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8. Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study

Author

Vollmuth, Philipp, Foltyn, Martha, Huang, Raymond Y, Galldiks, Norbert, Petersen, Jens, Isensee, Fabian, van den Bent, Martin J, Barkhof, Frederik, Park, Ji Eun, Park, Yae Won, Ahn, Sung Soo, Brugnara, Gianluca, Meredig, Hagen, Jain, Rajan, Smits, Marion, Pope, Whitney B, Maier-Hein, Klaus, Weller, Michael, Wen, Patrick Y, Wick, Wolfgang, and Bendszus, Martin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Rare Diseases, Neurosciences, Brain Cancer, Clinical Research, Brain Disorders, Cancer, Humans, Glioblastoma, Brain Neoplasms, Artificial Intelligence, Reproducibility of Results, Glioma, Artificial intelligence (AI)-based decision support, RANO, tumor response assessment, tumor volumetry, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundTo assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria.MethodsA series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling.ResultsThe CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02).ConclusionsAI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

Published
2023

23. Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum.

Author

Bang, Minji, Park, Kisung, Choi, Seoung‐Ho, Ahn, Sung Soo, Kim, Jinna, Lee, Seung‐Koo, Park, Yae Won, and Lee, Sang‐Hyuk

Subjects
MAGNETIC resonance imaging, RADIOMICS, FEATURE selection, ARTIFICIAL intelligence, STRUCTURAL models
Abstract

Aims: The cerebellum is involved in higher‐order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1‐weighted magnetic resonance imaging (T1‐MRI) of the cerebellum. Methods: A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1‐MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. Results: We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82–0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second‐order size zone non‐uniformity feature from the right lobule IX and first‐order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. Conclusion: The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease‐defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker‐based decision‐making in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Differentiation of Recurrent Glioblastoma from Radiation Necrosis Using Diffusion Radiomics: Machine Learning Model Development and External Validation

Author

Park, Yae Won, Park, Ji Eun, Ahn, Sung Soo, Kim, Hwiyoung, Kim, Ho Sung, Lee, Seung-Koo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kia, Seyed Mostafa, editor, Mohy-ud-Din, Hassan, editor, Abdulkadir, Ahmed, editor, Bass, Cher, editor, Habes, Mohamad, editor, Rondina, Jane Maryam, editor, Tax, Chantal, editor, Wang, Hongzhi, editor, Wolfers, Thomas, editor, Rathore, Saima, editor, and Ingalhalikar, Madhura, editor

Published
2020
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48. Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification

Author

Park, Yae Won, primary, Kim, Sooyon, additional, Han, Kyunghwa, additional, Ahn, Sung Soo, additional, Moon, Ju Hyung, additional, Kim, Eui Hyun, additional, Kim, Jinna, additional, Kang, Seok-Gu, additional, Kim, Se Hoon, additional, Lee, Seung-Koo, additional, and Chang, Jong Hee, additional

Published
2023
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