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Kaur J, Kaur P. A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques. Comput Biol Med 2024; 179:108910. [PMID: 39032244 DOI: 10.1016/j.compbiomed.2024.108910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/23/2024]
Abstract
Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.
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Affiliation(s)
- Jaspreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, India.
| | - Prabhpreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, India.
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Nguyen T, Vennatt J, Downs L, Surabhi V, Stanietzky N. Advanced Imaging of Hepatocellular Carcinoma: A Review of Current and Novel Techniques. J Gastrointest Cancer 2024:10.1007/s12029-024-01094-8. [PMID: 39158837 DOI: 10.1007/s12029-024-01094-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/20/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common primary carcinoma arising from the liver. Although HCC can arise de novo, the vast majority of cases develop in the setting of chronic liver disease. Hepatocarcinogenesis follows a well-studied process during which chronic inflammation and cellular damage precipitate cellular and genetic aberrations, with subsequent propagation of precancerous and cancerous lesions. Surveillance of individuals at high risk of HCC, early diagnosis, and individualized treatment are keys to reducing the mortality associated with this disease. Radiological imaging plays a critical role in the diagnosis and management of these patients. HCC is a unique cancer in that it can be diagnosed with confidence by imaging that meets all radiologic criteria, obviating the risks associated with tissue sampling. This article discusses conventional and emerging imaging techniques for the evaluation of HCC.
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Affiliation(s)
- Trinh Nguyen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - Jaijo Vennatt
- Department of Diagnostic Radiology, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Lincoln Downs
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - Venkateswar Surabhi
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - Nir Stanietzky
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA.
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Dai H, Xiao Y, Fu C, Grimm R, von Busch H, Stieltjes B, Choi MH, Xu Z, Chabin G, Yang C, Zeng M. Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI. J Magn Reson Imaging 2024. [PMID: 38826142 DOI: 10.1002/jmri.29404] [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: 01/23/2024] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 06/04/2024] Open
Abstract
BACKGROUND The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE Retrospective. SUBJECTS 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). DATA CONCLUSION AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Haoran Dai
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuyao Xiao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Robert Grimm
- MR Predevelopment, Siemens Healthineers AG, Erlangen, Germany
| | - Heinrich von Busch
- Innovation Owner Artificial Intelligence for Oncology, Siemens Healthineers AG, Erlangen, Germany
| | | | - Moon Hyung Choi
- Eunpyeong St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | - Zhoubing Xu
- Technology Excellence, Digital Technology and Innovation, Siemens Healthineers, Princeton, New Jersey, USA
| | - Guillaume Chabin
- Technology Excellence, Digital Technology and Innovation, Siemens Healthecare SAS, Paris, France
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
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4
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Xie H, Song C, Jian L, Guo Y, Li M, Luo J, Li Q, Tan T. A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma. BMC Med Imaging 2024; 24:121. [PMID: 38789936 PMCID: PMC11127329 DOI: 10.1186/s12880-024-01300-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
OBJECTIVES At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.
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Affiliation(s)
- Hui Xie
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Chaoling Song
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Lei Jian
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Yeang Guo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Mei Li
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Jiang Luo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Qing Li
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, Netherlands.
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Wunderlich AP, Lisson C, Götz M. Editorial for "Multi-Phase MRI-Based Radiomics for Predicting Histological Grade of Hepatocellular Carcinoma". J Magn Reson Imaging 2024. [PMID: 38411266 DOI: 10.1002/jmri.29323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/28/2024] Open
Affiliation(s)
- Arthur P Wunderlich
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Ulm, Germany
- Division for Experimental Radiology, Ulm University Medical Center, Ulm, Germany
| | - Catharina Lisson
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Ulm, Germany
- Division for Experimental Radiology, Ulm University Medical Center, Ulm, Germany
| | - Michael Götz
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Ulm, Germany
- Division for Experimental Radiology, Ulm University Medical Center, Ulm, Germany
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Li C, Tian Y, Sun F, Lei G, Cheng J, Tian C, Yu H, Deng Z, Lu S, Wang L, Xiao R, Bai C, Yang P. A Recombinant Oncolytic Influenza Virus Carrying GV1001 Triggers an Antitumor Immune Response. Hum Gene Ther 2024; 35:48-58. [PMID: 37646399 DOI: 10.1089/hum.2022.206] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023] Open
Abstract
Oncolytic viruses are able to lyse tumor cells selectively in the liver without killing normal hepatocytes, in addition to activating the immune response. Oncolytic virus therapy is expected to revolutionize the treatment of liver cancer, including hepatocellular carcinoma (HCC), one of the most frequent and fatal malignancies. In this study, reverse genetics techniques were exploited to load NA fragments of the A/PuertoRico/8/34 virus (PR8) with GV1001 peptides derived from human telomerase reverse transcriptase. An in vitro assessment of the therapeutic effect of the recombinant oncolytic virus was followed by an in vivo study in mice with HCC. The recombinant virus was verified by sequencing of the recombinant viral gene sequence, and viral virulence was detected by hemagglutination assays and based on the 50% tissue culture infectious dose (TCID50). The morphological structure of the virus was observed by electron microscopy, and GV1001 peptide was localized by cellular immunofluorescence. The selective cytotoxicity of the recombinant oncolytic virus in vitro was demonstrated in cultured HCC cells and normal hepatocytes, as only the tumor cells were killed; the normal cells were not significantly altered. Consistent with the in vitro results, the recombinant oncolytic influenza virus significantly inhibited liver tumor growth in mice in vivo, in addition to inducing an antitumor immune response, including an increase in the number of CD4+ and CD8+ T lymphocytes and, in turn, improving survival. Our results suggest that oncolytic influenza virus carrying GV1001 is a promising immunotherapy in patients with HCC.
