Ding
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xu Li
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhenyu Zhou
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tianchen Xie
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhenyu Shi
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
18
|
Durot C, Durot E, Mulé S, Morland D, Godard F, Quinquenel A, Delmer A, Soyer P, Hoeffel C. Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance. Diagnostics (Basel) 2023; 13:2237. [PMID: 37443630 DOI: 10.3390/diagnostics13132237] [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: 05/03/2023] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next treatment (TTNT), and overall survival in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Seventy-two patients with follicular lymphoma were retrospectively included. Texture analysis was performed on unenhanced CT images extracted from 18-FDG PET/CT examinations that were obtained within one month before treatment. Skewness at a fine texture scale (SSF = 2) was an independent predictor of PFS (hazard ratio = 3.72 (95% CI: 1.15, 12.11), p = 0.028), PFS 24 (hazard ratio = 13.38; 95% CI: 1.29, 138.13; p = 0.029), and TTNT (hazard ratio = 5.11; 95% CI: 1.18, 22.13; p = 0.029). Skewness values above -0.015 at SSF = 2 were significantly associated with lower PFS, PFS 24, and TTNT. Kurtosis without filtration was an independent predictor of PFS (SSF = 0; HR = 1.22 (95% CI: 1.04, 1.44), p = 0.013), and TTNT (SSF = 0; hazard ratio = 1.23; 95% CI: 1.04, 1.46; p = 0.013). This study shows that pretreatment unenhanced CT texture analysis-derived tumor skewness and kurtosis may be used as predictive biomarkers of PFS and TTNT in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance.
Collapse
Affiliation(s)
- Carole Durot
- Department of Radiology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Eric Durot
- Department of Hematology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil, France
- Faculté de Médecine, Université Paris-Est Créteil, 61 Avenue du Général de Gaulle, 94000 Créteil, France
| | - David Morland
- Department of Nuclear Medicine, Godinot Institute, 1 Rue du Général Koenig, 51100 Reims, France
- CReSTIC, EA 3804, University of Reims Champagne-Ardenne, UFR Moulin de la Housse, 51867 Reims, France
| | - François Godard
- Department of Radiology, Henri Mondor University Hospital, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil, France
| | - Anne Quinquenel
- Department of Hematology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Alain Delmer
- Department of Hematology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France
- Faculté de Médecine, Université Paris Cité, 75006 Paris, France
| | - Christine Hoeffel
- Department of Radiology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
- CReSTIC, EA 3804, University of Reims Champagne-Ardenne, UFR Moulin de la Housse, 51867 Reims, France
| |
Collapse
|
19
|
Kumar A, Jha AK, Agarwal JP, Yadav M, Badhe S, Sahay A, Epari S, Sahu A, Bhattacharya K, Chatterjee A, Ganeshan B, Rangarajan V, Moyiadi A, Gupta T, Goda JS. Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain. J Pers Med 2023; 13:920. [PMID: 37373909 DOI: 10.3390/jpm13060920] [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: 04/24/2023] [Revised: 05/22/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).
Collapse
Affiliation(s)
- Anuj Kumar
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Ashish Kumar Jha
- Department of Nuclear Medicine, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Jai Prakash Agarwal
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Manender Yadav
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Suvarna Badhe
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Ayushi Sahay
- Department of Pathology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Sridhar Epari
- Department of Pathology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Arpita Sahu
- Department of Radiodiagnosis, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Kajari Bhattacharya
- Department of Radiodiagnosis, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Abhishek Chatterjee
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospital, 235 Euston Road, London NW1 2BU, UK
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Aliasgar Moyiadi
- Department of Neurosurgery, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Tejpal Gupta
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| | - Jayant S Goda
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India
| |
Collapse
|
20
|
Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
Collapse
Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| |
Collapse
|
21
|
Gong XQ, Liu N, Tao YY, Li L, Li ZM, Yang L, Zhang XM. Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma. Sci Rep 2023; 13:7710. [PMID: 37173350 PMCID: PMC10182068 DOI: 10.1038/s41598-023-34763-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/07/2023] [Indexed: 05/15/2023] Open
Abstract
The purpose of this study was to explore the effectiveness of radiomics based on multisequence MRI in predicting the expression of PD-1/PD-L1 in hepatocellular carcinoma (HCC). One hundred and eight patients with HCC who underwent contrast-enhanced MRI 2 weeks before surgical resection were enrolled in this retrospective study. Corresponding paraffin sections were collected for immunohistochemistry to detect the expression of PD-1 and PD-L1. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Univariate and multivariate analyses were used to select potential clinical characteristics related to PD-1 and PD-L1 expression. Radiomics features were extracted from the axial fat-suppression T2-weighted imaging (FS-T2WI) images and the arterial phase and portal venous phase images from the axial dynamic contrast-enhanced MRI, and the corresponding feature sets were generated. The least absolute shrinkage and selection operator (LASSO) was used to select the optimal radiomics features for analysis. Logistic regression analysis was performed to construct single-sequence and multisequence radiomics and radiomic-clinical models. The predictive performance was judged by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts. In the whole cohort, PD-1 expression was positive in 43 patients, and PD-L1 expression was positive in 34 patients. The presence of satellite nodules served as an independent predictor of PD-L1 expression. The AUC values of the FS-T2WI, arterial phase, portal venous phase and multisequence models in predicting the expression of PD-1 were 0.696, 0.843, 0.863, and 0.946 in the training group and 0.669, 0.792, 0.800 and 0.815 in the validation group, respectively. The AUC values of the FS-T2WI, arterial phase, portal venous phase, multisequence and radiomic-clinical models in predicting PD-L1 expression were 0.731, 0.800, 0.800, 0.831 and 0.898 in the training group and 0.621, 0.743, 0.771, 0.810 and 0.779 in the validation group, respectively. The combined models showed better predictive performance. The results of this study suggest that a radiomics model based on multisequence MRI has the potential to predict the preoperative expression of PD-1 and PD-L1 in HCC, which could become an imaging biomarker for immune checkpoint inhibitor (ICI)-based treatment.
