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Ost DE. Artificial intelligence applications for the diagnosis of pulmonary nodules. Curr Opin Pulm Med 2025; 31:344-351. [PMID: 40326426 DOI: 10.1097/mcp.0000000000001179] [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] [Indexed: 05/07/2025]
Abstract
PURPOSE OF REVIEW This review evaluates the role of artificial intelligence (AI) in diagnosing solitary pulmonary nodules (SPNs), focusing on clinical applications and limitations in pulmonary medicine. It explores AI's utility in imaging and blood/tissue-based diagnostics, emphasizing practical challenges over technical details of deep learning methods. RECENT FINDINGS AI enhances computed tomography (CT)-based computer-aided diagnosis (CAD) through steps like nodule detection, false positive reduction, segmentation, and classification, leveraging convolutional neural networks and machine learning. Segmentation achieves Dice similarity coefficients of 0.70-0.92, while malignancy classification yields areas under the curve of 0.86-0.97. AI-driven blood tests, incorporating RNA sequencing and clinical data, report AUCs up to 0.907 for distinguishing benign from malignant nodules. However, most models lack prospective, multiinstitutional validation, risking overfitting and limited generalizability. The "black box" nature of AI, coupled with overlapping inputs (e.g., nodule size, smoking history) with physician assessments, complicates integration into clinical workflows and precludes standard Bayesian analysis. SUMMARY AI shows promise for SPN diagnosis but requires rigorous validation in diverse populations and better clinician training for effective use. Rather than replacing judgment, AI should serve as a second opinion, with its reported performance metrics understood as study-specific, not directly applicable at the bedside due to double-counting issues.
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Affiliation(s)
- David E Ost
- Professor of Medicine, MD Anderson Cancer Center, 1400 Pressler St., Unit 1462 FCT12.5099, Houston, Texas, USA
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Liu Z, Yang L, Liang J, Wen B, He Z, Xie Y, Luo H, Yang Q, Liu L, Luo D, Li L, Zhang N. Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules. Eur Radiol 2025; 35:2968-2978. [PMID: 39604651 DOI: 10.1007/s00330-024-11221-5] [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: 03/14/2024] [Accepted: 10/23/2024] [Indexed: 11/29/2024]
Abstract
PURPOSE To investigate the incremental benefit of adding radiomic features to conventional semantic radiological feature-based differential diagnosis between benign and malignant lung nodules. METHODS From May 2017 to March 2021, 393 patients with 465 pathologically confirmed lung nodules were enrolled with 54 patients with 54 lung nodules as external testing. Based on manually segmented lung nodules, 1409 radiomics features were extracted. Sixteen radiological features were obtained. The least absolute shrinkage and selection operator (LASSO) was used to select the most informative features from the two features set separately. Support vector machine (SVM) and logistic regression (LR) were used to build the models (radiomics model, radiological model, and combined model) with performance compared using the DeLong test. RESULTS After feature selection, six radiological features, including shape, vascular convergence sign (type III), margin, density, pleural traction sign, and spiculation, and nine radiomics features were selected. In the independent testing and external testing, combined models had significantly higher AUCs than the corresponding radiomic models for both the SVM classifier (AUC: 0.871 vs. 0.773, p = 0.029; 0.810 vs. 0.706, p = 0.037) and LR classifier (AUC: 0.871 vs. 0.742, p = 0.008; 0.828 vs. 0.712, p = 0.044), and the corresponding radiological model for both the SVM classifier (AUC: 0.871 vs. 0.803, p = 0.015; 0.810 vs. 0.730, p = 0.045) and LR classifier (AUC: 0.871 vs. 0.818, p = 0.034; 0.828 vs. 0.756, p = 0.040). CONCLUSION Radiomics features could add incremental benefits to the conventional radiological feature-based differential diagnosis. KEY POINTS Question Conventional semantic radiological feature-based differential diagnosis between benign and malignant lung nodules needs further improvement. Findings The model combining radiological features and radiomic features significantly outperforms a radiomic model and a radiological model. Clinical relevance Radiomic features could complement conventional radiological features to improve the differential diagnosis of lung nodules in the clinical setting.
