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Wu Y, Cao F, Lei H, Zhang S, Mei H, Ni L, Pang J. Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study. Abdom Radiol (NY) 2024; 49:3096-3106. [PMID: 38733392 PMCID: PMC11335970 DOI: 10.1007/s00261-024-04351-3] [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: 03/08/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors. MATERIALS AND METHODS In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach. RESULTS A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original_shape_Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method. CONCLUSIONS The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance.
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Affiliation(s)
- Yaohai Wu
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Fei Cao
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hanqi Lei
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Shiqiang Zhang
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hongbing Mei
- Department of Urology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Liangchao Ni
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jun Pang
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
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Bang M, Park K, Choi SH, Ahn SS, Kim J, Lee SK, Park YW, Lee SH. Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum. Psychiatry Clin Neurosci 2024. [PMID: 38953397 DOI: 10.1111/pcn.13707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 05/26/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024]
Abstract
AIMS The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum. METHODS A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. RESULTS We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. CONCLUSION The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.
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Affiliation(s)
- Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Kisung Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Seoung-Ho Choi
- National Program Excellence in Software at Kwangwoon University, Seoul, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
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Wang XM, Zhang XJ. Role of radiomics in staging liver fibrosis: a meta-analysis. BMC Med Imaging 2024; 24:87. [PMID: 38609843 PMCID: PMC11010385 DOI: 10.1186/s12880-024-01272-x] [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: 06/13/2023] [Accepted: 04/10/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis. METHOD After literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were performed to achieve pooled estimates of area under receiver-operator curve (AUROC), accuracy, sensitivity, and specificity of radiomics in staging liver fibrosis compared to histopathology. RESULTS Fifteen studies (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% [95% CI: 65, 73] males) were included. AUROC values of radiomics for detecting significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4) were 0.91 [95%CI: 0.89, 0.94], 0.92 [95%CI: 0.90, 0.95], and 0.94 [95%CI: 0.93, 0.96] in training cohorts and 0.89 [95%CI: 0.83, 0.91], 0.89 [95%CI: 0.83, 0.94], and 0.93 [95%CI: 0.91, 0.95] in validation cohorts, respectively. For diagnosing significant fibrosis, advanced fibrosis, and cirrhosis the sensitivity of radiomics was 84.0% [95%CI: 76.1, 91.9], 86.9% [95%CI: 76.8, 97.0], and 92.7% [95%CI: 89.7, 95.7] in training cohorts, and 75.6% [95%CI: 67.7, 83.5], 80.0% [95%CI: 70.7, 89.3], and 92.0% [95%CI: 87.8, 96.1] in validation cohorts, respectively. Respective specificity was 88.6% [95% CI: 83.0, 94.2], 88.4% [95% CI: 81.9, 94.8], and 91.1% [95% CI: 86.8, 95.5] in training cohorts, and 86.8% [95% CI: 83.3, 90.3], 94.0% [95% CI: 89.5, 98.4], and 88.3% [95% CI: 84.4, 92.2] in validation cohorts. Limitations included use of several methods for feature selection and classification, less availability of studies evaluating a particular radiological modality, lack of a direct comparison between radiology and radiomics, and lack of external validation. CONCLUSION Although radiomics offers good diagnostic accuracy in detecting liver fibrosis, its role in clinical practice is not as clear at present due to comparability and validation constraints.
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Affiliation(s)
- Xiao-Min Wang
- School of Medical Imaging, Tianjin Medical University, No.1, Guangdong Road, Hexi District, Tianjin, 300203, China.
| | - Xiao-Jing Zhang
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
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Champendal M, Müller H, Prior JO, Dos Reis CS. A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging. Eur J Radiol 2023; 169:111159. [PMID: 37976760 DOI: 10.1016/j.ejrad.2023.111159] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/26/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE To review eXplainable Artificial Intelligence/(XAI) methods available for medical imaging/(MI). METHOD A scoping review was conducted following the Joanna Briggs Institute's methodology. The search was performed on Pubmed, Embase, Cinhal, Web of Science, BioRxiv, MedRxiv, and Google Scholar. Studies published in French and English after 2017 were included. Keyword combinations and descriptors related to explainability, and MI modalities were employed. Two independent reviewers screened abstracts, titles and full text, resolving differences through discussion. RESULTS 228 studies met the criteria. XAI publications are increasing, targeting MRI (n = 73), radiography (n = 47), CT (n = 46). Lung (n = 82) and brain (n = 74) pathologies, Covid-19 (n = 48), Alzheimer's disease (n = 25), brain tumors (n = 15) are the main pathologies explained. Explanations are presented visually (n = 186), numerically (n = 67), rule-based (n = 11), textually (n = 11), and example-based (n = 6). Commonly explained tasks include classification (n = 89), prediction (n = 47), diagnosis (n = 39), detection (n = 29), segmentation (n = 13), and image quality improvement (n = 6). The most frequently provided explanations were local (78.1 %), 5.7 % were global, and 16.2 % combined both local and global approaches. Post-hoc approaches were predominantly employed. The used terminology varied, sometimes indistinctively using explainable (n = 207), interpretable (n = 187), understandable (n = 112), transparent (n = 61), reliable (n = 31), and intelligible (n = 3). CONCLUSION The number of XAI publications in medical imaging is increasing, primarily focusing on applying XAI techniques to MRI, CT, and radiography for classifying and predicting lung and brain pathologies. Visual and numerical output formats are predominantly used. Terminology standardisation remains a challenge, as terms like "explainable" and "interpretable" are sometimes being used indistinctively. Future XAI development should consider user needs and perspectives.
