101
|
Lee D, Wang D, Yang XR, Shi J, Landi MT, Zhu B. SUITOR: Selecting the number of mutational signatures through cross-validation. PLoS Comput Biol 2022; 18:e1009309. [PMID: 35377867 PMCID: PMC9009674 DOI: 10.1371/journal.pcbi.1009309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 04/14/2022] [Accepted: 03/09/2022] [Indexed: 11/19/2022] Open
Abstract
For de novo mutational signature analysis, the critical first step is to decide how many signatures should be expected in a cancer genomics study. An incorrect number could mislead downstream analyses. Here we present SUITOR (Selecting the nUmber of mutatIonal signaTures thrOugh cRoss-validation), an unsupervised cross-validation method that requires little assumptions and no numerical approximations to select the optimal number of signatures without overfitting the data. In vitro studies and in silico simulations demonstrated that SUITOR can correctly identify signatures, some of which were missed by other widely used methods. Applied to 2,540 whole-genome sequenced tumors across 22 cancer types, SUITOR selected signatures with the smallest prediction errors and almost all signatures of breast cancer selected by SUITOR were validated in an independent breast cancer study. SUITOR is a powerful tool to select the optimal number of mutational signatures, facilitating downstream analyses with etiological or therapeutic importance.
Collapse
Affiliation(s)
- Donghyuk Lee
- Department of Statistics, Pusan National University, Busan, Korea
| | - Difei Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Xiaohong R. Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| |
Collapse
|
102
|
Ding C, Shi T, Wu G, Man J, Han H, Cui Y. The anti-cancer role of microRNA-143 in papillary thyroid carcinoma by targeting high mobility group AT-hook 2. Bioengineered 2022; 13:6629-6640. [PMID: 35213273 PMCID: PMC8973723 DOI: 10.1080/21655979.2022.2044277] [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] [Indexed: 12/17/2022] Open
Abstract
Papillary thyroid carcinoma (PTC), a common thyroid cancer (TC) subtype, rapidly increases in occurrence. MicroRNAs (miRNAs), which are non-coding small RNAs, have been demonstrated to play a role in cancer pathogenic mechanisms. Although miR-143 is involved in suppressing certain malignant tumor progression, its biological role is unknown in PTC. The present study found that miR-143 levels were strongly lower in PTC patient samples and cell lines, implying that miR-143 may play a biological role in PTC. Down-regulation of miR-143 resulted in the increased expression of HMGA2. Furthermore, HMGA2 was found to be a direct target of miR-143. A dual-luciferase assay confirmed a direct binding site for miR-143 was confirmed on HMGA2 using a dual-luciferase assay. Next, over-expression of miR-143 suppressed PTC cell growth as analyzed by MTT, clone formation, and Ki-67 immunofluorescence staining assays. miR-143 mimics transfection downregulated the expression of PCNA, CDK4, CDK1, and Cyclin E1. In addition, wound healing and trans-well assays revealed that miR-143 up-regulation inhibited PTC cells invasion and migration. Co-transfection of HMGA2 expression vector restored HMGA2 expression and rescued PTC cells proliferation capability in miR-143 mimics transfected PTC cells, indicating that miR-143 inhibited PTC cells proliferation via HMGA2. These observations were also obtained in xenografts experiments in nude mice. Altogether, our study shed light on miR-143ʹs anti-cancer biological functions in PTC progression through targeting HMGA2, suggesting that restoration of miR-143 could be a potential therapeutic approach for PTC treatment.
Collapse
Affiliation(s)
- Chao Ding
- Departments of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Tiefeng Shi
- Departments of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Gang Wu
- Departments of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jianting Man
- Departments of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyu Han
- Departments of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yunfu Cui
- Departments of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| |
Collapse
|
103
|
Wu X, Li M, Cui XW, Xu G. Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer. Phys Med Biol 2022; 67. [PMID: 35042207 DOI: 10.1088/1361-6560/ac4c47] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/18/2022] [Indexed: 12/11/2022]
Abstract
Objective. The incidence of primary thyroid cancer has risen steadily over the past decades because of overdiagnosis and overtreatment through the improvement in imaging techniques for screening, especially in ultrasound examination. Metastatic status of lymph nodes is important for staging the type of primary thyroid cancer. Deep learning algorithms based on ultrasound images were thus developed to assist radiologists on the diagnosis of lymph node metastasis. The objective of this study is to integrate more clinical context (e.g., health records and various image modalities) into, and explore more interpretable patterns discovered by, deep learning algorithms for the prediction of lymph node metastasis in primary thyroid cancer patients.Approach. A deep multimodal learning network was developed in this study with a novel index proposed to compare the contribution of different modalities when making the predictions.Main results. The proposed multimodal network achieved an average F1 score of 0.888 and an average area under the receiver operating characteristic curve (AUC) value of 0.973 in two independent validation sets, and the performance was significantly better than that of three single-modality deep learning networks. Moreover, among three modalities used in this study, the deep multimodal learning network relied generally more on image modalities than the data modality of clinic records when making the predictions.Significance. Our work is beneficial to prospective clinic trials of radiologists on the diagnosis of lymph node metastasis in primary thyroid cancer, and will better help them understand how the predictions are made in deep multimodal learning algorithms.
Collapse
Affiliation(s)
- Xinglong Wu
- Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China.,Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Mengying Li
- Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| |
Collapse
|
104
|
Jiang H, Li A, Ji Z, Tian M, Zhang H. Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment. Mol Imaging Biol 2022; 24:537-549. [PMID: 35031945 DOI: 10.1007/s11307-022-01703-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/23/2021] [Accepted: 01/03/2022] [Indexed: 02/07/2023]
Abstract
Radiomic analysis provides information on the underlying tumour heterogeneity in lymphoma, reflecting the real-time evolution of malignancy. 2-Deoxy-2-[18F] fluoro-D-glucose positron emission tomography ([18F] FDG PET/CT) imaging is recommended before, during, and at the end of treatment for almost all lymphoma patients. This methodology offers high specificity and sensitivity, which can aid in accurate staging and assist in prompt treatment. Pretreatment [18F] FDG PET/CT-based radiomics facilitates improved diagnostic ability, guides individual treatment regimens, and boosts outcome prognosis based on heterogeneity as well as the biological, pathological, and metabolic status of the lymphoma. This technique has attracted considerable attention given its numerous applications in medicine. In the current review, we will briefly describe the basic radiomics workflow and types of radiomic features. Details of current applications of baseline [18F] FDG PET/CT-based radiomics in lymphoma will be discussed, such as differential diagnosis from other primary malignancies, diagnosis of bone marrow involvement, and response and prognostic prediction. We will also describe how this technique provides a unique noninvasive platform to assess tumour heterogeneity. Newly emerging PET radiotracers and multimodality technology will improve diagnostic specificity and further clarify tumor biology and even genetic variations in lymphoma, potentially promoting the development of precision medicine.
