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Borghesi A, Coviello FL, Scrimieri A, Ciolli P, Ravanelli M, Farina D. Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01648-z. [PMID: 37227661 DOI: 10.1007/s11547-023-01648-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
PURPOSE Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population. MATERIAL AND METHODS We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses. RESULTS In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth. CONCLUSION According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.
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Affiliation(s)
- Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy.
| | - Felice Leopoldo Coviello
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Alessandra Scrimieri
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Pietro Ciolli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
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Atasever S, Azginoglu N, Terzi DS, Terzi R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clin Imaging 2023; 94:18-41. [PMID: 36462229 DOI: 10.1016/j.clinimag.2022.11.003] [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: 05/31/2022] [Revised: 10/17/2022] [Accepted: 11/01/2022] [Indexed: 11/13/2022]
Abstract
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.
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Affiliation(s)
- Sema Atasever
- Computer Engineering Department, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey.
| | - Nuh Azginoglu
- Computer Engineering Department, Kayseri University, Kayseri, Turkey.
| | | | - Ramazan Terzi
- Computer Engineering Department, Amasya University, Amasya, Turkey.
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Shi Y, Wei N, Wang K, Wu J, Tao T, Li N, Lv B. Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy. Front Oncol 2023; 13:1122247. [PMID: 36950553 PMCID: PMC10025314 DOI: 10.3389/fonc.2023.1122247] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/17/2023] [Indexed: 03/08/2023] Open
Abstract
Background Chronic atrophic gastritis (CAG) is a precancerous condition. It is not easy to detect CAG in endoscopy. Improving the detection rate of CAG under endoscopy is essential to reduce or interrupt the occurrence of gastric cancer. This study aimed to construct a deep learning (DL) model for CAG recognition based on endoscopic images to improve the CAG detection rate during endoscopy. Methods We collected 10,961 endoscopic images and 118 video clips from 4,050 patients. For model training and testing, we divided them into two groups based on the pathological results: CAG and chronic non-atrophic gastritis (CNAG). We compared the performance of four state-of-the-art (SOTA) DL networks for CAG recognition and selected one of them for further improvement. The improved network was called GAM-EfficientNet. Finally, we compared GAM-EfficientNet with three endoscopists and analyzed the decision basis of the network in the form of heatmaps. Results After fine-tuning and transfer learning, the sensitivity, specificity, and accuracy of GAM-EfficientNet reached 93%, 94%, and 93.5% in the external test set and 96.23%, 89.23%, and 92.37% in the video test set, respectively, which were higher than those of the three endoscopists. Conclusions The CAG recognition model based on deep learning has high sensitivity and accuracy, and its performance is higher than that of endoscopists.
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Affiliation(s)
- Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Ning Wei
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Kunhong Wang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Jingjing Wu
- Department of Internal Medicine, Zhangdian Maternal and Child Health Care Hospital, Zibo, Shandong, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Na Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
- *Correspondence: Bing Lv, ; Na Li,
| | - Bing Lv
- School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China
- *Correspondence: Bing Lv, ; Na Li,
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Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:703-712. [PMID: 35544377 DOI: 10.2214/ajr.22.27487] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.
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Li M, Jiang Z, Shen W, Liu H. Deep learning in bladder cancer imaging: A review. Front Oncol 2022; 12:930917. [PMID: 36338676 PMCID: PMC9631317 DOI: 10.3389/fonc.2022.930917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.
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Affiliation(s)
- Mingyang Li
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zekun Jiang
- Ministry of Education (MoE) Key Lab of Artificial Intelligence, Artificial Intelligence (AI) Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Shen
- Ministry of Education (MoE) Key Lab of Artificial Intelligence, Artificial Intelligence (AI) Institute, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Haitao Liu, ; Wei Shen,
| | - Haitao Liu
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Haitao Liu, ; Wei Shen,
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Zhang G, Wu Z, Xu L, Zhang X, Zhang D, Mao L, Li X, Xiao Y, Guo J, Ji Z, Sun H, Jin Z. Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer. Front Oncol 2021; 11:654685. [PMID: 34178641 PMCID: PMC8226179 DOI: 10.3389/fonc.2021.654685] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/27/2021] [Indexed: 12/12/2022] Open
Abstract
Background Clinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa. Methods A total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists. Results The DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort. Conclusion The proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa.
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Affiliation(s)
- Gumuyang Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhe Wu
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, China
| | - Lili Xu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoxiao Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Daming Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Li Mao
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Xiuli Li
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Guo
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, China
| | - Zhigang Ji
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Wu G, Jochems A, Refaee T, Ibrahim A, Yan C, Sanduleanu S, Woodruff HC, Lambin P. Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging 2021; 48:3961-3974. [PMID: 33693966 PMCID: PMC8484174 DOI: 10.1007/s00259-021-05242-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/03/2021] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. METHODS Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. CONCLUSION The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."
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Affiliation(s)
- Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands. .,Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. .,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Chenggong Yan
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Sebastian Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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