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Zhou Y, Feng P, Tian F, Fong H, Yang H, Zhu H. A CT-based radiomics model for predicting lymph node metastasis in hepatic alveolar echinococcosis patients to support lymph node dissection. Eur J Med Res 2024; 29:409. [PMID: 39113113 PMCID: PMC11304587 DOI: 10.1186/s40001-024-01999-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/27/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Hepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients. METHODS A total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS A total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation. CONCLUSION In this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.
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Affiliation(s)
- Yinshu Zhou
- First School of Clinical Medicine, Jinan University, No.601 Huangpu Avenue West, Guangzhou, 510632, China
| | - Pengcai Feng
- General Surgery Department, Qinghai Provincial People's Hospital, Xining, 810000, Qinghai, China
| | - Fengyuan Tian
- General Surgery Department, Qinghai Provincial People's Hospital, Xining, 810000, Qinghai, China
| | - Hin Fong
- First School of Clinical Medicine, Jinan University, No.601 Huangpu Avenue West, Guangzhou, 510632, China
| | - Haoran Yang
- School of Medicine, Jinan University, No.601 Huangpu Avenue West, Guangzhou, 510632, China
| | - Haihong Zhu
- General Surgery Department, Qinghai Provincial People's Hospital, Xining, 810000, Qinghai, China.
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2
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Bektaş M, Pereira JK, Daams F, van der Peet DL. ChatGPT in surgery: a revolutionary innovation? Surg Today 2024; 54:964-971. [PMID: 38421439 PMCID: PMC11266448 DOI: 10.1007/s00595-024-02800-6] [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: 09/21/2023] [Accepted: 12/13/2023] [Indexed: 03/02/2024]
Abstract
ChatGPT has brought about a new era of digital health, as this model has become prominent and been rapidly developing since its release. ChatGPT may be able to facilitate improvements in surgery as well; however, the influence of ChatGPT on surgery is largely unknown at present. Therefore, the present study reports on the current applications of ChatGPT in the field of surgery, evaluating its workflow, practical implementations, limitations, and future perspectives. A literature search was performed using the PubMed and Embase databases. The initial search was performed from its inception until July 2023. This study revealed that ChatGPT has promising capabilities in areas of surgical research, education, training, and practice. In daily practice, surgeons and surgical residents can be aided in performing logistics and administrative tasks, and patients can be more efficiently informed about the details of their condition. However, priority should be given to establishing proper policies and protocols to ensure the safe and reliable use of this model.
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Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jaime Ken Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
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Zhou H, Zhao Q, Huang W, Liang Z, Cui C, Ma H, Luo C, Li S, Ruan G, Chen H, Zhu Y, Zhang G, Liu S, Liu L, Li H, Yang H, Xie H. A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma. Radiother Oncol 2024; 197:110367. [PMID: 38834152 DOI: 10.1016/j.radonc.2024.110367] [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: 08/11/2023] [Revised: 05/28/2024] [Accepted: 06/01/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. METHODS We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). RESULTS In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. CONCLUSIONS The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.
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Affiliation(s)
- Haoyang Zhou
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, PR China.
| | - Qin Zhao
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Wenjie Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Zhiying Liang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Chunyan Cui
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Huali Ma
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Chao Luo
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Shuqi Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Guangying Ruan
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Hongbo Chen
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, PR China.
| | - Yuliang Zhu
- Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, ZhongShan, PR China.
| | - Guoyi Zhang
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People's Hospital of Foshan, Foshan, PR China.
| | - Shanshan Liu
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, PR China.
| | - Lizhi Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Haojiang Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Hui Yang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
| | - Hui Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
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Tang Y, Su YX, Zheng JM, Zhuo ML, Qian QF, Shen QL, Lin P, Chen ZK. Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer. J Transl Med 2024; 22:690. [PMID: 39075486 PMCID: PMC11288107 DOI: 10.1186/s12967-024-05479-y] [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: 01/27/2024] [Accepted: 07/03/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. METHODS Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. RESULTS Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77-0.91) and validation (AUC = 0.85, 95% CI = 0.73-0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. CONCLUSIONS Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.
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Affiliation(s)
- Yi Tang
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Yi-Xi Su
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Jin-Mei Zheng
- Department of Radiology, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Min-Ling Zhuo
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Qing-Fu Qian
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Qing-Ling Shen
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Peng Lin
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China.
| | - Zhi-Kui Chen
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China.
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Liu W, Zhang B, Liu T, Jiang J, Liu Y. Artificial Intelligence in Pancreatic Image Analysis: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:4749. [PMID: 39066145 PMCID: PMC11280964 DOI: 10.3390/s24144749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.
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Affiliation(s)
- Weixuan Liu
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Bairui Zhang
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Tao Liu
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;
| | - Juntao Jiang
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yong Liu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
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Castellana R, Fanni SC, Roncella C, Romei C, Natrella M, Neri E. Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis. Eur J Radiol 2024; 176:111510. [PMID: 38781919 DOI: 10.1016/j.ejrad.2024.111510] [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: 02/29/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS PubMed, CENTRAL, Scopus, Web of Science and IEEE databases were searched to identify relevant studies published up until February 11, 2024. Two reviewers screened all papers independently for eligibility. Studies reporting the accuracy of CT-based radiomics or deep learning models for detecting LNM in PDAC, using histopathology as the reference standard, were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2, the Radiomics Quality Score (RQS) and the the METhodological RadiomICs Score (METRICS). Overall sensitivity (SE), specificity (SP), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated. RESULTS Four radiomics studies comprising 213 patients and four deep learning studies with 272 patients were included. The average RQS total score was 12.00 ± 3.89, corresponding to an RQS percentage of 33.33 ± 10.80, while the average METRICS score was 63.60 ± 10.88. A significant and strong positive correlation was found between RQS and METRICS (p = 0.016; r = 0.810). The pooled SE, SP, DOR, and AUC of all the studies were 0.83 (95 %CI = 0.77-0.88), 0.76 (95 %CI = 0.62-0.86), 15.70 (95 %CI = 8.12-27.50) and 0.85 (95 %CI = 0.77-0.88). Meta-regression analysis results indicated that neither the study type (radiomics vs deep learning) nor the dataset size of the studies had a significant effect on the DOR (p = 0.09 and p = 0.26, respectively). CONCLUSION Based on our meta-analysis findings, preoperative CT-based radiomics algorithms and deep learning models demonstrate favorable performance in predicting LNM in patients with PDAC, with a strong correlation between RQS and METRICS of the included studies.
