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He Y, Han Y, Zou L, Yao T, Zhang Y, Lv X, Jiang M, Long L, Li M, Cheng X, Jiang G, Peng Z, Tao L, Meng J, Xie W. Succinate promotes pulmonary fibrosis through GPR91 and predicts death in idiopathic pulmonary fibrosis. Sci Rep 2024; 14:14376. [PMID: 38909094 PMCID: PMC11193722 DOI: 10.1038/s41598-024-64844-5] [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: 04/04/2023] [Accepted: 06/13/2024] [Indexed: 06/24/2024] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is believed to be associated with a notable disruption of cellular energy metabolism. By detecting the changes of energy metabolites in the serum of patients with pulmonary fibrosis, we aimed to investigate the diagnostic and prognostic value of energy metabolites in IPF, and further elucidated the mechanism of their involvement in pulmonary fibrosis. Through metabolomics research, it was discovered that the TCA cycle intermediates changed dramatically in IPF patients. In another validation cohort of 55 patients with IPF compared to 19 healthy controls, it was found that succinate, an intermediate product of TCA cycle, has diagnostic and prognostic value in IPF. The cut-off levels of serum succinate were 98.36 μM for distinguishing IPF from healthy controls (sensitivity, 83.64%; specificity, 63.16%; likelihood ratio, 2.27, respectively). Moreover, a high serum succinate level was independently associated with higher rates of disease progression (OR 13.087, 95%CI (2.819-60.761)) and mortality (HR 3.418, 95% CI (1.308-8.927)). In addition, accumulation of succinate and increased expression of the succinate receptor GPR91 were found in both IPF patients and BLM mouse models of pulmonary fibrosis. Reducing succinate accumulation in BLM mice alleviated pulmonary fibrosis and 21d mortality, while exogenous administration of succinate can aggravate pulmonary fibrosis in BLM mice. Furthermore, GPR91 deficiency protected against lung fibrosis caused by BLM. In vitro, succinate promoted the activation of lung fibroblasts by activating ERK pathway through GPR91. In summary, succinate is a promising biomarker for diagnosis and prognosis of IPF. The accumulation of succinate may promote fibroblast activation through GPR91 and pulmonary fibrosis.
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Affiliation(s)
- Yijun He
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Yuanyuan Han
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China
| | - Lijun Zou
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Tingting Yao
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Yan Zhang
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Xin Lv
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China
| | - Mao Jiang
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Lingzhi Long
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Mengyu Li
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Xiaoyun Cheng
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
- Department of Pulmonary and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Guoliang Jiang
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
| | - Zhangzhe Peng
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China
- National International Collaborative Research Center for Medical Metabolomics, Changsha, China
| | - Lijian Tao
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China
- National International Collaborative Research Center for Medical Metabolomics, Changsha, China
| | - Jie Meng
- Department of Pulmonary and Critical Care Medicine, Third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Changsha, 410013, Hunan, China.
- Hunan Key Laboratory of Organ Fibrosis, Changsha, China.
- National International Collaborative Research Center for Medical Metabolomics, Changsha, China.
| | - Wei Xie
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China.
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Ren H, Wang Q, Xiao Z, Mo R, Guo J, Hide GR, Tu M, Zeng Y, Ling C, Li P. Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-00967-5. [PMID: 38565729 DOI: 10.1007/s10278-024-00967-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/01/2023] [Accepted: 11/14/2023] [Indexed: 04/04/2024]
Abstract
This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.
