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Du Y, Wang T, Qu L, Li H, Guo Q, Wang H, Liu X, Wu X, Song Z. Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network. Bioengineering (Basel) 2024; 11:472. [PMID: 38790338 PMCID: PMC11117786 DOI: 10.3390/bioengineering11050472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/22/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024] Open
Abstract
In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.
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Affiliation(s)
- Yijiang Du
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (Y.D.)
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China
| | - Tingting Wang
- Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Linhao Qu
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (Y.D.)
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China
| | - Haiming Li
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Qinhao Guo
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Haoran Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (Y.D.)
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China
| | - Xinyuan Liu
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xiaohua Wu
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Zhijian Song
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (Y.D.)
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China
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Agrawal S, Agrawal RK, Kumaran SS, Rana B, Srivastava AK. Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis. Cereb Cortex 2024; 34:bhae132. [PMID: 38679476 DOI: 10.1093/cercor/bhae132] [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: 12/24/2023] [Revised: 03/02/2024] [Accepted: 03/15/2024] [Indexed: 05/01/2024] Open
Abstract
Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.
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Affiliation(s)
- Snigdha Agrawal
- School of Computer and Systems Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi-110067, India
| | - Ramesh Kumar Agrawal
- School of Computer and Systems Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi-110067, India
| | - S Senthil Kumaran
- Department of NMR, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110029, India
| | - Bharti Rana
- Department of Computer Science, University of Delhi, Delhi-110007, India
| | - Achal Kumar Srivastava
- Department of Neurology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110029, India
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Yang J, Cai H, Liu N, Huang J, Pan Y, Zhang B, Tong M, Zhang Z. Application of radiomics in ischemic stroke. J Int Med Res 2024; 52:3000605241238141. [PMID: 38565321 PMCID: PMC10993685 DOI: 10.1177/03000605241238141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.
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Affiliation(s)
- Jie Yang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huabo Cai
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ning Liu
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiajie Huang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yun Pan
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minfeng Tong
- Department of Neurosurgery, Department of Neuro Intensive Care Unit, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Wei L, Pan X, Deng W, Chen L, Xi Q, Liu M, Xu H, Liu J, Wang P. Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables. Front Med (Lausanne) 2024; 11:1328073. [PMID: 38495120 PMCID: PMC10940383 DOI: 10.3389/fmed.2024.1328073] [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: 10/26/2023] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes. Methods A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC. Results A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86. Conclusion Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.
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Affiliation(s)
- Lai Wei
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, China
| | - Xianpan Pan
- Department of Research United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Wei Deng
- Department of Research United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lei Chen
- Department of Research United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Qian Xi
- Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ming Liu
- Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Huali Xu
- Department of Radiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Liu
- Department of Radiology, Zhabei Central Hospital, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, China
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Beaudoin AM, Ho JK, Lam A, Thijs V. Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment. Can Assoc Radiol J 2024:8465371241234545. [PMID: 38420881 DOI: 10.1177/08465371241234545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered by the included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.
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Affiliation(s)
- Ann-Marie Beaudoin
- Université de Sherbrooke, Sherbrooke, QC, Canada
- The Florey, Heidelberg, VIC, Australia
| | - Jan Kee Ho
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | | | - Vincent Thijs
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia
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Zhang K, Jiang Y, Zeng H, Zhu H. Application and risk prediction of thrombolytic therapy in cardio-cerebrovascular diseases: a review. Thromb J 2023; 21:90. [PMID: 37667349 PMCID: PMC10476453 DOI: 10.1186/s12959-023-00532-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
Cardiocerebrovascular diseases (CVDs) are the leading cause of death worldwide, consuming huge healthcare budget. For CVD patients, the prompt assessment and appropriate administration is the crux to save life and improve prognosis. Thrombolytic therapy, as a non-invasive approach to achieve recanalization, is the basic component of CVD treatment. Still, there are risks that limits its application. The objective of this review is to give an introduction on the utilization of thrombolytic therapy in cardiocerebrovascular blockage diseases, including coronary heart disease and ischemic stroke, and to review the development in risk assessment of thrombolytic therapy, comparing the performance of traditional scales and novel artificial intelligence-based risk assessment models.
