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Zhang H, Liu J, Liu W, Chen H, Yu Z, Yuan Y, Wang P, Qin J. MHD-Net: Memory-Aware Hetero-Modal Distillation Network for Thymic Epithelial Tumor Typing With Missing Pathology Modality. IEEE J Biomed Health Inform 2024; 28:3003-3014. [PMID: 38470599 DOI: 10.1109/jbhi.2024.3376462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.
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Zhou H, Bai HX, Jiao Z, Cui B, Wu J, Zheng H, Yang H, Liao W. Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study. Eur J Radiol 2023; 168:111136. [PMID: 37832194 DOI: 10.1016/j.ejrad.2023.111136] [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] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023]
Abstract
PURPOSE The study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic assessment. METHOD A total of 681 patients with TETs from three independent hospitals were included and separated into derivation cohort and external test cohort. Handcrafted and deep learning features were extracted from preoperative contrast-enhanced CT images and selected to build three radiomics signatures (radiomics signature [Rad_Sig], deep learning signature [DL_Sig] and deep learning radiomics signature [DLR_Sig]) to predict risk categorization of TETs. A deep learning-based radiomic nomogram (DLRN) was then depicted to visualize the classification evaluation. The performance of predictive models was compared using the receiver operating characteristic and decision curve analysis (DCA). RESULTS Among three radiomics signatures, DLR_Sig demonstrated optimum performance with an AUC of 0.883 for the derivation cohort and 0.749 for the external test cohort. Combining DLR_Sig with age and gender, DLRN was depict and exhibited optimum performance among all radiomics models with an AUC of 0.965, accuracy of 0.911, sensitivity of 0.921 and specificity of 0.902 in the derivation cohort, and an AUC of 0.786, accuracy of 0.774, sensitivity of 0.778 and specificity of 0.771 in the external test cohort. The DCA showed that DLRN had greater clinical benefit than other radiomics signatures. CONCLUSIONS Our study developed and validated a DLRN to accurately predict the risk categorization of TETs, which has potential to facilitate individualized treatment and improve patient prognosis evaluation.
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Affiliation(s)
- Hao Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Harrison X Bai
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Zhicheng Jiao
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI 02903, USA
| | - Biqi Cui
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jing Wu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Haijun Zheng
- Department of Radiology, First People's Hospital of Chenzhou, University of South China, Chenzhou 423000, China
| | - Huan Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China.
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China.
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Chen X, Feng B, Xu K, Chen Y, Duan X, Jin Z, Li K, Li R, Long W, Liu X. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Eur Radiol 2023; 33:6804-6816. [PMID: 37148352 DOI: 10.1007/s00330-023-09690-1] [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: 08/03/2022] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.
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Affiliation(s)
- Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Bao Feng
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Kuncai Xu
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Yehang Chen
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Xiaobei Duan
- Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Zhifa Jin
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province, 519000, People's Republic of China
| | - Ronggang Li
- Department of Pathology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China.
| | - Xueguo Liu
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong Province, 518107, People's Republic of China.
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Yu C, Li T, Yang X, Xin L, Zhao Z, Yang Z, Zhang R. The maximal contrast-enhanced range of CT for differentiating the WHO pathological subtypes and risk subgroups of thymic epithelial tumors. Br J Radiol 2023; 96:20221076. [PMID: 37486626 PMCID: PMC10546431 DOI: 10.1259/bjr.20221076] [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: 11/17/2022] [Revised: 07/01/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023] Open
Abstract
OBJECTIVE To explore the value of maximal contrast-enhanced (CEmax) range using contrast-enhanced CT (CECT) imaging in differentiating the pathological subtypes and risk subgroups of thymic epithelial tumors (TETs). METHODS The pre-treatment-CECT images of 319 TET patients from May 2012 to November 2021 were analyzed retrospectively. The CEmax was defined as the maximum difference between the CT value of the solid tumor on pre-contrast and contrast-enhanced images. The mean CEmax value was calculated at three different tumor levels. RESULTS There was a significant difference in the CEmax among the eight main pathological subtypes [types A, AB, B1, B2, and B3 thymoma, thymic carcinoma (TC), low-grade neuroendocrine tumor (NET) and high-grade NET] (p < 0.001). Among the eight subtypes, the CEmax values of types A, AB, and low-risk NET were higher than those of the other subtypes (all p < 0.001), and there was no difference among types B1-B3 and high-risk NET (all p > 0.05). There was no difference for CEmax values between NET and TC (p = 0.491). For the risk subgroups, the CEmax of TC (including NET) was 35.35 ± 11.41 HU, which was lower than that of low-risk thymoma (A and AB) (57.73±21.24 HU) (P < 0.001) and was higher than that of high-risk thymoma (B1-B3) (27.37±8.27 HU) (P < 0.001). The CEmax cut-off values were 38.5 HU and 30.5 HU respectively (AUC: 0.829 and 0.712; accuracy, 72.4% and 67.7%). CONCLUSION The tumor CEmax on CECT helps differentiate the pathological subtypes and risk subgroups of TETs. ADVANCES IN KNOWLEDGE In this study, an improved simplified risk grouping method was proposed based on the traditional (2004 edition) simplified risk grouping method for TETs. If Type B1 thymoma is classified as high-risk, radiologists using this improved method may improve the accuracy in differentiating risk level of TETs compared with the traditional method.