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Affiliation(s)
- He Ren
- Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Qiubo Wang
- Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhengguang Xiao
- Department of Radiology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Runwei Mo
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200030, China
| | - Jiachen Guo
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gareth Richard Hide
- Department of Surgery, Faculty of Health Sciences Medical School, University of the Witwatersrand, Parktown, Johannesburg, South Africa
| | - Mengting Tu
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yanan Zeng
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Chen Ling
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Ping Li
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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Lucà S, Pagliuca F, Perrotta F, Ronchi A, Mariniello DF, Natale G, Bianco A, Fiorelli A, Accardo M, Franco R. Multidisciplinary Approach to the Diagnosis of Idiopathic Interstitial Pneumonias: Focus on the Pathologist's Key Role. Int J Mol Sci 2024; 25:3618. [PMID: 38612431 PMCID: PMC11011777 DOI: 10.3390/ijms25073618] [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: 02/01/2024] [Revised: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Idiopathic Interstitial Pneumonias (IIPs) are a heterogeneous group of the broader category of Interstitial Lung Diseases (ILDs), pathologically characterized by the distortion of lung parenchyma by interstitial inflammation and/or fibrosis. The American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary consensus classification of the IIPs was published in 2002 and then updated in 2013, with the authors emphasizing the need for a multidisciplinary approach to the diagnosis of IIPs. The histological evaluation of IIPs is challenging, and different types of IIPs are classically associated with specific histopathological patterns. However, morphological overlaps can be observed, and the same histopathological features can be seen in totally different clinical settings. Therefore, the pathologist's aim is to recognize the pathologic-morphologic pattern of disease in this clinical setting, and only after multi-disciplinary evaluation, if there is concordance between clinical and radiological findings, a definitive diagnosis of specific IIP can be established, allowing the optimal clinical-therapeutic management of the patient.
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Affiliation(s)
- Stefano Lucà
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Francesca Pagliuca
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Fabio Perrotta
- Department of Translational Medical Science, Università degli Studi della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (F.P.); (D.F.M.); (A.B.)
| | - Andrea Ronchi
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Domenica Francesca Mariniello
- Department of Translational Medical Science, Università degli Studi della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (F.P.); (D.F.M.); (A.B.)
| | - Giovanni Natale
- Division of Thoracic Surgery, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy; (G.N.); (A.F.)
| | - Andrea Bianco
- Department of Translational Medical Science, Università degli Studi della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (F.P.); (D.F.M.); (A.B.)
| | - Alfonso Fiorelli
- Division of Thoracic Surgery, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia, 2, 80138 Naples, Italy; (G.N.); (A.F.)
| | - Marina Accardo
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
| | - Renato Franco
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (S.L.); (F.P.); (A.R.); (M.A.)
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Li Y, Deng W, Zhou Y, Luo Y, Wu Y, Wen J, Cheng L, Liang X, Wu T, Wang F, Huang Z, Tan C, Liu Y. A nomogram based on clinical factors and CT radiomics for predicting anti-MDA5+ DM complicated by RP-ILD. Rheumatology (Oxford) 2024; 63:809-816. [PMID: 37267146 DOI: 10.1093/rheumatology/kead263] [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: 12/01/2022] [Revised: 03/30/2023] [Accepted: 05/08/2023] [Indexed: 06/04/2023] Open
Abstract
OBJECTIVES Anti-melanoma differentiation-associated gene 5 antibody-positive (anti-MDA5+) DM complicated by rapidly progressive interstitial lung disease (RP-ILD) has a high incidence and poor prognosis. The objective of this study was to establish a model for the prediction and early diagnosis of anti-MDA5+ DM-associated RP-ILD based on clinical manifestations and imaging features. METHODS A total of 103 patients with anti-MDA5+ DM were included. The patients were randomly split into training and testing sets of 72 and 31 patients, respectively. After image analysis, we collected clinical, imaging and radiomics features from each patient. Feature selection was performed first with the minimum redundancy and maximum relevance algorithm and then with the best subset selection method. The final remaining features comprised the radscore. A clinical model and imaging model were then constructed with the selected independent risk factors for the prediction of non-RP-ILD and RP-ILD. We also combined these models in different ways and compared their predictive abilities. A nomogram was also established. The predictive performances of the models were assessed based on receiver operating characteristics curves, calibration curves, discriminability and clinical utility. RESULTS The analyses showed that two clinical factors, dyspnoea (P = 0.000) and duration of illness in months (P = 0.001), and three radiomics features (P = 0.001, 0.044 and 0.008, separately) were independent predictors of non-RP-ILD and RP-ILD. However, no imaging features were significantly different between the two groups. The radiomics model built with the three radiomics features performed worse than the clinical model and showed areas under the curve (AUCs) of 0.805 and 0.754 in the training and test sets, respectively. The clinical model demonstrated a good predictive ability for RP-ILD in MDA5+ DM patients, with an AUC, sensitivity, specificity and accuracy of 0.954, 0.931, 0.837 and 0.847 in the training set and 0.890, 0.875, 0.800 and 0.774 in the testing set, respectively. The combination model built with clinical and radiomics features performed slightly better than the clinical model, with an AUC, sensitivity, specificity and accuracy of 0.994, 0.966, 0.977 and 0.931 in the training set and 0.890, 0.812, 1.000 and 0.839 in the testing set, respectively. The calibration curve and decision curve analyses showed satisfactory consistency and clinical utility of the nomogram. CONCLUSION Our results suggest that the combination model built with clinical and radiomics features could reliably predict the occurrence of RP-ILD in MDA5+ DM patients.
