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Jeong JH, Lee KS, Park SM, Kim SR, Kim MN, Chae SC, Hur SH, Seong IW, Oh SK, Ahn TH, Jeong MH. Prediction of longitudinal clinical outcomes after acute myocardial infarction using a dynamic machine learning algorithm. Front Cardiovasc Med 2024; 11:1340022. [PMID: 38646154 PMCID: PMC11027893 DOI: 10.3389/fcvm.2024.1340022] [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: 11/17/2023] [Accepted: 03/27/2024] [Indexed: 04/23/2024] Open
Abstract
Several regression-based models for predicting outcomes after acute myocardial infarction (AMI) have been developed. However, prediction models that encompass diverse patient-related factors over time are limited. This study aimed to develop a machine learning-based model to predict longitudinal outcomes after AMI. This study was based on a nationwide prospective registry of AMI in Korea (n = 13,104). Seventy-seven predictor candidates from prehospitalization to 1 year of follow-up were included, and six machine learning approaches were analyzed. Primary outcome was defined as 1-year all-cause death. Secondary outcomes included all-cause deaths, cardiovascular deaths, and major adverse cardiovascular event (MACE) at the 1-year and 3-year follow-ups. Random forest resulted best performance in predicting the primary outcome, exhibiting a 99.6% accuracy along with an area under the receiver-operating characteristic curve of 0.874. Top 10 predictors for the primary outcome included peak troponin-I (variable importance value = 0.048), in-hospital duration (0.047), total cholesterol (0.047), maintenance of antiplatelet at 1 year (0.045), coronary lesion classification (0.043), N-terminal pro-brain natriuretic peptide levels (0.039), body mass index (BMI) (0.037), door-to-balloon time (0.035), vascular approach (0.033), and use of glycoprotein IIb/IIIa inhibitor (0.032). Notably, BMI was identified as one of the most important predictors of major outcomes after AMI. BMI revealed distinct effects on each outcome, highlighting a U-shaped influence on 1-year and 3-year MACE and 3-year all-cause death. Diverse time-dependent variables from prehospitalization to the postdischarge period influenced the major outcomes after AMI. Understanding the complexity and dynamic associations of risk factors may facilitate clinical interventions in patients with AMI.
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Affiliation(s)
- Joo Hee Jeong
- Division of Cardiology, Department of Internal Medicine, Anam Hospital, Korea University Medicine, Seoul, Republic of Korea
| | - Kwang-Sig Lee
- Korea University College of Medicine, AI Center, Anam Hospital, Seoul, Republic of Korea
| | - Seong-Mi Park
- Division of Cardiology, Department of Internal Medicine, Anam Hospital, Korea University Medicine, Seoul, Republic of Korea
| | - So Ree Kim
- Division of Cardiology, Department of Internal Medicine, Anam Hospital, Korea University Medicine, Seoul, Republic of Korea
| | - Mi-Na Kim
- Division of Cardiology, Department of Internal Medicine, Anam Hospital, Korea University Medicine, Seoul, Republic of Korea
| | - Shung Chull Chae
- Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Seung-Ho Hur
- Cardiovascular Medicine, Keimyung University Dongsan Medical Center, Daegu, Republic of Korea
| | - In Whan Seong
- Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University, College of Medicine, Daejeon, Republic of Korea
| | - Seok Kyu Oh
- Division of Cardiology, Department of Internal Medicine, Wonkwang University School of Medicine, Iksan, Republic of Korea
| | - Tae Hoon Ahn
- Department of Cardiology, Na-eun Hospital, Incheon, Republic of Korea
| | - Myung Ho Jeong
- Department of Cardiology, Chonnam National University Hospital, Gwangju, Republic of Korea
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Lee JS, Choi ES, Lee H, Son S, Lee KS, Ahn KH. Machine learning analysis for the association between breast feeding and metabolic syndrome in women. Sci Rep 2024; 14:4138. [PMID: 38374105 PMCID: PMC10876622 DOI: 10.1038/s41598-024-53137-6] [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] [Received: 03/07/2023] [Accepted: 01/29/2024] [Indexed: 02/21/2024] Open
Abstract
This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010-2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age.
