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Siddiqui MIA, Parihar P, Mishra GV, Sood A, Saboo K. World Health Organization (WHO) Grade 1 Astrocytoma in a Female From Rural India: A Case Report. Cureus 2023; 15:e50554. [PMID: 38226132 PMCID: PMC10788676 DOI: 10.7759/cureus.50554] [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: 09/13/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
Astrocytomas are rare in adults and less common in the parietal and temporal regions of the brain parenchyma. The current case is of a 26-year-old female patient who presented with a four-month history of headaches and a two-month history of vomiting. The patient's MRI brain showed an ill-defined, thick-walled lesion in the right parietal and temporal region with mass effect, which on histopathology confirmed to be a case of WHO Grade 1 astrocytoma. This manuscript describes the imaging and histopathological appearance of WHO Grade 1 astrocytoma in an adult female.
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Affiliation(s)
| | - Pratap Parihar
- Department of Radiodiagnosis, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Gaurav V Mishra
- Department of Radiodiagnosis, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anshul Sood
- Department of Radiodiagnosis, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Keyur Saboo
- Department of Medicine, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Yang S, Yang X, Wang H, Gu Y, Feng J, Qin X, Feng C, Li Y, Liu L, Fan G, Liao X, He S. Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma. Front Med (Lausanne) 2022; 8:802471. [PMID: 35118095 PMCID: PMC8804494 DOI: 10.3389/fmed.2021.802471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA.MethodsPatients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of patients with SCA in the training dataset and feature importance was obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability.ResultsA total of 818 patients with SCA were included in this study, with an average age of 30.84 ± 21.97 years and an average follow-up time of 117.57 ± 113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy, and post-operation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade, and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in the training and testing dataset illustrated good calibration, with C-indexes of 0.783 and 0.769. The area under the curves (AUCs) of 5-year survival prediction were 0.82 and 0.843, while 10-year survival predictions were 0.849 and 0.881, for training and testing datasets, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performances of nomograms were verified to be superior to that of single indicators, and the prediction performance of nomograms for CSS is also excellent.ConclusionsNomograms for patients with SCA prognosis prediction demonstrated good calibration, discrimination, and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation is required for the established web-based online calculators.
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Affiliation(s)
- Sheng Yang
- Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- Spinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China
| | - Xun Yang
- Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- Department of Orthopedics, The First Affiliated Hospital, Shenzhen University, Shenzhen, China
- Shenzhen Second People's Hospital, Shenzhen, China
| | - Huiwen Wang
- Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yuelin Gu
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Behavioral and Cognitive Neuroscience Center, Fudan University, Shanghai, China
| | - Jingjing Feng
- The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xianfeng Qin
- College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China
| | - Chaobo Feng
- Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- Spinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China
| | - Yufeng Li
- Department of Orthopedics, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Lijun Liu
- Department of Orthopedics, The First Affiliated Hospital, Shenzhen University, Shenzhen, China
- Shenzhen Second People's Hospital, Shenzhen, China
| | - Guoxin Fan
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China
- Department of Pain Medicine, Shenzhen Municipal Key Laboratory for Pain Medicine, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
- *Correspondence: Guoxin Fan
| | - Xiang Liao
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Pain Medicine, Shenzhen Municipal Key Laboratory for Pain Medicine, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
- Xiang Liao
| | - Shisheng He
- Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- Spinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China
- Shisheng He
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