Collapse
Affiliation(s)
- Han Jiang
- PET-CT Center, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Ang Li
- PET-CT Center, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Zhongyou Ji
- PET-CT Center, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. .,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China. .,Key Laboratory of Medical Molecular Imaging of Zhejiang Province, 8 Hangzhou, Hangzhou, China.
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. .,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China. .,Key Laboratory of Medical Molecular Imaging of Zhejiang Province, 8 Hangzhou, Hangzhou, China. .,College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China. .,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.
| |
Collapse
|
105
|
AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
|
106
|
Wang XS, Xu XH, Jiang G, Ling YH, Ye TT, Zhao YW, Li K, Lei YT, Hu HQ, Chen MW, Wang H. Lack of Association Between Helicobacter pylori Infection and the Risk of Thyroid Nodule Types: A Multicenter Case-Control Studyin China. Front Cell Infect Microbiol 2022; 11:766427. [PMID: 34970506 PMCID: PMC8713074 DOI: 10.3389/fcimb.2021.766427] [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: 08/29/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
The prevalence of Helicobacter pylori infection is high worldwide, while numerous research has focused on unraveling the relationship between H. pylori infection and extragastric diseases. Although H. pylori infection has been associated with thyroid diseases, including thyroid nodule (TN), the relationship has mainly focused on potential physiological mechanisms and has not been validated by large population epidemiological investigations. Therefore, we thus designed a case-control study comprising participants who received regular health examination between 2017 and 2019. The cases and controls were diagnosed via ultrasound, while TN types were classified according to the guidelines of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). Moreover, H. pylori infection was determined by C14 urea breath test, while its relationship with TN type risk and severity was analyzed using binary and ordinal logistic regression analyses. A total of 43,411 participants, including 13,036 TN patients and 30,375 controls, were finally recruited in the study. The crude odds ratio (OR) was 1.07 in Model 1 (95% CI = 1.03-1.14) without adjustment compared to the H. pylori non-infection group. However, it was negative in Model 2 (OR = 1.02, 95% CI = 0.97-1.06) after being adjusted for gender, age, body mass index (BMI), and blood pressure and in Model 3 (OR = 1.01, 95% CI = 0.97-1.06) after being adjusted for total cholesterol, triglyceride, low-density lipoprotein, and high-density lipoprotein on the basis of Model 2. Control variables, including gender, age, BMI, and diastolic pressure, were significantly correlated with the risk of TN types. Additionally, ordinal logistic regression results revealed that H. pylori infection was positively correlated with malignant differentiation of TN (Model 1: OR = 1.06, 95% CI = 1.02-1.11), while Model 2 and Model 3 showed negative results (Model 2: OR = 1.01, 95% CI = 0.96-1.06; Model 3: OR = 1.01, 95% CI = 0.96-1.05). In conclusion, H. pylori infection was not significantly associated with both TN type risk and severity of its malignant differentiation. These findings provide relevant insights for correcting possible misconceptions regarding TN type pathogenesis and will help guide optimization of therapeutic strategies for thyroid diseases.
Collapse
Affiliation(s)
- Xiao-Song Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.,The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xi-Hai Xu
- The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Health Management Center, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Gang Jiang
- Department of Social Medicine and Health Management, School of Health Management, Anhui Medical University, Hefei, China
| | - Yu-Huan Ling
- Department of Social Medicine and Health Management, School of Health Management, Anhui Medical University, Hefei, China
| | - Tian-Tian Ye
- Department of Social Medicine and Health Management, School of Health Management, Anhui Medical University, Hefei, China
| | - Yun-Wu Zhao
- The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Kun Li
- Health Management Center, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yu-Ting Lei
- Health Management Center, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hua-Qing Hu
- The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ming-Wei Chen
- The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Heng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.,The First Affiliated Hospital of Anhui Medical University, Hefei, China
| |
Collapse
|
107
|
Lian KM, Lin T. Role of color-coded virtual touch tissue imaging in suspected thyroid nodules. Technol Health Care 2022; 30:673-682. [PMID: 34511520 DOI: 10.3233/thc-213156] [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: 10/20/2022]
Abstract
BACKGROUND Conventional ultrasound (US) is the most widely used imaging test for thyroid nodule surveillance. OBJECTIVE We used the color-coded virtual touch tissue imaging (VTI) in the Acoustic Radiation Force Impulse (ARFI) technique to assess the hardness of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) TR3-5 nodules. The ability of color-coded VTI (CV) to discriminate between benign and malignant nodules was investigated. METHODS In this retrospective study, US and CV were performed on 211 TR3-5 thyroid lesions in 181 consecutive patients. All nodules were operated on to obtain pathological results. A multivariate logistic regression model was chosen to integrate the data obtained from the US and CV. RESULTS The area under the receiver operating characteristic (ROC) curve for the model was 0.945 (95% CI, 0.914 to 0.976). The cutoff value of predictive probability for diagnosing malignant thyroid nodules was 10.64%, the sensitivity was 94.43%, and the specificity was 83.12%. Through comparing with US and CV, respectively, it had been observed that the regression model had the best performance (all P< 0.001). However, when the US was compared with CV, the difference was not significant (P= 0.3304). CONCLUSIONS A combination of US and CV should be recommended for suspected malignant thyroid nodules in clinical practice.
Collapse
Affiliation(s)
- Kai-Mei Lian
- Department of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Teng Lin
- Department of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| |
Collapse
|
108
|
Wen Q, Wang Z, Traverso A, Liu Y, Xu R, Feng Y, Qian L. A radiomics nomogram for the ultrasound-based evaluation of central cervical lymph node metastasis in papillary thyroid carcinoma. Front Endocrinol (Lausanne) 2022; 13:1064434. [PMID: 36531493 PMCID: PMC9748155 DOI: 10.3389/fendo.2022.1064434] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/14/2022] [Indexed: 12/03/2022] Open
Abstract
PURPOSE To develop and validate a radiomics nomogram based on ultrasound (US) to predict central cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). METHODS PTC patients with pathologically confirmed presence or absence of central cervical LN metastasis in our hospital between March 2021 and November 2021 were enrolled as the training cohort. Radiomics features were extracted from the preoperative US images, and a radiomics signature was constructed. Univariate and multivariate logistic regression analyses were used to screen out the independent risk factors, and a radiomics nomogram was established. The performance of the model was verified in the independent test cohort of PTC patients who underwent thyroidectomy and cervical LN dissection in our hospital from December 2021 to March 2022. RESULTS In the independent test cohort, the radiomics model based on long-axis cross-section and short-axis cross-section images outperformed the radiomics models based on either one of these sections (the area under the curve (AUC), 0.69 vs. 0.62 and 0.66). The radiomics signature consisted of 4 selected features. The US radiomics nomogram included the radiomics signature, age, gender, BRAF V600E mutation status, and extrathyroidal extension (ETE) status. In the independent test cohort, the AUC of the receiver operating curve(ROC) of this nomogram was 0.76, outperformingthe clinical model and the radiomics model (0.63 and 0.69, respectively), and also much better than preoperative US examination (AUC, 0.60). Decision curve analysis indicated that the radiomics nomogram was clinically useful. CONCLUSIONS This study presents an efficient and useful US radiomics nomogram that can provide comprehensive information to assist clinicians in the individualized preoperative prediction of central cervical LN metastasis in PTC patients.
