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Zhang J, Zhang J, Jin J, Jiang X, Yang L, Fan S, Zhang Q, Chi M. Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis. Front Cardiovasc Med 2024; 11:1323918. [PMID: 38433757 PMCID: PMC10904648 DOI: 10.3389/fcvm.2024.1323918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024] Open
Abstract
Background With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis. Objective This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots. Methods The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. Results A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound". Conclusions AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.
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Affiliation(s)
- Jirong Zhang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Jimei Zhang
- College of Public Health, The University of Sydney, NSW, Sydney, Australia
| | - Juan Jin
- The First Department of Cardiovascular, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, HL, China
| | - Xicheng Jiang
- College of basic medicine, Heilongjiang University of Chinese Medicine, Harbin, HL, China
| | - Linlin Yang
- Cardiovascular Disease Branch, Dalian Second People's Hospital, Dalian, LN, China
| | - Shiqi Fan
- Harbin hospital of traditional Chinese medicine, Harbin, HL, China
| | - Qiao Zhang
- School of Pharmacy, Harbin University of Commerce, Harbin, HL, China
| | - Ming Chi
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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Lei J, Huang Y, Chen Y, Xia L, Yi B. The effect of the re-segmentation method on improving the performance of rectal cancer image segmentation models. Technol Health Care 2024; 32:1629-1640. [PMID: 38517809 DOI: 10.3233/thc-230690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
BACKGROUND Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patientâs lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with deep learning still cannot be compared with manual segmentation, and a major obstacle to cutting rectal tumors with deep learning is the lack of high-quality data sets. OBJECTIVE We propose to use our Re-segmentation Method to manually correct the model segmentation area and put it into training and training ideas. The data set has been made publicly available. Methods: A total of 354 rectal cancer CT images and 308 rectal region images labeled by experts from Jiangxi Cancer Hospital were included in the data set. Six network architectures are used to train the data set, and the region predicted by the model is manually revised and then put into training to improve the ability of model segmentation and then perform performance measurement. RESULTS In this study, we use the Resegmentation Method for various popular network architectures. CONCLUSION By comparing the evaluation indicators before and after using the Re-segmentation Method, we prove that our proposed Re-segmentation Method can further improve the performance of the rectal cancer image segmentation model.
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Affiliation(s)
- Jie Lei
- School of Software, Nanchang University, Nanchang, Jiangxi, China
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - YiJun Huang
- School of Software, Nanchang University, Nanchang, Jiangxi, China
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - YangLin Chen
- Jiangxi Cancer Hospital, Nanchang, Jiangxi, China
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - Linglin Xia
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - Bo Yi
- Jiangxi Cancer Hospital, Nanchang, Jiangxi, China
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Lu N, Guan X, Zhu J, Li Y, Zhang J. A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation. Cancers (Basel) 2023; 15:4497. [PMID: 37760468 PMCID: PMC10526233 DOI: 10.3390/cancers15184497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE This study aimed to build a deep learning system using enhanced computed tomography (CT) portal-phase images for predicting colorectal cancer patients' preoperative staging and RAS gene mutation status. METHODS The contrast-enhanced CT image dataset comprises the CT portal-phase images from a retrospective cohort of 231 colorectal cancer patients. The deep learning system was developed via migration learning for colorectal cancer detection, staging, and RAS gene mutation status prediction. This study used pre-trained Yolov7, vision transformer (VIT), swin transformer (SWT), EfficientNetV2, and ConvNeXt. 4620, and contrast-enhanced CT images and annotated tumor bounding boxes were included in the tumor identification and staging dataset. A total of 19,700 contrast-enhanced CT images comprise the RAS gene mutation status prediction dataset. RESULTS In the validation cohort, the Yolov7-based detection model detected and staged tumors with a mean accuracy precision (IoU = 0.5) (mAP_0.5) of 0.98. The area under the receiver operating characteristic curve (AUC) in the test set and validation set for the VIT-based prediction model in predicting the mutation status of the RAS genes was 0.9591 and 0.9554, respectively. The detection network and prediction network of the deep learning system demonstrated great performance in explaining contrast-enhanced CT images. CONCLUSION In this study, a deep learning system was created based on the foundation of contrast-enhanced CT portal-phase imaging to preoperatively predict the stage and RAS mutation status of colorectal cancer patients. This system will help clinicians choose the best treatment option to increase colorectal cancer patients' chances of survival and quality of life.