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Affiliation(s)
- Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Long Yang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - JiuPing Liang
- Department of Radiology, Shenzhen Bao'an District Songgang People's Hospital, Shenzhen, China
| | - Binbin Wen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zikun He
- Department of Radiology, Shenzhen Bao'an District Songgang People's Hospital, Shenzhen, China
| | - Yongsheng Xie
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Honghong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Qian Yang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Lijian Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Li Li
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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Horne A, Abravan A, Fornacon-Wood I, O’Connor JPB, Price G, McWilliam A, Faivre-Finn C. Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal. Br J Radiol 2025; 98:653-668. [PMID: 40100283 PMCID: PMC12012345 DOI: 10.1093/bjr/tqaf051] [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: 08/07/2024] [Revised: 12/18/2024] [Accepted: 02/25/2025] [Indexed: 03/20/2025] Open
Abstract
Radiomics is a health technology that has the potential to extract clinically meaningful biomarkers from standard of care imaging. Despite a wealth of exploratory analysis performed on scans acquired from patients with lung cancer and existing guidelines describing some of the key steps, no radiomic-based biomarker has been widely accepted. This is primarily due to limitations with methodology, data analysis, and interpretation of the available studies. There is currently a lack of guidance relating to the entire radiomic workflow from study design to critical appraisal. This guide, written with early career lung cancer researchers, describes a more complete radiomic workflow. Lung cancer image analysis is the focus due to some of the unique challenges encountered such as patient movement from breathing. The guide will focus on CT imaging as these are the most common scans performed on patients with lung cancer. The aim of this article is to support the production of high-quality research that has the potential to positively impact outcome of patients with lung cancer.
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Affiliation(s)
- Ashley Horne
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
- Department of Thoracic Oncology, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Azadeh Abravan
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
| | - Isabella Fornacon-Wood
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
| | - James P B O’Connor
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, SW7 3RP, United Kingdom
| | - Gareth Price
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
| | - Alan McWilliam
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9NT, United Kingdom
- Department of Thoracic Oncology, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
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Borji A, Kronreif G, Angermayr B, Hatamikia S. Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images. Front Med (Lausanne) 2025; 12:1555907. [PMID: 40313555 PMCID: PMC12045028 DOI: 10.3389/fmed.2025.1555907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2025] [Accepted: 03/24/2025] [Indexed: 05/03/2025] Open
Abstract
Background Recent advances in machine learning are transforming medical image analysis, particularly in cancer detection and classification. Techniques such as deep learning, especially convolutional neural networks (CNNs) and vision transformers (ViTs), are now enabling the precise analysis of complex histopathological images, automating detection, and enhancing classification accuracy across various cancer types. This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. Early and accurate detection of OS is essential for improving patient outcomes and reducing mortality. However, the increasing prevalence of cancer and the demand for personalized treatments create challenges in achieving precise diagnoses and customized therapies. Methods We propose a novel hybrid model that combines convolutional neural networks (CNN) and vision transformers (ViT) to improve diagnostic accuracy for OS using hematoxylin and eosin (H&E) stained histopathological images. The CNN model extracts local features, while the ViT captures global patterns from histopathological images. These features are combined and classified using a Multi-Layer Perceptron (MLP) into four categories: non-tumor (NT), non-viable tumor (NVT), viable tumor (VT), and non-viable ratio (NVR). Results Using the Cancer Imaging Archive (TCIA) dataset, the model achieved an accuracy of 99.08%, precision of 99.10%, recall of 99.28%, and an F1-score of 99.23%. This is the first successful four-class classification using this dataset, setting a new benchmark in OS research and offering promising potential for future diagnostic advancements.
