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Chen J, Ni L, Gong J, Wu J, Qian T, Wang M, Huang J, Liu K. Quantitative parameters of dual-layer spectral detector computed tomography for evaluating differentiation grade and lymphovascular and perineural invasion in colorectal adenocarcinoma. Eur J Radiol 2024; 178:111594. [PMID: 38986232 DOI: 10.1016/j.ejrad.2024.111594] [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: 03/08/2024] [Revised: 06/20/2024] [Accepted: 06/28/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE To explore the predictive value of dual-layer spectral detector CT (SDCT) quantitative parameters for determining differentiation grade, lymphovascular invasion (LVI) and perineural invasion (PNI) in colorectal adenocarcinoma (CRAC) patients. METHODS A total of 106 eligible patients with CRAC were included in this study. Spectral parameters, including CT values at 40 and 100 keV, the effective atomic number (Zeff), the iodine concentration (IC), the slope of the spectral Hounsfield unit (HU) curve (λHU), and the normalized iodine concentration (NIC) in the arterial phase (AP) and venous phase (VP), were compared according to the differentiation grade and the status of LVI and PNI. The diagnostic accuracies of the quantitative parameters with statistical significance were determined via receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated. RESULTS There were 57 males and 49 females aged 43-86 (69 ± 10) years. The measured values of the spectral quantitative parameters of the CRAC were consistent within the observer (ICC range: 0.800-0.926). The 40 keV-AP, IC-AP, NIC-AP, 40 keV-VP, and IC-VP were significantly different among the different differentiation grades in the CRAC (P = 0.040, AUC = 0.673; P = 0.035, AUC = 0.684; P = 0.031, AUC = 0.639; P = 0.044, AUC = 0.663 and P = 0.035, AUC = 0.666, respectively). A statistically significant difference was observed in 40 keV-VP, 100 keV-VP, Zeff-VP, IC-VP, and λHU-VP between LVI-positive and LVI-negative patients (P = 0.003, AUC = 0.688; P = 0.015, AUC = 0.644; P = 0.001, AUC = 0.688; P = 0.001, AUC = 0.703 and P = 0.003, AUC = 0.677, respectively). There were no statistically significant differences in the values of the spectral parameters of the PNI state of patients with CRAC (P > 0.05). CONCLUSION The quantitative parameters of SDCT had good diagnostic efficacy in differentiating between different grades and statuses of LVI in patients with CRAC; however, SDCT did not have value for identifying the state of PNI.
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Affiliation(s)
- Jinghua Chen
- Department of Radiology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Taicang, China
| | - Lei Ni
- Department of Radiology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Taicang, China
| | - Jingjing Gong
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Jie Wu
- Department of Radiology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Taicang, China
| | - Tingting Qian
- Department of Pathology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Taicang, China
| | - Mengjia Wang
- Department of Pathology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Taicang, China
| | - Jian Huang
- Department of Radiology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Taicang, China
| | - Kefu Liu
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
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Lu W, Tan X, Zhong Y, Wang P, Ge Y, Zhang H, Hu S. Spectral CT in the evaluation of perineural invasion status in rectal cancer. Jpn J Radiol 2024:10.1007/s11604-024-01575-7. [PMID: 38709434 DOI: 10.1007/s11604-024-01575-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/15/2024] [Indexed: 05/07/2024]
Abstract
PURPOSE To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer. METHODS Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test. RESULTS The preoperative spectral CT parameters (40-70 keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%. CONCLUSION Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency.
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Affiliation(s)
- Wenzheng Lu
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Xiaoying Tan
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Yanqi Zhong
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Peng Wang
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China.
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Yang L, Wang B, Shi X, Li B, Xie J, Wang C. Application research of radiomics in colorectal cancer: A bibliometric study. Medicine (Baltimore) 2024; 103:e37827. [PMID: 38608072 PMCID: PMC11018182 DOI: 10.1097/md.0000000000037827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/15/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Radiomics has shown great potential in the clinical field of colorectal cancer (CRC). However, few bibliometric studies have systematically analyzed existing research in this field. The purpose of this study is to understand the current research status and future development directions of CRC. METHODS Search the English documents on the application of radiomics in the field of CRC research included in the Web of Science Core Collection from its establishment to October 2023. VOSviewer and CiteSpace software were used to conduct bibliometric and visual analysis of online publications related to countries/regions, authors, journals, references, and keywords in this field. RESULTS A total of 735 relevant documents published from Web of Science Core Collection to October 2023 were retrieved, and a total of 419 documents were obtained based on the screening criteria, including 376 articles and 43 reviews. The number of publications is increasing year by year. Among them, China publishes the most relevant documents (n = 238), which is much higher than Italy (n = 69) and the United States (n = 63). Tian Jie is the author with the most publications and citations (n = 17, citations = 2128), GE Healthcare is the most productive institution (n = 26), Frontiers in Oncology is the journal with the most publications (n = 60), and European Radiology is the most cited journal (n = 776). Hot spots for the application of radiomics in CRC include magnetic resonance, neoadjuvant chemoradiotherapy, survival, texture analysis, and machine learning. These directions are the current hot spots for the application of radiomics research in CRC and may be the direction of continued development in the future. CONCLUSION Through bibliometric analysis, the application of radiomics in CRC has been increasing year by year. The application of radiomics improves the accuracy of preoperative diagnosis, prediction, and prognosis of CRC. The results of bibliometrics analysis provide a valuable reference for the research direction of radiomics. However, radiomics still faces many challenges in the future, such as the single nature of the data source which may affect the comprehensiveness of the results. Future studies can further expand the data sources and build a multicenter public database to more comprehensively reflect the research status and development trend of CRC radiomics.
