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Da-Ano R, Andrade-Miranda G, Tankyevych O, Visvikis D, Conze PH, Rest CCL. Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion. Sci Rep 2024; 14:16720. [PMID: 39030240 PMCID: PMC11271510 DOI: 10.1038/s41598-024-66487-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: 01/26/2024] [Accepted: 07/01/2024] [Indexed: 07/21/2024] Open
Abstract
Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.
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Affiliation(s)
- Ronrick Da-Ano
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
| | | | - Olena Tankyevych
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
- Nuclear Medicine, University of Poitiers, Poitiers, France
| | | | - Pierre-Henri Conze
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
- IMT Atlantique, Brest, France
| | - Catherine Cheze Le Rest
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
- Nuclear Medicine, University of Poitiers, Poitiers, France
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Du Y, Xiao Y, Guo W, Yao J, Lan T, Li S, Wen H, Zhu W, He G, Zheng H, Chen H. Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours. Biomed Eng Online 2024; 23:41. [PMID: 38594729 PMCID: PMC11003110 DOI: 10.1186/s12938-024-01234-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: 02/05/2024] [Accepted: 04/02/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
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Affiliation(s)
- Yangchun Du
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Yanju Xiao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Wenwen Guo
- Department of Pathology, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Jinxiu Yao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Tongliu Lan
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Sijin Li
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Huoyue Wen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Wenying Zhu
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Guangling He
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Hongyu Zheng
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
| | - Haining Chen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
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Jin Y, Liu J, Zhou Y, Chen R, Chen H, Duan W, Chen Y, Zhang XL. CRDet: A circle representation detector for lung granulomas based on multi-scale attention features with center point calibration. Comput Med Imaging Graph 2024; 113:102354. [PMID: 38341946 DOI: 10.1016/j.compmedimag.2024.102354] [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: 09/14/2023] [Revised: 02/04/2024] [Accepted: 02/04/2024] [Indexed: 02/13/2024]
Abstract
Lung granuloma is a very common lung disease, and its specific diagnosis is important for determining the exact cause of the disease as well as the prognosis of the patient. And, an effective lung granuloma detection model based on computer-aided diagnostics (CAD) can help pathologists to localize granulomas, thereby improving the efficiency of the specific diagnosis. However, for lung granuloma detection models based on CAD, the significant size differences between granulomas and how to better utilize the morphological features of granulomas are both critical challenges to be addressed. In this paper, we propose an automatic method CRDet to localize granulomas in histopathological images and deal with these challenges. We first introduce the multi-scale feature extraction network with self-attention to extract features at different scales at the same time. Then, the features will be converted to circle representations of granulomas by circle representation detection heads to achieve the alignment of features and ground truth. In this way, we can also more effectively use the circular morphological features of granulomas. Finally, we propose a center point calibration method at the inference stage to further optimize the circle representation. For model evaluation, we built a lung granuloma circle representation dataset named LGCR, including 288 images from 50 subjects. Our method yielded 0.316 mAP and 0.571 mAR, outperforming the state-of-the-art object detection methods on our proposed LGCR.
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Affiliation(s)
- Yu Jin
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China.
