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Zhao YS, Lai QP, Tang H, Luo RJ, He ZW, Huang W, Wang LY, Zhang ZT, Lin SH, Qin WJ, Xu F. Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning. Front Med (Lausanne) 2024; 11:1386161. [PMID: 38784232 PMCID: PMC11112035 DOI: 10.3389/fmed.2024.1386161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Background Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest. Objectives This study aimed to provide evidence for the early warning and management of fungal infections. Methods We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affiliated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h after ICU admission were included in the ICU-AF cohort. A predictive model of ICU-AF was obtained using the Least Absolute Shrinkage and Selection Operator and machine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relationships between the ICU-AF model, antifungal therapy and empirical antifungal therapy were analyzed. Results A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with importance ≥0.05 in the optimal model, namely, times of arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation. The area under the curve of the model for predicting ICU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter (p = 0.011, OR = 1.057, 95% CI = 1.053-1.104) and invasive mechanical ventilation (p = 0.007, OR = 1.056, 95%CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF. The times of arterial catheter (p = 0.004, OR = 1.098, 95%CI = 0.855-0.970) were an independent risk factor for empirical antifungal therapy. Conclusion The most important risk factors for ICU-AF are the six time-related features of clinical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection. Furthermore, this model can help ICU physicians to assess whether empiric antifungal therapy should be administered to ICU patients who are susceptible to fungal infections.
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Affiliation(s)
- Yi-Si Zhao
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
| | - Qing-Pei Lai
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hong Tang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ren-Jie Luo
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Wei He
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Huang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liu-Yang Wang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng-Tao Zhang
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shi-Hui Lin
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wen-Jian Qin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Fang Xu
- Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Doyle JP, Patel PH, Petrou N, Shur J, Orton M, Kumar S, Bhogal RH. Radiomic applications in upper gastrointestinal cancer surgery. Langenbecks Arch Surg 2023; 408:226. [PMID: 37278924 DOI: 10.1007/s00423-023-02951-z] [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: 02/19/2023] [Accepted: 05/21/2023] [Indexed: 06/07/2023]
Abstract
INTRODUCTION Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care. OBJECTIVE Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy. CONCLUSION Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.
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Affiliation(s)
- Joseph P Doyle
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Pranav H Patel
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Nikoletta Petrou
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Joshua Shur
- Department of Radiology, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Matthew Orton
- Department of Radiology, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Sacheen Kumar
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
- Upper GI Surgical Oncology Research Group, The Institute for Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Ricky H Bhogal
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK.
- Upper GI Surgical Oncology Research Group, The Institute for Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
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Menon N, Guidozzi N, Chidambaram S, Markar SR. Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy. Dis Esophagus 2023; 36:doad034. [PMID: 37236811 PMCID: PMC10789236 DOI: 10.1093/dote/doad034] [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: 02/19/2023] [Revised: 05/04/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.
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Affiliation(s)
- Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Mori M, Palumbo D, De Cobelli F, Fiorino C. Does radiomics play a role in the diagnosis, staging and re-staging of gastroesophageal junction adenocarcinoma? Updates Surg 2023; 75:273-279. [PMID: 36114920 DOI: 10.1007/s13304-022-01377-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/04/2022] [Indexed: 01/24/2023]
Abstract
Radiomics is an emerging field of investigation in medicine consisting in the extraction of quantitative features from conventional medical images and exploring their potentials in improving diagnosis, prognosis and outcome prediction after therapy. Clinical applications are still limited, mostly due to reproducibility and repeatability issues as well as to limited interpretability of predictive radiomic-based features/signatures. In the specific case of gastroesophageal junction (GEJ) adenocarcinoma, the expectancies are particularly high, mainly due to its increasing incidence and to the limited performance of conventional imaging techniques in assessing correct diagnosis and accurate pre-surgical tumor characterization. Accordingly, current literature was reviewed, emphasizing the methodological quality. In addition, papers were scored according to the Radiomic Quality Score (RQS), weighting more the clinical applicability and generalizability of the resulting models. According to the criteria of the search, only two papers were retained: the resulting technical quality was relatively high for both, while the corresponding RQS were 15 and 19 (on a scale of 31). Although the potentials of radiomics in the setting of GEJ adenocarcinoma are relevant, they remain largely unexplored, warranting an urgent need of high-quality, possibly prospective, multicenter studies.