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Affiliation(s)
- Roberto Castellana
- Diagnostic and Interventional Radiology, "Parini" Regional Hospital, Azienda USL della Valle d'Aosta, Viale Ginevra 3 11100, Aosta, Italy.
| | - Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
| | - Claudia Roncella
- Radiology Unit, Apuane Hospital, Azienda USL Toscana Nord Ovest, Via Mattei 21, 54100, Massa, Italy
| | - Chiara Romei
- Department of Diagnostic Imaging, Diagnostic Radiology 2, Pisa University Hospital, Via Paradisa 2, 56124, Pisa, Italy
| | - Massimiliano Natrella
- Diagnostic and Interventional Radiology, "Parini" Regional Hospital, Azienda USL della Valle d'Aosta, Viale Ginevra 3 11100, Aosta, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
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Deng C, Hu J, Tang P, Xu T, He L, Zeng Z, Sheng J. Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis. Front Oncol 2024; 14:1395159. [PMID: 38957322 PMCID: PMC11217320 DOI: 10.3389/fonc.2024.1395159] [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: 03/03/2024] [Accepted: 05/30/2024] [Indexed: 07/04/2024] Open
Abstract
Background The performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC. Methods We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities. Results Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists. Discussion In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).
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Affiliation(s)
| | | | | | | | | | | | - Jianfeng Sheng
- Department of Thyroid, Head, Neck and Maxillofacial Surgery, the Third Hospital of Mianyang & Sichuan Mental Health Center, Mianyang, Sichuan, China
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Oh YL, Byeon SJ, Suh YJ. Prediction model for pheochromocytoma/paraganglioma using nCounter assay. J Surg Oncol 2024; 129:1481-1489. [PMID: 38634406 DOI: 10.1002/jso.27653] [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: 12/23/2023] [Revised: 03/05/2024] [Accepted: 03/30/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND World Health Organization defined pheochromocytomas/paragangliomas (PPGL) as malignant tumors in 2017 because the existing classification system could not reflect locally aggressive behavior sufficiently. However, predicting the likelihood of metastasis remains a crucial part of the treatment strategy. METHODS From one tertiary care hospital and one secondary hospital, 97 PPGL cases were selected. Medical records of PPGL cases with the presence of formalin-fixed and paraffin-embedded (FFPE) tissue of primary lesion were reviewed. For FFPE tissues, a nCounter assay was conducted to determine differently expressed genes between metastatic and non-metastatic PPGL groups. Performances of prediction models for the likelihood of metastasis were calculated. RESULTS Of a total of 97 PPGL cases, 39, 20, and 38 were classified as benign, malignant, and validation, respectively. In the nCounter assay, CDK1, TYMS, and TOP2A genes showed significant differences in expression. Tumor size was positively correlated with CDK1 expression level. The Lasso regression model showed supreme performance of sensitivity 91.7% and specificity 95.5% when those significant factors were considered. CONCLUSION Machine learning of multi-modal classifiers can be used to create a prediction model for metastasis of PPGL with high sensitivity and specificity using nCounter assay. Moreover, CDK1 inhibitors could be considered for developing drug treatment.
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Affiliation(s)
- Young Lyun Oh
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sun-Ju Byeon
- Department of Pathology, Yuseong Sun Hospital, Daejeon, Korea
| | - Yong Joon Suh
- Department of Breast and Endocrine Surgery, Hallym University Sacred Heart Hospital, Anyang, Korea
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Xiao T, Kong S, Zhang Z, Hua D, Liu F. A review of big data technology and its application in cancer care. Comput Biol Med 2024; 176:108577. [PMID: 38739981 DOI: 10.1016/j.compbiomed.2024.108577] [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: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.
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Affiliation(s)
- Tianyun Xiao
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
| | - Zichen Zhang
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Dianbo Hua
- Beijing Sitairui Cancer Data Analysis Joint Laboratory, Beijing, 101149, China
| | - Fengchun Liu
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China
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Yamada S, Hashimoto D, Yamamoto T, Yamaki S, Oshima K, Murotani K, Sekimoto M, Nakao A, Satoi S. Reconsideration of the clinical impact of neoadjuvant therapy in resectable and borderline resectable pancreatic cancer: A dual-institution collaborative clinical study. Pancreatology 2024; 24:592-599. [PMID: 38548551 DOI: 10.1016/j.pan.2024.03.012] [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: 12/12/2023] [Revised: 03/05/2024] [Accepted: 03/23/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE We investigated true indication of neoadjuvant therapy (NAT) in resectable pancreatic cancer and the optimal surgical timing in borderline resectable pancreatic cancer. METHODS A total of 687 patients with resectable or borderline resectable pancreatic cancer were enrolled. Survival analysis was performed by intention-to-treat analysis and propensity score matching (PSM) was conducted. RESULTS In resectable disease, the NAT group showed better overall survival (OS) compared with the upfront group. Multivariate analysis identified CA19-9 level (≥100 U/mL) and lymph node metastasis to be prognostic factors, and a tumor size of 25 mm was the optimal cut-off value to predict lymph node metastasis. There was no significant survival difference between patients with a tumor size ≤25 mm and CA19-9 < 100 U/mL and those in the NAT group. In borderline resectable disease, OS in the NAT group was significantly better than that in the upfront group. CEA (≥5 ng/mL) and CA19-9 (≥100 U/mL) were identified as prognostic factors; however, the OS of patients fulfilling these factors was worse than that of the NAT group. CONCLUSIONS NAT could be unnecessary in patients with tumor size ≤25 mm and CA19-9 < 100 U/mL in resectable disease. In borderline resectable disease, surgery should be delayed until tumor marker levels are well controlled.