Collapse
Affiliation(s)
- Quan Wen
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhixiang Wang
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Yujiang Liu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ruifang Xu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ying Feng
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- *Correspondence: Linxue Qian, ; Ying Feng,
| | - Linxue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- *Correspondence: Linxue Qian, ; Ying Feng,
| |
Collapse
|
109
|
Qian X, Zheng H, Xue K, Chen Z, Hu Z, Zhang L, Wan J. Recurrence Risk of Liver Cancer Post-hepatectomy Using Machine Learning and Study of Correlation With Immune Infiltration. Front Genet 2021; 12:733654. [PMID: 34956309 PMCID: PMC8692778 DOI: 10.3389/fgene.2021.733654] [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: 06/30/2021] [Accepted: 11/24/2021] [Indexed: 12/24/2022] Open
Abstract
Postoperative recurrence of liver cancer is the main obstacle to improving the survival rate of patients with liver cancer. We established an mRNA-based model to predict the risk of recurrence after hepatectomy for liver cancer and explored the relationship between immune infiltration and the risk of recurrence after hepatectomy for liver cancer. We performed a series of bioinformatics analyses on the gene expression profiles of patients with liver cancer, and selected 18 mRNAs as biomarkers for predicting the risk of recurrence of liver cancer using a machine learning method. At the same time, we evaluated the immune infiltration of the samples and conducted a joint analysis of the recurrence risk of liver cancer and found that B cell, B cell naive, T cell CD4+ memory resting, and T cell CD4+ were significantly correlated with the risk of postoperative recurrence of liver cancer. These results are helpful for early detection, intervention, and the individualized treatment of patients with liver cancer after surgical resection, and help to reveal the potential mechanism of liver cancer recurrence.
Collapse
Affiliation(s)
- Xiaowen Qian
- Department of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Huilin Zheng
- Department of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Ke Xue
- Department of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Zheng Chen
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, China
| | - Zhenhua Hu
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, China.,Key Laboratory of Combined Multi-Organ Transplantation, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Ministry of Public Health Key Laboratory of Organ Transplantation, Hangzhou, China.,Division of Hepatobiliary and Pancreatic Surgery, Yiwu Central Hospital, Yiwu, China
| | - Lei Zhang
- Department of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China.,Department of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Jian Wan
- Department of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| |
Collapse
|
110
|
Shi J, Dong Y, Jiang W, Qin F, Wang X, Cui L, Liu Y, Jin Y, Luo Y, Jiang X. MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study. Magn Reson Imaging 2021; 88:1-8. [PMID: 34968703 DOI: 10.1016/j.mri.2021.12.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/29/2021] [Accepted: 12/22/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses. METHODS We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters. RESULTS Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models. CONCLUSION The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.
Collapse
Affiliation(s)
- Jiaxin Shi
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Wenyan Jiang
- Scientific Research and Academic Department, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Fengying Qin
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Linpeng Cui
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China
| | - Yan Liu
- The Affiliated Reproductive Hospital of China Medical University, Liaoning Research Institute of Family Planning, Shenyang 110031, PR China
| | - Ying Jin
- The Affiliated Reproductive Hospital of China Medical University, Liaoning Research Institute of Family Planning, Shenyang 110031, PR China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China.
| |
Collapse
|
111
|
Zhao J, Wang H, Zhang Y, Wang R, Liu Q, Li J, Li X, Huang H, Zhang J, Zeng Z, Zhang J, Yi Z, Zeng F. Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer. Radiother Oncol 2021; 167:195-202. [PMID: 34968471 DOI: 10.1016/j.radonc.2021.12.031] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 12/03/2021] [Accepted: 12/21/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND PURPOSE The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC. MATERIALS AND METHODS A total of 423 CRC patients were divided into cohort 1 (training set, n = 238, testing set, n = 101) and cohort 2 (validation set, n = 84). Among them, 84 patients' tumour tissues were collected for RNA sequencing. The DPL features were extracted from enhanced venous-phase computed tomography of CRC using an autoencoder. A DPL model was constructed with the least absolute shrinkage and selection operator algorithm. Carcinoembryonic antigen and carbohydrate antigen 19-9 were incorporated into the DPL model to construct a combined model. The model performance was assessed by receiver operating characteristic curves, calibration curves and decision curves. The correlations between DPL features, which have been selected, and genes were analysed by Spearman correlation, and the genes correlated with DPL features were used to transcriptomic analysis. RESULTS The DPL model, integrated with 20 DPL features, showed a good discrimination performance in predicting the LNM, with areas under the curves (AUCs) of 0.79, 0.73 and 0.70 in the training set, testing set and validation set, respectively. The combined model had a better performance, with AUCs of 0.81, 0.77 and 0.73 in the three sets, respectively. Decision curve analysis confirmed the clinical application value of the DPL model and combined model. Furthermore, catabolic processes and immune-related pathways were identified and related with the selected DPL features. CONCLUSION This study presented a DPL model and a combined model for LNM prediction. We explored the potential genomic phenotypes related with DPL features. In addition, the model could potentially be utilized to facilitate the individualized prediction of LNM in CRC.
Collapse
Affiliation(s)
- Jiaojiao Zhao
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Han Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Yin Zhang
- Oncology Department, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Rui Wang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qin Liu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jie Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Xue Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Hanyu Huang
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Jie Zhang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Zhaoping Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jun Zhang
- General surgery, Dazhou Central Hospital, Dazhou, Sichuan, China.