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Affiliation(s)
- Martina Mori
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.,Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Diego Palumbo
- Department of Radiology, San Raffaele Scientific Institute, Milan, Italy.,School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology, San Raffaele Scientific Institute, Milan, Italy.,School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Fiorino
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy. .,Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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Peng H, Xue T, Chen Q, Li M, Ge Y, Feng F. Computed Tomography-Based Radiomics Nomogram for Predicting the Postoperative Prognosis of Esophageal Squamous Cell Carcinoma: A Multicenter Study. Acad Radiol 2022; 29:1631-1640. [PMID: 35300908 DOI: 10.1016/j.acra.2022.01.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVES The aim of this study was to evaluate and identify the predictive value of combining CT radiomics features and clinical features to determine recurrence-free survival (RFS) and overall survival (OS) after surgery in patients with esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS A total of 372 patients with surgically and pathologically confirmed ESCC from 2 institutions were retrospectively included. All patients from institution 1 were randomized at a 7:3 ratio into a training cohort (n=206) and an internal validation cohort (n=88), and patients from institution 2 were used as an external validation cohort (n=78). The association between the radiomics features and RFS and OS was assessed in the training cohort and verified in the validation cohort. Furthermore, the performance of the radiomics nomogram was evaluated by combining the radiomics score (rad-score) and clinical risk factors. RESULTS The radiomics nomogram that combined radiomics features and clinical risk factors was better than the clinical nomogram and radiomics model alone at predicting RFS and OS in ESCC patients. All calibration curves showed significant consistency between predicted survival and actual survival. CONCLUSION Radiomics features could be used to stratify patients with ESCC following radical resection into high- and low-risk groups. Furthermore, the radiomics nomograms provided better predictive accuracy than other predictive models and might serve as a therapeutic decision-making reference for clinicians and be used to monitor the risks of recurrence and death.
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Affiliation(s)
- Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China (H.P., T.X., Q.C., M.L., F.F.); GE Healthcare China, Shanghai, China (Y.G)
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China (H.P., T.X., Q.C., M.L., F.F.); GE Healthcare China, Shanghai, China (Y.G)
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China (H.P., T.X., Q.C., M.L., F.F.); GE Healthcare China, Shanghai, China (Y.G)
| | - Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China (H.P., T.X., Q.C., M.L., F.F.); GE Healthcare China, Shanghai, China (Y.G)
| | - Yaqiong Ge
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China (H.P., T.X., Q.C., M.L., F.F.); GE Healthcare China, Shanghai, China (Y.G)
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China (H.P., T.X., Q.C., M.L., F.F.); GE Healthcare China, Shanghai, China (Y.G).
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Alharbe NR, Munshi RM, Khayyat MM, Khayyat MM, Abdalaha Hamza SH, Aljohani AA. Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4629178. [PMID: 36156959 PMCID: PMC9507698 DOI: 10.1155/2022/4629178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/13/2022] [Accepted: 08/10/2022] [Indexed: 11/20/2022]
Abstract
Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches.
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Affiliation(s)
| | - Raafat M. Munshi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Manal M. Khayyat
- Department of Information Systems, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Mashael M. Khayyat
- Department of Information Systems and Technology, Faculty of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Saadia Hassan Abdalaha Hamza
- Department of Computer Science College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Saudi Arabia
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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
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Peng H, Yang Q, Xue T, Chen Q, Li M, Duan S, Cai B, Feng F. Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma. Br J Radiol 2022; 95:20210918. [PMID: 34908477 PMCID: PMC8822548 DOI: 10.1259/bjr.20210918] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE The present study explored the value of preoperative CT radiomics in predicting lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC). METHODS A retrospective analysis of 294 pathologically confirmed ESCC patients undergoing surgical resection and their preoperative chest-enhanced CT arterial images were used to delineate the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Radiomics features were extracted from single-slice, three-slice, and full-volume regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) regression method was applied to select valuable radiomics features. Radiomics models were constructed using logistic regression method and were validated using leave group out cross-validation (LGOCV) method. The performance of the three models was evaluated using the receiver characteristic curve (ROC) and decision curve analysis (DCA). RESULTS A total of 1218 radiomics features were separately extracted from single-slice ROIs, three-slice ROIs, and full-volume ROIs, and 16, 13 and 18 features, respectively, were retained after optimization and screening to construct a radiomics prediction model. The results showed that the AUC of the full-volume model was higher than that of the single-slice and three-slice models. According to LGOCV, the full-volume model showed the highest mean AUC for the training cohort and the validation cohort. CONCLUSION The full-volume radiomics model has the best predictive performance and thus can be used as an auxiliary method for clinical treatment decision making. ADVANCES IN KNOWLEDGE LVI is considered to be an important initial step for tumor dissemination. CT radiomics features correlate with LVI in ESCC and can be used as potential biomarkers for predicting LVI in ESCC.
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Affiliation(s)
| | | | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China
| | - Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China
| | | | - Bo Cai
- Nantong Center for Disease Control and Prevention Institue of Chronic, Noncommunicable Diseases Prevention and Control, Nantong, China
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China
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