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Zhao M, Li Z, Gu X, Yang X, Gao Z, Wang S, Fu J. The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis. J Gynecol Oncol 2024; 36:36.e26. [PMID: 39058366 DOI: 10.3802/jgo.2025.36.e26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/17/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%-87%), specificity of 74% (95% CI=69%-79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82-0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity.This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.
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Affiliation(s)
- Mengli Zhao
- Department of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Li
- ENT institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Xiaowei Gu
- Department of Radiation Oncology, Jiangyin Hospital Affiliated to Nantong University, Jiangyin, China
| | - Xiaojing Yang
- Department of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongrong Gao
- Department of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shanshan Wang
- Department of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Fu
- Department of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Wu L, Li S, Li S, Lin Y, Wei D. Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1416378. [PMID: 39026971 PMCID: PMC11254676 DOI: 10.3389/fonc.2024.1416378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024] Open
Abstract
Background The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC). Methods A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC. Results A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively. Conclusion MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.
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Affiliation(s)
| | | | | | | | - Dayou Wei
- Department of Medical Ultrasound, Maoming People’s Hospital, Maoming, Guangdong, China
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Sheen H, Cho W, Kim C, Han MC, Kim H, Lee H, Kim DW, Kim JS, Hong CS. Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis. Phys Med 2024; 123:103414. [PMID: 38906047 DOI: 10.1016/j.ejmp.2024.103414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 06/23/2024] Open
Abstract
PURPOSE This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION The protocol of this study was registered on PROSPERO (CRD42023426565).
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Affiliation(s)
- Heesoon Sheen
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, South Korea
| | - Wonyoung Cho
- Research Institute, Oncosoft Inc., Seoul, South Korea
| | - Changhwan Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Min Cheol Han
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Hojin Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Ho Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Dong Wook Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Research Institute, Oncosoft Inc., Seoul, South Korea; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
| | - Chae-Seon Hong
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
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Zhang J, Fang J, Xu Y, Si G. How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives. Diagnostics (Basel) 2024; 14:1393. [PMID: 39001283 PMCID: PMC11241154 DOI: 10.3390/diagnostics14131393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.
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Affiliation(s)
- Jiaming Zhang
- Department of Radiology, Clinical Medical College, Southwest Medical University, Luzhou 646699, China; (J.Z.); (J.F.)
| | - Jiayi Fang
- Department of Radiology, Clinical Medical College, Southwest Medical University, Luzhou 646699, China; (J.Z.); (J.F.)
| | - Yanneng Xu
- Department of Radiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou 646699, China;
| | - Guangyan Si
- Department of Radiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou 646699, China;
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Guo YX, Lan JL, Song YX, Bu WQ, Tang Y, Wu ZX, Meng HT, Wu D, Yang H, Guo YC. Different machine learning methods based on maxillary sinus in sex estimation for northwestern Chinese Han population. Int J Legal Med 2024:10.1007/s00414-024-03255-7. [PMID: 38760564 DOI: 10.1007/s00414-024-03255-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND & OBJECTIVE Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex estimation. Due to the complex nature of sex estimation, several studies have been conducted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. MATERIAL & METHODS In this study, linear data of the maxillary sinus in the population of northwest China by using Cone-Beam Computed Tomography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex estimation with R 4.3.1. CBCT images from 477 samples of Han population (75 males and 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of both the left and right maxillary sinuses and distance of lateral wall between two maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. RESULTS Overall, the accuracy of the sex estimation for individuals over 18 years old on the testing set was 77.78%, with a slightly higher accuracy rate for males at 78.12% compared to females at 77.42%. However, accuracy of sex estimation for individuals under 18 was challenging. In comparison to logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorporating age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. Meanwhile, all variables showed a linear correlation with age. CONCLUSION The linear measurements of the maxillary sinus could be a valuable tool for sex estimation in individuals aged 18 and over. A robust RF model has been developed for sex estimation within the Han population residing in the northwestern region of China. The accuracy of sex estimation could be higher when age is used as a predictive variable.
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Affiliation(s)
- Yu-Xin Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Jun-Long Lan
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Yu-Xuan Song
- College of Forensic Science, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Wen-Qin Bu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Yu Tang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Zi-Xuan Wu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Hao-Tian Meng
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Di Wu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Hui Yang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China
| | - Yu-Cheng Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China.
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China.