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Affiliation(s)
- Chunhai Yu
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Ting Li
- Department of Nephrology, Taiyuan People's Hospital, Taiyuan, China
| | - Xiaotang Yang
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Lei Xin
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Zhikai Zhao
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Zhao Yang
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Ruiping Zhang
- First Hospital of Shanxi Medical University, Taiyuan, China
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Takumi K, Nagano H, Oose A, Gohara M, Kamimura K, Nakajo M, Harada-Takeda A, Ueda K, Tabata K, Yoshiura T. Extracellular volume fraction derived from equilibrium contrast-enhanced CT as a diagnostic parameter in anterior mediastinal tumors. Eur J Radiol 2023; 165:110891. [PMID: 37245341 DOI: 10.1016/j.ejrad.2023.110891] [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: 04/10/2023] [Revised: 05/08/2023] [Accepted: 05/22/2023] [Indexed: 05/30/2023]
Abstract
PURPOSE To assess the usefulness of extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT (CECT) for diagnosing anterior mediastinal tumors. METHOD This study included 161 histologically confirmed anterior mediastinal tumors (55 low-risk thymomas, 57 high-risk thymomas, 32 thymic carcinomas, and 17 malignant lymphomas) that were assessed by pretreatment CECT. ECV fraction was calculated using measurements obtained within the lesion and the aorta on unenhanced and equilibrium phase CECT. ECV fraction was compared among anterior mediastinal tumors using one-way ANOVA or t-test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of ECV fraction to differentiate thymic carcinomas/lymphomas from thymomas. RESULTS ECV fraction differed significantly among the anterior mediastinal tumors (p < 0.001). ECV fraction of thymic carcinomas was significantly higher than those of low-risk thymomas, high-risk thymomas, and lymphomas (p < 0.001, p < 0.001, and p = 0.006, respectively). ECV fraction of lymphomas was significantly higher than that of low-risk thymomas (p < 0.001). ECV fraction was significantly higher in thymic carcinomas/lymphomas than in thymomas (40.1 % vs. 27.7 %, p < 0.001). The optimal cutoff value to differentiate thymic carcinomas/lymphomas from thymomas was 38.5 % (AUC, 0.805; 95 %CI, 0.736-0.863). CONCLUSIONS ECV fraction derived from equilibrium CECT is helpful in diagnosing anterior mediastinal tumors. High ECV fraction is indicative of thymic carcinomas/lymphomas, particularly thymic carcinomas.
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Affiliation(s)
- Koji Takumi
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan.
