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Sun Y, Lu Z, Yang H, Jiang P, Zhang Z, Liu J, Zhou Y, Li P, Zeng Q, Long Y, Li L, Du B, Zhang X. Prediction of lateral lymph node metastasis in rectal cancer patients based on MRI using clinical, deep transfer learning, radiomic, and fusion models. Front Oncol 2024; 14:1433190. [PMID: 39099685 PMCID: PMC11294238 DOI: 10.3389/fonc.2024.1433190] [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: 05/15/2024] [Accepted: 07/02/2024] [Indexed: 08/06/2024] Open
Abstract
Introduction Lateral lymph node (LLN) metastasis in rectal cancer significantly affects patient treatment and prognosis. This study aimed to comprehensively compare the performance of various predictive models in predicting LLN metastasis. Methods In this retrospective study, data from 152 rectal cancer patients who underwent lateral lymph node (LLN) dissection were collected. The cohort was divided into a training set (n=86) from Tianjin Union Medical Center (TUMC), and two testing cohorts: testing cohort (TUMC) (n=37) and testing cohort from Gansu Provincial Hospital (GSPH) (n=29). A clinical model was established using clinical data; deep transfer learning models and radiomics models were developed using MRI images of the primary tumor (PT) and largest short-axis LLN (LLLN), visible LLN (VLLN) areas, along with a fusion model that integrates features from both deep transfer learning and radiomics. The diagnostic value of these models for LLN metastasis was analyzed based on postoperative LLN pathology. Results Models based on LLLN image information generally outperformed those based on PT image information. Rradiomics models based on LLLN demonstrated improved robustness on external testing cohorts compared to those based on VLLN. Specifically, the radiomics model based on LLLN imaging achieved an AUC of 0.741 in the testing cohort (TUMC) and 0.713 in the testing cohort (GSPH) with the extra trees algorithm. Conclusion Data from LLLN is a more reliable basis for predicting LLN metastasis in rectal cancer patients with suspicious LLN metastasis than data from PT. Among models performing adequately on the internal test set, all showed declines on the external test set, with LLLN_Rad_Models being less affected by scanning parameters and data sources.
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Affiliation(s)
- Yi Sun
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Zhongxiang Lu
- The First Clinical College of Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China
| | - Hongjie Yang
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | | | - Zhichun Zhang
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Jiafei Liu
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Yuanda Zhou
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Peng Li
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Qingsheng Zeng
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Yu Long
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Laiyuan Li
- Gansu Provincial Hospital, Gansu Clinical Medical Research Center for Anorectal Diseases, Lanzhou, Gansu, China
| | - Binbin Du
- Gansu Provincial Hospital, Gansu Clinical Medical Research Center for Anorectal Diseases, Lanzhou, Gansu, China
| | - Xipeng Zhang
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
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Wang Q, Zhu FX, Shi M. Clinical and pathological features of advanced rectal cancer with submesenteric root lymph node metastasis: Meta-analysis. World J Gastrointest Oncol 2024; 16:3299-3307. [PMID: 39072165 PMCID: PMC11271772 DOI: 10.4251/wjgo.v16.i7.3299] [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: 03/25/2024] [Revised: 04/23/2024] [Accepted: 05/08/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Advanced rectal cancer with submesenteric lymph node metastasis is a common complication of advanced rectal cancer, which has an important impact on the treatment and prognosis of patients. AIM To investigate the clinical and pathological characteristics of inferior mesenteric artery (IMA) root lymph node metastases in patients with rectal cancer. The findings of this study provided us with fresh medical information that assisted us in determining the appropriate treatment for these patients. METHODS Our study searched PubMed, Google Scholar, and other databases and searched the relevant studies and reports on the risk factors of IMA root lymph node metastasis of rectal cancer published in the self-built database until December 31, 2023. After data extraction, the Newcastle-Ottawa scale was used to evaluate the quality of the included literature, and RevMan5.3 software was used for meta-analysis and heterogeneity testing. The fixed effect modules without heterogeneity were selected to combine the effect size, and the random effect modules with heterogeneity were selected to combine the effect size. The cause of heterogeneity was found through sensitivity analysis, and the data of various risk factors were combined to obtain the final effect size, odds ratio (OR) value, and 95% confidence interval (CI). Publication bias was tested by drawing funnel plots. RESULTS A total of seven literature were included in this study. By combining the OR value of logistic multivariate regression and the 95%CI of various risk factors, we concluded that the risk factors for lymph node metastasis in the IMA region of rectal cancer were as follows: Preoperative carcinoembryonic antigen (CEA) > 5 ng/mL (OR = 0.32, 95%CI: 0.18-0.55, P < 0.05), tumor located above peritoneal reflexive (OR = 3.10, 95%CI: 1.78-5.42, P < 0.05), tumor size ≥ 5 cm (OR = 0.36, 95%CI: 0.22-0.57, P < 0.05), pathological type (mucinous adenocarcinoma/sig-ring cell carcinoma) (OR = 0.23, 95%CI: 0.13-0.41, P < 0.05), degree of tumor differentiation (low differentiation) (OR = 0.17, 95%CI: 0.10-0.31, P < 0.05), tumor stage (T3-4 stage) (OR = 0.11, 95%CI: 0.04-0.26, P < 0.05), gender and age were not risk factors for IMA root lymph node metastasis in rectal cancer (P > 0.05). CONCLUSION Preoperative CEA level, tumor location, tumor size, tumor pathologic type, tumor differentiation, and T stage were correlated with IMA root lymph node metastasis.