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Affiliation(s)
- Arezoo Borji
- Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria
- Research Center for Clinical AI-Research in Omics and Medical Data Science (CAROM), Department of Medicine, Danube Private University (DPU), Krems an der Donau, Austria
- Department of Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Gernot Kronreif
- Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria
| | - Bernhard Angermayr
- Patho im Zentrum, Saint Pölten, Austria
- Department of Medicine, Danube Private University, Krems an der Donau, Austria
| | - Sepideh Hatamikia
- Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria
- Research Center for Clinical AI-Research in Omics and Medical Data Science (CAROM), Department of Medicine, Danube Private University (DPU), Krems an der Donau, Austria
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5
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Salmanpour MR, Gorji A, Mousavi A, Fathi Jouzdani A, Sanati N, Maghsudi M, Leung B, Ho C, Yuan R, Rahmim A. Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets. Cancers (Basel) 2025; 17:285. [PMID: 39858067 PMCID: PMC11763441 DOI: 10.3390/cancers17020285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Revised: 01/09/2025] [Accepted: 01/13/2025] [Indexed: 01/27/2025] Open
Abstract
OBJECTIVE This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). METHODS We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS-PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by the Random Forest algorithm) to 199 LCa cases, using the same HMLS techniques. Furthermore, principal component analysis (PCA) linked with four survival prediction algorithms were utilized in the survival hazard ratio analysis. RESULTS The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average C-index of 0.80, with a log rank p-value << 0.001, confirmed by external testing. CONCLUSIONS Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone, can significantly achieve high predictive performance.
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Affiliation(s)
- Mohammad R. Salmanpour
- BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (C.H.); (A.R.)
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V6L 1L7, Canada; (A.G.); (A.M.); (A.F.J.); (N.S.); (M.M.)
| | - Arman Gorji
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V6L 1L7, Canada; (A.G.); (A.M.); (A.F.J.); (N.S.); (M.M.)
- Neuroscience and Artificial Intelligence Research Group (NAIRG), Department of Neuroscience, Hamadan University of Medical Sciences, Hamadan 6517838736, Iran
| | - Amin Mousavi
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V6L 1L7, Canada; (A.G.); (A.M.); (A.F.J.); (N.S.); (M.M.)
| | - Ali Fathi Jouzdani
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V6L 1L7, Canada; (A.G.); (A.M.); (A.F.J.); (N.S.); (M.M.)
- Neuroscience and Artificial Intelligence Research Group (NAIRG), Department of Neuroscience, Hamadan University of Medical Sciences, Hamadan 6517838736, Iran
| | - Nima Sanati
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V6L 1L7, Canada; (A.G.); (A.M.); (A.F.J.); (N.S.); (M.M.)
- Neuroscience and Artificial Intelligence Research Group (NAIRG), Department of Neuroscience, Hamadan University of Medical Sciences, Hamadan 6517838736, Iran
| | - Mehdi Maghsudi
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V6L 1L7, Canada; (A.G.); (A.M.); (A.F.J.); (N.S.); (M.M.)
| | - Bonnie Leung
- BC Cancer, Vancouver Center, Vancouver, BC V5Z 1L3, Canada;
| | - Cheryl Ho
- BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (C.H.); (A.R.)
| | - Ren Yuan
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- BC Cancer, Vancouver Center, Vancouver, BC V5Z 1L3, Canada;
| | - Arman Rahmim
- BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (C.H.); (A.R.)