| | - Yuanyuan Zhou
- Department of Immunology, TaiKang Medical School (School of Basic Medical Sciences), Wuhan University, Wuhan, China; Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan, China
| | - Rong Chen
- Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Hua Chen
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Wensi Duan
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Yuqi Chen
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Xiao-Lian Zhang
- Department of Immunology, TaiKang Medical School (School of Basic Medical Sciences), Wuhan University, Wuhan, China; Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan, China
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Liu K, Lin X, Chen X, Chen B, Li S, Li K, Chen H, Li L. Development and validation of a deep learning signature for predicting lymphovascular invasion and survival outcomes in clinical stage IA lung adenocarcinoma: A multicenter retrospective cohort study. Transl Oncol 2024; 42:101894. [PMID: 38324961 PMCID: PMC10851213 DOI: 10.1016/j.tranon.2024.101894] [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: 09/28/2023] [Revised: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/09/2024] Open
Abstract
PURPOSE The presence of lymphovascular invasion (LVI) influences the management and outcomes of patients with clinical stage IA lung adenocarcinoma. The objective was the development of a deep learning (DL) signature for the prediction of LVI and stratification of prognosis. METHODS A total of 2077 patients from three centers were retrospectively enrolled and divided into a training set (n = 1515), an internal validation set (n = 381), and an external set (n = 181). A -three-dimensional residual neural network was used to extract the DL signature and three models, namely, the clinical, DL, and combined models, were developed. Diagnostic efficiency was assessed by ROC curves and AUC values. Kaplan-Meier curves and Cox proportional hazards regression analyses were conducted to evaluate links between various factors and disease-free survival. RESULTS The DL model could effectively predict LVI, shown by AUC values of 0.72 (95 %CI: 0.68-0.76) and 0.63 (0.54-0.73) in the internal and external validation sets, respectively. The incorporation of DL signature and clinical-radiological factors increased the AUC to 0.74 (0.71-0.78) and 0.77 (0.70-0.84) in comparison with the DL and clinical models (AUC of 0.71 [0.68-0.75], 0.71 [0.61-0.81]) in the internal and external validation sets, respectively. Pathologic LVI, LVI predicted by both DL and combined models were associated with unfavorable prognosis (all p < 0.05). CONCLUSION The effectiveness of the DL signature in the diagnosis of LVI and prognosis prediction in patients with clinical stage IA lung adenocarcinoma was demonstrated. These findings suggest the potential of the model in clinical decision-making.
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Affiliation(s)
- Kunfeng Liu
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Xiaofeng Lin
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Xiaojuan Chen
- Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, PR China
| | - Biyun Chen
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Sheng Li
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, PR China
| | - Huai Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, PR China
| | - Li Li
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
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Li Y, Chen D, Liu S, Lin J, Wang W, Huang J, Tan L, Liang L, Wang Z, Peng K, Li Q, Jian W, Zhang Y, Peng C, Chen H, Zhang X, Zheng J. Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT. Mycoses 2024; 67:e13692. [PMID: 38214431 DOI: 10.1111/myc.13692] [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: 10/04/2023] [Revised: 12/16/2023] [Accepted: 12/22/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research. OBJECTIVES To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT. METHODS Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou. A deep learning framework was employed to develop two models: an undelineated supervised training (UST) model utilising original CT images, and a delineated supervised training (DST) model utilising CT images with manual lesion annotations provided by physicians. A subset of 20 cases was randomly selected from the entire dataset and reviewed by clinicians through a network questionnaire. The sensitivity, specificity and accuracy of the models and the clinicians were calculated. RESULTS A total of 395 PC cases and 249 LA cases were included in the final analysis. The internal validation results for the UST model showed a sensitivity of 85.3%, specificity of 81.0%, accuracy of 83.6% and an area under the curve (AUC) of 0.93. Similarly, the DST model exhibited a sensitivity of 88.2%, specificity of 88.1%, accuracy of 88.2% and an AUC of 0.94. The external validation of the two models yielded AUC values of 0.74 and 0.77, respectively. The average sensitivity, specificity and accuracy of 102 clinicians were determined to be 63.1%, 53.7% and 59.3%, respectively. CONCLUSIONS Both models outperformed the clinicians in distinguishing between PC and LA on chest CT, with the UST model exhibiting comparable performance to the DST model.