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Affiliation(s)
- Yanhong Li
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Wen Deng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhou
- Department of Respiratory and Critical Care Medicine, Chengdu First People's Hospital, Chengdu, China
| | - Yubin Luo
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Yinlan Wu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Ji Wen
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Lu Cheng
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Xiuping Liang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Tong Wu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence, Shanghai, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chunyu Tan
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Yi Liu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
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Ferrigno I, Verzellesi L, Ottone M, Bonacini M, Rossi A, Besutti G, Bonelli E, Colla R, Facciolongo N, Teopompi E, Massari M, Mancuso P, Ferrari AM, Pattacini P, Trojani V, Bertolini M, Botti A, Zerbini A, Giorgi Rossi P, Iori M, Salvarani C, Croci S. CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection. Inflamm Res 2024:10.1007/s00011-024-01852-1. [PMID: 38308760 DOI: 10.1007/s00011-024-01852-1] [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: 09/22/2023] [Revised: 01/17/2024] [Accepted: 01/21/2024] [Indexed: 02/05/2024] Open
Abstract
OBJECTIVE AND DESIGN We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients. MATERIAL OR SUBJECTS Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled. METHODS CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses. RESULTS CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1. CONCLUSION Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.
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Affiliation(s)
- Ilaria Ferrigno
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- PhD Program in Clinical and Experimental Medicine, University of Modena and Reggio Emilia, Modena, Italy
| | - Laura Verzellesi
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Marta Ottone
- Unit of Epidemiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Martina Bonacini
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Alessandro Rossi
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giulia Besutti
- Unit of Radiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Department of Surgery, Medicine, Dentistry and Morphological Sciences With Interest in Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy
| | - Efrem Bonelli
- Unit of Radiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Clinical Chemistry and Endocrinology Laboratory, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rossana Colla
- Clinical Chemistry and Endocrinology Laboratory, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Nicola Facciolongo
- Unit of Respiratory Diseases, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Elisabetta Teopompi
- Multidisciplinary Internal Medicine Unit, Guastalla Hospital, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Marco Massari
- Unit of Infectious Diseases, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Pamela Mancuso
- Unit of Epidemiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Anna Maria Ferrari
- Department of Emergency, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Pierpaolo Pattacini
- Unit of Radiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Valeria Trojani
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Marco Bertolini
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Botti
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Alessandro Zerbini
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Paolo Giorgi Rossi
- Unit of Epidemiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Mauro Iori
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Carlo Salvarani
- Department of Surgery, Medicine, Dentistry and Morphological Sciences With Interest in Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Rheumatology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Stefania Croci
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
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Huang W, Deng H, Li Z, Xiong Z, Zhou T, Ge Y, Zhang J, Jing W, Geng Y, Wang X, Tu W, Dong P, Liu S, Fan L. Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules. Front Oncol 2023; 13:1255007. [PMID: 37664069 PMCID: PMC10470826 DOI: 10.3389/fonc.2023.1255007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Objective To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. Methods This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise. Results The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%. Conclusion The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.