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Affiliation(s)
- Cong Li
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
| | - Yuying Tian
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
| | - Fang Sun
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Guanglin Lei
- The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jinxia Cheng
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Chongyu Tian
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hongyu Yu
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
| | - Zhuoya Deng
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Shuai Lu
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Lishi Wang
- School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
| | - Ruixue Xiao
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
| | - Changqing Bai
- Department of Respiratory, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Penghui Yang
- Faculty of Hepato-Pancreato-Biliary Surgery, Institute of Hepatobiliary Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
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Fotouhi M, Samadi Khoshe Mehr F, Delazar S, Shahidi R, Setayeshpour B, Toosi MN, Arian A. Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning. Eur J Radiol Open 2023; 11:100535. [PMID: 37964787 PMCID: PMC10641154 DOI: 10.1016/j.ejro.2023.100535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 11/16/2023] Open
Abstract
Purpose The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model. Methods 115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model. Results For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001. Conclusions The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.
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Affiliation(s)
- Maryam Fotouhi
- Advanced Diagnostic and Interventional Radiology (ADIR), Radiology department, Imam Khomeini Hospital Complex, Tehran University of Medical Science, Iran
| | - Fardin Samadi Khoshe Mehr
- Research Centre for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
| | - Sina Delazar
- Advanced Diagnostic and Interventional Radiology (ADIR), Radiology department, Imam Khomeini Hospital Complex, Tehran University of Medical Science, Iran
| | - Ramin Shahidi
- School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | | | - Mohssen Nassiri Toosi
- Imam Khomeini Hospital Complex, Liver Transplantation Research Centre, Tehran University of Medical Sciences, Tehran, Iran
| | - Arvin Arian
- Advanced Diagnostic and Interventional Radiology (ADIR), Radiology department, Imam Khomeini Hospital Complex, Tehran University of Medical Science, Iran
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Hettihewa K, Kobchaisawat T, Tanpowpong N, Chalidabhongse TH. MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging. Sci Rep 2023; 13:20098. [PMID: 37973987 PMCID: PMC10654423 DOI: 10.1038/s41598-023-46580-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023] Open
Abstract
Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead.
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Affiliation(s)
- Kasun Hettihewa
- Perceptual Intelligent Computing Laboratory, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
| | | | - Natthaporn Tanpowpong
- Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Thanarat H Chalidabhongse
- Perceptual Intelligent Computing Laboratory, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
- Applied Digital Technology in Medicine (ATM) Research Group, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
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10
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Ali MT, Gebreil A, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Sleman A, Giridharan GA, Barnes G, Elbaz AS. A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework. Sci Rep 2023; 13:17048. [PMID: 37813914 PMCID: PMC10562430 DOI: 10.1038/s41598-023-43478-z] [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: 03/09/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.
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Affiliation(s)
- Mohamed T Ali
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmad Gebreil
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmed Elnakib
- Electrical and Computer Engineering, Penn State Erie-The Behrend College, Erie, PA, 16563, USA
| | - Ahmed Shalaby
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Sleman
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | | | - Gregory Barnes
- Department of Neurology and Pediatric Research Institute, University of Louisville, Louisville, KY, 40202, USA
| | - Ayman S Elbaz
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.