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Astaraki M, Häger W, Lazzeroni M, Toma-Dasu I. Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling. Phys Med 2025; 129:104881. [PMID: 39724784 DOI: 10.1016/j.ejmp.2024.104881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 12/03/2024] [Accepted: 12/20/2024] [Indexed: 12/28/2024] Open
Abstract
PURPOSE We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) time. METHODS An established reaction-diffusion model was employed to simulate the spatiotemporal evolution of cancerous regions in T1-MRI images of GBM patients. The effects of the parameters of this model on the simulated tumor borders were quantified and the optimal values were used to estimate the distribution of infiltrative cells (CTVmodel). Radiomics and deep learning models were examined to predict the OS time by analyzing the GTV, clinical CTV, and CTVmodel. RESULTS The study involves 126 subjects for model development and 62 independent subjects for testing. Evaluation of the proposed approach demonstrates comparable predictive power for OS time that is achieved with the clinically defined CTV. Specifically, for the binary survival prediction, short vs. long time, the proposed CTVmodelresulted in improvements of prognostic power, in terms of AUROC, both for the validation (0.77 from 0.75) and the testing (0.73 from 0.71) set. Quantitative comparisons for three-class prediction and survival regression models exhibited a similar trend of comparable performance. CONCLUSION The findings highlight the potential of biophysical modeling for estimating and incorporating the spread of infiltrating cells into CTV delineation. Further clinical investigations are required to validate the clinical efficacy.
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Affiliation(s)
- Mehdi Astaraki
- Division of Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
| | - Wille Häger
- Division of Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Marta Lazzeroni
- Division of Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Iuliana Toma-Dasu
- Division of Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
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Letchumanan N, Hanaoka S, Takenaga T, Suzuki Y, Nakao T, Nomura Y, Yoshikawa T, Abe O. Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm. J Med Imaging (Bellingham) 2025; 12:014501. [PMID: 39776665 PMCID: PMC11702674 DOI: 10.1117/1.jmi.12.1.014501] [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: 08/30/2024] [Revised: 11/20/2024] [Accepted: 12/01/2024] [Indexed: 01/11/2025] Open
Abstract
Purpose The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance. Approach We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods. In the first method, a radiomics-based approach, we utilized radiomics features and machine learning (ML) algorithms. The entire breast region was chosen as the region of interest for extracting radiomics features. Then, a binary breast image was created from which we extracted 668 features and analyzed them using various ML algorithms. In the second method, a complex convolutional neural network (CNN) with a modified ResNet architecture and various kernel sizes was applied to raw mammography images for the prediction task. A nested, stratified five-fold cross-validation was done for both parts A and B to compute accuracy, sensitivity, specificity, and area under the receiver operating curve (AUROC). Hyperparameter tuning was also done to enhance the model's performance and reliability. Results The radiomics approach's light gradient boosting model gave 68.9% accuracy, 30.7% sensitivity, 89.5% specificity, and 0.63 AUROC. The CNN method achieved an AUROC of 0.58 over 20 epochs. Conclusion Radiomics outperformed CNN by 0.05 in terms of AUROC. This may be due to the more straightforward interpretability and clinical relevance of predefined radiomics features compared with the complex, abstract features learned by CNNs.
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Affiliation(s)
- Nishta Letchumanan
- The University of Tokyo, Department of Radiology, Graduate School of Medicine, Tokyo, Japan
| | - Shouhei Hanaoka
- The University of Tokyo Hospital, Department of Radiology, Tokyo, Japan
| | - Tomomi Takenaga
- The University of Tokyo Hospital, Department of Radiology, Tokyo, Japan
| | - Yusuke Suzuki
- The University of Tokyo Hospital, Department of Breast and Endocrine Surgery, Tokyo, Japan
| | - Takahiro Nakao
- The University of Tokyo Hospital, Department of Computational Diagnostic Radiology and Preventive Medicine, Tokyo, Japan
| | - Yukihiro Nomura
- The University of Tokyo Hospital, Department of Computational Diagnostic Radiology and Preventive Medicine, Tokyo, Japan
- Chiba University, Center for Frontier Medical Engineering, Chiba, Japan
| | - Takeharu Yoshikawa
- The University of Tokyo Hospital, Department of Computational Diagnostic Radiology and Preventive Medicine, Tokyo, Japan
| | - Osamu Abe
- The University of Tokyo Hospital, Department of Radiology, Tokyo, Japan
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Adachi T, Nakamura M, Matsuo Y, Karasawa K, Kokubo M, Sakamoto T, Hiraoka M, Mizowaki T. Prospective external validation of radiomics-based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non-small-cell lung cancer: A multi-institutional analysis. J Appl Clin Med Phys 2024; 25:e14475. [PMID: 39178139 PMCID: PMC11466494 DOI: 10.1002/acm2.14475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 07/03/2024] [Indexed: 08/25/2024] Open
Abstract
BACKGROUND AND PURPOSE This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT). MATERIALS AND METHODS The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test. RESULTS In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116). CONCLUSION Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.