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Affiliation(s)
- Qi Wang
- Department of Colorectal Surgery, Shaoxing People’s Hospital, Shaoxing 312000, Zhejiang Province, China
| | - Fu-Xiang Zhu
- Department of Anorectal Surgery, People’s Hospital of Wuhan University, Wuhan 430060, Hubei Province, China
| | - Min Shi
- Department of Immunization Program, Shaoxing Center for Disease Control and Prevention, Shaoxing 312000, Zhejiang Province, China
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Ye YX, Yang L, Kang Z, Wang MQ, Xie XD, Lou KX, Bao J, Du M, Li ZX. Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer. World J Gastrointest Oncol 2024; 16:1849-1860. [PMID: 38764830 PMCID: PMC11099437 DOI: 10.4251/wjgo.v16.i5.1849] [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: 12/18/2023] [Revised: 01/23/2024] [Accepted: 03/04/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge. AIM To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs. METHODS In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis. RESULTS The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively. CONCLUSION The nomogram model showed the best performance in predicting metastasis of evaluable LNs.
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Affiliation(s)
- Yong-Xia Ye
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Liu Yang
- Department of Colorectal Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Zheng Kang
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Mei-Qin Wang
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Xiao-Dong Xie
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Ke-Xin Lou
- Department of Pathology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Jun Bao
- Colorectal Center, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Mei Du
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Zhe-Xuan Li
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
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Yang H, Jiang P, Dong L, Li P, Sun Y, Zhu S. Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer. Updates Surg 2023; 75:2225-2234. [PMID: 37556079 DOI: 10.1007/s13304-023-01618-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/26/2023] [Indexed: 08/10/2023]
Abstract
This study aimed to develop a radiomics model for predicting lateral lymph node (LLN) metastasis in rectal cancer patients using MR-T2WI and CT images, and assess its clinical value. This prospective study included rectal cancer patients with complete MR-T2WI and portal enhanced CT images who underwent LLN dissection at Tianjin Union Medical Center between June 2017 and November 2022. Primary lesions and LLN were segmented using 3D slicer. Radiomics features were extracted from the region of interest using pyradiomics in Python. Least absolute shrinkage and selection operator algorithm and backward stepwise regression were employed for feature selection. Three LLN metastasis radiomics prediction models were established via multivariable logistic regression analysis. The performance of the model was evaluated using receiver operating characteristic curve analysis, and the area under the curve (AUC), sensitivity, specificity were calculated for the training, validation, and test sets. A nomogram was constructed for visualization, and decision curve analysis (DCA) was performed to evaluate clinical value. We included 94 eligible patients in the analysis. For each patient, we extracted a total of 1344 radiomics features. The CT combined with MR-T2WI model had the highest AUC for all sets compared to CT and MR-T2WI models. AUC values for the CT combined with MR-T2WI model in the training, validation, and test sets were 0.957, 0.901, and 0.936, respectively. DCA revealed high prediction value for the combined MR-T2WI and CT model. A radiomics model based on CT and MR-T2WI data effectively predicted LLN metastasis in rectal cancer patients preoperatively.
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Affiliation(s)
- Hongjie Yang
- Nankai University, Tianjin, 300071, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | | | - Longchun Dong
- Department of Radiology, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Peng Li
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Yi Sun
- Nankai University, Tianjin, 300071, China.
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China.
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China.
| | - Siwei Zhu
- Nankai University, Tianjin, 300071, China.