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.
| |
Collapse
|
112
|
Bo L, Zhang Z, Jiang Z, Yang C, Huang P, Chen T, Wang Y, Yu G, Tan X, Cheng Q, Li D, Liu Z. Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features. Front Med (Lausanne) 2021; 8:748144. [PMID: 34869438 PMCID: PMC8636043 DOI: 10.3389/fmed.2021.748144] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/06/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy. Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively. Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance.
Collapse
Affiliation(s)
- Linlin Bo
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zijian Zhang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Chao Yang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Pu Huang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Tingyin Chen
- Department of Network Information Center, Xiangya Hospital, Centra South University, Changsha, China
| | - Yifan Wang
- Department of Network Information Center, Xiangya Hospital, Centra South University, Changsha, China
| | - Gang Yu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Xiao Tan
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
113
|
Guo Y, Xu J, Li X, Zheng L, Pan W, Qiu M, Mao S, Huang D, Yang X. Classification and Diagnosis of Residual Thyroid Tissue in SPECT Images Based on Fine-Tuning Deep Convolutional Neural Network. Front Oncol 2021; 11:762643. [PMID: 34778083 PMCID: PMC8581297 DOI: 10.3389/fonc.2021.762643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 10/06/2021] [Indexed: 12/28/2022] Open
Abstract
Patients with thyroid cancer will take a small dose of 131I after undergoing a total thyroidectomy. Single-photon emission computed tomography (SPECT) is used to diagnose whether thyroid tissue remains in the body. However, it is difficult for human eyes to observe the specificity of SPECT images in different categories, and it is difficult for doctors to accurately diagnose the residual thyroid tissue in patients based on SPECT images. At present, the research on the classification of thyroid tissue residues after thyroidectomy is still in a blank state. This paper proposes a ResNet-18 fine-tuning method based on the convolutional neural network model. First, preprocess the SPECT images to improve the image quality and remove background interference. Secondly, use the preprocessed image samples to fine-tune the pretrained ResNet-18 model to obtain better features and finally use the Softmax classifier to diagnose the residual thyroid tissue. The method has been tested on SPECT images of 446 patients collected by local hospital and compared with the widely used lightweight network SqueezeNet model and ShuffleNetV2 model. Due to the small data set, this paper conducted 10 random grouping experiments. Each experiment divided the data set into training set and test set at a ratio of 3:1. The accuracy and sensitivity rates of the model proposed in this paper are 96.69% and 94.75%, which are significantly higher than other models (p < 0.05). The specificity and precision rates are 99.6% and 99.96%, respectively, and there is no significant difference compared with other models. (p > 0.05). The area under the curve of the proposed model, SqueezeNet, and ShuffleNetv2 are 0.988 (95% CI, 0.941-1.000), 0.898 (95% CI, 0.819-0.951) (p = 0.0257), and 0.885 (95% CI, 0.803-0.941) (p = 0.0057) (p < 0.05). We prove that this thyroid tissue residue classification system can be used as a computer-aided diagnosis method to effectively improve the diagnostic accuracy of thyroid tissue residues. While more accurately diagnosing patients with residual thyroid tissue in the body, we try our best to avoid the occurrence of overtreatment, which reflects its potential clinical application value.
Collapse
Affiliation(s)
- Yinxiang Guo
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, China
- School of Science, Guangxi University of Science and Technology, Liuzhou, China
- Department of Public Health, School of Medicine, Guangxi University of Science and Technology, Liuzhou, China
| | - Jianing Xu
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, China
- School of Microelectronics and Materials Engineering, Guangxi University of Science and Technology, Liuzhou, China
| | - Xiangzhi Li
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, China
- Department of Public Health, School of Medicine, Guangxi University of Science and Technology, Liuzhou, China
| | - Lin Zheng
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, China
- School of Science, Guangxi University of Science and Technology, Liuzhou, China
- Department of Public Health, School of Medicine, Guangxi University of Science and Technology, Liuzhou, China
| | - Wei Pan
- Department of Nuclear Medicine, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China
| | - Meiting Qiu
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, China
| | - Shuyi Mao
- Department of Nuclear Medicine, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China
| | - Dongfei Huang
- Department of Nuclear Medicine, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China
| | - Xiaobo Yang
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, China
- Department of Public Health, School of Medicine, Guangxi University of Science and Technology, Liuzhou, China
| |
Collapse
|
114
|
Wu G, Jiang Z, Cai Y, Zhang X, Lv Y, Li S, Lin G, Bao Z, Liu S, Gu W. Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery. Front Neurosci 2021; 15:707944. [PMID: 34602967 PMCID: PMC8482874 DOI: 10.3389/fnins.2021.707944] [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: 05/14/2021] [Accepted: 08/13/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR. Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model. Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients’ brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively. Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.
Collapse
Affiliation(s)
- Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Zhaoshun Jiang
- Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yuxi Cai
- Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xixue Zhang
- Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yating Lv
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Zhijun Bao
- Department of Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, China
| | - Songbin Liu
- Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Weidong Gu
- Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China
| |
Collapse
|
115
|
Dai G, Zhang X, Liu W, Li Z, Wang G, Liu Y, Xiao Q, Duan L, Li J, Song X, Li G, Bai S. Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients. Front Oncol 2021; 11:721591. [PMID: 34595115 PMCID: PMC8476908 DOI: 10.3389/fonc.2021.721591] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/30/2021] [Indexed: 02/05/2023] Open
Abstract
Purpose To find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves' ophthalmopathy (GO). Methods Position errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input. Results The best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively. Conclusion ML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.