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Affiliation(s)
- Takanori Adachi
- Department of Radiation Oncology and Image‐Applied TherapyGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image‐Applied TherapyGraduate School of MedicineKyoto UniversityKyotoJapan
- Department of Advanced Medical PhysicsGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Yukinori Matsuo
- Department of Radiation Oncology and Image‐Applied TherapyGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Katsuyuki Karasawa
- Division of Radiation OncologyDepartmentof RadiologyTokyo Metropolitan Cancer and Infectious Diseases Center Komagome HospitalTokyoJapan
| | - Masaki Kokubo
- Department of Radiation OncologyKobe City Medical Center General HospitalHyogoJapan
| | - Takashi Sakamoto
- Department of Radiation OncologyKyoto Katsura HospitalKyotoJapan
| | - Masahiro Hiraoka
- Department of Radiation OncologyJapanese Red Cross Society Wakayama Medical CenterWakayamaJapan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image‐Applied TherapyGraduate School of MedicineKyoto UniversityKyotoJapan
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Ong W, Lee A, Tan WC, Fong KTD, Lai DD, Tan YL, Low XZ, Ge S, Makmur A, Ong SJ, Ting YH, Tan JH, Kumar N, Hallinan JTPD. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review. Cancers (Basel) 2024; 16:2988. [PMID: 39272846 PMCID: PMC11394591 DOI: 10.3390/cancers16172988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 08/14/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Aric Lee
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Wei Chuan Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Kuan Ting Dominic Fong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Daoyong David Lai
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Yi Liang Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shao Jin Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Yong Han Ting
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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10
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Xu N, Wang J, Dai G, Lu T, Li S, Deng K, Song J. EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1086-1099. [PMID: 38361006 PMCID: PMC11169294 DOI: 10.1007/s10278-024-01022-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/29/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset. EME semantic features were extracted to construct an EME-prognostic model to stratify the prognosis of EGFR-mutant NSCLC patients receiving EGFR-TKI. A comparison of EME and radiomics was conducted. Additionally, we included patients from The Cancer Genome Atlas lung adenocarcinoma dataset with both CT images and RNA sequencing data to explore the biological associations between EME score and EGFR-related biological processes. EME obtained an area under the curve (AUC) of 0.907 (95% CI 0.840-0.926) on the test dataset, superior to the radiomics model (P = 0.007). The EME and radiomics fusion model showed better (AUC, 0.941) but not significantly increased performance (P = 0.895) compared with EME. In prognostic stratification, the EME-prognostic model achieved the best performance (C-index, 0.711). Moreover, the EME-prognostic score showed strong associations with biological pathways related to EGFR expression and EGFR-TKI efficacy. EME demonstrated a non-invasive and biologically interpretable approach to predict EGFR status, stratify survival prognosis, and correlate biological pathways in patients with NSCLC.
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Affiliation(s)
- Nan Xu
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China
| | - Jiajun Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Gang Dai
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, 230036, China
| | - Tao Lu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Shu Li
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, 230036, China
| | - Jiangdian Song
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China.
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11
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Qiu J, Mitra J, Ghose S, Dumas C, Yang J, Sarachan B, Judson MA. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Diagnostics (Basel) 2024; 14:1049. [PMID: 38786347 PMCID: PMC11120014 DOI: 10.3390/diagnostics14101049] [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: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.