Collapse
Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Li Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Zu-Mao Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| |
Collapse
|
22
|
Jiang L, Zhang Z, Guo S, Zhao Y, Zhou P. Clinical-Radiomics Nomogram Based on Contrast-Enhanced Ultrasound for Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma. Cancers (Basel) 2023; 15:cancers15051613. [PMID: 36900404 PMCID: PMC10001290 DOI: 10.3390/cancers15051613] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/08/2023] Open
Abstract
This study aimed to establish a new clinical-radiomics nomogram based on ultrasound (US) for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). We collected 211 patients with PTC between June 2018 and April 2020, then we randomly divided these patients into the training set (n = 148) and the validation set (n = 63). 837 radiomics features were extracted from B-mode ultrasound (BMUS) images and contrast-enhanced ultrasound (CEUS) images. The maximum relevance minimum redundancy (mRMR) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and backward stepwise logistic regression (LR) were applied to select key features and establish a radiomics score (Radscore), including BMUS Radscore and CEUS Radscore. The clinical model and clinical-radiomics model were established using the univariate analysis and multivariate backward stepwise LR. The clinical-radiomics model was finally presented as a clinical-radiomics nomogram, the performance of which was evaluated by the receiver operating characteristic curves, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). The results show that the clinical-radiomics nomogram was constructed by four predictors, including gender, age, US-reported LNM, and CEUS Radscore. The clinical-radiomics nomogram performed well in both the training set (AUC = 0.820) and the validation set (AUC = 0.814). The Hosmer-Lemeshow test and the calibration curves demonstrated good calibration. The DCA showed that the clinical-radiomics nomogram had satisfactory clinical utility. The clinical-radiomics nomogram constructed by CEUS Radscore and key clinical features can be used as an effective tool for individualized prediction of cervical LNM in PTC.
Collapse
Affiliation(s)
- Liqing Jiang
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Zijian Zhang
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China;
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
| | - Shiyan Guo
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Yongfeng Zhao
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
- Correspondence:
| |
Collapse
|
23
|
Characterization of spatially mapped volumetric molecular ultrasound signals for predicting response to anti-vascular therapy. Sci Rep 2023; 13:1686. [PMID: 36717575 PMCID: PMC9886917 DOI: 10.1038/s41598-022-26273-0] [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: 02/03/2022] [Accepted: 12/13/2022] [Indexed: 01/31/2023] Open
Abstract
Quantitative three-dimensional molecular ultrasound is a promising technology for longitudinal imaging applications such as therapy monitoring; the risk profile is favorable compared to positron emission tomography and computed tomography. However, clinical translation of quantitative methods for this technology are limited in that they assume that tumor tissues are homogeneous, and often depend on contrast-destruction events that can produce unintended bioeffects. Here, we develop quantitative features (henceforth image features) that capture tumor spatial information, and that are extracted without contrast destruction. We compare these techniques with the contrast-destruction derived differential targeted enhancement parameter (dTE) in predicting response to therapy. We found thirty-three reproducible image features that predict response to antiangiogenic therapy, without the need for a contrast agent disruption pulse. Multiparametric analysis shows that several of these image features can differentiate treated versus control animals with comparable performance to post-destruction measurements, suggesting that these can potentially replace parameters such as the dTE. The highest performing pre-destruction image features showed strong linear correlations with conventional dTE parameters with less overall variance. Thus, our study suggests that image features obtained during the wash in of the molecular agent, pre-destruction, may replace conventional post-destruction image features or the dTE parameter.
Collapse
|
24
|
Li M, Gu H, Xue T, Peng H, Chen Q, Zhu X, Duan S, Feng F. CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study. Br J Radiol 2023; 96:20220568. [PMID: 36318241 PMCID: PMC10997017 DOI: 10.1259/bjr.20220568] [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: 06/01/2022] [Revised: 09/20/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To develop and externally validate a CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion (LVI) in patients with colorectal cancer (CRC). METHODS 357 patients derived from 2 centers with pathologically confirmed CRC were included in this retrospective study. Two-dimensional (2D) and three-dimensional (3D) radiomics features were extracted from portal venous phase CT images. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. The radiomics nomogram was developed by integrating the radiomics score (rad-score) and the clinical risk factor. RESULTS The rad-score was significantly higher in the LVI+ group than in the LVI- group (p < 0.05). The area under the curve (AUC), accuracy, sensitivity and specificity of the 3D radiomics model were higher than those of the 2D radiomics model. The AUCs of 3D and 2D radiomics models in the training set were 0.82 (95% CI: 0.75-0.89) and 0.74 (95% CI: 0.66-0.82); in the internal validation set were 0.75 (95% CI: 0.65-0.85) and 0.67 (95% CI: 0.56-0.78); in the external validation set were 0.75 (95% CI: 0.64-0.86) and 0.57 (95% CI: 0.45-0.69); respectively. The AUCs of the nomogram integrating the optimal 3D rad-score and clinical risk factors (CT-reported T stage, CT-reported lymph node status) in the internal set and external validation set were 0.82 (95% CI: 0.73-0.91) and 0.80 (95% CI: 0.68-0.91), respectively. CONCLUSION Both 2D and 3D radiomics models can predict LVI status of CRC. The nomogram combining the optimal 3D rad-score and clinical risk factors further improved predictive performance. ADVANCES IN KNOWLEDGE This is the first study to compare the difference in performance of CT-based 2D and 3D radiomics models for the pre-operative prediction of LVI in CRC. The prediction of the nomogram could be improved by combining the 3D radiomics model with the imaging model, suggesting its potential for clinical application.