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Affiliation(s)
- Kexin Zhang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yao Jiang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Hesong Zeng
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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Predictive Value of CT Perfusion in Hemorrhagic Transformation after Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. Brain Sci 2023; 13:brainsci13010156. [PMID: 36672136 PMCID: PMC9856940 DOI: 10.3390/brainsci13010156] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/02/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Background: Existing studies indicate that some computed tomography perfusion (CTP) parameters may predict hemorrhagic transformation (HT) after acute ischemic stroke (AIS), but there is an inconsistency in the conclusions alongside a lack of comprehensive comparison. Objective: To comprehensively evaluate the predictive value of CTP parameters in HT after AIS. Data sources: A systematical literature review of existing studies was conducted up to 1st October 2022 in six mainstream databases that included original data on the CTP parameters of HT and non-HT groups or on the diagnostic performance of relative cerebral blood flow (rCBF), relative permeability-surface area product (rPS), or relative cerebral blood volume (rCBV) in patients with AIS that completed CTP within 24 h of onset. Data Synthesis: Eighteen observational studies were included. HT and non-HT groups had statistically significant differences in CBF, CBV, PS, rCBF, rCBV, and rPS (p < 0.05 for all). The hierarchical summary receiver operating characteristic (HSROC) revealed that rCBF (area under the curve (AUC) = 0.9), rPS (AUC = 0.89), and rCBV (AUC = 0.85) had moderate diagnostic performances in predicting HT. The pooled sensitivity and specificity of rCBF were 0.85 (95% CI, 0.75−0.91) and 0.83 (95% CI, 0.63−0.94), respectively. Conclusions: rCBF, rPS, and rCBV had moderate diagnostic performances in predicting HT, and rCBF had the best pooled sensitivity and specificity.
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Analysis of SOCS3 gene genotype in patients with acute cerebral infarction and its relationship with disease progression. Clin Neurol Neurosurg 2022; 224:107518. [PMID: 36434897 DOI: 10.1016/j.clineuro.2022.107518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/04/2022] [Accepted: 11/05/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This reseaech aimed to analyze the polymorphism of SOCS3 gene in patients with acute cerebral infarction (ACI), and to explore its relationship with disease progression. METHODS A total of 310 patients with ACI were categorized into mild stroke group and moderate to severe stroke group according to the National Institute of Stroke Scale (NIHSS) score at admission. Based on NIHSS scores at 7 days after admission, patients were further divided into advanced group and non-advanced group. According to the results of SOCS3 gene detection, three genotypes of rs8064821 were detected: CC, CA and AA. The distribution difference of the genotypes and their predictive possibility were reckoned. RESULTS The distribution of SOCS3 genotype at rs8064821 in patients with ACI was CC (161 cases), CA (127 cases), and AA (22 cases), with frequencies of 51.93 %, 40.97 %, and 7.10 %, respectively. Compared with mild group, the proportion of CC genotype was lower, while the proportion of CA and AA genotypes was higher in moderate-severe stroke group (P < 0.01). Compared with non-advanced group, patients who advanced to neurological deficit had a lower proportion of CC genotype, and a higher proportion of CA and AA genotype (P < 0.01). CONCLUSION CA and AA genotypes were the main genotypes of SOCS3 in patients with ACI in northern Anhui of China. The disease was more severe in patients with CA and AA, who are more likely to develop neurological deficits.
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Choo YJ, Chang MC. Use of Machine Learning in Stroke Rehabilitation: A Narrative Review. BRAIN & NEUROREHABILITATION 2022; 15:e26. [PMID: 36742082 PMCID: PMC9833483 DOI: 10.12786/bn.2022.15.e26] [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: 07/01/2022] [Revised: 09/09/2022] [Accepted: 10/07/2022] [Indexed: 12/05/2022] Open
Abstract
A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recovery of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recovery of language, cognitive, and sensory function, as well as prescribing appropriate rehabilitation treatments.
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Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Korea
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