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Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO, University of Applied Sciences Western Switzerland, Lausanne, CH, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, CH, Switzerland.
| | - Henning Müller
- Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO Valais) Sierre, CH, Switzerland; Medical faculty, University of Geneva, CH, Switzerland.
| | - John O Prior
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, CH, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV), Lausanne, CH, Switzerland.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO, University of Applied Sciences Western Switzerland, Lausanne, CH, Switzerland.
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Ahn S, Lee SH, Lee KS. Impact of Intolerance of Uncertainty on Brain Structural Changes in Panic Disorder. Psychiatry Investig 2023; 20:1069-1076. [PMID: 37997335 PMCID: PMC10678144 DOI: 10.30773/pi.2023.0181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/06/2023] [Accepted: 08/20/2023] [Indexed: 11/25/2023] Open
Abstract
OBJECTIVE This study investigated the impact of intolerance of uncertainty (IU) on structural changes in the brain and symptom severity in patients with panic disorder. METHODS This study included 90 participants diagnosed with panic disorder. The IU Scale, Panic Disorder Severity Scale (PDSS), Beck Depression Inventory-II (BDI-II), Penn State Worry Questionnaire (PSWQ), Self-Forgiveness Scale (SFS), and Short Form 36 Health Survey (SF) were used. A voxel-wise correlation analysis was conducted to investigate the structural differences in the gray matter. RESULTS As IU increased, the cortical thickness of the right lingual gyrus decreased significantly, while the gray matter volume of the right pars triangularis increased. The cortical thickness of the right lingual gyrus showed a significant negative correlation with the BDI-II score and a positive correlation with the SFS. Additionally, the gray matter volume of the right pars triangularis was positively correlated with the PDSS, PSWQ, and BDI-II scores and negatively correlated with the mental health domain of the SF. CONCLUSION According to our findings, elevated IU in participants with panic disorder was associated with cortical thinning in the lingual gyrus and increased gray matter volume in the pars triangularis. These structural alterations may also have an impact on perceived quality of life, as well as high levels of depression and anxiety.
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Affiliation(s)
- Sungjun Ahn
- Department of Psychiatry, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Kang Soo Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
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Li Q, Wang W, Hu Z. Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype. Front Psychiatry 2023; 14:1091730. [PMID: 36911127 PMCID: PMC10001895 DOI: 10.3389/fpsyt.2023.1091730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/06/2023] [Indexed: 03/14/2023] Open
Abstract
INTRODUCTION Anxiety disorder is the most common psychiatric disorder among adolescents, with generalized anxiety disorder (GAD) being a common subtype of anxiety disorder. Current studies have revealed abnormal amygdala function in patients with anxiety compared with healthy people. However, the diagnosis of anxiety disorder and its subtypes still lack specific features of amygdala from T1-weighted structural magnetic resonance (MR) imaging. The purpose of our study was to investigate the feasibility of using radiomics approach to distinguish anxiety disorder and its subtype from healthy controls on T1-weighted images of the amygdala, and provide a basis for the clinical diagnosis of anxiety disorder. METHODS T1-weighted MR images of 200 patients with anxiety disorder (including 103 GAD patients) as well as 138 healthy controls were obtained in the Healthy Brain Network (HBN) dataset. We extracted 107 radiomics features for the left and right amygdala, respectively, and then performed feature selection using the 10-fold LASSO regression algorithm. For the selected features, we performed group-wise comparisons, and use different machine learning algorithms, including linear kernel support vector machine (SVM), to achieve the classification between the patients and healthy controls. RESULTS For the classification task of anxiety patients vs. healthy controls, 2 and 4 radiomics features were selected from left and right amygdala, respectively, and the area under receiver operating characteristic curve (AUC) of linear kernel SVM in cross-validation experiments was 0.6739±0.0708 for the left amygdala features and 0.6403±0.0519 for the right amygdala features; for classification task for GAD patients vs. healthy controls, 7 and 3 features were selected from left and right amygdala, respectively, and the cross-validation AUCs were 0.6755±0.0615 for the left amygdala features and 0.6966±0.0854 for the right amygdala features. In both classification tasks, the selected amygdala radiomics features had higher discriminatory significance and effect sizes compared with the amygdala volume. DISCUSSION Our study suggest that radiomics features of bilateral amygdala potentially could serve as a basis for the clinical diagnosis of anxiety disorder.
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Affiliation(s)
- Qingfeng Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenzheng Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhishan Hu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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