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Affiliation(s)
- Lihong Yang
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Binjie Wang
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Xiaoying Shi
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Bairu Li
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Jiaqiang Xie
- Department of Breast and Thyroid Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Changfu Wang
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
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Liu NJ, Liu MS, Tian W, Zhai YN, Lv WL, Wang T, Guo SL. The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study. Insights Imaging 2024; 15:101. [PMID: 38578423 PMCID: PMC10997560 DOI: 10.1186/s13244-024-01664-1] [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: 11/13/2023] [Accepted: 03/04/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND We aimed to explore the application value of various machine learning (ML) algorithms based on multicenter CT radiomics in identifying peripheral nerve invasion (PNI) of colorectal cancer (CRC). METHODS A total of 268 patients with colorectal cancer who underwent CT examination in two hospitals from January 2016 to December 2022 were considered. Imaging and clinicopathological data were collected through the Picture Archiving and Communication System (PACS). The Feature Explorer software (FAE) was used to identify the peripheral nerve invasion of colorectal patients in center 1, and the best feature selection and classification channels were selected. Finally, the best feature selection and classifier pipeline were verified in center 2. RESULTS The six-feature models using RFE feature selection and GP classifier had the highest AUC values, which were 0.610, 0.699, and 0.640, respectively. FAE generated a more concise model based on one feature (wavelet-HLL-glszm-LargeAreaHighGrayLevelEmphasis) and achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively, using the "one standard error" rule. Using ANOVA feature selection, the GP classifier had the best AUC value in a one-feature model, with AUC values of 0.611, 0.663, and 0.643 on the validation, internal test, and external test sets, respectively. Similarly, when using the "one standard error" rule, the model based on one feature (wave-let-HLL-glszm-LargeAreaHighGrayLevelEmphasis) achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively. CONCLUSIONS Combining artificial intelligence and radiomics features is a promising approach for identifying peripheral nerve invasion in colorectal cancer. This innovative technique holds significant potential for clinical medicine, offering broader application prospects in the field. CRITICAL RELEVANCE STATEMENT The multi-channel ML method based on CT radiomics has a simple operation process and can be used to assist in the clinical screening of patients with CRC accompanied by PNI. KEY POINTS • Multi-channel ML in the identification of peripheral nerve invasion in CRC. • Multi-channel ML method based on CT-radiomics can detect the PNI of CRC. • Early preoperative identification of PNI in CRC is helpful to improve the formulation of treatment strategies and the prognosis of patients.
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Affiliation(s)
- Nian-Jun Liu
- The First School of Clinical Medical, Lanzhou University, LanzhouGansu, 73000, China
- Department of Radiology, the First Hospital of Lanzhou University, LanzhouGansu, 73000, China
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanzhouGansu, 73000, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, LanzhouGansu, 73000, China
- Gansu Province clinical research center for radiology imaging, LanzhouGansu, 73000, China
| | - Mao-Sen Liu
- Lichuan People's Hospital, Lichuan, 445400, Hubei, China
| | - Wei Tian
- The First School of Clinical Medical, Lanzhou University, LanzhouGansu, 73000, China
- Department of Radiology, the First Hospital of Lanzhou University, LanzhouGansu, 73000, China
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanzhouGansu, 73000, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, LanzhouGansu, 73000, China
- Gansu Province clinical research center for radiology imaging, LanzhouGansu, 73000, China
| | - Ya-Nan Zhai
- The First School of Clinical Medical, Lanzhou University, LanzhouGansu, 73000, China
- Department of Radiology, the First Hospital of Lanzhou University, LanzhouGansu, 73000, China
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanzhouGansu, 73000, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, LanzhouGansu, 73000, China
- Gansu Province clinical research center for radiology imaging, LanzhouGansu, 73000, China
| | - Wei-Long Lv
- The First School of Clinical Medical, Lanzhou University, LanzhouGansu, 73000, China
- Department of Radiology, the First Hospital of Lanzhou University, LanzhouGansu, 73000, China
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanzhouGansu, 73000, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, LanzhouGansu, 73000, China
- Gansu Province clinical research center for radiology imaging, LanzhouGansu, 73000, China
| | - Tong Wang
- The First School of Clinical Medical, Lanzhou University, LanzhouGansu, 73000, China
- Department of Radiology, the First Hospital of Lanzhou University, LanzhouGansu, 73000, China
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanzhouGansu, 73000, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, LanzhouGansu, 73000, China
- Gansu Province clinical research center for radiology imaging, LanzhouGansu, 73000, China
| | - Shun-Lin Guo
- The First School of Clinical Medical, Lanzhou University, LanzhouGansu, 73000, China.