Collapse
Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Hongmei Gu
- Department of Radiology, Affiliated Hospital of Nantong
University, Nantong, PR China
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Xinghua Zhu
- Department of Pathology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| |
Collapse
|
25
|
Andersen MB, Harders SW, Thygesen J, Ganeshan B, Torp Madsen HH, Rasmussen F. Potential impact of texture analysis in contrast enhanced CT in non-small cell lung cancer as a marker of survival: A retrospective feasibility study. Medicine (Baltimore) 2022; 101:e31855. [PMID: 36482650 PMCID: PMC9726401 DOI: 10.1097/md.0000000000031855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The objective of this feasibility study was to assess computed tomography (CT) texture analysis (CTTA) of pulmonary lesions as a predictor of overall survival in patients with suspected lung cancer on contrast-enhanced computed tomography (CECT). In a retrospective pilot study, 94 patients (52 men and 42 women; mean age, 67.2 ± 10.8 yrs) from 1 center with non-small cell lung cancer (NSCLC) underwent CTTA on the primary lesion by 2 individual readers. Both simple and multivariate Cox regression analyses correlating textural parameters with overall survival were performed. Statistically significant parameters were selected, and optimal cutoff values were determined. Kaplan-Meier plots based on these results were produced. Simple Cox regression analysis showed that normalized uniformity had a hazard ratio (HR) of 16.059 (3.861-66.788, P < .001), and skewness had an HR of 1.914 (1.330-2.754, P < .001). The optimal cutoff values for both parameters were 0.8602 and 0.1554, respectively. Normalized uniformity, clinical stage, and skewness were found to be prognostic factors for overall survival in multivariate analysis. Tumor heterogeneity, assessed by normalized uniformity and skewness on CECT may be a prognostic factor for overall survival.
Collapse
Affiliation(s)
- Michael Brun Andersen
- Department of Radiology, Aarhus University Hospital, Skejby, Denmark
- Department of Radiology, Copenhagen University Hospital, Gentofte, Denmark
- Department of Clinical Medicine, Copenhagen University, Copenhagen, Denmark
- * Correspondence: Michael Brun Andersen, Department of Radiology, Copenhagen University Hospital, Gentofte Hospitalsvej 1, Hellerup 2900, Denmark (e-mail: )
| | | | - Jesper Thygesen
- Department of Clinical Engineering c/o Aarhus University Hospital, Central Denmark Region, Skejby, Denmark
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London, London, UK
| | | | - Finn Rasmussen
- Department of Radiology, Aarhus University Hospital, Skejby, Denmark
| |
Collapse
|
26
|
Mahmoudi T, Radmard AR, Salehnia A, Ahmadian A, Davarpanah AH, Kafieh R, Arabalibeik H. Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images. J Biomed Phys Eng 2022; 12:655-668. [PMID: 36569560 PMCID: PMC9759639 DOI: 10.31661/jbpe.v0i0.2102-1283] [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: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 12/02/2022]
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.
Collapse
Affiliation(s)
- Tahereh Mahmoudi
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran Iran
| | - Aneseh Salehnia
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran Iran
| | - Alireza Ahmadian
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir H Davarpanah
- MD, Department of Radiology and Imaging Sciences, Emory University School of Medicine 1364 Clifton Rd NE Atlanta, Georgia 30322, USA
| | - Raheleh Kafieh
- PhD, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Arabalibeik
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
27
|
Gu XL, Cui Y, Wang K, Xing Q, Li XT, Zhu HT, Li ZW, Sun YS. Qualitative and quantitative parameters on hepatobiliary phase of gadoxetic acid-enhanced MR imaging for predicting pathological response to preoperative systemic therapy in colorectal liver metastases. Eur J Radiol 2022; 157:110572. [DOI: 10.1016/j.ejrad.2022.110572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/26/2022] [Accepted: 10/23/2022] [Indexed: 11/03/2022]
|
28
|
Xue T, Peng H, Chen Q, Li M, Duan S, Feng F. A CT-Based Radiomics Nomogram in Predicting the Postoperative Prognosis of Colorectal Cancer: A Two-center Study. Acad Radiol 2022; 29:1647-1660. [PMID: 35346564 DOI: 10.1016/j.acra.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/25/2022] [Accepted: 02/06/2022] [Indexed: 01/26/2023]
Abstract
RATIONALE AND OBJECTIVES This retrospective study aimed to develop a practical model to determine overall survival after surgery in patients with colorectal cancer according to radiomics signatures based on computed tomography (CT) images and clinical predictors. MATERIALS AND METHODS A total of 121 colorectal cancer (CRC) patients were selected to construct the model, and 51 patients and 114 patients were selected for internal validation and external testing. The radiomics features were extracted from each patient's CT images. Univariable Cox regression and least absolute shrinkage and selection operator regression were used to select radiomics features. The performance of the nomogram was evaluated by calibration curves and the c-index. Kaplan-Meier analysis was used to compare the overall survival between these subgroups. RESULTS The radiomics features of the CRC patients were significantly correlated with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of CRC patients' overall survival. The calibration curves showed that the predicted survival time was close to the actual survival time. According to Kaplan-Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group were higher than those of the high-risk group. CONCLUSION The nomogram combining the optimal radiomics signature and clinical predictors further improved the predicted accuracy of survival prognosis for CRC patients. These findings might affect treatment strategies and enable a step forward for precise medicine.
Collapse
Affiliation(s)
- Ting Xue
- Nantong University, Nantong, Jiangsu, PR China
| | - Hui Peng
- Nantong University, Nantong, Jiangsu, PR China
| | | | - Manman Li
- Nantong University, Nantong, Jiangsu, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu 226361, PR China.
| |
Collapse
|
29
|
Zhou W, Lv Y, Hu X, Luo Y, Li J, Zhu H, Hai Y. Study on the changes of CT texture parameters before and after HCC treatment in the efficacy evaluation and survival predication of patients with HCC. Front Oncol 2022; 12:957737. [PMID: 36387217 PMCID: PMC9650244 DOI: 10.3389/fonc.2022.957737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/17/2022] [Indexed: 11/26/2022] Open
Abstract
Objective To investigate texture parameters of contrast-enhanced computed tomography (CT) images before and after transarterial chemoembolization (TACE) as a tool for assessing the therapeutic response and survival predication in hepatocellular carcinoma (HCC). Materials and methods Data of 77 HCC patients who underwent three-phase dynamic contrast-enhanced CT examination within 4 weeks before and 4–8 weeks after TACE were collected and efficacy evaluation was performed according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST) standard. The remission group consisted of 31 patients (12 with complete remission+19 with partial remission), while the non-remission group consisted of 46 patients (27 with stable disease+19 with progressive disease). Full-volume manual delineation of the region of interest (ROI) and texture analysis of the ROI were performed on the CT images using FireVoxel software. Changes in the 48 texture parameters from three-phase CT images before and after TACE were calculated and compared between the two groups. The receiver operating characteristic (ROC) curve and the areas under the curve (AUC) were used to analyze the diagnostic performance of texture parameters. A multifactorial Cox model was used for predicting survival. The C-indices of texture parameter difference values with predictive value, texture features model, and texture features combined with mRECIST in predicting OS were compared with those of mRECIST. Results A total of 41 changes in texture parameters were statistically significant between the remission and non-remission groups. The receiver operating characteristic (ROC) curve showed that the AUC of changes in the 90th percentile in the arterial phase was the largest at 0.842. When the cut-off value was 70.50, the Youden index was the largest (0.621), and the sensitivity and specificity were 0.710 and 0.911, respectively. Three changes in texture parameters were independent factors associated with patient survival, with a hazard of 0.173, 2.068, and 1.940, respectively. The C-index of the OS predicted by the texture features model was not statistically different from that of the mRECIST (0.695 vs. 0.668, p=0.493). While the C-indices of skewness in the portal venous phase combined with mRECIST (0.729, p=0.015), skewness in the delayed phase combined with mRECIST (0.715, p=0.044), and skewness in both two phases combined with mRECIST (0.728, p=0.017) were statistically different. Conclusion Changes in the texture parameters of CT images before and after TACE treatment can be used to obtain relevant grayscale histogram parameters for evaluating the early efficacy of TACE in HCC treatment. And the texture analysis combined with mRECIST may be superior to the mRECIST alone in predicting survival in HCC after TACE treatment.