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Affiliation(s)
- Na Lu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Xiao Guan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Jianguo Zhu
- Department of Radiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China;
| | - Yuan Li
- Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China;
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
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Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol 2023; 13:1065402. [PMID: 36761957 PMCID: PMC9905815 DOI: 10.3389/fonc.2023.1065402] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA, United States
| | | | - Tarig Elhakim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States,*Correspondence: Dania Daye,
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Chenhui Yao,
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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Nakanishi R, Morooka K, Omori K, Toyota S, Tanaka Y, Hasuda H, Koga N, Nonaka K, Hu Q, Nakaji Y, Nakanoko T, Ando K, Ota M, Kimura Y, Oki E, Oda Y, Yoshizumi T. Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images. Ann Surg Oncol 2022; 30:3506-3514. [PMID: 36512260 DOI: 10.1245/s10434-022-12926-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/19/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND To develop an artificial intelligence-based model to predict recurrence after curative resection for stage I-III colorectal cancer from digitized pathological slides. PATIENTS AND METHODS In this retrospective study, 471 consecutive patients who underwent curative resection for stage I-III colorectal cancer at our institution from 2004 to 2015 were enrolled, and 512 randomly selected tiles from digitally scanned images of hematoxylin and eosin-stained tumor tissue sections were used to train a convolutional neural network. Five-fold cross-validation was used to validate the model. The association between recurrence and the model's output scores were analyzed in the test cohorts. RESULTS The area under the receiver operating characteristic curve of the cross-validation was 0.7245 [95% confidence interval (CI) 0.6707-0.7783; P < 0.0001]. The score successfully classified patients into those with better and worse recurrence free survival (P < 0.0001). Multivariate analysis revealed that a high score was significantly associated with worse recurrence free survival [odds ratio (OR) 1.857; 95% CI 1.248-2.805; P = 0.0021], which was independent from other predictive factors: male sex (P = 0.0238), rectal cancer (P = 0.0396), preoperative abnormal carcinoembryonic antigen (CEA) level (P = 0.0216), pathological T3/T4 stage (P = 0.0162), and pathological positive lymph node metastasis (P < 0.0001). CONCLUSIONS The artificial intelligence-based prediction model discriminated patients with a high risk of recurrence. This approach could help decision-makers consider the benefits of adjuvant chemotherapy.
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Ding Q, Sun Y, Zhang J, Yao Y, Huang D, Jiang Y. Utility and specificity of plasma heat shock protein 90 alpha, CEA, and CA199 as the diagnostic test in colorectal cancer liver metastasis. J Gastrointest Oncol 2022; 13:2497-2504. [PMID: 36388698 PMCID: PMC9660089 DOI: 10.21037/jgo-22-797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/22/2022] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Plasma heat shock protein 90 alpha (Hsp90α) has been suggested as a novel biomarker for the diagnosis and prognosis of cancer. Carcinoembryonic antigen (CEA) and carbohydrate antigen199 (CA199) are traditional tumor biomarkers for colorectal cancer (CRC). Previous studies have shown that Hsp90α and the combination of Hsp90α and CEA are optimal biomarkers for CRC at an early stage. However, research on the use of Hsp90α alone or in combination with CEA and/or CA199 in diagnosing CRC development, particularly liver metastasis, is limited. This study sought to investigate the value of Hsp90α alone or in combination with CEA/CA199 in diagnosing CRC liver metastasis. METHODS The clinical data of 472 CRC patients were retrospectively analyzed, which were confirmed by clinical manifestations and a histopathological examination associated with an imaging diagnosis. The levels of Hsp90α, and CEA, and CA199 were assessed by enzyme-linked immunoassays and electrochemiluminescence immunoassays. Liver metastasis was diagnosed by imaging or pathology of the liver. Logistic regression models were used to analyze associations between Hsp90α, CEA, and CA199, and liver metastasis in CRC. The areas under the curves (AUCs) were used to compare the utility of Hsp90α, CEA, and CA199 in the diagnosis of CRC liver metastasis (CRLM). Additionally, we compared the diagnostic utility of the models, including the Hsp90α plus 1 of the other serum markers, and a combination of the 3 serum makers. RESULTS The plasma levels of Hsp90α, CEA, and CA199 were positively associated with a higher risk of CRLM [odds ratios (OR) ranging from 1.36-2.72]. The AUCs of CEA, CA199, and Hsp90α for CRLM were 0.80, 0.69, and 0.55, respectively. The AUCs for the combination of Hsp90α and CEA, combination of Hsp90α and CA199, combinations of Hsp90α, CEA, and CA199 were 0.75, 0.66, 0.76, respectively. The combination of Hsp90α, CEA, and CA199 did not improve the diagnostic utility for liver metastasis in CRC. CONCLUSIONS The level of Hsp90α was elevated in CRC and was associated with CRLM. Thus, the Hsp90α is a potential biomarker for CRLM. CEA has the largest diagnostic utility for CRLM. Adding Hsp90α to CEA/CA199 did not improve their diagnostic utility for CRLM.
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Affiliation(s)
- Qi Ding
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yubei Sun
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Jinguo Zhang
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yiwei Yao
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Dabing Huang
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yan Jiang
- Department of Gynecology Oncology, Anhui Provincial Cancer Hospital, West Branch of the First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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