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Affiliation(s)
- Jue Seong Lee
- Department of Pediatrics, Korea University College of Medicine, Korea University Anam Hospital, Seoul, South Korea
| | - Eun-Saem Choi
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Korea University Anam Hospital, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea
| | - Hwasun Lee
- Department of Biostatistics, Korea University College of Medicine, Seoul, South Korea
| | - Serhim Son
- Department of Biostatistics, Korea University College of Medicine, Seoul, South Korea
| | - Kwang-Sig Lee
- AI Center, Korea University College of Medicine, Korea University Anam Hospital, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
| | - Ki Hoon Ahn
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Korea University Anam Hospital, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
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Lim ST, Choi HS, Kim K, Hahn S, Cho IJ, Noh H, Lee JI, Han A. Hounsfield Units Predict Survival of Patients With Estrogen Receptor-Positive and Human Epithelial Growth Factor Receptor 2-Negative Breast Cancer. Clin Breast Cancer 2023; 23:e424-e433.e3. [PMID: 37438195 DOI: 10.1016/j.clbc.2023.06.012] [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: 04/04/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUNDS Tumor vascularity plays a fundamental role in cancer progression, including breast cancer. This study aimed to elucidate tumor vascularity and its impact on patient survival in the context of breast cancer subtypes using Hounsfield units (HU) on contrast-enhanced computed tomography (CT). MATERIALS AND METHODS Patients with early-stage breast cancer who completed planned treatment between 2003 and 2013 were retrospectively assessed. RESULTS The final cohort comprised 440 patients. Of the 440 patients, 262 had estrogen receptor (ER)-positive disease and 119 had human epidermal growth factor receptor 2 (HER2)-overexpressing disease. The tumor-to-aorta ratio of Hounsfield units (TAR) was related to significantly worse recurrence-free interval (RFI) (P < .001) and overall survival (OS) (P < .001) in patients with TAR > 0.33 for RFI and > 0.35 for OS than their counterparts. In the subgroup analysis, the survival disadvantage was limited only to patients with ER-positive and HER2-negative disease (P < .001 for both RFI and OS). CONCLUSION This study showed that TAR, which reflects tumor vascularity, was significantly related to patients' RFI and OS, suggesting its capacity as a feasible biomarker. This study also showed that TAR was associated with the survival in patients with ER-positive and HER2-negative disease.
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Affiliation(s)
- Seung Taek Lim
- Department of Oncology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hyang Suk Choi
- Department of Surgery, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Kwangmin Kim
- Department of Surgery, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Seok Hahn
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - In-Jeong Cho
- Department of Surgery, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hany Noh
- Department of Surgery, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Jong-In Lee
- Department of Oncology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Airi Han
- Department of Surgery, Yonsei University Wonju College of Medicine, Wonju, Korea.
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Costa G, Cavinato L, Fiz F, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible. J Digit Imaging 2023; 36:1038-1048. [PMID: 36849835 PMCID: PMC10287605 DOI: 10.1007/s10278-023-00799-9] [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: 05/30/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/01/2023] Open
Abstract
Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.
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Affiliation(s)
- Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Lara Cavinato
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy.
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, Via M. Gavazzeni 21, 24125, Bergamo, Italy.
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Al-Jabbar M, Alshahrani M, Senan EM, Ahmed IA. Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted. Diagnostics (Basel) 2023; 13:diagnostics13101753. [PMID: 37238243 DOI: 10.3390/diagnostics13101753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/09/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Breast cancer is the second most common type of cancer among women, and it can threaten women's lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient's abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.
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Affiliation(s)
- Mohammed Al-Jabbar
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Mohammed Alshahrani
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
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Kim HY, Bae MS, Seo BK, Lee JY, Cho KR, Woo OH, Song SE, Cha J. Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study. Bioengineering (Basel) 2023; 10:bioengineering10050504. [PMID: 37237574 DOI: 10.3390/bioengineering10050504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. METHODS This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan-Meier analysis. RESULTS Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). CONCLUSIONS Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients.
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Affiliation(s)
- Hyo-Young Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Min-Sun Bae
- Department of Radiology, Inha University Hospital, Inha University College of Medicine, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Republic of Korea
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Republic of Korea
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jaehyung Cha
- Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
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Jiang L, Zhang Z, Guo S, Zhao Y, Zhou P. Clinical-Radiomics Nomogram Based on Contrast-Enhanced Ultrasound for Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma. Cancers (Basel) 2023; 15:cancers15051613. [PMID: 36900404 PMCID: PMC10001290 DOI: 10.3390/cancers15051613] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/08/2023] Open
Abstract
This study aimed to establish a new clinical-radiomics nomogram based on ultrasound (US) for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). We collected 211 patients with PTC between June 2018 and April 2020, then we randomly divided these patients into the training set (n = 148) and the validation set (n = 63). 837 radiomics features were extracted from B-mode ultrasound (BMUS) images and contrast-enhanced ultrasound (CEUS) images. The maximum relevance minimum redundancy (mRMR) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and backward stepwise logistic regression (LR) were applied to select key features and establish a radiomics score (Radscore), including BMUS Radscore and CEUS Radscore. The clinical model and clinical-radiomics model were established using the univariate analysis and multivariate backward stepwise LR. The clinical-radiomics model was finally presented as a clinical-radiomics nomogram, the performance of which was evaluated by the receiver operating characteristic curves, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). The results show that the clinical-radiomics nomogram was constructed by four predictors, including gender, age, US-reported LNM, and CEUS Radscore. The clinical-radiomics nomogram performed well in both the training set (AUC = 0.820) and the validation set (AUC = 0.814). The Hosmer-Lemeshow test and the calibration curves demonstrated good calibration. The DCA showed that the clinical-radiomics nomogram had satisfactory clinical utility. The clinical-radiomics nomogram constructed by CEUS Radscore and key clinical features can be used as an effective tool for individualized prediction of cervical LNM in PTC.
