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Jia W, Li F, Cui Y, Wang Y, Dai Z, Yan Q, Liu X, Li Y, Chang H, Zeng Q. Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases. Acad Radiol 2024:S1076-6332(24)00221-6. [PMID: 38702214 DOI: 10.1016/j.acra.2024.04.012] [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: 02/17/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 05/06/2024]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION The DLR model is an effective method for identifying the primary source of liver metastases.
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Affiliation(s)
- Wenjing Jia
- 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.
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China.
| | - Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 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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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [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: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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Li Y, Li J, Meng M, Duan S, Shi H, Hang J. Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer. Diagnostics (Basel) 2023; 13:2937. [PMID: 37761304 PMCID: PMC10528017 DOI: 10.3390/diagnostics13182937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
The origin of metastatic liver tumours (arising from gastric or colorectal sources) is closely linked to treatment choices and survival prospects. However, in some instances, the primary lesion remains elusive even after an exhaustive diagnostic investigation. Consequently, we have devised and validated a radiomics nomogram for ascertaining the primary origin of liver metastases stemming from gastric cancer (GCLMs) and colorectal cancer (CCLMs). This retrospective study encompassed patients diagnosed with either GCLMs or CCLMs, comprising a total of 277 GCLM cases and 278 CCLM cases. Radiomic characteristics were derived from venous phase computed tomography (CT) scans, and a radiomics signature (RS) was computed. Multivariable regression analysis demonstrated that gender (OR = 3.457; 95% CI: 2.102-5.684; p < 0.001), haemoglobin levels (OR = 0.976; 95% CI: 0.967-0.986; p < 0.001), carcinoembryonic antigen (CEA) levels (OR = 0.500; 95% CI: 0.307-0.814; p = 0.005), and RS (OR = 2.147; 95% CI: 1.127-4.091; p = 0.020) exhibited independent associations with GCLMs as compared to CCLMs. The nomogram, combining RS with clinical variables, demonstrated strong discriminatory power in both the training (AUC = 0.71) and validation (AUC = 0.78) cohorts. The calibration curve, decision curve analysis, and clinical impact curves revealed the clinical utility of this nomogram and substantiated its enhanced diagnostic performance.
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Affiliation(s)
- Yuying Li
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
- Graduate College, Dalian Medical University, Dalian 116000, China
| | - Jingjing Li
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
- Graduate College, Dalian Medical University, Dalian 116000, China
| | - Mingzhu Meng
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai 201100, China;
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
| | - Junjie Hang
- Department of Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
- Department of Oncology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China
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Liu K, Qin S, Ning J, Xin P, Wang Q, Chen Y, Zhao W, Zhang E, Lang N. Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI. Cancers (Basel) 2023; 15:cancers15112974. [PMID: 37296938 DOI: 10.3390/cancers15112974] [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: 04/20/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists.
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Affiliation(s)
- Ke Liu
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Siyuan Qin
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Jinlai Ning
- Department of Informatics, King's College London, London WC2B 4BG, UK
| | - Peijin Xin
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Qizheng Wang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Weili Zhao
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Enlong Zhang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