Collapse
Affiliation(s)
- Wei Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yinzhang Lv
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Yinzhang Lv,
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Luo
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haidan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yucheng Hai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| |
Collapse
|
30
|
Yuan H, Zou Y, Gao Y, Zhang S, Zheng X, You X. Correlation analysis between unenhanced and enhanced CT radiomic features of lung cancers presenting as solid nodules and their efficacy for predicting hilar and mediastinal lymph node metastases. FRONTIERS IN RADIOLOGY 2022; 2:911179. [PMID: 37492652 PMCID: PMC10365119 DOI: 10.3389/fradi.2022.911179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/21/2022] [Indexed: 07/27/2023]
Abstract
Objectives If hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer. Methods Two hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models. Results A total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p < 0.05). Among them, the shape features had the strongest correlation (mean r = 0.98). There were 5 features in the enhanced model and the unenhanced model, which had important predicting significance. The AUCs were 0.811 and 0.803, respectively. There was no significant difference in the predictive performance of the two models (DeLong's test, p = 0.05). Conclusion Our study models achieved higher accuracy for predicting hilar and mediastinal lymph node metastasis of solid nodule lung cancer and have some value in promoting the staging accuracy of lung cancer. Our results show that CT radiomics features have potential to predict hilar and mediastinal lymph node metastases in solid nodular lung cancer. In addition, enhanced and unenhanced CT radiomics models had comparable predictive power in predicting hilar and mediastinal lymph node metastases.
Collapse
Affiliation(s)
- Huanchu Yuan
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Yujian Zou
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Yun Gao
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Shihao Zhang
- Department of Pathology, Dongguan People’s Hospital, Dongguan, China
| | - Xiaolin Zheng
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Xiaoting You
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| |
Collapse
|
31
|
A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
Collapse
|
32
|
Yu Z, Ding J, Pang H, Fang H, He F, Xu C, Li X, Ren K. A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis. BMC Urol 2022; 22:147. [PMID: 36096829 PMCID: PMC9469588 DOI: 10.1186/s12894-022-01099-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/29/2022] [Indexed: 11/10/2022] Open
Abstract
Background To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7). Methods In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7. Results A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson’s correlation coefficient was statistically significant between Rad-score and CK7. Conclusion We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision. Supplementary Information The online version contains supplementary material available at 10.1186/s12894-022-01099-0.
Collapse
Affiliation(s)
- Ziyang Yu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Jie Ding
- Radiology, Xiang'an Hospital of Xiamen University, Xiamen, China
| | - Huize Pang
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongkun Fang
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Furong He
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Chenxi Xu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Xuedan Li
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
| | - Ke Ren
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China. .,Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China. .,Radiology, Xiang'an Hospital of Xiamen University, Xiamen, China.
| |
Collapse
|
33
|
Bellini D, Carbone I, Rengo M, Vicini S, Panvini N, Caruso D, Iannicelli E, Tombolini V, Laghi A. Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI. Tomography 2022; 8:2059-2072. [PMID: 36006071 PMCID: PMC9416446 DOI: 10.3390/tomography8040173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
Background: To evaluate the diagnostic performance of a Machine Learning (ML) algorithm based on Texture Analysis (TA) parameters in the prediction of Pathological Complete Response (pCR) to Neoadjuvant Chemoradiotherapy (nChRT) in Locally Advanced Rectal Cancer (LARC) patients. Methods: LARC patients were prospectively enrolled to undergo pre- and post-nChRT 3T MRI for initial loco-regional staging. TA was performed on axial T2-Weighted Images (T2-WI) to extract specific parameters, including skewness, kurtosis, entropy, and mean of positive pixels. For the assessment of TA parameter diagnostic performance, all patients underwent complete surgical resection, which served as a reference standard. ROC curve analysis was carried out to determine the discriminatory accuracy of each quantitative TA parameter to predict pCR. A ML-based decisional tree was implemented combining all TA parameters in order to improve diagnostic accuracy. Results: Forty patients were considered for final study population. Entropy, kurtosis and MPP showed statistically significant differences before and after nChRT in patients with pCR; in particular, when patients with Pathological Partial Response (pPR) and/or Pathological Non-Response (pNR) were considered, entropy and skewness showed significant differences before and after nChRT (all p < 0.05). In terms of absolute value changes, pre- and post-nChRT entropy, and kurtosis showed significant differences (0.31 ± 0.35, in pCR, −0.02 ± 1.28 in pPR/pNR, (p = 0.04); 1.87 ± 2.19, in pCR, −0.06 ± 3.78 in pPR/pNR (p = 0.0005); 107.91 ± 274.40, in pCR, −28.33 ± 202.91 in pPR/pNR, (p = 0.004), respectively). According to ROC curve analysis, pre-treatment kurtosis with an optimal cut-off value of ≤3.29 was defined as the best discriminative parameter, resulting in a sensitivity and specificity in predicting pCR of 81.5% and 61.5%, respectively. Conclusions: TA parameters extracted from T2-WI MRI images could play a key role as imaging biomarkers in the prediction of response to nChRT in LARC patients. ML algorithms can be used to efficiently combine all TA parameters in order to improve diagnostic accuracy.