| | - Hiroaki Nagano
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Arata Oose
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Misaki Gohara
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Kiyohisa Kamimura
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Masatoyo Nakajo
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Aya Harada-Takeda
- General Thoracic Surgery Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Kazuhiro Ueda
- General Thoracic Surgery Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Kazuhiro Tabata
- Human Pathology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
| | - Takashi Yoshiura
- Departments of Radiology Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima City 890-8544, Japan
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Takumi K, Nagano H, Myogasako T, Nakano T, Fukukura Y, Ueda K, Tabata K, Tanimoto A, Yoshiura T. Feasibility of iodine concentration and extracellular volume fraction measurement derived from the equilibrium phase dual-energy CT for differentiating thymic epithelial tumors. Jpn J Radiol 2023; 41:45-53. [PMID: 36029365 PMCID: PMC9813095 DOI: 10.1007/s11604-022-01331-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/15/2022] [Indexed: 01/09/2023]
Abstract
PURPOSE To assess the diagnostic feasibility of iodine concentration (IC) and extracellular volume (ECV) fraction measurement using the equilibrium phase dual-energy CT (DECT) for the evaluation of thymic epithelial tumors (TETs). MATERIALS AND METHODS This study included 33 TETs (11 low-risk thymomas, 11 high-risk thymomas, and 11 thymic carcinomas) that were assessed by pretreatment DECT. IC was measured during the equilibrium phases and ECV fraction was calculated using IC of the thymic lesion and the aorta. IC and ECV fraction were compared among TET subtypes using the Kruskal-Wallis H test and Mann-Whitney U test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of IC and ECV fraction to diagnose thymic carcinoma. RESULTS IC during the equilibrium phase and ECV fraction differed among the three TET groups (both p < 0.001). IC during the equilibrium phase and ECV fraction was significantly higher in thymic carcinomas than in thymomas (1.9 mg/mL vs. 1.2 mg/mL, p < 0.001; 38.2% vs. 25.9%, p < 0.001; respectively). The optimal cutoff values of IC during the equilibrium phase and of ECV fraction to diagnose thymic carcinoma were 1.5 mg/mL (AUC, 0.955; sensitivity, 100%; specificity, 90.9%) and 26.8% (AUC, 0.888; sensitivity, 100%; specificity, 72.7%), respectively. CONCLUSION IC and ECV fraction measurement using DECT are helpful in diagnosing TETs. High IC during the equilibrium phase and high ECV fraction are suggestive of thymic carcinoma.
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Affiliation(s)
- Koji Takumi
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Hiroaki Nagano
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tsuyoshi Myogasako
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tsubasa Nakano
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Yoshihiko Fukukura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Kazuhiro Ueda
- Department of General Thoracic Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Kazuhiro Tabata
- Department of Human Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Akihide Tanimoto
- Department of Human Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Zhang H, Chen H, Qin J, Wang B, Ma G, Wang P, Zhong D, Liu J. MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing. Front Oncol 2022; 12:925903. [PMID: 36387248 PMCID: PMC9659861 DOI: 10.3389/fonc.2022.925903] [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: 04/22/2022] [Accepted: 10/11/2022] [Indexed: 08/14/2023] Open
Abstract
OBJECTIVES Accurate histological typing plays an important role in diagnosing thymoma or thymic carcinoma (TC) and predicting the corresponding prognosis. In this paper, we develop and validate a deep learning-based thymoma typing method for hematoxylin & eosin (H&E)-stained whole slide images (WSIs), which provides useful histopathology information from patients to assist doctors for better diagnosing thymoma or TC. METHODS We propose a multi-path cross-scale vision transformer (MC-ViT), which first uses the cross attentive scale-aware transformer (CAST) to classify the pathological information related to thymoma, and then uses such pathological information priors to assist the WSIs transformer (WT) for thymoma typing. To make full use of the multi-scale (10×, 20×, and 40×) information inherent in a WSI, CAST not only employs parallel multi-path to capture different receptive field features from multi-scale WSI inputs, but also introduces the cross-correlation attention module (CAM) to aggregate multi-scale features to achieve cross-scale spatial information complementarity. After that, WT can effectively convert full-scale WSIs into 1D feature matrices with pathological information labels to improve the efficiency and accuracy of thymoma typing. RESULTS We construct a large-scale thymoma histopathology WSI (THW) dataset and annotate corresponding pathological information and thymoma typing labels. The proposed MC-ViT achieves the Top-1 accuracy of 0.939 and 0.951 in pathological information classification and thymoma typing, respectively. Moreover, the quantitative and statistical experiments on the THW dataset also demonstrate that our pipeline performs favorably against the existing classical convolutional neural networks, vision transformers, and deep learning-based medical image classification methods. CONCLUSION This paper demonstrates that comprehensively utilizing the pathological information contained in multi-scale WSIs is feasible for thymoma typing and achieves clinically acceptable performance. Specifically, the proposed MC-ViT can well predict pathological information classes as well as thymoma types, which show the application potential to the diagnosis of thymoma and TC and may assist doctors in improving diagnosis efficiency and accuracy.
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Affiliation(s)
- Huaqi Zhang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Huang Chen
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Jin Qin
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Bei Wang
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Pengyu Wang
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Dingrong Zhong
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
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Dong W, Xiong S, Lei P, Wang X, Liu H, Liu Y, Zou H, Fan B, Qiu Y. Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization. Front Oncol 2022; 12:944005. [PMID: 36081562 PMCID: PMC9446086 DOI: 10.3389/fonc.2022.944005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/01/2022] [Indexed: 12/04/2022] Open
Abstract
Objective This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images. Materials and Methods The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis. Results Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram. Conclusion We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.