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Wang Y, Li X, Konanur M, Konkel B, Seyferth E, Brajer N, Liu JG, Bashir MR, Lafata KJ. Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality. Med Phys 2022. [PMID: 36548913 DOI: 10.1002/mp.16181] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/27/2022] [Accepted: 11/08/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with clinical data can be non-trivial. Optimal integration requires robust data fusion, that is, the process of integrating multiple data sources to produce more useful information than captured by individual data sources. Here, we introduce the concept of fusion quality for deep learning problems involving imaging and clinical data. We first provide a general theoretical framework and numerical validation of our technique. To demonstrate real-world applicability, we then apply our technique to optimize the fusion of CT imaging and hepatic blood markers to estimate portal venous hypertension, which is linked to prognosis in patients with cirrhosis of the liver. PURPOSE To develop a measurement method of optimal data fusion quality deep learning problems utilizing both imaging data and clinical data. METHODS Our approach is based on modeling the fully connected layer (FCL) of a convolutional neural network (CNN) as a potential function, whose distribution takes the form of the classical Gibbs measure. The features of the FCL are then modeled as random variables governed by state functions, which are interpreted as the different data sources to be fused. The probability density of each source, relative to the probability density of the FCL, represents a quantitative measure of source-bias. To minimize this source-bias and optimize CNN performance, we implement a vector-growing encoding scheme called positional encoding, where low-dimensional clinical data are transcribed into a rich feature space that complements high-dimensional imaging features. We first provide a numerical validation of our approach based on simulated Gaussian processes. We then applied our approach to patient data, where we optimized the fusion of CT images with blood markers to predict portal venous hypertension in patients with cirrhosis of the liver. This patient study was based on a modified ResNet-152 model that incorporates both images and blood markers as input. These two data sources were processed in parallel, fused into a single FCL, and optimized based on our fusion quality framework. RESULTS Numerical validation of our approach confirmed that the probability density function of a fused feature space converges to a source-specific probability density function when source data are improperly fused. Our numerical results demonstrate that this phenomenon can be quantified as a measure of fusion quality. On patient data, the fused model consisting of both imaging data and positionally encoded blood markers at the theoretically optimal fusion quality metric achieved an AUC of 0.74 and an accuracy of 0.71. This model was statistically better than the imaging-only model (AUC = 0.60; accuracy = 0.62), the blood marker-only model (AUC = 0.58; accuracy = 0.60), and a variety of purposely sub-optimized fusion models (AUC = 0.61-0.70; accuracy = 0.58-0.69). CONCLUSIONS We introduced the concept of data fusion quality for multi-source deep learning problems involving both imaging and clinical data. We provided a theoretical framework, numerical validation, and real-world application in abdominal radiology. Our data suggests that CT imaging and hepatic blood markers provide complementary diagnostic information when appropriately fused.
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Affiliation(s)
- Yuqi Wang
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Xiang Li
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Meghana Konanur
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Brandon Konkel
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | | | - Nathan Brajer
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Jian-Guo Liu
- Department of Mathematics, Duke University, Durham, North Carolina, USA.,Department of Physics, Duke University, Durham, North Carolina, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University, Durham, North Carolina, USA.,Department of Medicine, Gastroenterology, Duke University, Durham, North Carolina, USA
| | - Kyle J Lafata
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.,Department of Radiology, Duke University, Durham, North Carolina, USA.,Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol 2022; 28:3398-3409. [PMID: 36158262 PMCID: PMC9346461 DOI: 10.3748/wjg.v28.i27.3398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/18/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.
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Affiliation(s)
- Liu-Liu Cao
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Mei Peng
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xiang Xie
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
| | - Shu-Yan Huang
- Department of Medical Ultrasound, The First People's Hospital of Huaihua, Huaihua 418000, Hunan Province, China
| | - Jia-Yu Wang
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3626, Switzerland
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Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI. Sci Rep 2022; 12:7924. [PMID: 35562532 PMCID: PMC9106680 DOI: 10.1038/s41598-022-11997-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/12/2022] [Indexed: 12/05/2022] Open
Abstract
With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.
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Landwehr JP, Kühl N, Walk J, Gnädig M. Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems. BUSINESS & INFORMATION SYSTEMS ENGINEERING 2022. [PMCID: PMC8973684 DOI: 10.1007/s12599-022-00745-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature.