Collapse
Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
- Correspondence: ; Tel.: +39-351836065
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Nicola Panvini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Damiano Caruso
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Elsa Iannicelli
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Vincenzo Tombolini
- Department of Radiotherapy, Policlinico Umberto I, “Sapienza” University of Rome, 00161 Rome, Italy
| | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| |
Collapse
|
34
|
Li J, Zhang Y, Yin D, Shang H, Li K, Jiao T, Fang C, Cui Y, Liu M, Pan J, Zeng Q. CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease. Front Neurosci 2022; 16:974096. [PMID: 36033623 PMCID: PMC9403315 DOI: 10.3389/fnins.2022.974096] [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: 06/20/2022] [Accepted: 07/20/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose To build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD). Methods Fifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC). Results Of the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively. Conclusion The TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.
Collapse
Affiliation(s)
- Jizhen Li
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, China
| | - Yan Zhang
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, China
| | - Di Yin
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Hui Shang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Kejian Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Tianyu Jiao
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Caiyun Fang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Ming Liu
- Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, China
| | - Jun Pan
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
- *Correspondence: Qingshi Zeng,
| |
Collapse
|
35
|
Liao H, Li Y, Yang Y, Liu H, Zhang J, Liang H, Yan G, Liu Y. Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China. Diagnostics (Basel) 2022; 12:diagnostics12081915. [PMID: 36010267 PMCID: PMC9406915 DOI: 10.3390/diagnostics12081915] [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: 07/13/2022] [Revised: 08/06/2022] [Accepted: 08/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background: We designed and validated the value of multiple radiomics models for diagnosing histological grade of pancreatic ductal adenocarcinoma (PDAC), holding a promise of assisting in precision medicine and providing clinical therapeutic strategies. Methods: 198 PDAC patients receiving surgical resection and pathological confirmation were enrolled and classified as 117 low-grade PDAC and 81 high-grade PDAC group. An external validation group was used to assess models’ performance. Available radiomics features were selected using GBDT algorithm on the basis of the arterial and venous phases, respectively. Five different machine learning models were built including k-nearest neighbour, logistic regression, naive bayes model, support vector machine, and random forest using ten times tenfold cross-validation. Multivariable logistic regression analysis was applied to establish clinical model and combined model. The models’ performance was assessed according to its predictive performance, calibration curves, and decision curves. A nomogram was established for visualization. Survival analysis was conducted for stratifying the overall survival prior to treatment. Results: In the training group, the RF model demonstrated the optimal predictive ability and robustness with an AUC of 0.943; the SVM model achieved the secondary performance, followed by Bayes model. In the external validation group, these three models (Bayes, RF, SVM) also achieved the top three predictive ability. A clinical model was built by selected clinical features with an AUC of 0.728, and combined model was established by an RF model and a clinical model with an AUC of 0.961. The log-rank test revealed that the low-grade group survived longer than the high-grade group. Conclusions: The multiphasic CECT radiomics models offered an accurate and noninvasive perspective to differentiate histological grade in PDAC and advantages of machine learning models including RF, SVM and Bayes were more remarkable.
Collapse
Affiliation(s)
- Hongfan Liao
- College of Medical Informatics of Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yaying Yang
- Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Huan Liu
- GE Healthcare, Shanghai 201203, China
| | - Jiao Zhang
- Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing 401120, China
| | - Hongwei Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Gaowu Yan
- Department of Radiology, Suining Central Hospital, Suining 429000, China
| | - Yanbing Liu
- College of Medical Informatics of Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, China
- Correspondence:
| |
Collapse
|
36
|
A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks? Eur Radiol 2022; 32:8443-8452. [PMID: 35904618 DOI: 10.1007/s00330-022-08922-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST). RESULTS Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design. CONCLUSION First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage. KEY POINTS • Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.
Collapse
|
37
|
Yu Z, Xu C, Zhang Y, Ji F. A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis. BMC Med Imaging 2022; 22:133. [PMID: 35896975 PMCID: PMC9327229 DOI: 10.1186/s12880-022-00862-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/21/2022] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVES To construct a noninvasive radiomics model for evaluating the pathological degree and an individualized treatment strategy for patients with the manifestation of ground glass nodules (GGNs) on CT images. METHODS The retrospective primary cohort investigation included patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semiautomatically, and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by ANOVA, Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, a random forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features. RESULTS In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Three intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.960 (0.963 for AAH/AIS, 0.940 for MIA, 0.978 for IAC) in the training set and 0.944 (0.955 for AAH/AIS, 0.952 for MIA, 0.926 for IAC) in the testing set for evaluation of the GGNs. CONCLUSION The triple classification based on intra- and peritumoral radiomics features derived from the noncontrast CT images had satisfactory performance and may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing of individualized treatment strategies.
Collapse
Affiliation(s)
- Ziyang Yu
- Department of Radiology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China.,School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Chenxi Xu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Ying Zhang
- Department of Radiology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China
| | - Fengying Ji
- Department of Radiology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China.
| |
Collapse
|
38
|
COŞKUN N, YÜKSEL AÖ, CANYİĞİT M, ÖZDEMİR E. Radiomics analysis of pre-treatment F-18 FDG PET/CT for predicting response to transarterial radioembolization in liver tumors. JOURNAL OF HEALTH SCIENCES AND MEDICINE 2022. [DOI: 10.32322/jhsm.1118649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Aim: To investigate the relationship between the textural features extracted from pre-treatment fluorine-18 fluorodeoxyglucose positron emission with computed tomography (F-18 FDG PET/CT) and the response to treatment in patients undergoing transarterial radioembolization (TARE) due to primary or metastatic liver tumors.
Material and Method: A total of 25 liver lesions from the pre-treatment F-18 PET/CT images of 14 patients were segmented manually. Standard uptake value (SUV) metrics and radiomics features were extracted for each lesion. Metabolic treatment response was determined according to PERCIST criteria in 18F-FDG PET/CT imaging performed 2 months after the treatment. Feature selection was done with recursive feature elimination (RFE). The association between selected features and treatment response was evaluated with logistic regression analysis.