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Affiliation(s)
- Yun Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Deyan Chen
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Shuyi Liu
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Junfeng Lin
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wei Wang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jinhai Huang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lunfang Tan
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lina Liang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhufeng Wang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kang Peng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiasheng Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenhua Jian
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Youwen Zhang
- Department of Neurology, Gaozhou People's Hospital, Gaozhou, China
| | - Chengbao Peng
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Huai Chen
- Department of Radiology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xia Zhang
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Jinping Zheng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Usuzaki T, Takahashi K, Takagi H, Ishikuro M, Obara T, Yamaura T, Kamimoto M, Majima K. Efficacy of exponentiation method with a convolutional neural network for classifying lung nodules on CT images by malignancy level. Eur Radiol 2023; 33:9309-9319. [PMID: 37477673 DOI: 10.1007/s00330-023-09946-w] [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: 02/12/2023] [Revised: 04/24/2023] [Accepted: 05/19/2023] [Indexed: 07/22/2023]
Abstract
OBJECTIVES The aim of this study was to examine the performance of a convolutional neural network (CNN) combined with exponentiating each pixel value in classifying benign and malignant lung nodules on computed tomography (CT) images. MATERIALS AND METHODS Images in the Lung Image Database Consortium-Image Database Resource Initiative (LIDC-IDRI) were analyzed. Four CNN models were then constructed to classify the lung nodules by malignancy level (malignancy level 1 vs. 2, malignancy level 1 vs. 3, malignancy level 1 vs. 4, and malignancy level 1 vs. 5). The exponentiation method was applied for exponent values of 1.0 to 10.0 in increments of 0.5. Accuracy, sensitivity, specificity, and area under the curve of receiver operating characteristics (AUC-ROC) were calculated. These statistics were compared between an exponent value of 1.0 and all other exponent values in each model by the Mann-Whitney U-test. RESULTS In malignancy 1 vs. 4, maximum test accuracy (MTA; exponent value = 2.0, 3.0, 3.5, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, and 10.0) and specificity (6.5, 7.0, and 9.0) were improved by up to 0.012 and 0.037, respectively. In malignancy 1 vs. 5, MTA (6.5 and 7.0) and sensitivity (1.5) were improved by up to 0.030 and 0.0040, respectively. CONCLUSIONS The exponentiation method improved the performance of the CNN in the task of classifying lung nodules on CT images as benign or malignant. The exponentiation method demonstrated two advantages: improved accuracy, and the ability to adjust sensitivity and specificity by selecting an appropriate exponent value. CLINICAL RELEVANCE STATEMENT Adjustment of sensitivity and specificity by selecting an exponent value enables the construction of proper CNN models for screening, diagnosis, and treatment processes among patients with lung nodules. KEY POINTS • The exponentiation method improved the performance of the convolutional neural network. • Contrast accentuation by the exponentiation method may derive features of lung nodules. • Sensitivity and specificity can be adjusted by selecting an exponent value.
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Affiliation(s)
- Takuma Usuzaki
- Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
| | - Kengo Takahashi
- Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hidenobu Takagi
- Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan
- Department of Advanced MRI Collaborative Research, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Mami Ishikuro
- Division of Molecular Epidemiology, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
| | - Taku Obara
- Division of Molecular Epidemiology, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
- Division of Molecular Epidemiology, Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
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Shi L, Sheng M, Wei Z, Liu L, Zhao J. CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis. Acad Radiol 2023; 30:3064-3075. [PMID: 37385850 DOI: 10.1016/j.acra.2023.05.026] [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: 04/21/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023]
Abstract
RATIONALE AND OBJECTIVES More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies. MATERIALS AND METHODS PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity. RESULTS In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs. CONCLUSION CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China (M.S.)
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Lei Liu
- Institutes of Intelligence Medicine, Fudan University, Shanghai, China (L.L.)
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China (J.Z.).
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Ma L, Wan C, Hao K, Cai A, Liu L. A novel fusion algorithm for benign-malignant lung nodule classification on CT images. BMC Pulm Med 2023; 23:474. [PMID: 38012620 PMCID: PMC10683224 DOI: 10.1186/s12890-023-02708-w] [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: 03/07/2023] [Accepted: 10/12/2023] [Indexed: 11/29/2023] Open
Abstract
The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.