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Affiliation(s)
- Julius Peter Landwehr
- Institute of Information Systems and Marketing (IISM) / Karlsruhe Service Research Institute (KSRI), Kaiserstraße 89, 76133 Karlsruhe, Germany
| | - Niklas Kühl
- Institute of Information Systems and Marketing (IISM) / Karlsruhe Service Research Institute (KSRI), Kaiserstraße 89, 76133 Karlsruhe, Germany
| | - Jannis Walk
- Institute of Information Systems and Marketing (IISM) / Karlsruhe Service Research Institute (KSRI), Kaiserstraße 89, 76133 Karlsruhe, Germany
| | - Mario Gnädig
- Netze BW GmbH, Schelmenwasenstraße 15, 70567 Stuttgart, Germany
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9
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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11
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Vaz K, Goodwin T, Kemp W, Roberts S, Majeed A. Artificial Intelligence in Hepatology: A Narrative Review. Semin Liver Dis 2021; 41:551-556. [PMID: 34327698 DOI: 10.1055/s-0041-1731706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
There has been a tremendous growth in data collection in hepatology over the last decade. This wealth of "big data" lends itself to the application of artificial intelligence in the development of predictive and diagnostic models with potentially greater accuracy than standard biostatistics. As processing power of computing systems has improved and data are made more accessible through the large databases and electronic health record, these more contemporary techniques for analyzing and interpreting data have garnered much interest in the field of medicine. This review highlights the current evidence base for the use of artificial intelligence in hepatology, focusing particularly on the areas of diagnosis and prognosis of advanced chronic liver disease and hepatic neoplasia.
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Affiliation(s)
- Karl Vaz
- Department of Gastroenterology and Hepatology, Austin Health, Melbourne, Australia
| | - Thomas Goodwin
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
| | - William Kemp
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| | - Stuart Roberts
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| | - Ammar Majeed
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
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Computed Tomography Image Feature under Intelligent Algorithms in Diagnosing the Effect of Humanized Nursing on Neuroendocrine Hormones in Patients with Primary Liver Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4563100. [PMID: 34659687 PMCID: PMC8514893 DOI: 10.1155/2021/4563100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/12/2021] [Accepted: 09/16/2021] [Indexed: 11/18/2022]
Abstract
This study was to explore the application value of computed tomography (CT) images processed by intelligent algorithm denoising in the evaluation of humanized nursing in postoperative neuroendocrine hormone changes in patients with primary liver cancer (PLC). In this study, a simple-structured recursive residual coding and decoding (RRCD) algorithm was constructed on the basis of residual network, which can effectively remove artifacts and noise in CT images and can also restore image details and lesion features well. In addition, 60 postoperative patients with primary liver cancer were collected and divided into routine nursing control group (30 cases) and humanized nursing experimental group (30 cases). After a period of nursing, CT images based on intelligent algorithms were evaluated by determining the hormone content. The results showed that the focal necrosis rate (FNR) of the experimental group was 6%. The adrenocorticotropic hormone (ACTH) levels of 6 and 15 days after admission (T3 and T4) were 41.25 ± 3.81 pg/mL and 19.55 ± 1.72 pg/mL, respectively. The cortisol levels of days 6, 15, and 30 after admission (T3, T4, and T5) were 424.86 ± 16.82 nmol/L, 277.98 ± 14.36 nmol/L, and 241.53 ± 13.27 nmol/L, respectively. Estradiol levels were 53.48 ± 11.19 pg/mL, 41.64 ± 9.28 pg/mL, and 30.59 ± 8.16 pg/mL, respectively. Testosterone levels were 2.18 ± 1.14 ng/mL, 1.78 ± 1.03 ng/mL, and 1.42 ± 0.69 ng/mL, respectively. Self-Rating Anxiety Scale (SAS) scores were 40.24 ± 5.81 points, 36.55 ± 5.02 points, and 32.53 ± 4.8 points, respectively. There were 24 cases, 27 cases, 23 cases, and 21 patients who followed no smoking and drinking, taking medication on time, diet control, and self-monitoring. The scores of physical function, self-cognition, emotional function, and social function were 62.59 ± 6.82 points, 69.26 ± 8.14 points, 73.89 ± 6.35 points, and 66.88 ± 7.04 points, which were better than those of the control group in all aspects (P < 0.05). In short, the humanized nursing course can enhance the compliance of the patients after the surgery, improve the quality of life, and inhibit the anxiety and depression of the patients, so it showed a positive effect on the neuroendocrine hormones and the prognosis of the patients.