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Affiliation(s)
- Jianwei Qiu
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Jhimli Mitra
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Soumya Ghose
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Camille Dumas
- Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA; (C.D.); (J.Y.)
| | - Jun Yang
- Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA; (C.D.); (J.Y.)
| | - Brion Sarachan
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Marc A. Judson
- Department of Medicine, Albany Medical College, Albany, NY 12208, USA;
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12
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Yang R, Zhang Y, Li W, Li Q, Liu X, Zhang F, Liang Z, Huang J, Li X, Tao L, Guo X. Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm). Biomed Phys Eng Express 2024; 10:045008. [PMID: 38684143 DOI: 10.1088/2057-1976/ad449a] [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: 01/05/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
Objectives. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by devising and externally validating a Multimodal Integrated Feature Neural Network (MIFNN). We hypothesize that the fusion of deep learning algorithms with morphological nodule features will significantly enhance diagnostic accuracy.Materials and Methods. Data were retrospectively collected from the Lung Nodule Analysis 2016 (LUNA16) dataset and four local centers in Beijing, China. The study includes patients with small pulmonary nodules (≤10 mm). We developed a neural network, termed MIFNN, that synergistically combines computed tomography (CT) images and morphological characteristics of pulmonary nodules. The network is designed to acquire clinically relevant deep learning features, thereby elevating the diagnostic accuracy of existing models. Importantly, the network's simple architecture and use of standard screening variables enable seamless integration into standard lung cancer screening protocols.Results. In summary, the study analyzed a total of 382 small pulmonary nodules (85 malignant) from the LUNA16 dataset and 101 small pulmonary nodules (33 malignant) obtained from four specialized centers in Beijing, China, for model training and external validation. Both internal and external validation metrics indicate that the MIFNN significantly surpasses extant state-of-the-art models, achieving an internal area under the curve (AUC) of 0.890 (95% CI: 0.848-0.932) and an external AUC of 0.843 (95% CI: 0.784-0.891).Conclusion. The MIFNN model significantly enhances the diagnostic accuracy of small pulmonary nodules, outperforming existing benchmarks by Zhanget alwith a 6.34% improvement for nodules less than 10 mm. Leveraging advanced integration techniques for imaging and clinical data, MIFNN increases the efficiency of lung cancer screenings and optimizes nodule management, potentially reducing false positives and unnecessary biopsies.Clinical relevance statement. The MIFNN enhances lung cancer screening efficiency and patient management for small pulmonary nodules, while seamlessly integrating into existing workflows due to its reliance on standard screening variables.
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Affiliation(s)
- Runhuang Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Qiang Li
- Beijing Medical Examination Center, Beijing, People's Republic of China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Zhigang Liang
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing, People's Republic of China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Xia Li
- Department of Mathematics and Statistics, La Trobe University, Melbourne, Australia
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
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13
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Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering (Basel) 2024; 11:50. [PMID: 38247927 PMCID: PMC10813496 DOI: 10.3390/bioengineering11010050] [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: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVE Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. METHODS In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.
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Affiliation(s)
| | | | | | | | | | - Wei Wei
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116014, China (S.W.); (Y.F.)
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14
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Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology 2023; 309:e222904. [PMID: 37815447 PMCID: PMC10623199 DOI: 10.1148/radiol.222904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 10/11/2023]
Abstract
The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
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Affiliation(s)
- Ashley Elizabeth Prosper
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Michael N. Kammer
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Fabien Maldonado
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Denise R. Aberle
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - William Hsu
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
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15
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Evangelista L, Fiz F, Laudicella R, Bianconi F, Castello A, Guglielmo P, Liberini V, Manco L, Frantellizzi V, Giordano A, Urso L, Panareo S, Palumbo B, Filippi L. PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature. Cancers (Basel) 2023; 15:3258. [PMID: 37370869 PMCID: PMC10296704 DOI: 10.3390/cancers15123258] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy. MATERIALS AND METHODS A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302. RESULTS Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy. CONCLUSIONS Radiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome.
