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  • 1
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    Online Resource
    Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review
    British Institute of Radiology ; 2021
    In:  The British Journal of Radiology Vol. 94, No. 1122 ( 2021-06-01), p. 20201272-
    Details
    In: The British Journal of Radiology, British Institute of Radiology, Vol. 94, No. 1122 ( 2021-06-01), p. 20201272-
    Abstract: To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). Methods: We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms “radiomics”, “PET/CT”, “NSCLC”, and “EGFR”. The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclass correlation coefficient (ICC) was used to determine inter rater agreement for the RQS. An overview of the methodologies used in steps of the radiomics workflow and current results are presented. Results: Six studies were included with sample sizes of 973 ranging from 115 to 248 patients. Methodologies in the radiomic workflow varied greatly. The first-order statistics were the most reproducible features. The RQS scores varied from 13.9 to 47.2%. All studies were scored below 50% due to defects on multiple segmentations, phantom study on all scanners, imaging at multiple time points, cut-off analyses, calibration statistics, prospective study, potential clinical utility, and cost-effectiveness analysis. The ICC results for majority of RQS items were excellent. The ICC for summed RQS was 0.986 [95% confidence interval (CI): 0.898–0.998]. Conclusions: The PET/CT-based radiomics signature could serve as a diagnostic indicator of EGFR mutation status in NSCLC patients. However, the current conclusions should be interpreted with care due to the suboptimal quality of the studies. Consensus for standardization of PET/CT-based radiomic workflow for EGFR mutation status in NSCLC patients is warranted to further improve research. Advances in knowledge: Radiomics can offer clinicians better insight into the prediction of EGFR mutation status in NSCLC patients, whereas the quality of relative studies should be improved before application to the clinical setting.
    Type of Medium: Online Resource
    ISSN: 0007-1285 , 1748-880X
    URL: Article
    DOI: 10.1259/bjr.20201272
    RVK:
    XA 34700
    Language: English
    Publisher: British Institute of Radiology
    Publication Date: 2021
    detail.hit.zdb_id: 1468548-6
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  • 2
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    Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study
    Elsevier BV ; 2024
    In:  Academic Radiology ( 2024-3)
    Details
    In: Academic Radiology, Elsevier BV, ( 2024-3)
    Type of Medium: Online Resource
    ISSN: 1076-6332
    URL: Article
    DOI: 10.1016/j.acra.2024.02.040
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
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  • 3
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    Tumor stiffness measured by 3D magnetic resonance elastography can help predict the aggressiveness of endometrial carcinoma: preliminary findings
    Springer Science and Business Media LLC ; 2021
    In:  Cancer Imaging Vol. 21, No. 1 ( 2021-12)
    Details
    In: Cancer Imaging, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2021-12)
    Abstract: Preoperative evaluation of aggressiveness, including tumor histological subtype, grade of differentiation, Federation International of Gynecology and Obstetrics (FIGO) stage, and depth of myometrial invasion, is significant for treatment planning and prognosis in endometrial carcinoma (EC). The purpose of this study was to evaluate whether three-dimensional (3D) magnetic resonance elastography (MRE) can help predict the aggressiveness of EC. Methods From August 2015 to January 2019, 82 consecutive patients with suspected uterine tumors underwent pelvic MRI and MRE scans, and 15 patients with confirmed EC after surgical resection were enrolled. According to pathological results (tumor grade, histological subtype, FIGO stage, and myometrial invasiveness), the patients were divided into two subgroups. The independent-samples t-test or Mann-Whitney U test was used to compare the stiffness between different groups. The diagnostic performance was determined with receiver operating characteristic (ROC) curve analysis. Results The stiffness of EC with ≥ 50 % ( n  = 6) myometrial invasion was significantly higher than that with 〈  50 % ( n  = 9) myometrial invasion (3.68 ± 0.59 kPa vs. 2.61 ± 0.72 kPa, p  = 0.009). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 77.8 % specificity for differentiating ≥ 50 % myometrial invasion from 〈  50 % myometrial invasion of EC. The stiffness of poorly differentiated EC (n = 8) was significantly higher than that of well/moderately differentiated EC ( n  = 7) (3.47 ± 0.64 kPa vs. 2.55 ± 0.82 kPa, p  = 0.028). Using a stiffness of 3.04 kPa as a cutoff value resulted in 75 % sensitivity and 71.4 % specificity for differentiating poorly differentiated from well/moderately differentiated EC. The stiffness of FIGO stage II/III EC was significantly higher than that of FIGO stage I EC (3.69 ± 0.65 kPa vs. 2.72 ± 0.76 kPa, p =  0.030). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 70 % specificity for differentiating FIGO stage I EC from FIGO stage II/III EC. The tumor stiffness value in type II (n = 3) EC was higher than that in type I (n = 12) EC (3.67 ± 0.59 kPa vs. 2.88 ± 0.85 kPa), but the difference was not significant ( p  = 0.136). Conclusions Tumor stiffness measured by 3D MRE may be potentially useful for predicting tumor grade, FIGO stage and myometrial invasion of EC and can aid in the preoperative risk stratification of EC.