Results: Eventually, 13 lesions responded to TARE, while 12 lesions remain stable or progressed. All standard uptake values and 27 out of 30 textural heterogeneity indicators were significantly higher in lesions that responded to treatment. SUVmax, kurtosis and dissimilarity features were selected by the RFE algorithm for the prediction of response to TARE. Logistic regression analysis revealed that all three parameters were significantly associated with treatment outcome.
Conclusion: Textural features extracted from pre-treatment F-18 FDG PET/CT in patients undergoing TARE due to liver tumors are promising biomarkers that can be potentially used to predict metabolic treatment response.
Collapse
Affiliation(s)
- Nazım COŞKUN
- SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, ANKARA ŞEHİR SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ
| | - Alptuğ Özer YÜKSEL
- SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, ANKARA ŞEHİR SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, NÜKLEER TIP ANABİLİM DALI
| | - Murat CANYİĞİT
- YILDIRIM BEYAZIT ÜNİVERSİTESİ, TIP FAKÜLTESİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, RADYOLOJİ ANABİLİM DALI
| | - Elif ÖZDEMİR
- YILDIRIM BEYAZIT ÜNİVERSİTESİ, TIP FAKÜLTESİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, NÜKLEER TIP ANABİLİM DALI
| |
Collapse
|
39
|
Ungan G, Lavandier AF, Rouanet J, Hordonneau C, Chauveau B, Pereira B, Boyer L, Garcier JM, Mansard S, Bartoli A, Magnin B. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J Comput Assist Radiol Surg 2022; 17:1867-1877. [PMID: 35650345 DOI: 10.1007/s11548-022-02662-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy. METHODS We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification. RESULTS The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p-value 0.001). CONCLUSION This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.
Collapse
Affiliation(s)
- Gulnur Ungan
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Anne-Flore Lavandier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jacques Rouanet
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Constance Hordonneau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Benoit Chauveau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Bruno Pereira
- Biostatistics Unit, DRCI, CHU Clermont Ferrand, 58 rue Montalembert, 63000, Clermont-Ferrand, France
| | - Louis Boyer
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jean-Marc Garcier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France.,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Sandrine Mansard
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Adrien Bartoli
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Benoit Magnin
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France. .,Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France. .,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
| |
Collapse
|
40
|
He W, Xia C, Chen X, Yu J, Liu J, Pu H, Li X, Liu S, Chen X, Peng L. Computed Tomography-Based Radiomics for Differentiation of Thymic Epithelial Tumors and Lymphomas in Anterior Mediastinum. Front Oncol 2022; 12:869982. [PMID: 35646676 PMCID: PMC9136168 DOI: 10.3389/fonc.2022.869982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/11/2022] [Indexed: 11/19/2022] Open
Abstract
Objective To investigate the differential diagnostic performance of computed tomography (CT)-based radiomics in thymic epithelial tumors (TETs) and lymphomas in anterior mediastinum. Methods There were 149 patients with TETs and 93 patients with lymphomas enrolled. These patients were assigned to a training set (n = 171) and an external validation set (n = 71). Dedicated radiomics prototype software was used to segment lesions on preoperative chest enhanced CT images and extract features. The multivariable logistic regression algorithm was used to construct three models according to clinico-radiologic features, radiomics features, and combined features, respectively. Performance of the three models was compared by using the area under the receiver operating characteristic curves (AUCs). Decision curve analysis was used to evaluate clinical utility of the three models. Results For clinico-radiologic model, radiomics signature model, and combined model, the AUCs were 0.860, 0.965, 0.975 and 0.843, 0.961, 0.955 in the training cohort and the test cohort, respectively (all P<0.05). The accuracies of each model were 0.836, 0.895, 0.918 and 0.845, 0.901, 0.859 in the two cohorts, respectively (all P<0.05). Compared with the clinico-radiologic model, better diagnostic performances were found in the radiomics signature model and the combined model. Conclusions Radiomics signature model and combined model exhibit outstanding and comparable differential diagnostic performances between TETs and lymphomas. The CT-based radiomics analysis might serve as an effective tool for accurately differentiating TETs from lymphomas before treatment.
Collapse
Affiliation(s)
- Wenzhang He
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jianqun Yu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Huaxia Pu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xue Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Shengmei Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyue Chen
- Computed Tomography (CT) Collaboration, Siemens Healthineers, Chengdu, China
| | - Liqing Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
41
|
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.
Collapse
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
| | | | | |
Collapse
|
42
|
Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G, Dinapoli N, Peng Q, Casà C, Gambacorta MA, Valentini V, Lang J. Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol 2022; 17:78. [PMID: 35428267 PMCID: PMC9013126 DOI: 10.1186/s13014-022-02048-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/04/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Predicting pathological complete response (pCR) in patients affected by locally advanced rectal cancer (LARC) who undergo neoadjuvant chemoradiotherapy (nCRT) is a challenging field of investigation, but many of the published models are burdened by a lack of reliable external validation. Aim of this study was to evaluate the applicability of a magnetic resonance imaging (MRI) radiomic-based pCR model developed and validated in Europe, to a different cohort of patients from an intercontinental cancer center. METHODS The original model was based on two clinical and two radiomics features extracted from T2-weighted 1.5 T MRI of 161 LARC patients acquired before nCRT, considered as training set. Such model is here validated using the T2-w 1.5 and 3 T staging MRI of 59 LARC patients with different clinical characteristics consecutively treated in mainland Chinese cancer center from March 2017 to January 2018. Model performance were evaluated in terms of area under the receiver operator characteristics curve (AUC) and relative parameters, such as accuracy, specificity, negative and positive predictive value (NPV and PPV). RESULTS An AUC of 0.83 (CI 95%, 0.71-0.96) was achieved for the intercontinental cohort versus a value of 0.75 (CI 95%, 0.61-0.88) at the external validation step reported in the original experience. Considering the best cut-off threshold identified in the first experience (0.26), the following predictive performance were obtained: 0.65 as accuracy, 0.64 as specificity, 0.70 as sensitivity, 0.91 as NPV and 0.28 as PPV. CONCLUSIONS Despite the introduction of significant different factors, the proposed model appeared to be replicable on a real-world data extra-European patients' cohort, achieving a TRIPOD 4 level.