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Affiliation(s)
- Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy
| | - Francesco Fiz
- Nuclear Medicine Department, E.O. “Ospedali Galliera”, 16128 Genoa, Italy;
- Nuclear Medicine Department and Clinical Molecular Imaging, University Hospital, 72076 Tübingen, Germany
| | - Riccardo Laudicella
- Unit of Nuclear Medicine, Biomedical Department of Internal and Specialist Medicine, University of Palermo, 90100 Palermo, Italy;
| | - Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti, 06125 Perugia, Italy;
| | - Angelo Castello
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Priscilla Guglielmo
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV—IRCCS, 35128 Padua, Italy;
| | - Virginia Liberini
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy;
| | - Luigi Manco
- Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy;
| | - Viviana Frantellizzi
- Department of Radiological Sciences, Oncology and Anatomo-Pathology, Sapienza University of Rome, 00161 Rome, Italy;
| | - Alessia Giordano
- Nuclear Medicine Unit, IRCCS CROB, Referral Cancer Center of Basilicata, 85028 Rionero in Vulture, Italy;
| | - Luca Urso
- Department of Nuclear Medicine PET/CT Centre, S. Maria della Misericordia Hospital, 45100 Rovigo, Italy;
| | - Stefano Panareo
- Nuclear Medicine Unit, Oncology and Haematology Department, University Hospital of Modena, 41124 Modena, Italy;
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, 06125 Perugia, Italy;
| | - Luca Filippi
- Nuclear Medicine Section, Santa Maria Goretti Hospital, 04100 Latina, Italy;
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16
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Duan S, Hua Y, Cao G, Hu J, Cui W, Zhang D, Xu S, Rong T, Liu B. Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics. Eur J Radiol 2023; 165:110899. [PMID: 37300935 DOI: 10.1016/j.ejrad.2023.110899] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/28/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs). METHODS We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features. RESULTS For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak. CONCLUSIONS The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.
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Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Wei Cui
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Duo Zhang
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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17
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Saied M, Raafat M, Yehia S, Khalil MM. Efficient pulmonary nodules classification using radiomics and different artificial intelligence strategies. Insights Imaging 2023; 14:91. [PMID: 37199791 DOI: 10.1186/s13244-023-01441-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/24/2023] [Indexed: 05/19/2023] Open
Abstract
OBJECTIVES This study aimed to explore and develop artificial intelligence approaches for efficient classification of pulmonary nodules based on CT scans. MATERIALS AND METHODS A number of 1007 nodules were obtained from 551 patients of LIDC-IDRI dataset. All nodules were cropped into 64 × 64 PNG images , and preprocessing was carried out to clean the image from surrounding non-nodular structure. In machine learning method, texture Haralick and local binary pattern features were extracted. Four features were selected using principal component analysis (PCA) algorithm before running classifiers. In deep learning, a simple CNN model was constructed and transfer learning was applied using VGG-16 and VGG-19, DenseNet-121 and DenseNet-169 and ResNet as pre-trained models with fine tuning. RESULTS In statistical machine learning method, the optimal AUROC was 0.885 ± 0.024 with random forest classifier and the best accuracy was 0.819 ± 0.016 with support vector machine. In deep learning, the best accuracy reached 90.39% with DenseNet-121 model and the best AUROC was 96.0%, 95.39% and 95.69% with simple CNN, VGG-16 and VGG-19, respectively. The best sensitivity reached 90.32% using DenseNet-169 and the best specificity attained was 93.65% when applying the DenseNet-121 and ResNet-152V2. CONCLUSION Deep learning methods with transfer learning showed several benefits over statistical learning in terms of nodule prediction performance and saving efforts and time in training large datasets. SVM and DenseNet-121 showed the best performance when compared with their counterparts. There is still more room for improvement, especially when more data can be trained and lesion volume is represented in 3D. CLINICAL RELEVANCE STATEMENT Machine learning methods offer unique opportunities and open new venues in clinical diagnosis of lung cancer. The deep learning approach has been more accurate than statistical learning methods. SVM and DenseNet-121 showed superior performance in pulmonary nodule classification.