    Type of Medium: Online Resource
    ISSN: 1470-7330
    URL: Article
    DOI: 10.1186/s40644-021-00420-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2104862-9
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  • 4
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    MRI-Based Radiomics for Preoperative Prediction of Lymphovascular Invasion in Patients With Invasive Breast Cancer
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-6-6)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-6-6)
    Abstract: Preoperative identification of lymphovascular invasion (LVI) in patients with invasive breast cancer is challenging due to absence of reliable biomarkers or tools in clinical settings. We aimed to establish and validate multiparametric magnetic resonance imaging (MRI)-based radiomic models to predict the risk of lymphovascular invasion (LVI) in patients with invasive breast cancer. Methods This retrospective study included a total of 175 patients with confirmed invasive breast cancer who had known LVI status and preoperative MRI from two tertiary centers. The patients from center 1 was randomly divided into a training set (n=99) and a validation set (n = 26), while the patients from center 2 was used as a test set (n=50). A total of 1409 radiomic features were extracted from the T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), respectively. A three-step feature selection including SelectKBest, interclass correlation coefficients (ICC), and least absolute shrinkage and selection operator (LASSO) was performed to identify the features most associated with LVI. Subsequently, a Support Vector Machine (SVM) classifier was trained to develop single-layer radiomic models and fusion radiomic models. Model performance was evaluated and compared by the area under the curve (AUC), sensitivity, and specificity. Results Based on one feature of wavelet-HLH_gldm_GrayLevelVariance, the ADC radiomic model achieved an AUC of 0.87 (95% confidence interval [CI]: 0.80–0.94) in the training set, 0.87 (0.70-1.00) in the validation set, and 0.77 (95%CI: 0.64-0.86) in the test set. However, the combination of radiomic features derived from other MR sequences failed to yield incremental value. Conclusions ADC-based radiomic model demonstrated a favorable performance in predicting LVI prior to surgery in patients with invasive breast cancer. Such model holds the potential for improving clinical decision-making regarding treatment for breast cancer.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2022.876624
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 5
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    A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation
    Frontiers Media SA ; 2022
    In:  Frontiers in Physiology Vol. 13 ( 2022-11-23)
    Details
    In: Frontiers in Physiology, Frontiers Media SA, Vol. 13 ( 2022-11-23)
    Abstract: Background: Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT). Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1,157 patients diagnosed with ICH is established to train ( n = 857), validate ( n = 100), and test ( n = 200) the Sym-TransNet. A healthy cohort of 200 subjects is added, establishing a test set with balanced positive and negative cases ( n = 400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. The DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare with Sym-TransNet, and statistical analysis is performed to prove the statistical significance of the proposed method. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the DICE coefficient of ICH lesions. Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716 ± 0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784 ± 0.039, 0.680 ± 0.049, 0.359 ± 0.186, 0.534 ± 0.455, and 0.337 ± 0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the DICE coefficient of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively. Conclusion: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects.