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Li W, Sun Y, Zhang G, Yang Q, Wang B, Ma X, Zhang H. Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnu-net. BMC Pediatr 2024; 24:321. [PMID: 38724944 PMCID: PMC11080230 DOI: 10.1186/s12887-024-04775-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 04/18/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Radiologic volumetric evaluation of Wilms' tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning. METHODS We included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to assess the performance. Next, we optimized the automatic segmentation results based on the ratio of the three-dimensional diameter of the lesion to improve the performance of volumetric assessment. RESULTS The DSC and HD95 was 0.83 ± 0.22 and 10.50 ± 8.98 mm. The absolute difference and percentage difference in tumor size was 72.27 ± 134.84 cm3 and 21.08% ± 30.46%. After optimization according to our method, it decreased to 40.22 ± 96.06 cm3 and 10.16% ± 9.70%. CONCLUSION We introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.
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Affiliation(s)
- Weikang Li
- Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China
| | - Yiran Sun
- Wenzhou Medical University, Wenzhou, China
| | - Guoxun Zhang
- Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China
| | - Qing Yang
- Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China
| | - Bo Wang
- Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China
| | - Xiaohui Ma
- Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China.
| | - Hongxi Zhang
- Department of Radiology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333, Binshneg Rd, Hangzhou, China.
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Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [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: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
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Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
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Tietz E, Müller-Franzes G, Zimmermann M, Kuhl CK, Keil S, Nebelung S, Truhn D. Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI. Diagnostics (Basel) 2024; 14:483. [PMID: 38472955 DOI: 10.3390/diagnostics14050483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/02/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
Increased attention has been given to MRI in radiation-free screening for malignant nodules in recent years. Our objective was to compare the performance of human readers and radiomic feature analysis based on stand-alone and complementary CT and MRI imaging in classifying pulmonary nodules. This single-center study comprises patients with CT findings of pulmonary nodules who underwent additional lung MRI and whose nodules were classified as benign/malignant by resection. For radiomic features analysis, 2D segmentation was performed for each lung nodule on axial CT, T2-weighted (T2w), and diffusion (DWI) images. The 105 extracted features were reduced by iterative backward selection. The performance of radiomics and human readers was compared by calculating accuracy with Clopper-Pearson confidence intervals. Fifty patients (mean age 63 +/- 10 years) with 66 pulmonary nodules (40 malignant) were evaluated. ACC values for radiomic features analysis vs. radiologists based on CT alone (0.68; 95%CI: 0.56, 0.79 vs. 0.59; 95%CI: 0.46, 0.71), T2w alone (0.65; 95%CI: 0.52, 0.77 vs. 0.68; 95%CI: 0.54, 0.78), DWI alone (0.61; 95%CI:0.48, 0.72 vs. 0.73; 95%CI: 0.60, 0.83), combined T2w/DWI (0.73; 95%CI: 0.60, 0.83 vs. 0.70; 95%CI: 0.57, 0.80), and combined CT/T2w/DWI (0.83; 95%CI: 0.72, 0.91 vs. 0.64; 95%CI: 0.51, 0.75) were calculated. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets, pulmonary nodule assessment through radiomics analysis is superior to using one modality alone, even exceeding human readers' performance.
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Affiliation(s)
- Eric Tietz
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225 Dusseldorf, Germany
| | - Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
| | - Markus Zimmermann
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
| | - Christiane Katharina Kuhl
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
| | - Sebastian Keil
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstr. 30, 52072 Aachen, Germany
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Luo Y, Huang Z, Gao Z, Wang B, Zhang Y, Bai Y, Wu Q, Wang M. Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma. Korean J Radiol 2024; 25:189-198. [PMID: 38288898 PMCID: PMC10831304 DOI: 10.3348/kjr.2023.0618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 02/01/2024] Open
Abstract
OBJECTIVE To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). MATERIALS AND METHODS A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. RESULTS Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. CONCLUSION The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.
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Affiliation(s)
- Yu Luo
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Zihan Gao
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanwei Zhang
- Department of Bethune International Peace Hospital, Department of Radiology, Shijiazhuang, China
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China.
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10
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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11
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Liu J, Cundy TP, Woon DTS, Lawrentschuk N. A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancers (Basel) 2024; 16:486. [PMID: 38339239 PMCID: PMC10854940 DOI: 10.3390/cancers16030486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.