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Affiliation(s)
- Suguru Yamada
- Department of Gastroenterological Surgery, Nagoya Central Hospital, Japan
| | | | | | - So Yamaki
- Department of Surgery, Kansai Medical University, Japan
| | - Kenji Oshima
- Department of Gastroenterological Surgery, Nagoya Central Hospital, Japan
| | - Kenta Murotani
- Biostatistics Center, Graduate School of Medicine, Kurume University, Japan
| | - Mitsugu Sekimoto
- Division of Surgical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Akimasa Nakao
- Department of Gastroenterological Surgery, Nagoya Central Hospital, Japan
| | - Sohei Satoi
- Department of Surgery, Kansai Medical University, Japan; Division of Surgical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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11
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Bilreiro C, Andrade L, Santiago I, Marques RM, Matos C. Imaging of pancreatic ductal adenocarcinoma - An update for all stages of patient management. Eur J Radiol Open 2024; 12:100553. [PMID: 38357385 PMCID: PMC10864763 DOI: 10.1016/j.ejro.2024.100553] [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: 10/15/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is a common and lethal cancer. From diagnosis to disease staging, response to neoadjuvant therapy assessment and patient surveillance after resection, imaging plays a central role, guiding the multidisciplinary team in decision-planning. Review aims and findings This review discusses the most up-to-date imaging recommendations, typical and atypical findings, and issues related to each step of patient management. Example cases for each relevant condition are presented, and a structured report for disease staging is suggested. Conclusion Despite current issues in PDAC imaging at different stages of patient management, the radiologist is essential in the multidisciplinary team, as the conveyor of relevant imaging findings crucial for patient care.
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Affiliation(s)
- Carlos Bilreiro
- Radiology Department, Champalimaud Foundation, Lisbon, Portugal
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
- Nova Medical School, Lisbon, Portugal
| | - Luísa Andrade
- Radiology Department, Champalimaud Foundation, Lisbon, Portugal
| | - Inês Santiago
- Radiology Department, Champalimaud Foundation, Lisbon, Portugal
| | - Rui Mateus Marques
- Nova Medical School, Lisbon, Portugal
- Radiology Department, Hospital de S. José, Lisbon, Portugal
| | - Celso Matos
- Radiology Department, Champalimaud Foundation, Lisbon, Portugal
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
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12
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Yin R, Dou Z, Wang Y, Zhang Q, Guo Y, Wang Y, Chen Y, Zhang C, Li H, Jian X, Qi L, Ma W. Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00231-9. [PMID: 38693025 DOI: 10.1016/j.acra.2024.04.024] [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: 01/22/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 05/03/2024]
Abstract
RATIONALE AND OBJECTIVES Peritoneal recurrence is the predominant pattern of recurrence in advanced ovarian cancer (AOC) and portends a dismal prognosis. Accurate prediction of peritoneal recurrence and disease-free survival (DFS) is crucial to identify patients who might benefit from intensive treatment. We aimed to develop a predictive model for peritoneal recurrence and prognosis in AOC. METHODS In this retrospective multi-institution study of 515 patients, an end-to-end multi-task convolutional neural network (MCNN) comprising a segmentation convolutional neural network (CNN) and a classification CNN was developed and tested using preoperative CT images, and MCNN-score was generated to indicate the peritoneal recurrence and DFS status in patients with AOC. We evaluated the accuracy of the model for automatic segmentation and predict prognosis. RESULTS The MCNN achieved promising segmentation performances with a mean Dice coefficient of 84.3% (range: 78.8%-87.0%). The MCNN was able to predict peritoneal recurrence in the training (AUC 0.87; 95% CI 0.82-0.90), internal test (0.88; 0.85-0.92), and external test set (0.82; 0.78-0.86). Similarly, MCNN demonstrated consistently high accuracy in predicting recurrence, with an AUC of 0.85; 95% CI 0.82-0.88, 0.83; 95% CI 0.80-0.86, and 0.85; 95% CI 0.83-0.88. For patients with a high MCNN-score of recurrence, it was associated with poorer DFS with P < 0.0001 and hazard ratios of 0.1964 (95% CI: 0.1439-0.2680), 0.3249 (95% CI: 0.1896-0.5565), and 0.3458 (95% CI: 0.2582-0.4632). CONCLUSION The MCNN approach demonstrated high performance in predicting peritoneal recurrence and DFS in patients with AOC.
