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Jiao T, Li F, Cui Y, Wang X, Li B, Shi F, Xia Y, Zhou Q, Zeng Q. Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images. J Magn Reson Imaging 2023; 58:1624-1635. [PMID: 36965182 DOI: 10.1002/jmri.28695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/10/2023] [Accepted: 03/10/2023] [Indexed: 03/27/2023] Open
Abstract
BACKGROUND Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. PURPOSE To distinguish primary site of BM and identify the best DL models. STUDY TYPE Retrospective. POPULATION A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. FIELD STRENGTH/SEQUENCE A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE). ASSESSMENT Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps. STATISTICAL TESTS The area under the receiver operating characteristics curve (AUC) assess each classification performance. RESULTS 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions. DATA CONCLUSION DL models may help to distinguish the origins of BM based on MRI data. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tianyu Jiao
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong First Medical University, Jinan, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Xiao Wang
- Department of Radiology, Jining No. 1 People's Hospital, Jining, China
| | - Butuo Li
- Department of Radiation Oncology, Shandong Cancer Hospital & Institute, Jinan, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Qing Zhou
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
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Hong Y, Yu Q, Mo F, Yin M, Xu C, Zhu S, Lin J, Xu G, Gao J, Liu L, Wang Y. Deep learning to predict esophageal variceal bleeding based on endoscopic images. J Int Med Res 2023; 51:3000605231200371. [PMID: 37818651 PMCID: PMC10566287 DOI: 10.1177/03000605231200371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/24/2023] [Indexed: 10/12/2023] Open
Abstract
OBJECTIVE Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.
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Affiliation(s)
- Yu Hong
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qianqian Yu
- Department of Oncology, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
| | - Feng Mo
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guoting Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Wang
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
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Nawaz S, Rahmani V, Pennicard D, Setty SPR, Klaudel B, Graafsma H. Explainable machine learning for diffraction patterns. J Appl Crystallogr 2023; 56:1494-1504. [PMID: 37791364 PMCID: PMC10543671 DOI: 10.1107/s1600576723007446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/24/2023] [Indexed: 10/05/2023] Open
Abstract
Serial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as 'hit' and 'miss', respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify the data into hit and miss categories in order to achieve data reduction. The quantitative performance established in previous work indicates that CNNs successfully classify serial crystallography data into desired categories [Ke, Brewster, Yu, Ushizima, Yang & Sauter (2018). J. Synchrotron Rad.25, 655-670], but no qualitative evidence on the internal workings of these networks has been provided. For example, there are no visualization methods that highlight the features contributing to a specific prediction while classifying data in serial crystallography experiments. Therefore, existing deep learning methods, including CNNs classifying serial crystallography data, are like a 'black box'. To this end, presented here is a qualitative study to unpack the internal workings of CNNs with the aim of visualizing information in the fundamental blocks of a standard network with serial crystallography data. The region(s) or part(s) of an image that mostly contribute to a hit or miss prediction are visualized.
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Affiliation(s)
- Shah Nawaz
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Vahid Rahmani
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - David Pennicard
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | | | | | - Heinz Graafsma
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
- Mid-Sweden University, Sundsvall, Sweden
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Gao J, Chen M, Li Y, Gao Y, Li Y, Cai S, Wang J. Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks. Front Neurosci 2021; 14:629630. [PMID: 33584183 PMCID: PMC7877487 DOI: 10.3389/fnins.2020.629630] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 12/29/2020] [Indexed: 12/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients' families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.
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Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingren Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Li
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yachun Gao
- School of Physics, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China
| | - Shimin Cai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
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Chuang WY, Chang SH, Yu WH, Yang CK, Yeh CJ, Ueng SH, Liu YJ, Chen TD, Chen KH, Hsieh YY, Hsia Y, Wang TH, Hsueh C, Kuo CF, Yeh CY. Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning. Cancers (Basel) 2020; 12:cancers12020507. [PMID: 32098314 PMCID: PMC7072217 DOI: 10.3390/cancers12020507] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/16/2020] [Accepted: 02/19/2020] [Indexed: 12/14/2022] Open
Abstract
Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC.
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Affiliation(s)
- Wen-Yu Chuang
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
| | - Shang-Hung Chang
- Center for Big Data Analytics and Statistics, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan;
| | - Wei-Hsiang Yu
- aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan; (W.-H.Y.); (C.-K.Y.)
| | - Cheng-Kun Yang
- aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan; (W.-H.Y.); (C.-K.Y.)
| | - Chi-Ju Yeh
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Shir-Hwa Ueng
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Chang Gung Molecular Medicine Research Center, Chang Gung University, No. 259, Wenhua First Road, Guishan District, Taoyuan City 333, Taiwan
| | - Yu-Jen Liu
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Tai-Di Chen
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Kuang-Hua Chen
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Yi-Yin Hsieh
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Yi Hsia
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Department of Pathology, MacKay Memorial Hospital, No. 92, Section 2, Zhongshan North Road, Zhongshan District, Taipei City 104, Taiwan
| | - Tong-Hong Wang
- Tissue Bank, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan;
| | - Chuen Hsueh
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Chang Gung Molecular Medicine Research Center, Chang Gung University, No. 259, Wenhua First Road, Guishan District, Taoyuan City 333, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan;
| | - Chao-Yuan Yeh
- aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan; (W.-H.Y.); (C.-K.Y.)
- Correspondence: ; Tel.: +886-2-27856892
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