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Affiliation(s)
- Jianliang Liu
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Thomas P. Cundy
- Discipline of Surgery, University of Adelaide, Adelaide, SA 5005, Australia
| | - Dixon T. S. Woon
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Nathan Lawrentschuk
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
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12
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Sadeghi MH, Sina S, Omidi H, Farshchitabrizi AH, Alavi M. Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Pol J Radiol 2024; 89:e30-e48. [PMID: 38371888 PMCID: PMC10867948 DOI: 10.5114/pjr.2024.134817] [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: 11/19/2023] [Accepted: 12/27/2023] [Indexed: 02/20/2024] Open
Abstract
Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.
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Affiliation(s)
| | - Sedigheh Sina
- Shiraz University, Shiraz, Iran
- Radiation Research Center, Shiraz University, Shiraz, Iran
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13
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HajiEsmailPoor Z, Kargar Z, Tabnak P. Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis. Eur J Radiol 2023; 168:111129. [PMID: 37820522 DOI: 10.1016/j.ejrad.2023.111129] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 09/03/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of radiomics in lymph node metastasis (LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic review and meta-analysis. METHOD A literature search of PubMed, EMBASE, and Web of Science was conducted to find relevant studies published until February 18th, 2023. Studies that reported the accuracy of radiomics in different imaging modalities for LNM prediction in PTC patients were selected. The methodological quality of included studies was evaluated by radiomics quality score (RQS) and quality assessment of diagnostic accuracy studies (QUADAS-2) tools. General characteristics and radiomics accuracy were extracted. Overall sensitivity, specificity, and area under the curve (AUC) were calculated for diagnostic accuracy evaluation. Spearman correlation coefficient and subgroup analysis were performed for heterogeneity exploration. RESULTS In total, 25 studies were included, of which 22 studies provided adequate data for meta-analysis. We conducted two types of meta-analysis: one focused solely on radiomics features models and the other combined radiomics and non-radiomics features models in the analysis. The pooled sensitivity, specificity, and AUC of radiomics and combined models were 0.75 [0.68, 0.80] vs. 0.77 [0.74, 0.80], 0.77 [0.74, 0.81] vs. 0.83 [0.78, 0.87] and 0.80 [0.73, 0.85] vs 0.82 [0.75, 0.88], respectively. The analysis showed a high heterogeneity level among the included studies. There was no threshold effect. The subgroup analysis demonstrated that utilizing ultrasonography, 2D segmentation, central and lateral LNM detection, automatic segmentation, and PyRadiomics software could slightly improve diagnostic accuracy. CONCLUSIONS Our meta-analysis shows that the radiomics has the potential for pre-operative LNM prediction in PTC patients. Although methodological quality is sufficient but we still need more prospective studies with larger sample sizes from different centers.
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Affiliation(s)
| | - Zana Kargar
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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14
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Chang H, Choi JY, Shim J, Kim M, Choi M. Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records. Healthc Inform Res 2023; 29:323-333. [PMID: 37964454 PMCID: PMC10651408 DOI: 10.4258/hir.2023.29.4.323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVES Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. METHODS The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. RESULTS Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. CONCLUSIONS Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.