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Affiliation(s)
- Rui Yin
- National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China; School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin 300203, China
| | - Zhaoxiang Dou
- Department of Breast Imaging, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Yanyan Wang
- Department of CT and MRI, Shanxi Tumor Hospital, Taiyuan 030013, China
| | - Qian Zhang
- Department of Radiology, Baoding No. 1 Central Hospital, Baoding 071030, China
| | - Yijun Guo
- Department of Breast Imaging, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Yigeng Wang
- Department of Radiology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Ying Chen
- Department of Gynecologic Oncology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Chao Zhang
- Department of Bone Cancer, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Huiyang Li
- Department of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xiqi Jian
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin 300203, China
| | - Lisha Qi
- Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Wenjuan Ma
- Department of Breast Imaging, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
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13
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Xu ZY, Li ZZ, Cao LM, Zhong NN, Liu XH, Wang GR, Xiao Y, Liu B, Bu LL. Seizing the fate of lymph nodes in immunotherapy: To preserve or not? Cancer Lett 2024; 588:216740. [PMID: 38423247 DOI: 10.1016/j.canlet.2024.216740] [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: 12/09/2023] [Revised: 02/07/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024]
Abstract
Lymph node dissection has been a long-standing diagnostic and therapeutic strategy for metastatic cancers. However, questions over myriad related complications and survival outcomes are continuously debated. Immunotherapy, particularly neoadjuvant immunotherapy, has revolutionized the conventional paradigm of cancer treatment, yet has benefited only a fraction of patients. Emerging evidence has unveiled the role of lymph nodes as pivotal responders to immunotherapy, whose absence may contribute to drastic impairment in treatment efficacy, again posing challenges over excessive lymph node dissection. Hence, centering around this theme, we concentrate on the mechanisms of immune activation in lymph nodes and provide an overview of minimally invasive lymph node metastasis diagnosis, current best practices for activating lymph nodes, and the prognostic outcomes of omitting lymph node dissection. In particular, we discuss the potential for future comprehensive cancer treatment with effective activation of immunotherapy driven by lymph node preservation and highlight the challenges ahead to achieve this goal.
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Affiliation(s)
- Zhen-Yu Xu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Xuan-Hao Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Guang-Rui Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Yao Xiao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China.
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14
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Miao Q, Wang X, Cui J, Zheng H, Xie Y, Zhu K, Chai R, Jiang Y, Feng D, Zhang X, Shi F, Tan X, Fan G, Liang K. Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging. Comput Biol Med 2024; 171:108125. [PMID: 38340439 DOI: 10.1016/j.compbiomed.2024.108125] [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: 10/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.
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Affiliation(s)
- Qi Miao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xuechun Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Bejing, China
| | - Haoxin Zheng
- Department of Computer Science, University of California, Los Angeles, USA
| | - Yan Xie
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Kexin Zhu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Ruimei Chai
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yuanxi Jiang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Dongli Feng
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiaodong Tan
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
| | - Keke Liang
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
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15
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Scherübl H. [Early detection of sporadic pancreatic cancer]. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2024; 62:412-419. [PMID: 37827502 DOI: 10.1055/a-2114-9847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.
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Affiliation(s)
- Hans Scherübl
- Klinik für Innere Medizin; Gastroenterol., GI Onkol. u. Infektiol., Vivantes Klinikum Am Urban, Berlin, Germany
- Akademisches Lehrkrankenhaus der Charité, Berlin, Germany
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16
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Liu J, Yu J, Wei Y, Li W, Lu J, Chen Y, Wang M. Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma. Thyroid Res 2024; 17:4. [PMID: 38369523 PMCID: PMC10875890 DOI: 10.1186/s13044-024-00191-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 01/06/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Whether prophylactic central lymph node dissection is necessary for patients with clinically node-negative (cN0) papillary thyroid microcarcinoma (PTMC) remains controversial. Herein, we aimed to establish an ultrasound (US) radiomics (Rad) score for assessing the probability of central lymph node metastasis (CLNM) in such patients. METHODS 480 patients (327 in the training cohort, 153 in the validation cohort) who underwent thyroid surgery for cN0 PTMC at two institutions between January 2018 and December 2020 were included. Radiomics features were extracted from the US images. Least absolute shrinkage and selection operator logistic regression were utilized to generate a Rad score. A nomogram consisting of the Rad score and clinical factors was then constructed for the training cohort. Both cohorts assessed model performance using discrimination, calibration, and clinical usefulness. RESULTS Based on the six most valuable radiomics features, the Rad score was calculated for each patient. A multivariate analysis revealed that a higher Rad score (P < 0.001), younger age (P = 0.006), and presence of capsule invasion (P = 0.030) were independently associated with CLNM. A nomogram integrating these three factors demonstrated good calibration and promising clinical utility in the training and validation cohorts. The nomogram yielded areas under the curve of 0.795 (95% confidence interval [CI], 0.745-0.846) and 0.774 (95% CI, 0.696-0.852) in the training and validation cohorts, respectively. CONCLUSIONS The radiomics nomogram may be a clinically useful tool for the individual prediction of CLNM in patients with cN0 PTMC.
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Affiliation(s)
- Jie Liu
- Department of Head and Neck Thyroid Surgery, Cangzhou Hospital of Integrated TCM-WM·Hebei, No.31 Huanghe West Road, 061000, Cangzhou, Hebei Province, China.