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Qin H, Wu YQ, Lin P, Gao RZ, Li X, Wang XR, Chen G, He Y, Yang H. Ultrasound Image-Based Radiomics: An Innovative Method to Identify Primary Tumorous Sources of Liver Metastases. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:1229-1244. [PMID: 32951217 DOI: 10.1002/jum.15506] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/17/2020] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVES To develop radiomic models of B-mode ultrasound (US) signatures for determining the origin of primary tumors in metastatic liver disease. METHODS A total of 254 patients with a diagnosis of metastatic liver disease were included in this retrospective study. The patients were divided into 3 groups depending on the origin of the primary tumor: group 1 (digestive tract versus non-digestive tract tumors), group 2 (breast cancer versus non-breast cancer), and group 3 (lung cancer versus other malignancies). The patients in each group were allocated to a training or testing set (a ratio of 8:2). The region of interest of liver metastasis was determined through manual differentiation of the tumors, and radiomic signatures were acquired from B-mode US images. Optimal features were selected to develop 3 radiomic models using multiple-dimensionality reduction and classifier screening. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess each model's performance. RESULTS A total of 5936 features were extracted, and 40, 6, and 14 optimal features were sequentially identified for the development of radiomic models for groups 1, 2, and 3, respectively, with training set AUC values of 0.938, 0.974, and 0.768 and testing set AUC values of 0.767, 0.768, and 0.750. The differences in age, sex, and number of liver metastatic lesions varied greatly between the 4 primary tumors (P < .050). CONCLUSIONS B-mode US radiomic models could be effective supplemental means to identify the origin of hepatic metastatic lesions (ie, unknown primary sites).
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Affiliation(s)
- Hui Qin
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yu-Quan Wu
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rui-Zhi Gao
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- Department of Life Sciences, GE Healthcare, Shanghai, China
| | - Xin-Rong Wang
- Department of Life Sciences, GE Healthcare, Shanghai, China
| | - Gang Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10023-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Shivakumar N, Chandrashekar A, Handa AI, Lee R. Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review. Postgrad Med J 2021; 98:e20. [PMID: 33688072 DOI: 10.1136/postgradmedj-2020-139620] [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: 12/16/2020] [Revised: 02/08/2021] [Accepted: 02/20/2021] [Indexed: 11/16/2022]
Abstract
CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.
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Affiliation(s)
- Natesh Shivakumar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Anirudh Chandrashekar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ashok Inderraj Handa
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Regent Lee
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
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Kim SS, Lee DH, Lee MW, Kim SY, Shin J, Choi JY, Choi BW. Construction of a Standard Dataset for Liver Tumors for Testing the Performance and Safety of Artificial Intelligence-Based Clinical Decision Support Systems. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2021; 82:1196-1206. [PMID: 36238394 PMCID: PMC9432358 DOI: 10.3348/jksr.2020.0177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/24/2020] [Accepted: 02/04/2021] [Indexed: 11/15/2022]
Affiliation(s)
- Seung-seob Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Dong Ho Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Min Woo Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - So Yeon Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jaeseung Shin
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jin-Young Choi
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Byoung Wook Choi
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Neubert A, Bourgeat P, Wood J, Engstrom C, Chandra SS, Crozier S, Fripp J. Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative. Med Phys 2020; 47:4939-4948. [PMID: 32745260 DOI: 10.1002/mp.14421] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/07/2020] [Accepted: 07/24/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE High resolution three-dimensional (3D) magnetic resonance (MR) images are well suited for automated cartilage segmentation in the human knee joint. However, volumetric scans such as 3D Double-Echo Steady-State (DESS) images are not routinely acquired in clinical practice which limits opportunities for reliable cartilage segmentation using (fully) automated algorithms. In this work, a method for generating synthetic 3D MR (syn3D-DESS) images with better contrast and higher spatial resolution from routine, low resolution, two-dimensional (2D) Turbo-Spin Echo (TSE) clinical knee scans is proposed. METHODS A UNet convolutional neural network is employed for synthesizing enhanced artificial MR images suitable for automated knee cartilage segmentation. Training of the model was performed on a large, publically available dataset from the OAI, consisting of 578 MR examinations of knee joints from 102 healthy individuals and patients with knee osteoarthritis. RESULTS The generated synthetic images have higher spatial resolution and better tissue contrast than the original 2D TSE, which allow high quality automated 3D segmentations of the cartilage. The proposed approach was evaluated on a separate set of MR images from 88 subjects with manual cartilage segmentations. It provided a significant improvement in automated segmentation of knee cartilages when using the syn3D-DESS images compared to the original 2D TSE images. CONCLUSION The proposed method can successfully synthesize 3D DESS images from 2D TSE images to provide images suitable for automated cartilage segmentation.
