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Zhong H, Huang Q, Zheng X, Wang Y, Qian Y, Chen X, Wang J, Duan S. Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks. BMC Med Imaging 2024; 24:151. [PMID: 38890572 PMCID: PMC11184875 DOI: 10.1186/s12880-024-01331-3] [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: 04/11/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.
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Affiliation(s)
- Hua Zhong
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China.
| | - Qianwen Huang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Xiaoli Zheng
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Yong Wang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Yanan Qian
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Jinan Wang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Shaoyin Duan
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
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Wang M, Zhuang B, Yu S, Li G. Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices. Front Oncol 2024; 14:1397505. [PMID: 38952558 PMCID: PMC11215019 DOI: 10.3389/fonc.2024.1397505] [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: 03/14/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.
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Affiliation(s)
- Mengxia Wang
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Bo Zhuang
- Department of Hepatobiliary Surgery, The Affliated Jinhua Hospital of Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Shian Yu
- Department of Hepatobiliary Surgery, The Affliated Jinhua Hospital of Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
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Ansari Y, Mourad O, Qaraqe K, Serpedin E. Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023. Front Physiol 2023; 14:1246746. [PMID: 37791347 PMCID: PMC10542398 DOI: 10.3389/fphys.2023.1246746] [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: 06/26/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017-2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.
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Affiliation(s)
- Yaqoob Ansari
- ECEN Program, Texas A&M University at Qatar, Doha, Qatar
| | | | - Khalid Qaraqe
- ECEN Program, Texas A&M University at Qatar, Doha, Qatar
| | - Erchin Serpedin
- ECEN Department, Texas A&M University, College Station, TX, United States
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Rai P, Ansari MY, Warfa M, Al-Hamar H, Abinahed J, Barah A, Dakua SP, Balakrishnan S. Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review. Cancer Med 2023. [PMID: 37191030 DOI: 10.1002/cam4.6089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Percutaneous thermal ablation has become the preferred therapeutic treatment option for liver cancers that cannot be resected. Since ablative zone tissue changes over time, it becomes challenging to determine therapy effectiveness over an extended period. Thus, an immediate post-procedural evaluation of the ablation zone is crucial, as it could influence the need for a second-look treatment or follow-up plan. Assessing treatment response immediately after ablation is essential to attain favorable outcomes. This study examines the efficacy of image fusion strategies immediately post-ablation in liver neoplasms to determine therapeutic response. METHODOLOGY A comprehensive systematic search using PRISMA methodology was conducted using EMBASE, MEDLINE (via PUBMED), and Cochrane Library Central Registry electronic databases to identify articles that assessed the immediate post-ablation response in malignant hepatic tumors with fusion imaging (FI) systems. The data were retrieved on relevant clinical characteristics, including population demographics, pre-intervention clinical history, lesion characteristics, and intervention type. For the outcome metrics, variables such as average fusion time, intervention metrics, technical success rate, ablative safety margin, supplementary ablation rate, technical efficacy rate, LTP rates, and reported complications were extracted. RESULTS Twenty-two studies were included for review after fulfilling the study eligibility criteria. FI's immediate technical success rate ranged from 81.3% to 100% in 17/22 studies. In 16/22 studies, the ablative safety margin was assessed immediately after ablation. Supplementary ablation was performed in 9 studies following immediate evaluation by FI. In 15/22 studies, the technical effectiveness rates during the first follow-up varied from 89.3% to 100%. CONCLUSION Based on the studies included, we found that FI can accurately determine the immediate therapeutic response in liver cancer ablation image fusion and could be a feasible intraprocedural tool for determining short-term post-ablation outcomes in unresectable liver neoplasms. There are some technical challenges that limit the widespread adoption of FI techniques. Large-scale randomized trials are warranted to improve on existing protocols. Future research should emphasize improving FI's technological capabilities and clinical applicability to a broader range of tumor types and ablation procedures.
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Affiliation(s)
- Pragati Rai
- Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | | | - Mohammed Warfa
- Department of Clinical Imaging, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA
| | - Hammad Al-Hamar
- Department of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar
| | - Julien Abinahed
- Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | - Ali Barah
- Department of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar
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Chandrasekar V, Ansari MY, Singh AV, Uddin S, Prabhu KS, Dash S, Khodor SA, Terranegra A, Avella M, Dakua SP. Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta. IEEE ACCESS 2023; 11:52726-52739. [DOI: 10.1109/access.2023.3272987] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2023]
Affiliation(s)
| | | | | | - Shahab Uddin
- Hamad Medical Corporation, Translational Research Institute, Academic Health System, Doha, Qatar
| | - Kirthi S. Prabhu
- Hamad Medical Corporation, Translational Research Institute, Academic Health System, Doha, Qatar
| | - Sagnika Dash
- Department of Obstetrics and Gynecology, Apollo Clinic, Doha, Qatar
| | - Souhaila Al Khodor
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | - Annalisa Terranegra
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | - Matteo Avella
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
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Ansari MY, Yang Y, Balakrishnan S, Abinahed J, Al-Ansari A, Warfa M, Almokdad O, Barah A, Omer A, Singh AV, Meher PK, Bhadra J, Halabi O, Azampour MF, Navab N, Wendler T, Dakua SP. A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci Rep 2022; 12:14153. [PMID: 35986015 PMCID: PMC9391485 DOI: 10.1038/s41598-022-16828-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.
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Ansari MY, Abdalla A, Ansari MY, Ansari MI, Malluhi B, Mohanty S, Mishra S, Singh SS, Abinahed J, Al-Ansari A, Balakrishnan S, Dakua SP. Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med Imaging 2022; 22:97. [PMID: 35610600 PMCID: PMC9128093 DOI: 10.1186/s12880-022-00825-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/09/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012–2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.
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Dakua SP, Nayak A. A review on treatments of hepatocellular carcinoma—role of radio wave ablation and possible improvements. EGYPTIAN LIVER JOURNAL 2022. [DOI: 10.1186/s43066-022-00191-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Currently, several treatment options are available for liver cancer depending on various factors such as location, size, shape, and liver function. Image fusion is required for the diagnosis, intervention, and follow-up of certain HCCs. Presently, mental fusion is the only way while diagnosing liver lesions by comparing the ultrasound (US) image with the computed tomography (CT) image. Nevertheless, mental fusion is bound to have errors. The objective of this paper is to study the present treatment options for hepatocellular carcinoma and review the present treatment options, list out their potential limitations, and present a possible alternative solution based on the findings to reduce errors and mistargeting.
Methods
This is a systematic review on the present treatment options for hepatocellular carcinoma, especially radio wave ablation.
Results
It is found that computer fusion is the possible alternative to the present mental registration.
Conclusions
Although computer fusion is the best alternative to use radio wave ablation, there have been a few open-ended questions to further explore.
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