| | - Jingchao Yu
- Department of Head and Neck Thyroid Surgery, Cangzhou Hospital of Integrated TCM-WM·Hebei, No.31 Huanghe West Road, 061000, Cangzhou, Hebei Province, China
| | - Yanan Wei
- Department of TCM Internal Medicine, Cangzhou Hospital of Integrated TCM-WM·Hebei, 061000, Cangzhou, China
| | - Wei Li
- Department of Head and Neck Thyroid Surgery, Cangzhou Hospital of Integrated TCM-WM·Hebei, No.31 Huanghe West Road, 061000, Cangzhou, Hebei Province, China
| | - Jinle Lu
- Department of Head and Neck Thyroid Surgery, Cangzhou Hospital of Integrated TCM-WM·Hebei, No.31 Huanghe West Road, 061000, Cangzhou, Hebei Province, China
| | - Yating Chen
- Department of Head and Neck Thyroid Surgery, Cangzhou Hospital of Integrated TCM-WM·Hebei, No.31 Huanghe West Road, 061000, Cangzhou, Hebei Province, China
| | - Meng Wang
- Department of Head and Neck Thyroid Surgery, Cangzhou Hospital of Integrated TCM-WM·Hebei, No.31 Huanghe West Road, 061000, Cangzhou, Hebei Province, China
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Thiemann N, Sonntag SR, Kreikenbohm M, Böhmerle G, Stagge J, Grisanti S, Martinetz T, Miura Y. Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis-Toward Retinal Metabolic Diagnostics. Diagnostics (Basel) 2024; 14:431. [PMID: 38396470 PMCID: PMC10888399 DOI: 10.3390/diagnostics14040431] [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: 11/11/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
The purpose of this study was to investigate the possibility of implementing an artificial intelligence (AI) approach for the analysis of fluorescence lifetime imaging ophthalmoscopy (FLIO) data even with small data. FLIO data, including the fluorescence intensity and mean fluorescence lifetime (τm) of two spectral channels, as well as OCT-A data from 26 non-smokers and 28 smokers without systemic and ocular diseases were used. The analysis was performed with support vector machines (SVMs), a well-known AI method for small datasets, and compared with the results of convolutional neural networks (CNNs) and autoencoder networks. The SVM was the only tested AI method, which was able to distinguish τm between non-smokers and heavy smokers. The accuracy was about 80%. OCT-A data did not show significant differences. The feasibility and usefulness of the AI in analyzing FLIO and OCT-A data without any apparent retinal diseases were demonstrated. Although further studies with larger datasets are necessary to validate the results, the results greatly suggest that AI could be useful in analyzing FLIO-data even from healthy subjects without retinal disease and even with small datasets. AI-assisted FLIO is expected to greatly advance early retinal diagnosis.
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Affiliation(s)
- Natalie Thiemann
- Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany
| | - Svenja Rebecca Sonntag
- Department of Ophthalmology, University of Luebeck, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Marie Kreikenbohm
- Department of Ophthalmology, University of Luebeck, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Giulia Böhmerle
- Department of Ophthalmology, University of Luebeck, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Jessica Stagge
- Department of Ophthalmology, University of Luebeck, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Salvatore Grisanti
- Department of Ophthalmology, University of Luebeck, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany
| | - Yoko Miura
- Department of Ophthalmology, University of Luebeck, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
- Institute of Biomedical Optics, University of Lübeck, 23538 Lübeck, Germany
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Wen Y, Song Z, Li Q, Zhang D, Li X, Yu J, Li Z, Ren X, Zhang J, Liu Q, Huang J, Zeng D, Tang Z. Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters. Insights Imaging 2024; 15:41. [PMID: 38353857 PMCID: PMC10866831 DOI: 10.1186/s13244-024-01617-8] [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: 09/12/2023] [Accepted: 12/21/2023] [Indexed: 02/17/2024] Open
Abstract
OBJECTIVE To construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS Data from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT-Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve. RESULTS Computed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT-Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT-Radiology nomogram was established based on the DECT-Radiology model, which showed the highest net benefit and satisfactory consistency. CONCLUSIONS The DECT-Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients. CRITICAL RELEVANCE STATEMENT The DECT-Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression. KEY POINTS • Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). • The DECT-Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression. • The nomogram may help screen out PDAC patients with high Ki-67 expression.
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Affiliation(s)
- Youjia Wen
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Zuhua Song
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Qian Li
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Dan Zhang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Xiaojiao Li
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Jiayi Yu
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Zongwen Li
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Xiaofang Ren
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Jiayan Zhang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Qian Liu
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Jie Huang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Dan Zeng
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Zhuoyue Tang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
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19
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Chen M, Jiang Y, Zhou X, Wu D, Xie Q. Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review. Diagnostics (Basel) 2024; 14:377. [PMID: 38396416 PMCID: PMC10888055 DOI: 10.3390/diagnostics14040377] [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: 01/05/2024] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.
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Affiliation(s)
| | | | | | - Di Wu
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518036, China; (M.C.); (Y.J.); (X.Z.)
| | - Qiuxia Xie
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518036, China; (M.C.); (Y.J.); (X.Z.)
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20
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Wang M, Liu Z, Ma L. Application of artificial intelligence in ultrasound imaging for predicting lymph node metastasis in breast cancer: A meta-analysis. Clin Imaging 2024; 106:110048. [PMID: 38065024 DOI: 10.1016/j.clinimag.2023.110048] [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: 08/01/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND This study aims to comprehensively evaluate the accuracy and effectiveness of ultrasound imaging based on artificial intelligence algorithms in predicting lymph node metastasis in breast cancer patients through a meta-analysis. METHODS We systematically searched PubMed, Embase, and Cochrane Library for literature published up to May 2023. The search terms included artificial intelligence, ultrasound, breast cancer, and lymph node. Studies meeting the inclusion criteria were selected, and data were extracted for analysis. The main evaluation indicators included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC). The heterogeneity was assessed using the Cochrane Q test combined with the I^2 statistic expressing the percentage of total effect variation that can be attributed to the effect variation between studies, as recommended by the Cochrane Handbook for heterogeneity quantification. A threshold p-value of 0.10 was considered to compensate for the low power of the Q test. Sensitivity analysis was performed to assess the stability of individual studies, and publication bias was determined with funnel plots. Additionally, fagan plots were used to assess clinical utility. RESULTS Ten studies involving 4726 breast cancer patients were included in the meta-analysis. The results showed that ultrasound imaging based on artificial intelligence algorithms had high accuracy and effectiveness in predicting lymph node metastasis in breast cancer patients. The pooled sensitivity was 0.88 (95% CI: 0.81-0.93; P < 0.001; I2 = 84.68), specificity was 0.75 (95% CI: 0.66-0.83; P < 0.001; I2 = 91.11), and AUC was 0.89 (95% CI: 0.86-0.91). The positive likelihood ratio was 3.5 (95% CI: 2.6-4.8), negative likelihood ratio was 0.16 (95% CI: 0.10-0.26), and diagnostic odds ratio was 23 (95% CI: 13-40). However, the combined sensitivity of ultrasound imaging based on non-AI algorithms for predicting lymph node metastasis in breast cancer patients was 0.78 (95%CI: 0.63-0.88), the specificity was 0.76 (95%CI: 0.63-0.86), and the AUC was 0.84 (95%CI: 0.80-0.87). The positive likelihood ratio was 3.3 (95% CI: 1.9-5.6), the negative likelihood ratio was 0.29 (95% CI: 0.15-0.54), and the diagnostic odds ratio was 11 (95% CI: 4-33). Due to limited sample size (n = 2), meta-analysis was not conducted for the outcome of predicting lymph node metastasis burden. CONCLUSION Ultrasound imaging based on artificial intelligence algorithms holds promise in predicting lymph node metastasis in breast cancer patients, demonstrating high accuracy and effectiveness. The application of this technology helps in the diagnosis and treatment decisions for breast cancer patients and is expected to become an important tool in future clinical practice.