- Department of Radiology, the First Hospital of Lanzhou University, LanzhouGansu, 73000, China.
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanzhouGansu, 73000, China.
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, LanzhouGansu, 73000, China.
- Gansu Province clinical research center for radiology imaging, LanzhouGansu, 73000, China.
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Zhou H, Zhou J, Qin C, Tian Q, Zhou S, Qin Y, Wu Y, Shi J, Feng F. Preoperative Prediction of Perineural Invasion in Oesophageal Squamous Cell Carcinoma Based on CT Radiomics Nomogram: A Multicenter Study. Acad Radiol 2024; 31:1355-1366. [PMID: 37949700 DOI: 10.1016/j.acra.2023.09.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/13/2023] [Accepted: 09/20/2023] [Indexed: 11/12/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of computed tomography (CT) radiomics nomogram in the preoperative prediction of perineural invasion (PNI) in oesophageal squamous cell carcinoma (ESCC) through a multicenter study. MATERIALS AND METHODS We retrospectively collected postoperative pathological data of 360 ESCC patients with definite PNI status (131 PNI-positive and 229 PNI-negative) from two centres. Radiomic features were extracted from the arterial-phase CT images, and the least absolute shrinkage and selection operator and logistic regression algorithm were used to screen valuable features for identifying the PNI status and calculating the radiomics score (Rad-score). A radiomics nomogram was established by integrating the Rad-score and clinical risk factors. A receiver operating characteristic curve was used to evaluate model performance, and decision curve analysis was used to evaluate the predictive performance of the radiomics nomogram in the training, internal validation, and external validation sets. RESULTS Twenty radiomics features were extracted from a full-volume tumour region of interest to construct the model, and the radiomics nomogram combined with radiomics features and clinical risk factors was superior to the clinical and radiomics models in predicting the PNI status of ESCC patients. The area under the curve values of the radiomics nomogram in the training, internal validation, and external validation sets were 0.856 (0.794-0.918), 0.832 (0.742-0.922), and 0.803 (0.709-0.898), respectively. CONCLUSION The radiomics nomogram based on CT has excellent predictive ability; it can non-invasively predict the preoperative PNI status of ESCC patients and provide a basis for preoperative decision-making.
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Affiliation(s)
- Hui Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Jianwen Zhou
- Department of Radiology, Dongtai People's Hospital, Yancheng, Jiangsu Province, China (J.Z.)
| | - Cai Qin
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Qi Tian
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Siyu Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Yihan Qin
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Yutao Wu
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Jian Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.).
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Liu J, Sun L, Zhao X, Lu X. Development and validation of a combined nomogram for predicting perineural invasion status in rectal cancer via computed tomography-based radiomics. J Cancer Res Ther 2023; 19:1552-1559. [PMID: 38156921 DOI: 10.4103/jcrt.jcrt_2633_22] [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: 12/15/2022] [Accepted: 07/05/2023] [Indexed: 01/03/2024]
Abstract
AIM This study aimed to create and validate a clinic-radiomics nomogram based on computed tomography (CT) imaging for predicting preoperative perineural invasion (PNI) of rectal cancer (RC). MATERIAL AND METHODS This study enrolled 303 patients with RC who were divided into training (n = 242) and test datasets (n = 61) in an 8:2 ratio with all their clinical outcomes. A total of 3,296 radiomic features were extracted from CT images. Five machine learning (ML) models (logistic regression (LR)/K-nearest neighbor (KNN)/multilayer perceptron (MLP)/support vector machine (SVM)/light gradient boosting machine (LightGBM)) were developed using radiomic features derived from the arterial and venous phase images, and the model with the best diagnostic performance was selected. By combining the radiomics and clinical signatures, a fused nomogram model was constructed. RESULTS After using the Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) to remove redundant features, the MLP model proved to be the most efficient among the five ML models. The fusion nomogram based on MLP prediction probability further improves the ability to predict the PNI status. The area under the curve (AUC) of the training and test sets was 0.883 and 0.889, respectively, which were higher than those of the clinical (training set, AUC = 0.710; test set, AUC = 0.762) and radiomic models (training set, AUC = 0.840; test set, AUC = 0.834). CONCLUSIONS The clinical-radiomics combined nomogram model based on enhanced CT images efficiently predicted the PNI status of patients with RC.
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Affiliation(s)
- Jiaxuan Liu
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
| | - Lingling Sun
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
| | - Xiang Zhao
- Institute of Innovative Science and Technology, Shenyang University, Liaoning, China
| | - Xi Lu
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
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