    Type of Medium: Online Resource
    ISSN: 1664-042X
    URL: Article
    DOI: 10.3389/fphys.2022.977427
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2564217-0
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  • 6
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    Bioartificial livers: a review of their design and manufacture
    IOP Publishing ; 2022
    In:  Biofabrication Vol. 14, No. 3 ( 2022-07-01), p. 032003-
    Details
    In: Biofabrication, IOP Publishing, Vol. 14, No. 3 ( 2022-07-01), p. 032003-
    Abstract: Acute liver failure (ALF) is a rapidly progressive disease with high morbidity and mortality rates. Liver transplantation and artificial liver (AL) support systems, such as ALs and bioartificial livers (BALs), are the two major therapies for ALF. Compared to ALs, BALs are composed of functional hepatocytes that provide essential liver functions, including detoxification, metabolite synthesis, and biotransformation. Furthermore, BALs can potentially provide effective support as a form of bridging therapy to liver transplantation or spontaneous recovery for patients with ALF. In this review, we systematically discussed the currently available state-of-the-art designs and manufacturing processes for BAL support systems. Specifically, we classified the cell sources and bioreactors that are applied in BALs, highlighted the advanced technologies of hepatocyte culturing and bioreactor fabrication, and discussed the current challenges and future trends in developing next-generation BALs for large-scale clinical applications.
    Type of Medium: Online Resource
    ISSN: 1758-5082 , 1758-5090
    URL: Article
    DOI: 10.1088/1758-5090/ac6e86
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2500944-8
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  • 7
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    Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings
    IOS Press ; 2021
    In:  Journal of X-Ray Science and Technology Vol. 29, No. 5 ( 2021-09-29), p. 785-796
    Details
    In: Journal of X-Ray Science and Technology, IOS Press, Vol. 29, No. 5 ( 2021-09-29), p. 785-796
    Abstract: Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility of applying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of applying AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including 2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with more scant doctors and higher rates of the infected population.
    Type of Medium: Online Resource
    ISSN: 0895-3996 , 1095-9114
    URL: Article
    DOI: 10.3233/XST-210894
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2021
    detail.hit.zdb_id: 2012019-9
    SSG: 11
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  • 8
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    Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients
    Elsevier BV ; 2023
    In:  iScience Vol. 26, No. 11 ( 2023-11), p. 108326-
    Details
    In: iScience, Elsevier BV, Vol. 26, No. 11 ( 2023-11), p. 108326-
    Type of Medium: Online Resource
    ISSN: 2589-0042
    URL: Article
    DOI: 10.1016/j.isci.2023.108326
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2927064-9
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  • 9
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    Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach
    Springer Science and Business Media LLC ; 2023
    In:  European Journal of Medical Research Vol. 28, No. 1 ( 2023-12-09)
    Details
    In: European Journal of Medical Research, Springer Science and Business Media LLC, Vol. 28, No. 1 ( 2023-12-09)
    Abstract: Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. Purpose This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. Methods We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). Results The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. Conclusion The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.
    Type of Medium: Online Resource
    ISSN: 2047-783X
    URL: Article
    DOI: 10.1186/s40001-023-01550-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2129989-4
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  • 10
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    DataSheet1.doc
    Frontiers Media SA ; 2022
    In:  Frontiers in Molecular Biosciences Vol. 9 ( 2022-4-8)
    Details
    In: Frontiers in Molecular Biosciences, Frontiers Media SA, Vol. 9 ( 2022-4-8)
    Abstract: As a major infectious disease, tuberculosis (TB) still poses a threat to people’s health in China. As a triage test for TB, reading chest radiography with traditional approach ends up with high inter-radiologist and intra-radiologist variability, moderate specificity and a waste of time and medical resources. Thus, this study established a deep convolutional neural network (DCNN) based artificial intelligence (AI) algorithm, aiming at diagnosing TB on posteroanterior chest X-ray photographs in an effective and accurate way. Altogether, 5,000 patients with TB and 4,628 patients without TB were included in the study, totaling to 9,628 chest X-ray photographs analyzed. Splitting the radiographs into a training set (80.4%) and a testing set (19.6%), three different DCNN algorithms, including ResNet, VGG, and AlexNet, were trained to classify the chest radiographs as images of pulmonary TB or without TB. Both the diagnostic accuracy and the area under the receiver operating characteristic curve were used to evaluate the performance of the three AI diagnosis models. Reaching an accuracy of 96.73% and marking the precise TB regions on the radiographs, ResNet algorithm-based AI outperformed the rest models and showed excellent diagnostic ability in different clinical subgroups in the stratification analysis. In summary, the ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application for TB diagnosis, especially in poor regions with high TB incidence.
    Type of Medium: Online Resource
    ISSN: 2296-889X
    URL: Article
    URL: Article
    DOI: 10.3389/fmolb.2022.874475
    DOI: 10.3389/fmolb.2022.874475.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2814330-9
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