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Affiliation(s)
- Minghui Wang
- Department of Breast Surgery, Affiliate Hospital of Chengde Medical University, Hebei 067000, China
| | - Zihui Liu
- Department of Pathology, Affiliate Hospital of Chengde Medical University, Hebei 067000, China
| | - Lihui Ma
- Department of Breast Surgery, Affiliate Hospital of Chengde Medical University, Hebei 067000, China.
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [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: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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22
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Patel H, Zanos T, Hewitt DB. Deep Learning Applications in Pancreatic Cancer. Cancers (Basel) 2024; 16:436. [PMID: 38275877 PMCID: PMC10814475 DOI: 10.3390/cancers16020436] [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: 12/05/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging technology in the care of patients with pancreatic cancer.
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Affiliation(s)
- Hardik Patel
- Northwell Health—The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA;
| | - Theodoros Zanos
- Northwell Health—The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA;
| | - D. Brock Hewitt
- Department of Surgery, NYU Grossman School of Medicine, New York, NY 10016, USA;
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Li D, Peng Q, Wang L, Cai W, Liang M, Liu S, Ma X, Zhao X. Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9. Eur Radiol 2024; 34:509-524. [PMID: 37507611 DOI: 10.1007/s00330-023-09980-8] [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: 10/08/2022] [Revised: 05/18/2023] [Accepted: 05/28/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVES To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). METHODS A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. RESULTS The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). CONCLUSIONS The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. CLINICAL RELEVANCE STATEMENT We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. KEY POINTS • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.
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Affiliation(s)
- Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Street South, Chaoyang District, Beijing, 100021, China
| | - Qing Peng
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Street South, Chaoyang District, Beijing, 100021, China
| | - Wei Cai
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Street South, Chaoyang District, Beijing, 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Street South, Chaoyang District, Beijing, 100021, China
| | - Siyun Liu
- GE Healthcare (China), Beijing, 100176, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Street South, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Street South, Chaoyang District, Beijing, 100021, China.
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Struckmeier AK, Yekta E, Agaimy A, Kopp M, Buchbender M, Moest T, Lutz R, Kesting M. Diagnostic accuracy of contrast-enhanced computed tomography in assessing cervical lymph node status in patients with oral squamous cell carcinoma. J Cancer Res Clin Oncol 2023; 149:17437-17450. [PMID: 37875746 PMCID: PMC10657302 DOI: 10.1007/s00432-023-05470-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/09/2023] [Indexed: 10/26/2023]
Abstract
OBJECTIVE Accurate preoperative prediction of lymph node (LN) status plays a pivotal role in determining the extension of neck dissection (ND) required for patients with oral squamous cell carcinoma (OSCC). This study aims to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting LN metastases (LNMs) and to explore clinicopathological factors associated with its reliability. METHODS Data from 239 patients with primary OSCC who underwent preoperative CT and subsequent radical surgery involving ND were retrospectively reviewed. Suspicious LNs were categorized into three groups: accentuated (< 10 mm), enlarged (≥ 10 mm), and melted. Statistical analysis encompassing correlation and comparative analysis, and determination of sensitivity, specificity, PPV, and NPV were performed. RESULTS Overall, sensitivity was significantly higher in the accentuated LNs group (83.54%) compared to the melted LNs group (39.24%, p < 0.05, t test). Conversely, specificity was significantly higher in the melted LNs group (98.19%) compared to the accentuated LNs group (55.15%, p < 0.05, t test). Accentuated LNs exhibited a false negative rate of 13.00%. False positive rates were 51.80%, 30.26% and 8.82%, respectively. Diagnostic accuracy for detecting LNMs in level IIa and IIb exceeded that of level III. Patients with solely accentuated LNs were more likely to have a small, well-differentiated tumor. However, no distinctions emerged in terms of the occurrence of T4 tumors among the three groups. CONCLUSION CT proves sufficient to predict LNMs in patients with OSCC. Looking ahead, the potential integration of artificial intelligence and deep learning holds promise to further enhance the reliability of CT in LNMs detection. However, this prospect necessitates further investigation.
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Affiliation(s)
- Ann-Kristin Struckmeier
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Glückstraße 11, 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany.
| | - Ebrahim Yekta
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Glückstraße 11, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
| | - Abbas Agaimy
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Markus Kopp
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
- Department of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Mayte Buchbender
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Glückstraße 11, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
| | - Tobias Moest
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Glückstraße 11, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
| | - Rainer Lutz
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Glückstraße 11, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
| | - Marco Kesting
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Glückstraße 11, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
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Wöltjen MM, Kröger JR. [Current CT developments in imaging of pancreatic diseases]. RADIOLOGIE (HEIDELBERG, GERMANY) 2023; 63:878-885. [PMID: 37947865 DOI: 10.1007/s00117-023-01230-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Diseases of the pancreas are often diagnosed late and can have fatal consequences for patients. PURPOSE Current computed tomography (CT) developments in imaging of pancreatic diseases. MATERIALS AND METHODS Evaluation of numerous studies, especially considering modern CT techniques such as dual-energy CT and photon-counting CT but also artificial intelligence (AI) algorithms for disease detection. RESULTS Spectral imaging using dual-energy CT and photon-counting CT offers numerous advantages in the detection of pancreatic disease and can thus improve diagnostic performance but also provide additional information on any therapeutic response. Likewise, advances in the development of AI algorithms are improving diagnostic performance. CONCLUSION In the future, we can expect increasingly improved detection of pancreatic diseases, thereby enabling patients to be treated more quickly, which will consequently result in improved outcomes.