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Affiliation(s)
- Aleš Neubert
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
| | - Jason Wood
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
| | - Craig Engstrom
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
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18
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Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, Dietrich CF. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019; 25:672-682. [PMID: 30783371 PMCID: PMC6378542 DOI: 10.3748/wjg.v25.i6.672] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 12/24/2018] [Accepted: 01/09/2019] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.
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Affiliation(s)
- Li-Qiang Zhou
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jia-Yu Wang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Song-Yuan Yu
- Department of Ultrasound, Tianyou Hospital Affiliated to Wuhan University of Technology, Wuhan 430030, Hubei Province, China
| | - Ge-Ge Wu
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Qi Wei
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - You-Bin Deng
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xing-Long Wu
- School of Mathematics and Computer Science, Wuhan Textitle University, Wuhan 430200, Hubei Province, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Christoph F Dietrich
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg, Würzburg 97980, Germany
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Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. Radiology 2019; 290:590-606. [PMID: 30694159 DOI: 10.1148/radiol.2018180547] [Citation(s) in RCA: 272] [Impact Index Per Article: 54.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are entailed in the design process of deep learning radiology research. In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional neural networks-to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. The survey of the studies is followed by a discussion about current challenges and future trends and their potential implications for radiology. This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks.
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Affiliation(s)
- Shelly Soffer
- From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.)
| | - Avi Ben-Cohen
- From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.)
| | - Orit Shimon
- From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.)
| | - Michal Marianne Amitai
- From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.)
| | - Hayit Greenspan
- From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.)
| | - Eyal Klang
- From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.)
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Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML. Deep learning in medical imaging and radiation therapy. Med Phys 2019; 46:e1-e36. [PMID: 30367497 PMCID: PMC9560030 DOI: 10.1002/mp.13264] [Citation(s) in RCA: 372] [Impact Index Per Article: 74.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 09/18/2018] [Accepted: 10/09/2018] [Indexed: 12/15/2022] Open
Abstract
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.
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Affiliation(s)
- Berkman Sahiner
- DIDSR/OSEL/CDRH U.S. Food and Drug AdministrationSilver SpringMD20993USA
| | - Aria Pezeshk
- DIDSR/OSEL/CDRH U.S. Food and Drug AdministrationSilver SpringMD20993USA
| | | | - Xiaosong Wang
- Imaging Biomarkers and Computer‐aided Diagnosis LabRadiology and Imaging SciencesNIH Clinical CenterBethesdaMD20892‐1182USA
| | - Karen Drukker
- Department of RadiologyUniversity of ChicagoChicagoIL60637USA
| | - Kenny H. Cha
- DIDSR/OSEL/CDRH U.S. Food and Drug AdministrationSilver SpringMD20993USA
| | - Ronald M. Summers
- Imaging Biomarkers and Computer‐aided Diagnosis LabRadiology and Imaging SciencesNIH Clinical CenterBethesdaMD20892‐1182USA
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Trivizakis E, Manikis GC, Nikiforaki K, Drevelegas K, Constantinides M, Drevelegas A, Marias K. Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation. IEEE J Biomed Health Inform 2018; 23:923-930. [PMID: 30561355 DOI: 10.1109/jbhi.2018.2886276] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of several clinical outcomes. In this paper, we propose and evaluate a novel three-dimensional (3-D) convolutional neural network (CNN) designed for tissue classification in medical imaging and applied for discriminating between primary and metastatic liver tumors from diffusion weighted MRI (DW-MRI) data. The proposed network consists of four consecutive strided 3-D convolutional layers with 3 × 3 × 3 kernel size and rectified linear unit (ReLU) as activation function, followed by a fully connected layer with 2048 neurons and a Softmax layer for binary classification. A dataset comprising 130 DW-MRI scans was used for the training and validation of the network. To the best of our knowledge this is the first DL solution for the specific clinical problem and the first 3-D CNN for cancer classification operating directly on whole 3-D tomographic data without the need of any preprocessing step such as region cropping, annotating, or detecting regions of interest. The classification performance results, 83% (3-D) versus 69.6% and 65.2% (2-D), demonstrated significant tissue classification accuracy improvement compared to two 2-D CNNs of different architectures also designed for the specific clinical problem with the same dataset. These results suggest that the proposed 3-D CNN architecture can bring significant benefit in DW-MRI liver discrimination and potentially, in numerous other tissue classification problems based on tomographic data, especially in size-limited, disease-specific clinical datasets.