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Affiliation(s)
- Ling Ma
- College of Software, Nankai University, Tianjin, 300350, China
| | - Chuangye Wan
- College of Software, Nankai University, Tianjin, 300350, China
| | - Kexin Hao
- College of Software, Nankai University, Tianjin, 300350, China
| | - Annan Cai
- College of Software, Nankai University, Tianjin, 300350, China
| | - Lizhi Liu
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
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Zhao T, Qi S, Yue Y, Zhang B, Li J, Wen Y, Yao Y, Qian W, Guan Y. CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:981-999. [PMID: 37424490 DOI: 10.3233/xst-230063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
BACKGROUND Pulmonary granulomatous nodules (GN) with spiculation or lobulation have a similar morphological appearance to solid lung adenocarcinoma (SADC) under computed tomography (CT). However, these two kinds of solid pulmonary nodules (SPN) have different malignancies and are sometimes misdiagnosed. OBJECTIVE This study aims to predict malignancies of SPNs by a deep learning model automatically. METHODS A chimeric label with self-supervised learning (CLSSL) is proposed to pre-train a ResNet-based network (CLSSL-ResNet) for distinguishing isolated atypical GN from SADC in CT images. The malignancy, rotation, and morphology labels are integrated into a chimeric label and utilized to pre-train a ResNet50. The pre-trained ResNet50 is then transferred and fine-tuned to predict the malignancy of SPN. Two image datasets of 428 subjects (Dataset1, 307; Dataset2, 121) from different hospitals are collected. Dataset1 is divided into training, validation, and test data by a ratio of 7:1:2 to develop the model. Dataset2 is utilized as an external validation dataset. RESULTS CLSSL-ResNet achieves an area under the ROC curve (AUC) of 0.944 and an accuracy (ACC) of 91.3%, which was much higher than that of the consensus of two experienced chest radiologists (77.3%). CLSSL-ResNet also outperforms other self-supervised learning models and many counterparts of other backbone networks. In Dataset2, AUC and ACC of CLSSL-ResNet are 0.923 and 89.3%, respectively. Additionally, the ablation experiment result indicates higher efficiency of the chimeric label. CONCLUSION CLSSL with morphology labels can increase the ability of feature representation by deep networks. As a non-invasive method, CLSSL-ResNet can distinguish GN from SADC via CT images and may support clinical diagnoses after further validation.
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Affiliation(s)
- Tianhu Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Baihua Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Jingxu Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yanhua Wen
- Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yubao Guan
- Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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10
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Liu P, Liang X, Liao S, Lu Z. Pattern Classification for Ovarian Tumors by Integration of Radiomics and Deep Learning Features. Curr Med Imaging 2022; 18:1486-1502. [PMID: 35578861 DOI: 10.2174/1573405618666220516122145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/21/2022] [Accepted: 03/03/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Ovarian tumor is a common female genital tumor, among which malignant tumors have a poor prognosis. The survival rate of 70% of patients with ovarian cancer is less than 5 years, while benign ovarian tumor is better, so the early diagnosis of ovarian cancer is important for the treatment and prognosis of patients. OBJECTIVES Our aim is to establish a classification model for ovarian tumors. METHODS We extracted radiomics and deep learning features from patients'CT images. The four-step feature selection algorithm proposed in this paper was used to obtain the optimal combination of features, then, a classification model was developed by combining those selected features and support vector machine. The receiver operating characteristic curve and an area under the curve (AUC) analysis were used to evaluate the performance of the classification model in both the training and test cohort. RESULTS The classification model, which combined radiomics features with deep learning features, demonstrated better classification performance with respect to the radiomics features model alone in training cohort (AUC 0.9289 vs. 0.8804, P < 0.0001, accuracy 0.8970 vs. 0.7993, P < 0.0001), and significantly improve the performance in the test cohort (AUC 0.9089 vs. 0.8446, P = 0.001, accuracy 0.8296 vs. 0.7259, P < 0.0001). CONCLUSION The experiments showed that deep learning features play an active role in the construction of classification model, and the proposed classification model achieved excellent classification performance, which can potentially become a new auxiliary diagnostic tool.
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Affiliation(s)
- Pengfei Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510000, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xiaokang Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510000, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Shengwu Liao
- Nanfang Hospital Southern Medical University, Guangzhou, China
| | - Zhentai Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510000, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
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11
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Wang L. Deep Learning Techniques to Diagnose Lung Cancer. Cancers (Basel) 2022; 14:cancers14225569. [PMID: 36428662 PMCID: PMC9688236 DOI: 10.3390/cancers14225569] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.