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Affiliation(s)
- Wenjun Huang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Heng Deng
- School of Medicine, Shanghai University, Shanghai, China
| | - Zhaobin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zhanda Xiong
- Department of Artificial Intelligence Medical Imaging, Tron Technology, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Yanming Ge
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China
| | - Jing Zhang
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Wenbin Jing
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Yayuan Geng
- Clinical Research Institute, Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
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7
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Chen J, Meng T, Xu J, Ooi JD, Eggenhuizen PJ, Liu W, Li F, Wu X, Sun J, Zhang H, Zhou YO, Luo H, Xiao X, Pei Y, Li W, Zhong Y. Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: a two-center study. Front Immunol 2023; 14:1084299. [PMID: 37503353 PMCID: PMC10369051 DOI: 10.3389/fimmu.2023.1084299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/19/2023] [Indexed: 07/29/2023] Open
Abstract
Background Previous studies from our group and other investigators have shown that lung involvement is one of the independent predictors for treatment resistance in patients with myeloperoxidase (MPO)-anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (MPO-AAV). However, it is unclear which image features of lung involvement can predict the therapeutic response in MPO-AAV patients, which is vital in decision-making for these patients. Our aim was to develop and validate a radiomics nomogram to predict treatment resistance of Chinese MPO-AAV patients based on low-dose multiple slices computed tomography (MSCT) of the involved lung with cohorts from two centers. Methods A total of 151 MPO-AAV patients with lung involvement (MPO-AAV-LI) from two centers were enrolled. Two different models (Model 1: radiomics signature; Model 2: radiomics nomogram) were built based on the clinical and MSCT data to predict the treatment resistance of MPO-AAV with lung involvement in training and test cohorts. The performance of the models was assessed using the area under the curve (AUC). The better model was further validated. A nomogram was constructed and evaluated by DCA and calibration curves, which further tested in all enrolled data and compared with the other model. Results Model 2 had a higher predicting ability than Model 1 both in training (AUC: 0.948 vs. 0.824; p = 0.039) and test cohorts (AUC: 0.913 vs. 0.898; p = 0.043). As a better model, Model 2 obtained an excellent predictive performance (AUC: 0.929; 95% CI: 0.827-1.000) in the validation cohort. The DCA curve demonstrated that Model 2 was clinically feasible. The calibration curves of Model 2 closely aligned with the true treatment resistance rate in the training (p = 0.28) and test sets (p = 0.70). In addition, the predictive performance of Model 2 (AUC: 0.929; 95% CI: 0.875-0.964) was superior to Model 1 (AUC: 0.862; 95% CI: 0.796-0.913) and serum creatinine (AUC: 0.867; 95% CI: 0.802-0.917) in all patients (all p< 0.05). Conclusion The radiomics nomogram (Model 2) is a useful, non-invasive tool for predicting the treatment resistance of MPO-AAV patients with lung involvement, which might aid in individualizing treatment decisions.
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Affiliation(s)
- Juan Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ting Meng
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jia Xu
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Joshua D. Ooi
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Inflammatory Diseases, Monash University, Clayton, VIC, Australia
| | | | - Wenguang Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Fang Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xueqin Wu
- Department of Nephrology, The third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jian Sun
- Department of Nephrology, The third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hao Zhang
- Department of Nephrology, The third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ya-Ou Zhou
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Luo
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wenzheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yong Zhong
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan, China
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8
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Gao J, Qi Q, Li H, Wang Z, Sun Z, Cheng S, Yu J, Zeng Y, Hong N, Wang D, Wang H, Yang F, Li X, Li Y. Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules. Front Oncol 2023; 13:1096453. [PMID: 36910632 PMCID: PMC9996279 DOI: 10.3389/fonc.2023.1096453] [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: 11/12/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Background Tumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness. Methods We identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People's Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves. Results In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004-1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058-3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004-1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259-27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057-9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399-0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750-7.717; p<0.001) were independent predictors for IAC. The areas under the curve (AUCs) in the training and validation cohorts indicated a better discriminative ability of the histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886). Conclusion We developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.