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Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol 2018; 28:4578-4585. [PMID: 29761358 DOI: 10.1007/s00330-018-5499-7] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/10/2018] [Accepted: 04/18/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. METHODS This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (FDLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance. RESULTS The FDLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using FDLCT scores were 0.74 (0.64-0.85), 0.76 (0.66-0.85) and 0.73 (0.62-0.84), respectively. CONCLUSIONS Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance. KEY POINTS • Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Shigeru Kiryu
- Department of Radiology, Graduate School of Medical Sciences, International University of Health and Welfare, 537-3 Iguchi, Nasushiobara, Tochigi, 329-2763, Japan.
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Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol 2018; 36:257-272. [PMID: 29498017 DOI: 10.1007/s11604-018-0726-3] [Citation(s) in RCA: 188] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 02/26/2018] [Indexed: 12/28/2022]
Abstract
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Shigeru Kiryu
- Department of Radiology, Graduate School of Medical Sciences, International University of Health and Welfare, 4-3 Kozunomori, Narita, Chiba, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
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Gesundheit 4.0 – Wie gehts uns denn morgen? Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2018; 61:334-339. [DOI: 10.1007/s00103-018-2702-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Hammes J, Täger P, Drzezga A. EBONI: A Tool for Automated Quantification of Bone Metastasis Load in PSMA PET/CT. J Nucl Med 2017; 59:1070-1075. [PMID: 29242401 DOI: 10.2967/jnumed.117.203265] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 11/23/2017] [Indexed: 12/18/2022] Open
Abstract
Prostate-specific membrane antigen (PSMA) PET/CT has a high diagnostic accuracy for lesion detection in metastatic prostate cancer, including bone metastases. Novel therapeutic approaches require valid biomarkers for standardized disease staging and for evaluation of progression and therapy response. Here, we introduce EBONI (Evaluation of Bone Involvement), a software tool to automatically quantify the bone metastasis load in PSMA PET/CT. Lesion quantity, mean and maximum lesional SUV, z score, and percentage of affected bone volume are determined. EBONI is open source and freely available. Methods: To validate EBONI, the results of automated quantification of 38 PSMA PET/CT scans with different levels of bone involvement were compared with visual expert reading. The influence of SUV threshold and Hounsfield unit thresholds was analyzed. Results: A high correlation between bone lesion quantity as determined visually and automatically was found (SUVmax, r2 = 0.97; SUVmean, r2 = 0.88; lesion count, r2 = 0.97). The Hounsfield unit threshold had no significant influence, whereas an SUV threshold of 2.5 proved optimal for automated lesion quantification. The systematic error of false-positive tissue misclassification was low, occurred mainly around the salivary and lacrimal glands, and could easily be corrected. There were no false-negative ratings. Conclusion: EBONI analysis is robust, quick (<3 min per scan), and 100% reproducible. It allows rater-independent quantification of bone metastasis in metastatic prostate cancer. It provides lesion quantification equivalent to that of visual assessment, as well as providing complementary information. It can be easily implemented as an add-on to visual analysis of PSMA PET/CT scans and has the potential to reduce turnaround time.
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Affiliation(s)
- Jochen Hammes
- Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Philipp Täger
- Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
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