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Affiliation(s)
- Lulu Wang
- Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
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12
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Zhang R, Wei Y, Shi F, Ren J, Zhou Q, Li W, Chen B. The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images. BMC Cancer 2022; 22:1118. [PMID: 36319968 PMCID: PMC9628173 DOI: 10.1186/s12885-022-10224-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/17/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules. METHODS Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio of 7:3, and convolutional neural networks (CNN) models and random forest (RF) models were established. Survival analyses were performed for patients with solid adenocarcinomas. RESULTS Totally 720 solid pulmonary nodules were enrolled, 348 benign and 372 malignant. The CNN model with clinical features achieved the highest AUC [0.819, 95% confidence interval (CI): 0.760-0.877] with a sensitivity of 0.778, specificity of 0.788 and accuracy of 0.783. No significant differences were observed between the CNN and radiomics models. There were 295 solid adenocarcinomas in survival analysis. Different disease-free survival was observed between the low-risk and high-risk groups divided according to the radiomics Rad-score. However, the groups based on deep learning signatures showed similar survival. Cox regression analysis indicated that the radiomics Rad-score (hazard ratio: 5.08, 95% CI: 2.61-9.90) was an independent predictor of recurrence. CONCLUSIONS The radiomics and deep learning models can well predict the malignancy of solid pulmonary nodules. Radiomics signatures also demonstrate prognostic value in solid adenocarcinomas.
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Affiliation(s)
- Rui Zhang
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jing Ren
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Weimin Li
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
| | - Bojiang Chen
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
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Bhattacharyya D, Thirupathi Rao N, Joshua ESN, Hu YC. A bi-directional deep learning architecture for lung nodule semantic segmentation. THE VISUAL COMPUTER 2022; 39:1-17. [PMID: 36097497 PMCID: PMC9453728 DOI: 10.1007/s00371-022-02657-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.
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Affiliation(s)
- Debnath Bhattacharyya
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522 502 India
| | - N. Thirupathi Rao
- Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, 530049 AP India
| | - Eali Stephen Neal Joshua
- Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, 530049 AP India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung City, 43301 Taiwan R.O.C
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Yi L, Peng Z, Chen Z, Tao Y, Lin Z, He A, Jin M, Peng Y, Zhong Y, Yan H, Zuo M. Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features. Front Oncol 2022; 12:924055. [PMID: 36147924 PMCID: PMC9485677 DOI: 10.3389/fonc.2022.924055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/22/2022] [Indexed: 11/17/2022] Open
Abstract
To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.
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15
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Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04256-y. [PMID: 35939114 DOI: 10.1007/s00432-022-04256-y] [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: 05/11/2022] [Accepted: 08/02/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE To investigate the incremental value of enhanced CT-based radiomics in discriminating between pulmonary tuberculosis (PTB) and pulmonary adenocarcinoma (PAC) presenting as solid nodules or masses and to develop an optimal radiomics model. METHODS A total of 128 lesions (from 123 patients) from three hospitals were retrospectively analyzed and were randomly divided into training and test datasets at a ratio of 7:3. Independent predictors in subjective image features were used to develop the subjective image model (SIM). The plain CT-based and enhanced CT-based radiomics features were screened by the correlation coefficient method, univariate analysis, and the least absolute shrinkage and selection operator, then used to build the plain CT radiomics model (PRM) and enhanced CT radiomics model (ERM), respectively. Finally, the combined model (CM) combining PRM and ERM was established. In addition, the performance of three radiologists and one respiratory physician was evaluated. The areas under the receiver operating characteristic curve (AUCs) were used to assess the performance of each model. RESULTS The differential diagnostic capability of the ERM (training: AUC = 0.933; test: AUC = 0.881) was better than that of the PRM (training: AUC = 0.861; test: AUC = 0.756) and the SIM (training: AUC = 0.760; test: AUC = 0.611). The CM was optimal (training: AUC = 0.948; test: AUC = 0.917) and outperformed the respiratory physician and most radiologists. CONCLUSIONS The ERM was more helpful than the PRM for identifying PTB and PAC that present as solid nodules or masses, and the CM was the best.
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