Collapse
Affiliation(s)
- Guyu Dai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xiangbin Zhang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Wenjie Liu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Zhibin Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Guangyu Wang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yaxin Liu
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Lian Duan
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyu Song
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
116
|
陈 可, 吕 国, 沈 浩, 王 月, 王 康, 杨 舒. [Establishment of a predictive model for central cervical lymph node metastasis of papillary thyroid carcinoma based on ACR TI-RADS score and evaluation of its diagnostic efficacy]. LIN CHUANG ER BI YAN HOU TOU JING WAI KE ZA ZHI = JOURNAL OF CLINICAL OTORHINOLARYNGOLOGY, HEAD, AND NECK SURGERY 2021; 35:773-778. [PMID: 34628827 PMCID: PMC10127827 DOI: 10.13201/j.issn.2096-7993.2021.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Indexed: 06/13/2023]
Abstract
Objective:To establish a predictive model for central lymph node metastasis(CLNM) of papillary thyroid carcinoma(PTC) based on ACR TI-RADS grades(ATR model) and evaluate its diagnostic efficacy. Methods:A total of 319 patients with PTC diagnosed from January 2019 to May 2020 were included, including 366 nodules were used as the modeling cohort to construct the risk prediction model. A total of 105 PTC patients with 121 nodules from June to August 2020 were included as the external validation cohort. The C-index of the model was calculated and the Hosmer-Lemeshow goodness-of-fit test was performed to compare the diagnostic efficiency of ACR model and those conventional imaging models. Results:The ATR model, Y=-3.719+0.765×gender+1.094×multifocality+0.08×maximum diameter+0.266×ACR TI-RADS score. In the training set, validation set and external validation cohort, the model C-index was 0.758(95%CI: 0.699-0.817), 0.717(95%CI: 0.619-0.815) and 0.756(95%CI: 0.671-0.840), respectively. The Hosmer-Lemeshow goodness of fit test showed that the prediction rate of the model was consistent with the actual incidence rate(P=0.918; P=0.581; P=0.366). With ≥0.434 as the diagnostic threshold, the model had the highest diagnostic efficacy (sensitivity: 86.0%, specificity: 56.3%, Youden index: 0.423). In the external validation cohort, there was no significant difference between C-US and CT(P>0.05). Compared with C-US and CT, the sensitivity(66.1% vs 16.1%, P<0.001; 66.1% vs 9.7%, P<0.001) and accuracy(68.6% vs 55.4%, P=0.041; 68.6% vs 52.9%, P=0.012) of ATR model were higher, and the negative predictive value was higher than that of CT(66.7% vs 50.9%, P=0.042), but there was no difference between ATR model and C-US(66.7% vs 52.3%, P=0.066); There was no significant difference among the three positive predictive values(70.7% vs 83.3%, P=0.211; 70.7% vs 85.7%, P=0.319), but the specificity of the model was low (71.2% vs 96.6%, P=0.001; 71.2% vs 98.3%, P<0.001). Conclusion:The predictive model based on ACR TI-RADS grades can predict CLNM of PTC more accurately and sensitively than traditional imaging examination.
Collapse
Affiliation(s)
- 可悦 陈
- 福建医科大学附属漳州市医院超声医学科(福建漳州,363000)Department of Ultrasound, Zhangzhou Hospital Affiliated to Fujian Medical University, Zhangzhou, 363000, China
| | - 国荣 吕
- 教育部泉州医学高等专科学校Department of Clinical Medicine, Quanzhou Medical College
| | - 浩霖 沈
- 福建医科大学附属漳州市医院超声医学科(福建漳州,363000)Department of Ultrasound, Zhangzhou Hospital Affiliated to Fujian Medical University, Zhangzhou, 363000, China
| | - 月桂 王
- 福建医科大学附属漳州市医院超声医学科(福建漳州,363000)Department of Ultrasound, Zhangzhou Hospital Affiliated to Fujian Medical University, Zhangzhou, 363000, China
| | - 康健 王
- 福建医科大学附属漳州市医院超声医学科(福建漳州,363000)Department of Ultrasound, Zhangzhou Hospital Affiliated to Fujian Medical University, Zhangzhou, 363000, China
| | - 舒萍 杨
- 福建医科大学附属漳州市医院超声医学科(福建漳州,363000)Department of Ultrasound, Zhangzhou Hospital Affiliated to Fujian Medical University, Zhangzhou, 363000, China
| |
Collapse
|
117
|
Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
Collapse
Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| |
Collapse
|
118
|
Lens Opacities Classification System III-based artificial intelligence program for automatic cataract grading. J Cataract Refract Surg 2021; 48:528-534. [PMID: 34433780 DOI: 10.1097/j.jcrs.0000000000000790] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/15/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCSIII). SETTING Eye and Ear, Nose, and Throat (EENT) Hospital, Fudan University, Shanghai, China. DESIGN AI training. METHODS Advanced deep-learning algorithms, including Faster R-CNN and ResNet, were applied to the localization and analysis of the region of interest. An internal dataset from the EENT Hospital of Fudan University and an external dataset from the Pujiang Eye Study were used for AI training, validation, and testing. The datasets were automatically labeled on the AI platform in terms of the capture mode and cataract grading based on the LOCSIII system. RESULTS The AI program showed reliable capture mode recognition, grading, and referral capability for nuclear and cortical cataract grading. In the internal and external datasets, 99.4% and 100% of automatic nuclear grading, respectively, had an absolute prediction error of ≤ 1.0, with a satisfactory referral capability (area under the curve [AUC]: 0.983 for the internal dataset; 0.977 for the external dataset). 75.0% (internal dataset) and 93.5% (external dataset) of the automatic cortical grades had an absolute prediction error of ≤ 1.0, with AUCs of 0.855 and 0.795 for referral, respectively. Good consistency was observed between automatic and manual grading when both nuclear and cortical cataracts were evaluated. However, automatic grading of posterior subcapsular cataracts was impractical. CONCLUSIONS The AI program proposed in this study shows robust grading and diagnostic performance for both nuclear and cortical cataracts, based on LOCSIII.
Collapse
|
119
|
Xia E, Chi Y, Jin L, Shen Y, Hirachan S, Bhandari A, Wang O. Preoperative prediction of lymph node metastasis in patients with papillary thyroid carcinoma by an artificial intelligence algorithm. Am J Transl Res 2021; 13:7695-7704. [PMID: 34377246 PMCID: PMC8340231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 05/13/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND It is necessary to identify patients at risk of developing lymph node metastasis prior to papillary thyroid carcinoma (PTC) surgery. This can be challenging due to limiting factors, and an artificial intelligence algorithm may be a viable option. OBJECTIVE In this study, we aimed to evaluate whether combining an artificial intelligence algorithm (support vector machine and probabilistic neural network) and clinico-pathologic data can preoperatively predict lymph node metastasis of papillary thyroid carcinoma (PTC). METHODS We retrospectively examined 251 PTCs with lymph node metastasis and 194 PTCs without lymph node metastasis. The artificial intelligence algorithm included the support vector machine (SVM) and the probabilistic neural network (PNN). RESULTS The ACR TI-RADS (Thyroid Imaging, Reporting and Data System), number of tumours, no well-defined margin, lymph node status and rim calcification on ultrasonography (US), age, sex, tumour size, and presence of Hashimoto's thyroiditis were significantly more frequent among PTCs with central lymph node metastasis than those without metastasis (P<0.05). The PNN classifier revealed an F1 score of 0.88 on the central lymph node metastasis test set. The SVM classifier revealed an F1 score of 0.93 on the lateral lymph node metastasis test set. Our study demonstrates that combining artificial intelligence algorithms and clinico-pathologic data can effectively predict the lymph node metastasis of papillary thyroid carcinoma prior to surgery.