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Affiliation(s)
- Wentao Dong
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Situ Xiong
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xiaolian Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hao Liu
- R&D, Yizhun Medical AI, Beijing, China
| | - Yangchun Liu
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Huachun Zou
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Bing Fan, ; Yingying Qiu,
| | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Bing Fan, ; Yingying Qiu,
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Yu C, Li T, Yang X, Zhang R, Xin L, Zhao Z, Cui J. Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors. BMC Med Imaging 2022; 22:37. [PMID: 35249531 PMCID: PMC8898532 DOI: 10.1186/s12880-022-00768-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/23/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs).
Methods
A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses.
Results
Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set.
Conclusions
CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.
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10
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Han X, Luo S, Liu B, Chen Y, Gao W, Wang Y, Liu X, Yu H, Zhang L, Ma G. Acute Angle of Multilobulated Contours Improves the Risk Classification of Thymomas. Front Med (Lausanne) 2021; 8:744587. [PMID: 34660649 PMCID: PMC8513789 DOI: 10.3389/fmed.2021.744587] [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: 07/20/2021] [Accepted: 08/30/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Computed tomography plays an important role in the identification and characterization of thymomas. It has been mainly used during preoperative evaluation for clinical staging. However, the reliable prediction of histological risk types of thymomas based on CT imaging features requires further study. In this study, we developed and validated a nomogram based on CT imaging and included new indices for individualized preoperative prediction of the risk classification of thymomas. Methods: We conducted a retrospective, multicenter study that included 229 patients from two Chinese medical centers. All the patients underwent cross-sectional CT imaging within 2 weeks before surgery. The results of pathological assessments were retrieved from existing reports of the excised lesions. The tumor perimeter that contacted the lung (TPCL) was evaluated and a new quantitative indicator, the acute angle (AA) formed by adjacent lobulations, was measured. Two predictive models of risk classification were created using the least absolute shrinkage and selection operator (LASSO) method in a training cohort for features selection. The model with a smaller Akaike information criterion was then used to create an individualized imaging nomogram, which we evaluated regarding its prediction ability and clinical utility. Results: A new CT imaging-based model incorporating AA was developed and validated, which had improved predictive performance during risk classification of thymomas when compared with a model using traditional imaging predictors. The new imaging nomogram with AA demonstrated its clinical utility by decision curve analysis. Conclusions: Acute angle can improve the performance of a CT-based predictive model during the preoperative risk classification of thymomas and should be considered a new imaging marker for the evaluation and treatment of patients with thymomas. On the contrary, TPCL is not useful as a predictor for the risk classification of thymomas in this study.
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Affiliation(s)
- Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Song Luo
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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11
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Savu C, Melinte A, Gibu A, Hallabrin I, Zafiu A, Tudor VA, Diaconu C, Gherghiceanu F, Furtunescu F, Radavoi D, Balescu I, Bacalbasa N. A Large Thymoma Resected via Left Antero-lateral Thoracotomy. CANCER DIAGNOSIS & PROGNOSIS 2021; 1:363-370. [PMID: 35403149 PMCID: PMC8988950 DOI: 10.21873/cdp.10048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 07/12/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND/AIM Thymomas are a rare type of mediastinal tumors with a slow growth rate. Because of this, they are well tolerated and patients usually present with large masses, which can extend in either of the thoracic cavities. The surgical approach for such tumors is dictated by the size and localization of the mass. CASE REPORT We present the case of a patient with a large thymoma, resected through surgery performed by left antero-lateral thoracotomy. The patient presented in our clinic with a persistent cough, dyspnea, chest pain and tightness. Standard thoracic X-ray revealed a bilateral increase in size of the mediastinal shadow, mainly on the left side, with well-defined margins and subcostal intensity. A thoracic computed tomography (CT) scan discovered a tumoral mass within the antero-superior mediastinum, with compression of the mediastinal organs; presentation being suggestive for a thymoma. Surgery was performed, removing a 15/13/10 cm thymoma with a weight of 1126 g. Pathological examination as well as immunohistochemistry confirmed our diagnosis of type AB thymoma, stage I Masaoka-Koga. CONCLUSION In conclusion, surgical treatment remains the main therapeutic option in thymomas, but it is often difficult to perform due to tumor size and local invasion. However, even in large thymomas of stages I and II, surgery can be performed using an antero-lateral thoracotomy.