Collapse
Affiliation(s)
- Luca Boldrini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Lucia Clara Orlandini
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Davide Cusumano
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Nicola Dinapoli
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Qian Peng
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Calogero Casà
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Vincenzo Valentini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jinyi Lang
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
43
|
Incidental Thyroid Nodule on Chest Computed Tomography: Application of Computed Tomography Texture Analysis in Prediction of Ultrasound Classification. J Comput Assist Tomogr 2022; 46:480-486. [PMID: 35405688 DOI: 10.1097/rct.0000000000001286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of the study was to evaluate the value of computed tomography (CT) texture analysis (CTTA) in predicting ultrasound (US) classification of incidentally detected thyroid nodule (ITN) on chest CT. METHODS A total of 117 ITNs (≥1 cm in the longest diameter) on chest CT scan of 107 patients was divided into 4 categories according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification on recent thyroid US within 3 months. Computed tomography texture features were extracted with or without filtration using commercial software. The texture features were compared between the benign (K-TIRADS 2; n = 21) and the suspicious (K-TIRADS 3, 4, 5; n = 96) nodules. Multivariate regression and area under the receiver operating characteristic curve analysis were performed to determine significant prediction factors of the suspicious nodules. RESULTS The mean value of positive pixels was significantly higher in the suspicious nodules except the unfiltered image (P < 0.05). Entropy of the suspicious nodules was significantly higher with unfiltered and fine filters (P < 0.05), and kurtosis of the suspicious nodules was significantly higher with medium and coarse filters (P < 0.05). A logistic regression model incorporating mean value of positive pixels and kurtosis with a medium filter using volumetric analysis demonstrated the best performance to predict the suspicious nodules with an area under the receiver operating characteristic curve of 0.842 (P < 0.001, sensitivity 82.3%, and specificity 81.0%). CONCLUSIONS Computed tomography texture analysis for ITN larger than 1 cm showed significant correlation with systematic thyroid US classification and presented excellent performance to predict the suspicious nodules.
Collapse
|
44
|
Yao W, Yang S, Ge Y, Fan W, Xiang L, Wan Y, Gu K, Zhao Y, Zha R, Bu J. Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Front Med (Lausanne) 2022; 9:819670. [PMID: 35402463 PMCID: PMC8987588 DOI: 10.3389/fmed.2022.819670] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/18/2022] [Indexed: 12/12/2022] Open
Abstract
Background Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI. Results We found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 0.61–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93). Conclusions Models derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.
Collapse
Affiliation(s)
- Wenjun Yao
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shuo Yang
- Department of Radiology, Anhui Mental Health Center, Hefei, China
| | | | - Wenlong Fan
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Li Xiang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yang Wan
- Department of Hematological Lab, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Kangchen Gu
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yan Zhao
- School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Rujing Zha
- Department of Radiology, Division of Life Science and Medicine, The First Affiliated Hospital of USTC, School of Life Science, University of Science and Technology of China, Hefei, China
| | - Junjie Bu
- School of Biomedical Engineering, Anhui Medical University, Hefei, China.,The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| |
Collapse
|
45
|
Yildirim M, Baykara M. Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma. Acta Neurol Belg 2022; 122:363-368. [PMID: 33555560 DOI: 10.1007/s13760-021-01607-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 01/18/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE Conventional magnetic resonance imaging (MRI) technics are insufficient in the differentiation of tumor progression from post-treatment changes in patients with treated glioblastoma. Previous studies have suggested that histogram analysis is a useful tool in the assessment of treatment response in different cancer types. The aim of the study was to to evaluate the effectiveness of MRI histogram analysis in the differentiation of tumor progression from pseudoprogression in patients with treated glioblastoma. METHODS Forty-six patients with glioblastoma who newly developed enhancing lesions following chemoradiation treatment were included in this retrospective study. Histogram analysis was performed from new enhancing lesions on T1-weighted contrast-enhanced MRI. Histogram analysis findings of patients with progression (23) and pseudoprogression (23) were compared. RESULTS Mean, minimum, median, maximum, standard deviation, variance, entropy, skewness, uniformity values were found to be significantly higher in progressive disease (p < 0.05). A receiver-operating characteristic (ROC) curve analysis was performed for mean value, and area under the curve (AUC) was found as 0.975. When the threshold value was selected as 528.86, two groups could be differentiated with 95.7% sensitivity and 87.0% specificity. CONCLUSION MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.
Collapse
|
46
|
Wan S, Wei Y, Zhang X, Yang C, Hu F, Song B. Computed Tomography-Based Texture Features for the Risk Stratification of Portal Hypertension and Prediction of Survival in Patients With Cirrhosis: A Preliminary Study. Front Med (Lausanne) 2022; 9:863596. [PMID: 35433759 PMCID: PMC9010529 DOI: 10.3389/fmed.2022.863596] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/22/2022] [Indexed: 12/21/2022] Open
Abstract
ObjectiveClinical evidence suggests that the risk stratification of portal hypertension (PH) plays a vital role in disease progression and patient outcomes. However, the gold standard for stratifying PH [portal vein pressure (PVP) measurement] is invasive and therefore not suitable for routine clinical practice. This study is aimed to stratify PH and predict patient outcomes using liver or spleen texture features based on computed tomography (CT) images non-invasively.MethodsA total of 114 patients with PH were included in this retrospective study and divided into high-risk PH (PVP ≥ 20 mm Hg, n = 57) or low-risk PH (PVP < 20 mm Hg, n = 57), a progression-free survival (PFS) group (n = 14), or a non-PFS group (n = 51) based on patients with rebleeding or death after the transjugular intrahepatic portosystemic shunt (TIPS) procedure. All patients underwent contrast-enhanced CT, and the laboratory data were recorded. Texture features of the liver or spleen were obtained by a manual drawing of the region of interest (ROI) and were performed in the portal venous phase. Logistic regression analysis was applied to select the significant features related to high-risk PH, and PFS-related features were determined by the Cox proportional hazards model and Kaplan-Meier analysis. Receiver operating characteristic (ROC) curves were used to test the diagnostic capacity of each feature.ResultsFive texture features (one first-order feature from the liver and four wavelet features from the spleen) and the international normalized ratio (INR) were identified as statistically significant for stratifying PH (p < 0.05). The best performance was achieved by the spleen-derived feature of wavelet.LLH_ngtdm_Busyness, with an AUC of 0.72. The only log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation feature from the liver was associated with PFS with a C-index of 0.72 (95% CI 0.566–0.885), which could stratify patients with PH into high- or low-risk groups. The 1-, 2-, and 3-year survival probabilities were 66.7, 50, and 33.3% for the high-risk group and 93.2, 91.5, and 84.4% for the low-risk group, respectively (p < 0.05).ConclusionCT-based texture features from the liver or spleen may have the potential to stratify PH and predict patient survival.