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Affiliation(s)
- Mohamed Saied
- Medical Biophysics, Department of Physics, Faculty of Science, Helwan University, Cairo, Egypt
| | - Mourad Raafat
- Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt
| | - Sherif Yehia
- Medical Biophysics, Department of Physics, Faculty of Science, Helwan University, Cairo, Egypt
| | - Magdy M Khalil
- School of Biotechnology, Badr University in Cairo (BUC) and Department of Physics, Faculty of Science, Helwan University, Cairo, Egypt.
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18
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Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly. Diagnostics (Basel) 2023; 13:diagnostics13030384. [PMID: 36766488 PMCID: PMC9914272 DOI: 10.3390/diagnostics13030384] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76-81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied-functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.
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Chang CC, Tang EK, Wei YF, Lin CY, Wu FZ, Wu MT, Liu YS, Yen YT, Ma MC, Tseng YL. Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan. Front Oncol 2023; 13:1105100. [PMID: 37143945 PMCID: PMC10151670 DOI: 10.3389/fonc.2023.1105100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs). Methods A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models. Result In the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275). Conclusion Our study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.
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Affiliation(s)
- Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - En-Kuei Tang
- Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yu-Feng Wei
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Chest Medicine, Department of Internal Medicine, E-Da Cancer Hospital, Kaohsiung, Taiwan
| | - Chia-Ying Lin
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Education, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Ming-Ting Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Sheng Liu
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yi-Ting Yen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- *Correspondence: Yi-Ting Yen, ; Mi-Chia Ma,
| | - Mi-Chia Ma
- Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan
- *Correspondence: Yi-Ting Yen, ; Mi-Chia Ma,
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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Wu YJ, Wu FZ, Yang SC, Tang EK, Liang CH. Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics (Basel) 2022; 12:diagnostics12051064. [PMID: 35626220 PMCID: PMC9139351 DOI: 10.3390/diagnostics12051064] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/14/2022] [Accepted: 04/22/2022] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.
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Affiliation(s)
- Yun-Ju Wu
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan;
| | - Fu-Zong Wu
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Correspondence:
| | - Shu-Ching Yang
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan;
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Fiorino C, Rancati T. Artificial intelligence applied to medicine: There is an "elephant in the room". Phys Med 2022; 98:8-10. [PMID: 35462274 DOI: 10.1016/j.ejmp.2022.04.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/09/2022] [Indexed: 11/27/2022] Open
Affiliation(s)
- Claudio Fiorino
- Medical Physics Department, San Raffaele Scientific Institute, Milano Italy.
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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22
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Zhang H. A Review of Convolutional Neural Network Development in Computer Vision. EAI ENDORSED TRANSACTIONS ON INTERNET OF THINGS 2022. [DOI: 10.4108/eetiot.v7i28.445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Convolutional neural networks have made admirable progress in computer vision. As a fast-growing computer field, CNNs are one of the classical and widely used network structures. The Internet of Things (IoT) has gotten a lot of attention in recent years. This has directly led to the vigorous development of AI technology, such as the intelligent luggage security inspection system developed by the IoT, intelligent fire alarm system, driverless car, drone technology, and other cutting-edge directions. This paper first outlines the structure of CNNs, including the convolutional layer, the downsampling layer, and the fully connected layer, all of which play an important role. Then some different modules of classical networks are described, and these modules are rapidly driving the development of CNNs. And then the current state of CNNs research in image classification, object segmentation, and object detection is discussed.
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