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Affiliation(s)
- Cornel Savu
- Department of Thoracic Surgery, "Marius Nasta" National Institute of Pneumology, Bucharest, Romania
- Department of Thoracic Surgery, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Alexandru Melinte
- Department of Thoracic Surgery, "Marius Nasta" National Institute of Pneumology, Bucharest, Romania
| | - Alexandru Gibu
- Department of Thoracic Surgery, "Marius Nasta" National Institute of Pneumology, Bucharest, Romania
| | - Ionut Hallabrin
- Department of Thoracic Surgery, "Marius Nasta" National Institute of Pneumology, Bucharest, Romania
| | - Alexandru Zafiu
- Department of Thoracic Surgery, "Marius Nasta" National Institute of Pneumology, Bucharest, Romania
| | - Vasilica-Adrian Tudor
- Department of Thoracic Surgery, "Marius Nasta" National Institute of Pneumology, Bucharest, Romania
| | - Camelia Diaconu
- Department of Internal Medicine, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
- Department of Internal Medicine, Clinical Emergency Hospital of Bucharest, Bucharest, Romania
| | - Florentina Gherghiceanu
- Department of Marketing and Medical Technology, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Florentina Furtunescu
- Department of Public Health and Management University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania
| | - Daniel Radavoi
- Department of Urology, "Prof. Dr. Th. Burghele" Clinical Hospital, Bucharest, Romania
- Department of Urology, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Irina Balescu
- Department of Surgery, "Ponderas" Academic Hospital, Bucharest, Romania
| | - Nicolae Bacalbasa
- Department of Obstetrics and Gynecology, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
- Department of Visceral Surgery, Center of Excellence in Translational Medicine "Fundeni" Clinical Institute, Bucharest, Romania
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12
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Gentili F, Monteleone I, Mazzei FG, Luzzi L, Del Roscio D, Guerrini S, Volterrani L, Mazzei MA. Advancement in Diagnostic Imaging of Thymic Tumors. Cancers (Basel) 2021; 13:cancers13143599. [PMID: 34298812 PMCID: PMC8303549 DOI: 10.3390/cancers13143599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 01/25/2023] Open
Abstract
Simple Summary Diagnostic imaging is pivotal for the diagnosis and staging of thymic tumors. It is important to distinguish thymoma and other tumor histotypes amenable to surgery from lymphoma. Furthermore, in cases of thymoma, it is necessary to differentiate between early and advanced disease before surgery since patients with locally advanced tumors require neoadjuvant chemotherapy for improving survival. This review aims to provide to radiologists a full spectrum of findings of thymic neoplasms using traditional and innovative imaging modalities. Abstract Thymic tumors are rare neoplasms even if they are the most common primary neoplasm of the anterior mediastinum. In the era of advanced imaging modalities, such as functional MRI, dual-energy CT, perfusion CT and radiomics, it is possible to improve characterization of thymic epithelial tumors and other mediastinal tumors, assessment of tumor invasion into adjacent structures and detection of secondary lymph nodes and metastases. This review aims to illustrate the actual state of the art in diagnostic imaging of thymic lesions, describing imaging findings of thymoma and differential diagnosis.
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Affiliation(s)
- Francesco Gentili
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (F.G.M.); (S.G.)
- Correspondence:
| | - Ilaria Monteleone
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
| | - Francesco Giuseppe Mazzei
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (F.G.M.); (S.G.)
| | - Luca Luzzi
- Thoracic Surgery Unit, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy;
| | - Davide Del Roscio
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
| | - Susanna Guerrini
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (F.G.M.); (S.G.)