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Nazario-Johnson L, Zaki HA, Tung GA. Use of Large Language Models to Predict Neuroimaging. J Am Coll Radiol 2023; 20:1004-1009. [PMID: 37423349 DOI: 10.1016/j.jacr.2023.06.008] [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: 05/11/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE Large language models (LLMs) have demonstrated a level of competency within the medical field. The aim of this study was to explore the ability of LLMs to predict the best neuroradiologic imaging modality given specific clinical presentations. In addition, the authors seek to determine if LLMs can outperform an experienced neuroradiologist in this regard. METHODS ChatGPT and Glass AI, a health care-based LLM by Glass Health, were used. ChatGPT was prompted to rank the three best neuroimaging modalities while taking the best responses from Glass AI and the neuroradiologist. The responses were compared with the ACR Appropriateness Criteria for 147 conditions. Clinical scenarios were passed into each LLM twice to account for stochasticity. Each output was scored out of 3 on the basis of the criteria. Partial scores were given for nonspecific answers. RESULTS ChatGPT and Glass AI scored 1.75 and 1.83, respectively, with no statistically significant difference. The neuroradiologist scored 2.20, significantly outperforming both LLMs. ChatGPT was also found to be the more inconsistent of the two LLMs, with the score difference between both outputs being statistically significant. Additionally, scores between different ranks output by ChatGPT were statistically significant. CONCLUSIONS LLMs perform well in selecting appropriate neuroradiologic imaging procedures when prompted with specific clinical scenarios. ChatGPT performed the same as Glass AI, suggesting that with medical text training, ChatGPT could significantly improve its function in this application. LLMs did not outperform an experienced neuroradiologist, indicating the need for continued improvement in the medical context.
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Affiliation(s)
- Lleayem Nazario-Johnson
- Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, Rhode Island
| | - Hossam A Zaki
- Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, Rhode Island.
| | - Glenn A Tung
- Associate Dean for Clinical Affairs, Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, Rhode Island
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Chen M, Kong C, Lin G, Chen W, Guo X, Chen Y, Cheng X, Chen M, Shi C, Xu M, Sun J, Lu C, Ji J. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study. EClinicalMedicine 2023; 63:102176. [PMID: 37662514 PMCID: PMC10474371 DOI: 10.1016/j.eclinm.2023.102176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023] Open
Abstract
Background For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.
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Affiliation(s)
- Mingzhen Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Xinyu Guo
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Yaning Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Xue Cheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Changsheng Shi
- Department of Interventional Radiology, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, Zhejiang, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Junhui Sun
- Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
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Fu N, Fu W, Chen H, Chai W, Qian X, Wang W, Jiang Y, Shen B. A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study. Int J Surg 2023; 109:2196-2203. [PMID: 37216230 PMCID: PMC10442094 DOI: 10.1097/js9.0000000000000469] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/04/2023] [Indexed: 05/24/2023]
Abstract
OBJECTIVES Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now. METHODS A multivariate model was established based on the multiview-guided two-stream convolution network (MTCN) radiomics algorithms, which focused on primary tumor and peri-tumor features. Regarding discriminative ability, survival fitting, and model accuracy, different models were compared. RESULTS Three hundred and sixty-three pancreatic cancer patients were divided in to train and test cohorts by 7:3. The modified MTCN (MTCN+) model was established based on age, CA125, MTCN scores, and radiologist judgement. The MTCN+ model outperformed the MTCN model and the artificial model in discriminative ability and model accuracy. [Train cohort area under curve (AUC): 0.823 vs. 0.793 vs. 0.592; train cohort accuracy (ACC): 76.1 vs. 74.4 vs. 56.7%; test cohort AUC: 0.815 vs. 0.749 vs. 0.640; test cohort ACC: 76.1 vs. 70.6 vs. 63.3%; external validation AUC: 0.854 vs. 0.792 vs. 0.542; external validation ACC: 71.4 vs. 67.9 vs. 53.5%]. The survivorship curves fitted well between actual LN status and predicted LN status regarding disease free survival and overall survival. Nevertheless, the MTCN+ model performed poorly in assessing the LN metastatic burden among the LN positive population. Notably, among the patients with small primary tumors, the MTCN+ model performed steadily as well (AUC: 0.823, ACC: 79.5%). CONCLUSIONS A novel MTCN+ preoperative LN status predictive model was established and outperformed the artificial judgement and deep-learning radiomics judgement. Around 40% misdiagnosed patients judged by radiologists could be corrected. And the model could help precisely predict the survival prognosis.