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Affiliation(s)
- Jian Gao
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Qingyi Qi
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Hao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Zhenfan Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Zewen Sun
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Sida Cheng
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Jie Yu
- Department of Thoracic Surgery, Qingdao Women and Children's Hospital, Qingdao, China
| | - Yaqi Zeng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Huiyang Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Feng Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Xiao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Yun Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
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9
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Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis. Diagnostics (Basel) 2022; 12:diagnostics12123038. [PMID: 36553045 PMCID: PMC9777463 DOI: 10.3390/diagnostics12123038] [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: 11/11/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
We investigated the feasibility of a new deep-learning (DL)-based lung analysis method for the evaluation of interstitial lung disease (ILD) by comparing it with evaluation using the traditional computer-aided diagnosis (CAD) system and patients’ clinical outcomes. We prospectively included 104 patients (84 with and 20 without ILD). An expert radiologist defined regions of interest in the typical areas of normal, ground-glass opacity, consolidation, consolidation with fibrosis (traction bronchiectasis), honeycombing, reticulation, traction bronchiectasis, and emphysema, and compared them with the CAD and DL-based analysis results. Next, we measured the extent of ILD lesions with the CAD and DL-based analysis and compared them. Finally, we compared the lesion extent on computed tomography (CT) images, as measured with the DL-based analysis, with pulmonary function tests results and patients’ overall survival. Pearson’s correlation analysis revealed a significant correlation between DL-based analysis and CAD results. Forced vital capacity was significantly correlated with DL-based analysis (r = 0.789, p < 0.001 for normal lung volume and r = −0.316, p = 0.001 for consolidation with fibrosis volume). Consolidation with fibrosis measured using DL-based analysis was independently associated with poor survival. The lesion extent measured using DL-based analysis showed a negative correlation with the pulmonary function test results and prognosis.
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10
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Wagner M, Brandenburg JM, Bodenstedt S, Schulze A, Jenke AC, Stern A, Daum MTJ, Mündermann L, Kolbinger FR, Bhasker N, Schneider G, Krause-Jüttler G, Alwanni H, Fritz-Kebede F, Burgert O, Wilhelm D, Fallert J, Nickel F, Maier-Hein L, Dugas M, Distler M, Weitz J, Müller-Stich BP, Speidel S. Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data. Surg Endosc 2022; 36:8568-8591. [PMID: 36171451 PMCID: PMC9613751 DOI: 10.1007/s00464-022-09611-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/03/2022] [Indexed: 01/06/2023]
Abstract
BACKGROUND Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics. METHODS We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features' clinical relevance and technical feasibility. RESULTS In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was "surgical skill and quality of performance" for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was "Instrument" (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were "intraoperative adverse events", "action performed with instruments", "vital sign monitoring", and "difficulty of surgery". CONCLUSION Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
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Affiliation(s)
- Martin Wagner
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), Heidelberg, Germany.
| | - Johanna M Brandenburg
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Sebastian Bodenstedt
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- Cluster of Excellence "Centre for Tactile Internet with Human-in-the-Loop" (CeTI), Technische Universität Dresden, 01062, Dresden, Germany
| | - André Schulze
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Alexander C Jenke
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
| | - Antonia Stern
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Marie T J Daum
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Lars Mündermann
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Fiona R Kolbinger
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany
| | - Nithya Bhasker
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
| | - Gerd Schneider
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Grit Krause-Jüttler
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Hisham Alwanni
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Fleur Fritz-Kebede
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Oliver Burgert
- Research Group Computer Assisted Medicine (CaMed), Reutlingen University, Reutlingen, Germany
| | - Dirk Wilhelm
- Department of Surgery, Faculty of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Johannes Fallert
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Felix Nickel
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Marius Distler
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Jürgen Weitz
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Beat-Peter Müller-Stich
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Stefanie Speidel
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- Cluster of Excellence "Centre for Tactile Internet with Human-in-the-Loop" (CeTI), Technische Universität Dresden, 01062, Dresden, Germany
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