| | - Luca Volterrani
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
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13
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Liu Z, Zhu Y, Yuan Y, Yang L, Wang K, Wang M, Yang X, Wu X, Tian X, Zhang R, Shen B, Luo H, Feng H, Feng S, Ke Z. 3D DenseNet Deep Learning Based Preoperative Computed Tomography for Detecting Myasthenia Gravis in Patients With Thymoma. Front Oncol 2021; 11:631964. [PMID: 34026611 PMCID: PMC8132943 DOI: 10.3389/fonc.2021.631964] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 04/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background Myasthenia gravis (MG) is the most common paraneoplastic syndromes of thymoma and closely related to thymus abnormalities. Timely detecting of the risk of MG would benefit clinical management and treatment decision for patients with thymoma. Herein, we developed a 3D DenseNet deep learning (DL) model based on preoperative computed tomography (CT) as a non-invasive method to detect MG in thymoma patients. Methods A large cohort of 230 thymoma patients in a hospital affiliated with a medical school were enrolled. 182 thymoma patients (81 with MG, 101 without MG) were used for training and model building. 48 cases from another hospital were used for external validation. A 3D-DenseNet-DL model and five radiomic models were performed to detect MG in thymoma patients. A comprehensive analysis by integrating machine learning and semantic CT image features, named 3D-DenseNet-DL-based multi-model, was also performed to establish a more effective prediction model. Findings By elaborately comparing the prediction efficacy, the 3D-DenseNet-DL effectively identified MG patients and was superior to other five radiomic models, with a mean area under ROC curve (AUC), accuracy, sensitivity, and specificity of 0.734, 0.724, 0.787, and 0.672, respectively. The effectiveness of the 3D-DenseNet-DL-based multi-model was further improved as evidenced by the following metrics: AUC 0.766, accuracy 0.790, sensitivity 0.739, and specificity 0.801. External verification results confirmed the feasibility of this DL-based multi-model with metrics: AUC 0.730, accuracy 0.732, sensitivity 0.700, and specificity 0.690, respectively. Interpretation Our 3D-DenseNet-DL model can effectively detect MG in patients with thymoma based on preoperative CT imaging. This model may serve as a supplement to the conventional diagnostic criteria for identifying thymoma associated MG.
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Affiliation(s)
- Zhenguo Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ying Zhu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Institution of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yujie Yuan
- Center of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lei Yang
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Kefeng Wang
- Department of Thoracic Surgery, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Minghui Wang
- Department of Thoracic Surgery, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoyu Yang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xi Wu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xi Tian
- Advanced Institute, Infervision, Beijing, China
| | | | - Bingqi Shen
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Honghe Luo
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Huiyu Feng
- Department of Neurology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shiting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zunfu Ke
- Institution of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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14
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Correlation of Computed Tomography Parameters with Histology, Stage and Prognosis in Surgically Treated Thymomas. ACTA ACUST UNITED AC 2020; 57:medicina57010010. [PMID: 33374432 PMCID: PMC7824084 DOI: 10.3390/medicina57010010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/17/2020] [Accepted: 12/18/2020] [Indexed: 11/16/2022]
Abstract
Background and objectives: The histological classification and staging of thymic tumors remains a matter of debate. The correlation of computed tomography (CT) parameters with tumor histology and stage also still has to be completely assessed. The aim of this study was therefore to analyze the correlation of radiological parameters with histological and staging classifications of thymomas evaluating their prognostic role. Methods: Data of 50 patients with thymoma submitted to a complete surgical treatment between 2005 and 2015 were retrospectively analyzed. Tumors were classified according to the WHO and Suster and Moran (S&M) histological classifications and to the Masaoka-Koga and tumor, node and metastases (TNM) staging systems. The correlation of CT features with histology and stage and the prognostic role of histopathological and radiological features were assessed. Results: Five-year overall (OS) and disease-free survival (DFS) were 90.3% and 81.1%, respectively. A significant correlation of DFS with the Masaoka-Koga (p = 0.001) and TNM staging systems (p = 0.002) and with the S&M (p = 0.02) and WHO histological classifications (p = 0.04) was observed. CT scan features correlated with tumor stage, histology and prognosis. Moderately differentiated tumors (WHO B3) had a significantly higher incidence of irregular shape and contours (p = 0.002 and p = 0.001, respectively) and pericardial contact (p = 0.036). A larger tumor volume (p = 0.03) and a greater length of pleural contact (p = 0.04) adversely influenced DFS. The presence of pleural (p < 0.001) or lung invasion (p = 0.02) and of pleural effusion (p = 0.004) was associated with a significantly worse OS. Conclusions: Pre-operative CT scan parameters correlate with stage and histology, and have a prognostic role in surgically treated thymomas.
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15
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Ren C, Li M, Zhang Y, Zhang S. Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Cancer Imaging 2020; 20:86. [PMID: 33308325 PMCID: PMC7731456 DOI: 10.1186/s40644-020-00364-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 11/26/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. METHODS Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots. RESULTS Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications. CONCLUSION A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.
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Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China. .,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.
| | - Mingli Li
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China
| | - Yunyan Zhang
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.,Department of Radiology, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
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