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Affiliation(s)
- Ningzhen Fu
- Department of General Surgery, Pancreatic Disease Center
- Research Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine
- Institute of Translational Medicine
- State Key Laboratory of Oncogenes and Related Genes, Shanghai, China
| | - Wenli Fu
- School of Biomedical Engineering, Shanghai Jiao Tong University
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center
- Research Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine
- Institute of Translational Medicine
- State Key Laboratory of Oncogenes and Related Genes, Shanghai, China
| | | | - Xiaohua Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University
| | - Weishen Wang
- Department of General Surgery, Pancreatic Disease Center
- Research Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine
- Institute of Translational Medicine
- State Key Laboratory of Oncogenes and Related Genes, Shanghai, China
| | - Yu Jiang
- Department of General Surgery, Pancreatic Disease Center
- Research Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine
- Institute of Translational Medicine
- State Key Laboratory of Oncogenes and Related Genes, Shanghai, China
| | - Baiyong Shen
- Department of General Surgery, Pancreatic Disease Center
- Research Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine
- Institute of Translational Medicine
- State Key Laboratory of Oncogenes and Related Genes, Shanghai, China
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Zhang G, Bao C, Liu Y, Wang Z, Du L, Zhang Y, Wang F, Xu B, Zhou SK, Liu R. 18F-FDG-PET/CT-based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery. EJNMMI Res 2023; 13:49. [PMID: 37231321 DOI: 10.1186/s13550-023-00985-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. METHODS A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation. RESULTS The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. CONCLUSION To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making.
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Affiliation(s)
- Gong Zhang
- Medical School of Chinese PLA, Beijing, China
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Chengkai Bao
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Yanzhe Liu
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Zizheng Wang
- Senior Department of Hepatology, The Fifth Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Lei Du
- Department of Nuclear Medicine, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Yue Zhang
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Fei Wang
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Baixuan Xu
- Department of Nuclear Medicine, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
| | - S Kevin Zhou
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China.
| | - Rong Liu
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China.
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Gu Q, He M, He Y, Dai A, Liu J, Chen X, Liu P. CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma. Discov Oncol 2023; 14:16. [PMID: 36735166 PMCID: PMC9898483 DOI: 10.1007/s12672-023-00624-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND To explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC). METHODS We retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient's body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method. RESULTS A total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively. CONCLUSIONS CT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.
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Affiliation(s)
- Qianbiao Gu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Mengqing He
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Yaqiong He
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Anqi Dai
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Jianbin Liu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Xiang Chen
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
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Li Q, Li X, Liu W, Yu J, Chen Y, Zhu M, Li N, Liu F, Wang T, Fang X, Li J, Lu J, Shao C, Bian Y. Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma. Front Oncol 2023; 13:1108545. [PMID: 36756153 PMCID: PMC9900003 DOI: 10.3389/fonc.2023.1108545] [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: 11/26/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Purpose To evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. Results A total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28-86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36-84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74). Conclusions The radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.
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Affiliation(s)
- Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,Department of Radiology, 96601 Military Hospital of PLA, Huangshan, Anhui, China
| | - Xuezhou Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Wenbin Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yukun Chen
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,*Correspondence: Yun Bian, ; Chengwei Shao,
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,*Correspondence: Yun Bian, ; Chengwei Shao,
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An P, Lin Y, Zhang J, Hu Y, Qin P, Ye Y, Li X, Feng G, Wang J. Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data. Technol Cancer Res Treat 2023; 22:15330338231186739. [PMID: 37464839 PMCID: PMC10363996 DOI: 10.1177/15330338231186739] [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: 01/18/2023] [Revised: 05/06/2023] [Accepted: 05/19/2023] [Indexed: 07/20/2023] Open
Abstract
Objective: To collect the clinical, pathological, and computed tomography (CT) data of 143 accepted surgical cases of pancreatic body tail cancer (PBTC) and to model and predict its prognosis. Methods: The clinical, pathological, and CT data of 143 PBTC patients who underwent surgical resection or endoscopic ultrasound biopsy and were pathologically diagnosed in Xiangyang No.1 People's Hospital Hospital from December 2012 to December 2022 were retrospectively analyzed. The Kaplan-Meier method was adopted to make survival curves based on the 1 to 5 years' follow-up data, and then the log-rank was employed to analyze the survival. According to the median survival of 6 months, the PBTC patients were divided into a group with a good prognosis (survival time ≥ 6 months) and a group with a poor prognosis (survival time < 6 months), and further the training set and test set were set at a ratio of 7/3. Then logistic regression was conducted to find independent risk factors, establish predictive models, and further the models were validated. Results: The Kaplan-Meier analysis showed that age, diabetes, tumor, node, and metastasis stage, CT enhancement mode, peripancreatic lymph node swelling, nerve invasion, surgery in a top hospital, tumor size, carbohydrate antigen 19-9, carcinoembryonic antigen, Radscore 1/2/3 were the influencing factors of PBTC recurrence. The overall average survival was 7.4 months in this study. The multivariate logistic analysis confirmed that nerve invasion, surgery in top hospital, dilation of the main pancreatic duct, and Radscore 2 were independent factors affecting the mortality of PBTC (P < .05). In the test set, the combined model achieved the best predictive performance [AUC 0.944, 95% CI (0.826-0.991)], significantly superior to the clinicopathological model [AUC 0.770, 95% CI (0.615-0.886), P = .0145], and the CT radiomics model [AUC 0.883, 95% CI (0.746-0.961), P = .1311], with a good clinical net benefit confirmed by decision curve. The same results were subsequently validated on the test set. Conclusion: The diagnosis and treatment of PBTC are challenging, and survival is poor. Nevertheless, the combined model benefits the clinical management and prognosis of PBTC.
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Affiliation(s)
- Peng An
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yong Lin
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pancreatic Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyan Zhang
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- Depatment of Radiology, Hubei Clinical Research Center of Parkinson’s disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, P.R.C
| | - Yan Hu
- Department of Pancreatic Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ping Qin
- Department of Pancreatic Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Depatment of Radiology, Hubei Clinical Research Center of Parkinson’s disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, P.R.C
- Department of internal medicine, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yingjian Ye
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiumei Li
- Depatment of Radiology, Hubei Clinical Research Center of Parkinson’s disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, P.R.C
- Department of Pharmacy and Laboratory, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Guoyan Feng
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jinsong Wang
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
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