Collapse
Affiliation(s)
- Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Zhang
- Pharmaceutical Diagnostics, GE Healthcare, Beijing, China
| | - Caiwei Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Fubi Hu
- Department of Radiology, First Affiliated Hospital of Chengdu Medical College, Chengdu, China
- *Correspondence: Fubi Hu,
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, Sanya People’s Hospital, Sanya, China
- Bin Song,
| |
Collapse
|
47
|
Yang Q, Mao Y, Xie H, Qin T, Mai Z, Cai Q, Wen H, Li Y, Zhang R, Liu L. Identifying Outcomes of Patients With Advanced Pancreatic Adenocarcinoma and RECIST Stable Disease Using Radiomics Analysis. JCO Precis Oncol 2022; 6:e2100362. [PMID: 35319966 PMCID: PMC8966975 DOI: 10.1200/po.21.00362] [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] [Indexed: 11/20/2022] Open
Abstract
Few studies have explored the biomarkers for predicting the heterogeneous outcomes of patients with advanced pancreatic adenocarcinoma showing stable disease (SD) on the initial postchemotherapy computed tomography. We aimed to devise a radiomics signature (RS) to predict these outcomes for further risk stratification.
Collapse
Affiliation(s)
- Qiuxia Yang
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yize Mao
- Department of Pancreatic-Biliary Surgical Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hui Xie
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Tao Qin
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhijun Mai
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Qian Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Hailin Wen
- Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center, Shenzhen, China
| | - Yong Li
- Department of Medical Imaging Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Rong Zhang
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lizhi Liu
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| |
Collapse
|
48
|
Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer. Acad Radiol 2022; 29 Suppl 2:S62-S72. [PMID: 33402298 DOI: 10.1016/j.acra.2020.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.
Collapse
|
49
|
Cheung BMF, Lau KS, Lee VHF, Leung TW, Kong FMS, Luk MY, Yuen KK. Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases. Radiat Oncol J 2022; 39:254-264. [PMID: 34986546 PMCID: PMC8743458 DOI: 10.3857/roj.2021.00311] [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: 02/27/2021] [Accepted: 06/28/2021] [Indexed: 11/26/2022] Open
Abstract
Purpose Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the ablative treatment of thoracic tumors. This study aims to identify predictors of treatment responses in patients affected by early stage non-small cell lung cancer (NSCLC) or pulmonary oligo-metastases treated with SBRT and to develop an accurate machine learning model to predict radiological response to SBRT. Materials and Methods Computed tomography (CT) images of 85 tumors (stage I–II NSCLC and pulmonary oligo-metastases) from 69 patients treated with SBRT were analyzed. Gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) or partial response (PR) were defined as responders. One hundred ten radiomic features were extracted using PyRadiomics module based on the GTV. The association of features with response to SBRT was evaluated. A model using support vector machine (SVM) was then trained to predict response based solely on the extracted radiomics features. Receiver operating characteristic curves were constructed to evaluate model performance of the identified radiomic predictors. Results Sixty-nine patients receiving thoracic SBRT from 2008 to 2018 were retrospectively enrolled. Skewness and root mean squared were identified as radiomic predictors of response to SBRT. The SVM machine learning model developed had an accuracy of 74.8%. The area under curves for CR, PR, and non-responder prediction were 0.86 (95% confidence interval [CI], 0.794–0.921), 0.946 (95% CI, 0.873–0.978), and 0.857 (95% CI, 0.789–0.915), respectively. Conclusion Radiomic analysis of pre-treatment CT scan is a promising tool that can predict tumor response to SBRT.
Collapse
Affiliation(s)
| | - Kin Sang Lau
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong
| | | | - To Wai Leung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong
| | | | - Mai Yee Luk
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong
| | - Kwok Keung Yuen
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong
| |
Collapse
|
50
|
Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2021:3953442. [PMID: 34976107 PMCID: PMC8716224 DOI: 10.1155/2021/3953442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 12/09/2022]
Abstract
This study retrospectively included some patients with colorectal cancer diagnosed by histopathology, to explore the feasibility of CT medical image texture analysis in predicting KRAS gene mutations in patients with colorectal cancer. Before any surgical procedure, all patients received an enhanced CT scan of the abdomen and pelvis, as well as genetic testing. To define patient groups, divide all patients into test and validation sets based on the order of patient enrollment. A radiologist took a look at the plain axial CT image of the tumor, as well as the portal vein CT image, at the corresponding level. The physician points the computer's cursor to the relevant area in the image, and TexRAD software programs together texture parameters based on various spatial scale factors, also known as total mean, total variance, statistical entropy, overall total average, mean total, positive mean, skewness value, kurtosis value, and general skewness. Using the same method again two weeks later, the observer and another physician measured the image of each patient again to see if the method was consistent between observers. With regard to clinical information, the KRAS gene mutation group and the wild group of participants in the test set and validation set each had values for the texture parameter. In a study of patients with colorectal cancer, the results demonstrated that CT texture parameters were correlated with the presence of the KRAS gene mutation. The best CT prediction model includes the values of the medium texture image's slope and the other CT fine texture image's value of entropy, the medium texture image's slope and kurtosis, and the medium texture image's mean and the other CT fine texture image's value of entropy. Regardless of the training set or the validation set, patients with and without KRAS gene mutations did not differ significantly in clinical characteristics. This method can be used to identify mutations in the KRAS gene in patients with colorectal cancer, making it practical to implement CT medical image texture analysis technology for that purpose.
Collapse
|