1
|
Li L, Chen Y, Xie H, Zheng P, Mu G, Li Q, Huang H, Shen Z. Machine Learning Model for Predicting Risk Factors of Prolonged Length of Hospital Stay in Patients with Aortic Dissection: a Retrospective Clinical Study. J Cardiovasc Transl Res 2024:10.1007/s12265-024-10565-z. [PMID: 39388090 DOI: 10.1007/s12265-024-10565-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 09/04/2024] [Indexed: 10/12/2024]
Abstract
The length of hospital stay (LOS) is crucial for assessing medical service quality. This study aimed to develop machine learning models for predicting risk factors of prolonged LOS in patients with aortic dissection (AD). The data of 516 AD patients were obtained from the hospital's medical system, with 111 patients in the prolonged LOS (> 30 days) group based on three quarters of the LOS in the entire cohort. Given the screened variables and prediction models, the XGBoost model demonstrated superior predictive performance in identifying prolonged LOS, due to the highest area under the receiver operating characteristic curve, sensitivity, and F1-score in both subsets. The SHapley Additive exPlanation analysis indicated that high density lipoprotein cholesterol, alanine transaminase, systolic blood pressure, percentage of lymphocyte, and operation time were the top five risk factors associated with prolonged LOS. These findings have a guiding value for the clinical management of patients with AD.
Collapse
Affiliation(s)
- Luo Li
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China
| | - Yihuan Chen
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China
| | - Hui Xie
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China
| | - Peng Zheng
- Department of Cardiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, Jiangsu, 210009, Nanjing, China
| | - Gaohang Mu
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China
| | - Qian Li
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China
| | - Haoyue Huang
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China.
| | - Zhenya Shen
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Soochow University, Suzhou Medical College, Soochow University, 899 Pinghai Road, Jiangsu, 215123, Suzhou, China.
| |
Collapse
|
2
|
Kong X, Mao Y, Luo Y, Xi F, Li Y, Ma J. Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification. Acta Radiol 2023; 64:2938-2947. [PMID: 37735892 DOI: 10.1177/02841851231199744] [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] [Indexed: 09/23/2023]
Abstract
BACKGROUND The 2021 World Health Organization (WHO) classification considers a histological low grade glioma with specific molecular characteristics as molecular glioblastoma (mGBM). Accurate identification of mGBM will aid in risk stratification of glioma patients. PURPOSE To explore the value of machine learning models based on magnetic resonance imaging (MRI) radiomics features in predicting mGBM. MATERIAL AND METHODS In total, 166 patients histologically diagnosed as low-grade diffuse glioma (WHO II and III) were included in the study. Fifty-three cases were reclassified as mGBM based on molecular status. Four dimensionality reduction methods including distance correlation (DC), gradient boosted decision tree (GBDT), least absolute shrinkage and selection operator (LASSO) and minimal redundancy maximal relevance (MRMR) were used to select the optimal signatures. Six machine learning algorithms including support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), logistic regression (LR), K-nearest neighbour (KNN) and decision tree (DT) were used to develop the classifiers. The relative SD was used to evaluate the stability of the models, and the area under the curve values in the independent test group were used to evaluate their performances. RESULTS NN_DC was determined as the optimal classifier due to the highest area under the curve of 0.891 in the test group. The classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value of NN_DC were 0.915, 0.842, 0.950, 0.889 and 0.927, respectively. CONCLUSION Machine learning models can predict mGBM non-invasively, which may help to develop personalized treatment strategies for neurosurgeons and provide an effective tool for accurate stratification in clinical trials.
Collapse
Affiliation(s)
- Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yu Mao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fengjun Xi
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
3
|
Musigmann M, Nacul NG, Kasap DN, Heindel W, Mannil M. Use Test of Automated Machine Learning in Cancer Diagnostics. Diagnostics (Basel) 2023; 13:2315. [PMID: 37510059 PMCID: PMC10378334 DOI: 10.3390/diagnostics13142315] [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: 05/31/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future.
Collapse
Affiliation(s)
- Manfred Musigmann
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Nabila Gala Nacul
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Dilek N Kasap
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Walter Heindel
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| | - Manoj Mannil
- University Clinic for Radiology, University Hospital Muenster, WWU Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany
| |
Collapse
|
4
|
Qin X, Xia L, Hu X, Xiao W, Huaming X, Xisheng X, Zhang C. A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy. Front Endocrinol (Lausanne) 2023; 14:1093452. [PMID: 36742388 PMCID: PMC9895811 DOI: 10.3389/fendo.2023.1093452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/04/2023] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy. METHODS Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis. RESULTS The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram. CONCLUSION ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.
Collapse
Affiliation(s)
- Xiachuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Linlin Xia
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaomin Hu
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Weihan Xiao
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Xian Huaming
- Department of Nephrology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Xie Xisheng
- Department of Nephrology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
- *Correspondence: Chaoxue Zhang, ; Xie Xisheng,
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- *Correspondence: Chaoxue Zhang, ; Xie Xisheng,
| |
Collapse
|
5
|
Zhao J, Sun L, Sun K, Wang T, Wang B, Yang Y, Wu C, Sun X. Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule. Front Oncol 2021; 11:759840. [PMID: 34858836 PMCID: PMC8630666 DOI: 10.3389/fonc.2021.759840] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/18/2021] [Indexed: 01/11/2023] Open
Abstract
Objective To establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability. Materials and Methods A total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit. Results We extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors-maximum diameter, lobulation, and pleural retraction-were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram. Conclusions The radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.
Collapse
Affiliation(s)
- Jiabi Zhao
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lin Sun
- Department of Radiation Medicine, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Ke Sun
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Tingting Wang
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Bin Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Yang
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| |
Collapse
|