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Affiliation(s)
- Hyejung Chang
- Department of Management, School of Management, Kyung Hee University, Seoul,
Korea
| | - Jae-Young Choi
- Department of Business Administration, College of Business, Hallym University, Chuncheon,
Korea
| | - Jaesun Shim
- Department of Municipal Hospital Policy & Management, Seoul Health Foundation, Seoul,
Korea
| | - Mihui Kim
- Department of Nursing Science, Jeonju University, Jeonju,
Korea
| | - Mona Choi
- College of Nursing, Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul,
Korea
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15
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Wang H, Xia Z, Xu Y, Sun J, Wu J. The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis. Prostate Cancer Prostatic Dis 2023; 26:602-613. [PMID: 37488275 DOI: 10.1038/s41391-023-00704-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND In clinical practice, there are currently a variety of nomograms for predicting lymph node metastasis (LNM) of prostate cancer. At the same time, some scholars have introduced machine learning (ML) into the prediction of LNM of prostate cancer. However, the predictive value of nomograms and ML remains controversial. Based on this situation, this systematic review and meta-analysis was performed to explore the predictive value of various nomograms currently recommended and newly-developed ML models for LNM in prostate cancer patients. EVIDENCE ACQUISITION Cochrane, PubMed, Embase, and Web of Science were searched up to November 1, 2022. The risk of bias in the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). The concordance index (C-index), sensitivity, and specificity were adopted to evaluate the predictive accuracy of the models. RESULTS Thirty-one studies (18,803 patients) were included. Seven kinds of nomograms currently recommended, dominated by Briganti nomogram or MSKCC nomogram, were covered in the included studies. For newly-developed ML models, the C-index for LNM prediction in the training set and validation set was 0.846 [95%CI (0.818, 0.873)] and 0.862 [95%CI (0.819-0.905)] respectively. Most ML models in the training set were based on Logistic Regression (LR), which had a sensitivity of 0.78 [95%CI (0.70, 0.85)] and a specificity of 0.85 [95%CI (0.77, 0.90)] in the training set, and a sensitivity of 0.81 [95%CI (0.67, 0.89)] and a specificity of 0.82 [95%CI (0.75, 0.88)] in the validation set. For the recommended nomograms, the C-index in the validation set was 0.745 [95%CI (0.701, 0.790)] for the Briganti nomogram and 0.714 [95%CI (0.662, 0.765)] for the MSKCC nomogram. CONCLUSION The predictive accuracy of ML is superior to existing clinically recommended nomograms, and appropriate updates can be conducted to existing nomograms according to special situations.
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Affiliation(s)
- Hao Wang
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Zhongyou Xia
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Yulai Xu
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Jing Sun
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Ji Wu
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China.
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16
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Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [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: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
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Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
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17
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Liu L, Yu H, Bai J, Xu Q, Zhang Y, Zhang X, Yu Z, Liu Y. Positive Association of Serum Vitamin B6 Levels with Intrapulmonary Lymph Node and/or Localized Pleural Metastases in Non-Small Cell Lung Cancer: A Retrospective Study. Nutrients 2023; 15:nu15102340. [PMID: 37242223 DOI: 10.3390/nu15102340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
The relationship between vitamin B levels and the development and progression of lung cancer remains inconclusive. We aimed to investigate the relationship between B vitamins and intrapulmonary lymph nodes as well as localized pleural metastases in patients with non-small cell lung cancer (NSCLC). This was a retrospective study including patients who underwent lung surgery for suspected NSCLC at our institution from January 2016 to December 2018. Logistic regression models were used to evaluate the associations between serum B vitamin levels and intrapulmonary lymph node and/or localized pleural metastases. Stratified analysis was performed according to different clinical characteristics and tumor types. A total of 1498 patients were included in the analyses. Serum vitamin B6 levels showed a positive association with intrapulmonary metastasis in a multivariate logistic regression (odds ratio (OR) of 1.016, 95% confidence interval (CI) of 1.002-1.031, p = 0.021). After multivariable adjustment, we found a high risk of intrapulmonary metastasis in patients with high serum vitamin B6 levels (fourth quartile (Q4) vs. Q1, OR of 1.676, 95%CI of 1.092 to 2.574, p = 0.018, p for trend of 0.030). Stratified analyses showed that the positive association between serum vitamin B6 and lymph node metastasis appeared to be stronger in females, current smokers, current drinkers, and those with a family history of cancer, squamous cell carcinoma, a tumor of 1-3 cm in diameter, or a solitary tumor. Even though serum vitamin B6 levels were associated with preoperative NSCLC upstaging, B6 did not qualify as a useful biomarker due to weak association and wide confidence intervals. Thus, it would be appropriate to prospectively investigate the relationship between serum vitamin B6 levels and lung cancer further.
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Affiliation(s)
- Lu Liu
- Department of Nutrition, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Hang Yu
- Department of Respiratory and Critical Medicine, Medical School of Chinese People's Liberation Army, Beijing 100853, China
| | - Jingmin Bai
- Department of Radiotherapy, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Qing Xu
- Department of Nutrition, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Yong Zhang
- Department of Nutrition, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Xinsheng Zhang
- Department of Nutrition, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Zhimeng Yu
- Department of Nutrition, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Yinghua Liu
- Department of Nutrition, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
- National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
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18
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Erasmus JJ, Vlahos I. Editorial Commentary: Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC. J Thorac Oncol 2023; 18:556-558. [PMID: 37087116 DOI: 10.1016/j.jtho.2023.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 04/24/2023]
Affiliation(s)
- Jeremy J Erasmus
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Ioannis Vlahos
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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