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Affiliation(s)
- Liqing Jiang
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Zijian Zhang
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China;
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
| | - Shiyan Guo
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Yongfeng Zhao
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
- Correspondence:
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Zhao X, Jiang D, Hu Z, Yang J, Liang D, Yuan B, Lin R, Wang H, Liao J, Zhao C. Machine learning and statistic analysis to predict drug treatment outcome in pediatric epilepsy patients with tuberous sclerosis complex. Epilepsy Res 2022; 188:107040. [PMID: 36332542 DOI: 10.1016/j.eplepsyres.2022.107040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 01/21/2023]
Abstract
OBJECTIVES We aimed to investigate the association between multi-modality features and epilepsy drug treatment outcomes and propose a machine learning model to predict epilepsy drug treatment outcomes with multi-modality features. METHODS This retrospective study consecutively enrolled 103 epilepsy children with rare TSC. Multi-modality data were used to characterize risk factors for epilepsy drug treatment outcome of TSC, including clinical data, TSC1, and TSC2 genes test results, magnetic resonance imaging (MRI), computerized tomography (CT), and electroencephalogram (EEG). Three common feature selection methods and six common machine learning models were used to find the best combination of feature selection and machine learning model for epilepsy drug treatment outcomes prediction with multi-modality features for TSC clinical application. RESULTS The analysis of variance based on selected 35 features combined with multilayer perceptron (MLP) model achieved the best area-under-curve score (AUC) of 0.812 (±0.005). Infantile spasms, EEG discharge type, epileptiform discharge in the right frontal area of EEG, drug-resistant epilepsy, gene mutation type, and type II lesions were positively correlated with drug treatment outcome. Age of onset and age of visiting doctors were negatively correlated with drug treatment outcome (p < 0.05). Our machine learning results found that among MRI features, lesion type is the most important in the outcome prediction, followed by location and quantity. CONCLUSION We developed and validated an effective prediction model for epilepsy drug treatment outcomes of TSC. Our results suggested that multi-modality features analysis and MLP-based machine learning can predict epilepsy drug treatment outcomes of TSC.
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Affiliation(s)
- Xia Zhao
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Dian Jiang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing 101400, China
| | - Zhanqi Hu
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Jun Yang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing 101400, China
| | - Dong Liang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing 101400, China; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Bixia Yuan
- Shenzhen Association Against Epilepsy, Shenzhen 518038, China
| | - Rongbo Lin
- Department of Emergency, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Haifeng Wang
- University of Chinese Academy of Sciences, Beijing 101400, China; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Jianxiang Liao
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China.
| | - Cailei Zhao
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen 518038, China.
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Prospective clinical research of radiomics and deep learning in oncology: A translational review. Crit Rev Oncol Hematol 2022; 179:103823. [PMID: 36152912 DOI: 10.1016/j.critrevonc.2022.103823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/13/2022] [Accepted: 09/20/2022] [Indexed: 10/31/2022] Open
Abstract
Radiomics and deep learning (DL) hold transformative promise and substantial and significant advances in oncology; however, most methods have been tested in retrospective or simulated settings. There is considerable interest in the biomarker validation, clinical utility, and methodological robustness of these studies and their deployment in real-world settings. This review summarizes the characteristics of studies, the level of prospective validation, and the overview of research on different clinical endpoints. The discussion of methodological robustness shows the potential for independent external replication of prospectively reported results. These in-depth analyses further describe the barriers limiting the translation of radiomics and DL into primary care options and provide specific recommendations regarding clinical deployment. Finally, we propose solutions for integrating novel approaches into the treatment environment to unravel the critical process of translating AI models into the clinical routine and explore strategies to improve personalized medicine.
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Affiliation(s)
- Xingping Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China; Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia.
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110189, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Liefa Liao
- School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330000, China; School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
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Wang F, Wang D, Xu Y, Jiang H, Liu Y, Zhang J. Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study. Front Oncol 2022; 12:848726. [PMID: 35387125 PMCID: PMC8979294 DOI: 10.3389/fonc.2022.848726] [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: 01/05/2022] [Accepted: 02/14/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives The molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively. Methods A total of 300 breast cancer patients (153 luminal types and 147 non-luminal types) who underwent routine chest CT examination were included in the study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model. Results During internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish luminal from the non-luminal type were 0.842 [95% CI: 0.728−0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640–0.866], 0.713, 0.767, and 0.676 in the test set. Conclusion The radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.
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Affiliation(s)
- Fei Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ye Xu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yang Liu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jinfeng Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
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