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Saleh GA, Elmokadem AH, Razek AA, El-Morsy A, Hamdy O, Eleraky ES, Saleh M. Utility of diffusion tensor imaging in differentiating benign from malignant hepatic focal lesions. Eur Radiol 2023; 33:1400-1411. [PMID: 35982336 DOI: 10.1007/s00330-022-09091-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To assess the diagnostic accuracy of diffusion tensor imaging (DTI) in the characterization of hepatic focal lesions (HFLs) and compare it to diffusion-weighted imaging (DWI). METHODS Prospective analysis was done for 49 patients (23 male and 26 female) with 74 HFLs who underwent dynamic MRI, DWI, and DTI. Apparent diffusion coefficient (ADC) values from DWI, fractional anisotropy (FA) values, and mean diffusivity (MD) values from DTI were measured by two independent radiologists. HFLs were classified into benign and malignant HFLs; the latter were subdivided into HCC and non-HCC lesions. Binary logistic regression was performed to analyze the associations between the DTI parameters and the distinction of malignant lesions. RESULTS The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10-3 mm2/s, ≤ 1.71 × 10-3 mm2/s, and > 0.29, respectively, are excellent discriminators for differentiating malignant and benign HFLs. The mean ADC and MD values of hemangiomas were significantly higher than HCC and non-HCC malignant lesions. In contrast, the mean FA values of hemangiomas were significantly lower than those of non-HCC malignant lesions and HCCs. The ADC and MD were very good discriminators at cutoff values of > 1.03 × 10-3 mm2/s and > 1.12 × 10-3 mm2/s, respectively. The FA at a cutoff value > 0.38 is an excellent discriminator for HCC versus non-HCC malignant lesions. Only FA value > 0.38 was a statistically significant independent predictor of HCC versus non-HCC lesions among the three parameters. There was an excellent inter-observer agreement with ICC > 0.9. CONCLUSION MD and FA of DTI are non-invasive, very good, and excellent discriminators superior to ADC measured by DWI for the differentiation of HFLs. KEY POINTS • The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10-3 mm2/s, ≤ 1.71 × 10-3 mm2/s, and > 0.29, respectively, are excellent discriminators for differentiating malignant and benign HFLs. • The mean ADC and MD values of hemangiomas were significantly higher than those of HCC and non-HCC malignant lesions. In contrast, the mean FA values of hemangiomas were significantly lower than those of non-HCC malignant lesions and HCCs, respectively. • Multivariate regression analysis revealed that only FA value > 0.38 was a statistically significant independent predictor of HCC vs. non-HCC lesions. A lesion with FA > 0.38 has 34 times higher odds of being HCC rather than non-HCC lesions.
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Affiliation(s)
- Gehad A Saleh
- Department of Radiology, Mansoura University, El Gomhoria St., Mansoura, 35516, Egypt
| | - Ali H Elmokadem
- Department of Radiology, Mansoura University, El Gomhoria St., Mansoura, 35516, Egypt.
| | - Ahmed Abdel Razek
- Department of Radiology, Mansoura University, El Gomhoria St., Mansoura, 35516, Egypt
| | - Ahmed El-Morsy
- Department of Radiology, Mansoura University, El Gomhoria St., Mansoura, 35516, Egypt
| | - Omar Hamdy
- Department of Surgical Oncology, Mansoura University, Mansoura, Egypt
| | | | - Marwa Saleh
- Department of Internal Medicine, Mansoura University, Mansoura, Egypt
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Prediction of Wilms' Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System. Diagnostics (Basel) 2023; 13:diagnostics13030486. [PMID: 36766591 PMCID: PMC9914296 DOI: 10.3390/diagnostics13030486] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/01/2023] [Accepted: 01/25/2023] [Indexed: 02/01/2023] Open
Abstract
Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms' tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms' tumors. A total of 63 patients (age range: 6 months-14 years) were included in this study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms' tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors' images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors' functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms' tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms' tumors.
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [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: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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Chen D, Liu X, Hu C, Hao R, Wang O, Xiao Y. Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis. Future Oncol 2022:1-14. [PMID: 36475996 DOI: 10.2217/fon-2022-0333] [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: 03/31/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022] Open
Abstract
Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.
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Affiliation(s)
- Danxiang Chen
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Xia Liu
- Department of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Chunlei Hu
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Rutian Hao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Ouchen Wang
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Yanling Xiao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
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15
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The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey. Bioengineering (Basel) 2022; 9:bioengineering9080366. [PMID: 36004891 PMCID: PMC9405367 DOI: 10.3390/bioengineering9080366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/28/2022] [Accepted: 08/01/2022] [Indexed: 11/16/2022] Open
Abstract
Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications.