Collapse
Affiliation(s)
- Erjie Xia
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang Province, People’s Republic of China
| | - Yili Chi
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang Province, People’s Republic of China
| | - Linli Jin
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang Province, People’s Republic of China
| | - Yanyan Shen
- Department of Breast Surgery, The Second Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang Province, People’s Republic of China
| | - Suzita Hirachan
- Department of Surgery, Breast Unit, Tribhuvan University Teaching HospitalKathmandu, Nepal
| | - Adheesh Bhandari
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang Province, People’s Republic of China
| | - Ouchen Wang
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang Province, People’s Republic of China
| |
Collapse
|
120
|
Qin H, Que Q, Lin P, Li X, Wang XR, He Y, Chen JQ, Yang H. Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery. Radiol Med 2021; 126:1312-1327. [PMID: 34236572 DOI: 10.1007/s11547-021-01393-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 06/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively. MATERIALS AND METHODS A retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models. RESULTS The area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC. CONCLUSION Our combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.
Collapse
Affiliation(s)
- Hui Qin
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China
| | - Qiao Que
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China
| | - Peng Lin
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China
| | - Xin Li
- Department of GE Healthcare Global Research, GE Healthcare, Shanghai, 201203, People's Republic of China
| | - Xin-Rong Wang
- Department of GE Healthcare Global Research, GE Healthcare, Shanghai, 201203, People's Republic of China
| | - Yun He
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China
| | - Jun-Qiang Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China.
| | - Hong Yang
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China.
| |
Collapse
|
121
|
Tong Y, Sun P, Yong J, Zhang H, Huang Y, Guo Y, Yu J, Zhou S, Wang Y, Wang Y, Ji Q, Wang Y, Chang C. Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study. Front Oncol 2021; 11:682998. [PMID: 34268116 PMCID: PMC8276635 DOI: 10.3389/fonc.2021.682998] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/09/2021] [Indexed: 12/19/2022] Open
Abstract
Background Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors. Methods In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis. Results The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001). Conclusions The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.
Collapse
Affiliation(s)
- Yuyang Tong
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Department of Surgical Oncology, The Ohio State University, Columbus, OH, United States
| | - Peixuan Sun
- Diagnostic Imaging Center, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juanjuan Yong
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hongbo Zhang
- Pharmaceutical Sciences Laboratory, Åbo Akademi University, Turku, Finland.,Turku Biosciences Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Yunxia Huang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Guo
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yulong Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yu Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qinghai Ji
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|
122
|
Zou Y, Shi Y, Liu J, Cui G, Yang Z, Liu M, Sun F. A Comparative Analysis of Six Machine Learning Models Based on Ultrasound to Distinguish the Possibility of Central Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma. Front Oncol 2021; 11:656127. [PMID: 34254039 PMCID: PMC8270759 DOI: 10.3389/fonc.2021.656127] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/06/2021] [Indexed: 12/23/2022] Open
Abstract
Current approaches to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) have failed to identify patients who would benefit from preventive treatment. Machine learning has offered the opportunity to improve accuracy by comparing the different algorithms. We assessed which machine learning algorithm can best improve CLNM prediction. This retrospective study used routine ultrasound data of 1,364 PTC patients. Six machine learning algorithms were compared to predict the possibility of CLNM. Predictive accuracy was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). The patients were randomly split into the training (70%), validation (15%), and test (15%) data sets. Random forest (RF) led to the best diagnostic model in the test cohort (AUC 0.731 ± 0.036, 95% confidence interval: 0.664–0.791). The diagnostic performance of the RF algorithm was most dependent on the following five top-rank features: extrathyroidal extension (27.597), age (17.275), T stage (15.058), shape (13.474), and multifocality (12.929). In conclusion, this study demonstrated promise for integrating machine learning methods into clinical decision-making processes, though these would need to be tested prospectively.
Collapse
Affiliation(s)
- Ying Zou
- Department of Radiology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yan Shi
- Department of Ultrasonography, Binzhou Medical University Hospital, Binzhou City, China
| | - Jihua Liu
- Department of Radiology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Guanghe Cui
- Department of Ultrasonography, Binzhou Medical University Hospital, Binzhou City, China
| | - Zhi Yang
- Department of Ultrasonography, Binzhou Medical University Hospital, Binzhou City, China
| | - Meiling Liu
- Department of Ultrasonography, Binzhou Medical University Hospital, Binzhou City, China
| | - Fang Sun
- Department of Ultrasonography, Binzhou Medical University Hospital, Binzhou City, China
| |
Collapse
|
123
|
Zhou H, Jiang T, Li Q, Zhang C, Zhang C, Liu Y, Cao J, Sun Y, Jin P, Luo J, Pan M, Huang P. US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients. Front Oncol 2021; 11:672055. [PMID: 34168992 PMCID: PMC8217663 DOI: 10.3389/fonc.2021.672055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/20/2021] [Indexed: 12/12/2022] Open
Abstract
The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.
Collapse
Affiliation(s)
- Hang Zhou
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Jiang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qunying Li
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yajing Liu
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Cao
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Sun
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peile Jin
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Luo
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minqiang Pan
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Pintong Huang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
124
|
Li Z, Xu K, Xu L, Dai J, Jin K, Zhu Y, Yang Y, Jiang G. Predictive Value of Folate Receptor-Positive Circulating Tumor Cells for the Preoperative Diagnosis of Lymph Node Metastasis in Patients with Lung Adenocarcinoma. SMALL METHODS 2021; 5:e2100152. [PMID: 34927918 DOI: 10.1002/smtd.202100152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/18/2021] [Indexed: 06/14/2023]
Abstract
Noninvasive assessments of the risk of lymph node metastasis (LNM) in patients with lung adenocarcinoma (LAD) are of great value for selecting individualized treatment options. However, the diagnostic accuracies of different preoperative LN evaluation methods in routine clinical practice are not satisfactory. Here, an assessment to detect folate receptor (FR)-positive circulating tumor cells (CTCs) based on ligand-targeted enzyme-linked polymerization is established. FR-positive CTCs have the potential to improve the specificity and sensitivity of diagnosing LNM in lung cancer patients. The addition of CTC level improved the diagnostic efficiency of the initial prediction model that comprises other clinical parameters. A nomogram for predicting preoperative LNM is established, which showed good prediction and calibration capacities and achieved an average area under the curve of 0.786. Good correlations are observed between the CTC level and nodal classifications, such as the number of positive LNs and the ratio of the number of positive LNs to removed LNs (LN ratio or LNR). The ligand-targeted enzyme-linked polymerization-assisted assessment of CTCs enables noninvasive detection and has a useful predictive value for the preoperative diagnosis of LNM in patients with LAD.