- Department of Oncology, Tianjin Union Medical Center, Tianjin, 300121, China.
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China.
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Huang W, Lin R, Ke X, Ni S, Zhang Z, Tang L. Utility of Machine Learning Algorithms in Predicting Preoperative Lymph Node Metastasis in Patients With Rectal Cancer Based on Three-Dimensional Endorectal Ultrasound and Clinical and Laboratory Data. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:2615-2627. [PMID: 37401518 DOI: 10.1002/jum.16297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/07/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023]
Abstract
BACKGROUND We aimed to investigate the value of a machine learning (ML) algorithm in the preoperative prediction of lymph node metastasis in patients with rectal cancer. METHODS Based on the histopathological results, 126 rectal cancer patients were divided into two groups: lymph node metastasis-positive and metastasis-negative groups. We collected clinical and laboratory data, three-dimensional endorectal ultrasound (3D-ERUS) findings, and parameters of the tumor for between-group comparisons. We constructed a clinical prediction model based on the ML algorithm, which demonstrated the best diagnostic performance. Finally, we analyzed the diagnostic results and processes of the ML model. RESULTS Between the two groups, there were significant differences in serum carcinoembryonic antigen (CEA) levels, tumor length, tumor breadth, circumferential extent of the tumor, resistance index (RI), and ultrasound T-stage (P < 0.05). The extreme gradient boosting (XGBoost) model had the best comprehensive diagnostic performance for predicting lymph node metastasis in patients with rectal cancer. Compared with experienced radiologists, the XGBoost model showed significantly higher diagnostic value in predicting lymph node metastasis; the area under curve (AUC) value of the receiver operating characteristic (ROC) curve of the XGBoost model and experienced radiologists was 0.82 and 0.60, respectively. CONCLUSIONS Preoperative predictive utility in lymph node metastasis was demonstrated by the XGBoost model based on the 3D-ERUS finding and related clinical information. This could be useful in guiding clinical decisions on the selection of different treatment strategies.
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Affiliation(s)
- Weiqin Huang
- Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Ruoxuan Lin
- Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Xiaohui Ke
- Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Shixiong Ni
- Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Zhen Zhang
- Department of Ultrasound, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Lina Tang
- Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
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Jin Y, Wang Y, Zhu Y, Li W, Tang F, Liu S, Song B. A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study. Medicine (Baltimore) 2023; 102:e34865. [PMID: 37832071 PMCID: PMC10578668 DOI: 10.1097/md.0000000000034865] [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: 05/13/2023] [Accepted: 07/31/2023] [Indexed: 10/15/2023] Open
Abstract
The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.
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Affiliation(s)
- Yumei Jin
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
- Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China
| | - Yewu Wang
- Department of Joint and Sports Medicine, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Yonghua Zhu
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Wenzhi Li
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Fengqiong Tang
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Shengmei Liu
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
| | - Bin Song
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
- Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Sichuan University, West China Hospital, Sichuan, China
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Peng W, Qiao H, Mo L, Guo Y. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023; 13:1167289. [PMID: 37519802 PMCID: PMC10374255 DOI: 10.3389/fonc.2023.1167289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.
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Affiliation(s)
- Wei Peng
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Linfeng Mo
- School of Health and Medicine, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
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Zhang XY, Wei Q, Wu GG, Tang Q, Pan XF, Chen GQ, Zhang D, Dietrich CF, Cui XW. Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review. Front Oncol 2023; 13:1197447. [PMID: 37333814 PMCID: PMC10272784 DOI: 10.3389/fonc.2023.1197447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.
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Affiliation(s)
- Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wei
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ge-Ge Wu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Tang
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Di Zhang
- Department of Medical Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | | | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Wang K, Chen P, Feng B, Tu J, Hu Z, Zhang M, Yang J, Zhan Y, Yao J, Xu D. Machine learning prediction of prostate cancer from transrectal ultrasound video clips. Front Oncol 2022; 12:948662. [PMID: 36091110 PMCID: PMC9459141 DOI: 10.3389/fonc.2022.948662] [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: 05/20/2022] [Accepted: 08/08/2022] [Indexed: 11/14/2022] Open
Abstract
Objective To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI). Methods We systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis. Results The AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05). Conclusion The prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.
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Affiliation(s)
- Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Peizhe Chen
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Bojian Feng
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jing Tu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jie Yang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Ying Zhan
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jincao Yao
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- *Correspondence: Dong Xu, ; Jincao Yao,
| | - Dong Xu
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
- *Correspondence: Dong Xu, ; Jincao Yao,
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