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Liu YF, Shu X, Qiao XF, Ai GY, Liu L, Liao J, Qian S, He XJ. Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer. Front Oncol 2022; 12:911426. [PMID: 35795067 PMCID: PMC9252170 DOI: 10.3389/fonc.2022.911426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 05/19/2022] [Indexed: 01/31/2023] Open
Abstract
Objective To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa). Methods A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohistochemical results between June 2016 and February 2021 was conducted. As indicated by P504s/P63 expression, the patients were divided into label 0 (atypical prostatic hyperplasia), label 1 (benign prostatic hyperplasia, BPH) and label 2 (PCa) groups. This study employed T2WI, DWI and ADC sequences to assess prostate diseases and manually segmented regions of interest (ROIs) with Artificial Intelligence Kit software for radiomics feature acquisition. Feature dimensionality reduction and selection were performed by using a mutual information algorithm. Based on screened features, P504s/P63 prediction models were established by random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), adaptive boosting (AdaBoost) and k-nearest neighbor (KNN) algorithms. The performance was evaluated by the area under the ROC curve (AUC) and accuracy. Results A total of 315 patients were enrolled. Among the 851 radiomic features, the 32 top features were derived from T2WI, in which the gray-level run length matrix (GLRLM) and gray-level cooccurrence matrix (GLCM) features accounted for the largest proportion. Among the five models, the RF algorithm performed best in general evaluations (microaverage AUC=0.920, macroaverage AUC=0.870) and provided the most accurate result in further sublabel prediction (the accuracies of label 0, 1, and 2 were 0.831, 0.831, and 0.932, respectively). In comparative sequence analyses, T2WI was the best single-sequence candidate (microaverage AUC=0.94 and macroaverage AUC=0.78). The merged datasets of T2WI, DWI, and ADC yielded optimal AUCs (microaverage AUC=0.930 and macroaverage AUC=0.900). Conclusions The radiomic-based RF classifier has the potential to be used to evaluate the presurgical P504s/P63 status and further diagnose PCa noninvasively and accurately.
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Affiliation(s)
- Yun-Fan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Feng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guang-Yong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Liu
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Jun Liao
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Shuang Qian
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Xiao-Jing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Xiao-Jing He,
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Jordanova E, Jankovic R, Naumovic R, Celic D, Ljubicic B, Simic-Ogrizovic S, Basta-Jovanovic G. The fractal and textural analysis of glomeruli in obese and non-obese patients. J Pathol Inform 2022; 13:100108. [PMID: 36277955 PMCID: PMC9583580 DOI: 10.1016/j.jpi.2022.100108] [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: 03/07/2022] [Revised: 05/29/2022] [Accepted: 05/29/2022] [Indexed: 11/25/2022] Open
Abstract
Background Fractal dimension is an indirect indicator of signal complexity. The aim was to evaluate the fractal and textural analysis parameters of glomeruli in obese and non-obese patients with glomerular diseases and association of these parameters with clinical features. Methods The study included 125 patients mean age 46 ± 15.2 years: obese (BMI ≥ 27 kg/m2—63 patients) and non-obese (BMI < 27 kg/m2—62 patients). Serum concentration of creatinine, protein, albumin, cholesterol, trygliceride, and daily proteinuria were measured. Formula Chronic Kidney Disease Epidemiology Colaboration (CKD-EPI) equation was calculated. Fractal (fractal dimension, lacunarity) and textural (angular second moment (ASM), textural correlation (COR), inverse difference moment (IDM), textural contrast (CON), variance) analysis parameters were compared between two groups. Results Obese patients had higher mean value of variance (t = 1.867), ASM (t = 1.532) and CON (t = 0.394) but without significant difference (P > 0.05) compared to non-obese. Mean value of COR (t = 0.108) and IDM (t = 0.185) were almost the same in two patient groups. Obese patients had higher value of lacunarity (t = 0.499) in comparison with non-obese, the mean value of fractal dimension (t = 0.225) was almost the same in two groups. Significantly positive association between variance and creatinine concentration (r = 0.499, P < 0.01), significantly negative association between variance and CKD-EPI (r = -0.448, P < 0.01), variance and sex (r = -0.339, P < 0.05) were found. Conclusions Variance showed significant correlation with serum creatinine concentration, CKD-EPI and sex. CON and IDM were significantly related to sex. Fractal and textural analysis parameters of glomeruli could become a supplement to histopathologic analysis of kidney tissue. Variance showed significant correlation with eGFR calculated by CKD- EPI formula. Significant correlation between variance and serum creatinine was found. Textural contrast and inverse difference moment were significantly related to sex. Fractal analysis of glomeruli could become supplement to histopathologic analysis. Textural analyses of kidney tissue should become useful to histopathologic analysis.
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