Collapse
Affiliation(s)
- Zhao Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No.507 Zhengmin Road, Shanghai, 200433, China
| | - Ke Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, No. 151 Yanjiang Road, Guangzhou, 510120, China
| | - Lekai Xu
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, No. 1 Beiertiao, Zhongguancun, Beijing, 100109, China
| | - Jie Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No.507 Zhengmin Road, Shanghai, 200433, China
| | - Kaiqi Jin
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No.507 Zhengmin Road, Shanghai, 200433, China
| | - Yuming Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No.507 Zhengmin Road, Shanghai, 200433, China
| | - Yang Yang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No.507 Zhengmin Road, Shanghai, 200433, China
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No.507 Zhengmin Road, Shanghai, 200433, China
| |
Collapse
|
125
|
Cao Y, Zhong X, Diao W, Mu J, Cheng Y, Jia Z. Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations. Cancers (Basel) 2021; 13:2436. [PMID: 34069887 PMCID: PMC8157383 DOI: 10.3390/cancers13102436] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 02/05/2023] Open
Abstract
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.
Collapse
Affiliation(s)
- Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Xiao Zhong
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Wei Diao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Jingshi Mu
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Yue Cheng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610040, China;
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| |
Collapse
|
126
|
Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021; 8:031902. [PMID: 33768134 PMCID: PMC7985651 DOI: 10.1117/1.jmi.8.3.031902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.
Collapse
Affiliation(s)
- James T. T. Coates
- Massachusetts General Hospital & Harvard Medical School, Center for Cancer Research, Boston, Massachusetts, United States
| | - Giacomo Pirovano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Issam El Naqa
- Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
| |
Collapse
|
127
|
Sun P, Mak TK, Li J, Wang C. Endoscopic Left Central Lymph Node Dissection of Thyroid Cancer: Safe, Feasible, and Relatively Easy. Surg Innov 2021; 28:747-753. [PMID: 33830818 DOI: 10.1177/1553350620983640] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Purpose. The purpose of this study was to explore the feasibility of left central lymph node dissection (CLND) in endoscopic thyroidectomy via chest-breast approach (ETCB). Methods. Retrospective analysis of 57 cases of left CLND (group A) via ETCB, 35 cases of open left CLND (group B), and 90 cases of right CLND via ETCB (Group C) were performed from October 2014 to October 2019. Surgical data, complications, and follow-up data were compared among group A and group B, group A and group C, respectively. Results. There were no significant differences between group A and group B in intraoperative blood loss, tumor size, lymph node (LN) metastasis rate, dissected LN number, metastatic LN number, serum thyroglobulin (sTg), radioactive iodine uptake (RAIU), radioactive technetium uptake (RATU), radionuclide imaging of the residual area (RITRA), and radionuclide imaging of suspicious lymph node metastasis (RISLNM). There were no significant differences between group A and group C in age, operation time, intraoperative blood loss, postoperative hospital stay, tumor size, LN metastasis rate, dissected LN number, metastatic LN number, hypoparathyroidism, sTg, RAIU, RATU, RITRA, and RISLNM. There were 5 cases of temporary recurrent laryngeal nerve (RLN) palsy and 1 case of recurrence in group C. Besides, 1 case of lymphatic leakage was in group A. Conclusion. For selected cases, endoscopic left CLND is safe, feasible, efficient, and more easier than endoscopic right CLND.
Collapse
Affiliation(s)
- Peng Sun
- Department of Thyroid Surgery, 162698Jinan University First Affiliated Hospital, Guangzhou, China
| | - Tsz Kin Mak
- Department of General Surgery, 162698Jinan University First Affiliated Hospital, Guangzhou, China
| | - Jinyi Li
- Department of Thyroid Surgery, 162698Jinan University First Affiliated Hospital, Guangzhou, China
| | - Cunchuan Wang
- Department of General Surgery, 162698Jinan University First Affiliated Hospital, Guangzhou, China
| |
Collapse
|
128
|
Masuda T, Nakaura T, Funama Y, Sugino K, Sato T, Yoshiura T, Baba Y, Awai K. Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies. Radiography (Lond) 2021; 27:920-926. [PMID: 33762147 DOI: 10.1016/j.radi.2021.03.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/14/2021] [Accepted: 03/02/2021] [Indexed: 02/07/2023]
Abstract
INTRODUCTION We compared the diagnostic performance of morphological methods such as the major axis, the minor axis, the volume and sphericity and of machine learning with texture analysis in the identification of lymph node metastasis in patients with thyroid cancer who had undergone contrast-enhanced CT studies. METHODS We sampled 772 lymph nodes with histology defined tissue types (84 metastatic and 688 benign lymph nodes) that were visualised on CT images of 117 patients. A support vector machine (SVM), free programming software (Python), and the scikit-learn machine learning library were used to discriminate metastatic-from benign lymph nodes. We assessed 96 texture and 4 morphological features (major axis, minor axis, volume, sphericity) that were reported useful for the differentiation between metastatic and benign lymph nodes on CT images. The area under the curve (AUC) obtained by receiver operating characteristic analysis of univariate logistic regression and SVM classifiers were calculated for the training and testing datasets. RESULTS The AUC for all classifiers in training and testing datasets was 0.96 and 0.86, at the SVM for machine learning. When we applied conventional methods to the training and testing datasets, the AUCs were 0.63 and 0.48 for the major axis, 0.70 and 0.44 for the minor axis, 0.66 and 0.43 for the volume, and 0.69 and 0.54 for sphericity, respectively. The SVM using texture features yielded significantly higher AUCs than univariate logistic regression models using morphological features (p = 0.001). CONCLUSION For the identification of metastatic lymph nodes from thyroid cancer on contrast-enhanced CT images, machine learning combined with texture analysis was superior to conventional diagnostic methods with the morphological parameters. IMPLICATIONS FOR PRACTICE Our findings suggest that in patients with thyroid cancer and suspected lymph node metastasis who undergo contrast-enhanced CT studies, machine learning using texture analysis is high diagnostic value for the identification of metastatic lymph nodes.
Collapse
Affiliation(s)
- T Masuda
- Department of Radiological Technology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan; Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.
| | - T Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan
| | - Y Funama
- Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - K Sugino
- Department of Surgery, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan
| | - T Sato
- Department of Diagnostic Radiology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan
| | - T Yoshiura
- Department of Radiological Technology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan
| | - Y Baba
- Saitama Medical University International Medical Center, 1397-1, Yamane, Hidaka-City, Saitama-Pref 350-1298, Japan
| | - K Awai
- Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| |
Collapse
|
129
|
Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Front Oncol 2021; 10:604051. [PMID: 33634025 PMCID: PMC7899964 DOI: 10.3389/fonc.2020.604051] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/21/2020] [Indexed: 12/12/2022] Open
Abstract
Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.
