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Vargas J, Pease M, Snyder MH, Blalock J, Wu S, Nwachuku E, Mittal A, Okonkwo DO, Kellogg RT. Automated Preoperative and Postoperative Volume Estimates Risk of Retreatment in Chronic Subdural Hematoma: A Retrospective, Multicenter Study. Neurosurgery 2024; 94:317-324. [PMID: 37747231 DOI: 10.1227/neu.0000000000002667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/17/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Several neurosurgical pathologies, ranging from glioblastoma to hemorrhagic stroke, use volume thresholds to guide treatment decisions. For chronic subdural hematoma (cSDH), with a risk of retreatment of 10%-30%, the relationship between preoperative and postoperative cSDH volume and retreatment is not well understood. We investigated the potential link between preoperative and postoperative cSDH volumes and retreatment. METHODS We performed a retrospective chart review of patients operated for unilateral cSDH from 4 level 1 trauma centers, February 2009-August 2021. We used a 3-dimensional deep learning, automated segmentation pipeline to calculate preoperative and postoperative cSDH volumes. To identify volume thresholds, we constructed a receiver operating curve with preoperative and postoperative volumes to predict cSDH retreatment rates and selected the threshold with the highest Youden index. Then, we developed a light gradient boosting machine to predict the risk of cSDH recurrence. RESULTS We identified 538 patients with unilateral cSDH, of whom 62 (12%) underwent surgical retreatment within 6 months of the index surgery. cSDH retreatment was associated with higher preoperative (122 vs 103 mL; P < .001) and postoperative (62 vs 35 mL; P < .001) volumes. Patients with >140 mL preoperative volume had nearly triple the risk of cSDH recurrence compared with those below 140 mL, while a postoperative volume >46 mL led to an increased risk for retreatment (22% vs 6%; P < .001). On multivariate modeling, our model had an area under the receiver operating curve of 0.76 (95% CI: 0.60-0.93) for predicting retreatment. The most important features were preoperative and postoperative volume, platelet count, and age. CONCLUSION Larger preoperative and postoperative cSDH volumes increase the risk of retreatment. Volume thresholds may allow identification of patients at high risk of cSDH retreatment who would benefit from adjunct treatments. Machine learning algorithm can quickly provide accurate estimates of preoperative and postoperative volumes.
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Affiliation(s)
- Jan Vargas
- Division of Neurosurgery, PRISMA Health, Greenville , South Carolina , USA
| | - Matthew Pease
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York , New York , USA
| | - M Harrison Snyder
- Department of Neurosurgery, Tufts Medical Center, Boston , Massachusetts , USA
| | - Jonathan Blalock
- University of South Carolina School of Medicine Greenville, Greenville , South Carolina , USA
| | - Shandong Wu
- Department of Neurosurgery, UPMC Healthcare System, Pittsburgh , Pennsylvania , USA
| | - Enyinna Nwachuku
- Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland , Ohio , USA
| | - Aditya Mittal
- Department of Neurosurgery, University of Pittsburgh Medical Center Medical School, Pittsburgh , Pennsylvania , USA
| | - David O Okonkwo
- Department of Neurosurgery, UPMC Healthcare System, Pittsburgh , Pennsylvania , USA
| | - Ryan T Kellogg
- Department of Neurosurgery, University of Virginia, Charlottesville , Virginia , USA
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Ya Y, Ji L, Jia Y, Zou N, Jiang Z, Yin H, Mao C, Luo W, Wang E, Fan G. Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features. Front Aging Neurosci 2022; 14:808520. [PMID: 35493923 PMCID: PMC9043762 DOI: 10.3389/fnagi.2022.808520] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance. Methods Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets. Results After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models. Conclusion The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.
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Affiliation(s)
- Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Lirong Ji
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Nan Zou
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Zhou X, Ye Q, Yang X, Chen J, Ma H, Xia J, Del Ser J, Yang G. AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus. Neural Comput Appl 2022; 35:1-10. [PMID: 35228779 PMCID: PMC8866920 DOI: 10.1007/s00521-022-07048-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Qinghao Ye
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA USA
| | - Xiaolin Yang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jiakun Chen
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Haiqin Ma
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Guang Yang
- Royal Brompton Hospital, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
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Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang G. Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study. Front Aging Neurosci 2020; 12:618538. [PMID: 33390930 PMCID: PMC7772233 DOI: 10.3389/fnagi.2020.618538] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 11/23/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework. Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning. Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9). Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Qinghao Ye
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Yinghui Jiang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Minhao Wang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., London, United Kingdom
| | | | - Evandro Fei Fang
- Department of Clinical Molecular Biology, University of Oslo, Oslo, Norway
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Jun Xia
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom.,National Heart and Lung Institute, Imperial College London, London, United Kingdom
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