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      A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

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          Abstract

          Background

          Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features.

          Methods

          In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram.

          Findings

          Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831–0•887) and 0•819 (95% CI, 0•775–0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively.

          Interpretation

          Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas.

          Funding

          This work was supported by the doi 10.13039/501100001809, National Natural Science Foundation of China; (81772006, 81922040); the doi 10.13039/501100004739, Youth Innovation Promotion Association; CAS (grant numbers 2019136); special fund project for doctoral training program of doi 10.13039/100012899, Lanzhou University; Second Hospital (grant numbers YJS-BD-33).

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          Most cited references35

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          Decision curve analysis.

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            Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review.

            Evolving interest in meningioma, the most common primary brain tumor, has refined contemporary management of these tumors. Problematic, however, is the paucity of prospective clinical trials that provide an evidence-based algorithm for managing meningioma. This review summarizes the published literature regarding the treatment of newly diagnosed and recurrent meningioma, with an emphasis on outcomes stratified by WHO tumor grade. Specifically, this review focuses on patient outcomes following treatment (either adjuvant or at recurrence) with surgery or radiation therapy inclusive of radiosurgery and fractionated radiation therapy. Phase II trials for patients with meningioma have recently completed accrual within the Radiation Therapy Oncology Group and the European Organisation for Research and Treatment of Cancer consortia, and Phase III studies are being developed. However, at present, there are no completed prospective, randomized trials assessing the role of either surgery or radiation therapy. Successful completion of future studies will require a multidisciplinary effort, dissemination of the current knowledge base, improved implementation of WHO grading criteria, standardization of response criteria and other outcome end points, and concerted efforts to address weaknesses in present treatment paradigms, particularly for patients with progressive or recurrent low-grade meningioma or with high-grade meningioma. In parallel efforts, Response Assessment in Neuro-Oncology (RANO) subcommittees are developing a paper on systemic therapies for meningioma and a separate article proposing standardized end point and response criteria for meningioma.
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              Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

              Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For this retrospective study, DLAD was developed by using 43 292 chest radiographs (normal radiograph-to-nodule radiograph ratio, 34 067:9225) in 34 676 patients (healthy-to-nodule ratio, 30 784:3892; 19 230 men [mean age, 52.8 years; age range, 18-99 years]; 15 446 women [mean age, 52.3 years; age range, 18-98 years]) obtained between 2010 and 2015, which were labeled and partially annotated by 13 board-certified radiologists, in a convolutional neural network. Radiograph classification and nodule detection performances of DLAD were validated by using one internal and four external data sets from three South Korean hospitals and one U.S. hospital. For internal and external validation, radiograph classification and nodule detection performances of DLAD were evaluated by using the area under the receiver operating characteristic curve (AUROC) and jackknife alternative free-response receiver-operating characteristic (JAFROC) figure of merit (FOM), respectively. An observer performance test involving 18 physicians, including nine board-certified radiologists, was conducted by using one of the four external validation data sets. Performances of DLAD, physicians, and physicians assisted with DLAD were evaluated and compared. Results According to one internal and four external validation data sets, radiograph classification and nodule detection performances of DLAD were a range of 0.92-0.99 (AUROC) and 0.831-0.924 (JAFROC FOM), respectively. DLAD showed a higher AUROC and JAFROC FOM at the observer performance test than 17 of 18 and 15 of 18 physicians, respectively (P < .05), and all physicians showed improved nodule detection performances with DLAD (mean JAFROC FOM improvement, 0.043; range, 0.006-0.190; P < .05). Conclusion This deep learning-based automatic detection algorithm outperformed physicians in radiograph classification and nodule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians' performances when used as a second reader. © RSNA, 2018 Online supplemental material is available for this article.
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                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                30 July 2020
                August 2020
                30 July 2020
                : 58
                : 102933
                Affiliations
                [a ]Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China
                [b ]Second Clinical School, Lanzhou University, Lanzhou, China
                [c ]Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
                [d ]CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China
                [e ]School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
                [f ]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nansihuan Xilu 119, Fengtai District, Beijing, China
                [g ]Department of Neurosurgery, The Municipal Hospital of Weihai, China
                [h ]CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
                [i ]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China
                [j ]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
                [k ]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
                [l ]Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, China
                Author notes
                [1]

                These authors contributed equally to this article.

                Article
                S2352-3964(20)30309-1 102933
                10.1016/j.ebiom.2020.102933
                7393568
                32739863
                cd5de9fb-2646-4d2b-85d4-148988fdf860
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 15 March 2020
                : 16 July 2020
                : 16 July 2020
                Categories
                Research paper

                meningioma,brain invasion,radiomics,magnetic resonance images

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