Collapse
Affiliation(s)
- Ling-Rui Li
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Du
- School of Computer Science, Wuhan University, Wuhan, China.,Institute of Artificial Intelligence, Wuhan University, Wuhan, China
| | - Han-Qing Liu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
130
|
Lee EK, Park YJ. Best Achievements in Clinical Thyroidology in 2020. Endocrinol Metab (Seoul) 2021; 36:30-35. [PMID: 33677923 PMCID: PMC7937845 DOI: 10.3803/enm.2021.103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 01/28/2021] [Indexed: 12/12/2022] Open
Abstract
This review highlights the most interesting research in thyroidology conducted in 2020. The publications of interest discussed below dealt with the following topics: thyroid dysfunction, risk of thyroid cancer, molecular diagnostics and new therapeutics for thyroid cancer, and thyroid disease in the coronavirus disease 2019 pandemic era.
Collapse
Affiliation(s)
- Eun Kyung Lee
- Center for Thyroid Cancer, National Cancer Center, Goyang, Korea
- Department of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Korea
| | - Young Joo Park
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| |
Collapse
|
131
|
Heng Y, Feng S, Yang Z, Cai W, Qiu W, Tao L. Features of Lymph Node Metastasis and Structural Recurrence in Papillary Thyroid Carcinoma Located in the Upper Portion of the Thyroid: A Retrospective Cohort Study. Front Endocrinol (Lausanne) 2021; 12:793997. [PMID: 35145480 PMCID: PMC8823057 DOI: 10.3389/fendo.2021.793997] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/27/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND This study aims to reveal the features of lymph node metastasis (LNM) and recurrence in papillary thyroid carcinoma (PTC) tumors located in the upper portion of the thyroid. METHODS A total of 1075 PTC patients were retrospectively reviewed, including 314 patients with a tumor in the upper portion of the thyroid. Another 103 PTC patients with upper portion diagonsis from three clinical centers were included for external validation. RESULTS The results showed no difference between the patients with a tumor in the upper portion of the thyroid and those with a tumor in the non-upper portion in terms of overall LNM rates. However, patients with a tumor in the upper portion were significantly more prone to LLNM and exhibited a significantly worse recurrence outcome than those with a tumor in other subregions. Multivariate analysis showed that four factors-age no more than 40, maximum tumor diameter no less than1.0 cm, the presence of thyroid capsular invasion, and tumor with ipsilateral nodular goiter-were independent risk factors for LLNM of the tumor in the upper thyroid. A predictive risk-scoring model was established based on these factors. CONCLUSIONS Patients with PTC located in the upper portion may have an exclusive lymphatic drainage pathway to the lateral neck region and are more prone to suffer from LLNM and tumor recurrence than those with a tumor located in other subregions. A new postoperative strategy selection flow chart was established based on our newly created risk-scoring model that can effectively predict the individualized possibility of LLNM for PTC patients with a tumor in the upper portion.
Collapse
Affiliation(s)
- Yu Heng
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Siqi Feng
- Department of General Surgery, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Zheyu Yang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei Cai
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Lei Tao, ; Wei Cai, ; Weihua Qiu,
| | - Weihua Qiu
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Lei Tao, ; Wei Cai, ; Weihua Qiu,
| | - Lei Tao
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China
- *Correspondence: Lei Tao, ; Wei Cai, ; Weihua Qiu,
| |
Collapse
|
132
|
Li J, Wu X, Mao N, Zheng G, Zhang H, Mou Y, Jia C, Mi J, Song X. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study. Front Endocrinol (Lausanne) 2021; 12:741698. [PMID: 34745008 PMCID: PMC8567994 DOI: 10.3389/fendo.2021.741698] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/04/2021] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES This study aimed to develop a computed tomography (CT)-based radiomics model to predict central lymph node metastases (CLNM) preoperatively in patients with papillary thyroid carcinoma (PTC). METHODS In this retrospective study, 678 patients with PTC were enrolled from Yantai Yuhuangding Hot3spital (n=605) and the Affiliated Hospital of Binzhou Medical University (n=73) within August 2010 to December 2020. The patients were randomly divided into a training set (n=423), an internal test set (n=182), and an external test set (n=73). Radiomics features of each patient were extracted from preoperative plain scan and contrast-enhanced CT images (arterial and venous phases). One-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator algorithm were used for feature selection. The K-nearest neighbor, logistics regression, decision tree, linear-support vector machine (linear-SVM), Gaussian-SVM, and polynomial-SVM algorithms were used to establish radiomics models for CLNM prediction. The clinical risk factors were selected by ANOVA and multivariate logistic regression. Incorporated with clinical risk factors, a combined radiomics model was established for the preoperative prediction of CLNM in patients with PTCs. The performance of the combined radiomics model was evaluated using the receiver operating characteristic (ROC) and calibration curves in the training and test sets. The clinical usefulness was evaluated through decision curve analysis (DCA). RESULTS A total of 4227 radiomic features were extracted from the CT images of each patient, and 14 non-zero coefficient features associated with CLNM were selected. Four clinical variables (sex, age, tumor diameter, and CT-reported lymph node status) were significantly associated with CLNM. Linear-SVM led to the best prediction model, which incorporated radiomic features and clinical risk factors. Areas under the ROC curves of 0.747 (95% confidence interval [CI] 0.706-0.782), 0.710 (95% CI 0.634-0.786), and 0.764 (95% CI 0.654-0.875) were obtained in the training, internal, and external test sets, respectively. The linear-SVM algorithm also showed better sensitivity (0.702 [95% CI 0.600-0.790] vs. 0.477 [95% CI 0.409-0.545]) and accuracy (0.670 [95% CI 0.600-0.738] vs. 0.642 [95% CI 0.569-0.712]) than an experienced radiologist in the internal test set in the combined radiomics model. The calibration plot reflected a favorable agreement between the actual and estimated probabilities of CLNM. The DCA indicated the clinical usefulness of the combined radiomics model. CONCLUSION The combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.
Collapse
Affiliation(s)
- Jingjing Li
- Second Clinical Medicine College, Binzhou Medical University, Yantai, China
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xinxin Wu
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Guibin Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Yantai, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
| | - Yakui Mou
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Chuanliang Jia
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Jia Mi
- Precision Medicine Research Center, Binzhou Medical University, Yantai, China
- *Correspondence: Xicheng Song, ; Jia Mi,
| | - Xicheng Song
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
- *Correspondence: Xicheng Song, ; Jia Mi,
| |
Collapse
|
133
|
Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
|