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Zhang AD, Shi QL, Zhang HT, Duan WH, Li Y, Ruan L, Han YF, Liu ZK, Li HF, Xiao JS, Shi GF, Wan X, Wang RZ. Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients. Abdom Radiol (NY) 2024:10.1007/s00261-024-04377-7. [PMID: 38831075 DOI: 10.1007/s00261-024-04377-7] [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: 03/17/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 06/05/2024]
Abstract
OBJECTIVE To investigate the feasibility and accuracy of predicting locoregional recurrence (LR) in elderly patients with esophageal squamous cell cancer (ESCC) who underwent radical radiotherapy using a pairwise machine learning algorithm. METHODS The 130 datasets enrolled were randomly divided into a training set and a testing set in a 7:3 ratio. Clinical factors were included and radiomics features were extracted from pretreatment CT scans using pyradiomics-based software, and a pairwise naive Bayes (NB) model was developed. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). To facilitate practical application, we attempted to construct an automated esophageal cancer diagnosis system based on trained models. RESULTS To the follow-up date, 64 patients (49.23%) had experienced LR. Ten radiomics features and two clinical factors were selected for modeling. The model demonstrated good prediction performance, with area under the ROC curve of 0.903 (0.829-0.958) for the training cohort and 0.944 (0.849-1.000) for the testing cohort. The corresponding accuracies were 0.852 and 0.914, respectively. Calibration curves showed good agreement, and DCA curve confirmed the clinical validity of the model. The model accurately predicted LR in elderly patients, with a positive predictive value of 85.71% for the testing cohort. CONCLUSIONS The pairwise NB model, based on pre-treatment enhanced chest CT-based radiomics and clinical factors, can accurately predict LR in elderly patients with ESCC. The esophageal cancer automated diagnostic system embedded with the pairwise NB model holds significant potential for application in clinical practice.
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Affiliation(s)
- An-du Zhang
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China
| | - Qing-Lei Shi
- School of Medicine, Chinese University of Hong Kong (Shenzhen), No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
| | - Hong-Tao Zhang
- Department of Oncology, Hebei General Hospital, NO. 348 Heping West Road, Xinhua District, Shijiazhuang, Hebei, 050051, People's Republic of China
| | - Wen-Han Duan
- School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yang Li
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China
| | - Li Ruan
- School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yi-Fan Han
- School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Zhi-Kun Liu
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China
| | - Hao-Feng Li
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
| | - Jia-Shun Xiao
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
| | - Gao-Feng Shi
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China.
| | - Xiang Wan
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China.
| | - Ren-Zhi Wang
- School of Medicine, Chinese University of Hong Kong (Shenzhen), No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China.
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Chukwujindu E, Faiz H, Ai-Douri S, Faiz K, De Sequeira A. Role of artificial intelligence in brain tumour imaging. Eur J Radiol 2024; 176:111509. [PMID: 38788610 DOI: 10.1016/j.ejrad.2024.111509] [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: 01/24/2024] [Revised: 04/29/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.
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Affiliation(s)
| | | | | | - Khunsa Faiz
- McMaster University, Department of Radiology, L8S 4L8, Canada.
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3
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Liu D, Lv W, Liu WV, Tian T, Qin Y, Li Y, Liu Q, Cai J, Gao S, Ding G, Zhao Y, Zhou Y, Xie Y, Zhu W. MRI Radiomics Features of Adenohypophysis Determine the Activation of Hypothalamic-Pituitary-Gonadal Axis in Peri-Puberty Children. J Magn Reson Imaging 2024; 59:1769-1776. [PMID: 37501392 DOI: 10.1002/jmri.28914] [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: 04/06/2023] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND The status of the hypothalamic-pituitary-gonadal (HPG) axis is important for assessing the onset of physiological or pathological puberty. The reference standard gonadotropin-releasing hormone (GnRH) stimulation test requires hospital admission and repeated blood samples. A simple noninvasive method would be beneficial. OBJECTIVES To explore a noninvasive method for evaluating HPG axis activation in children using an MRI radiomics model. STUDY TYPE Retrospective. POPULATION Two hundred thirty-nine children (83 male; 3.6-14.6 years) with hypophysial MRI and GnRH stimulation tests, randomly divided a training set (168 children) and a test set (71 children). FIELD STRENGTH/SEQUENCE 3.0 T, 3D isotropic fast spin echo (CUBE) T1-weighted imaging (T1WI) sequences. ASSESSMENT Radiomics features were extracted from sagittal 3D CUBE T1WI, and imaging signatures were generated using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Diagnostic performance for differential diagnosis of HPG status was compared between a radiomics model and MRI features (adenohypophyseal height [aPH] and volume [aPV]). STATISTICAL TESTS Receiver operating characteristic (ROC) and decision curve analysis (DCA). A P value <0.05 was considered statistically significant. RESULTS Eight hundred fifty-one radiomics features were extracted and reduced to 10 by the LASSO method in the training cohort. The radiomics model based on CUBE T1WI showed good performance in assessment of HPG axis activation with an area under the ROC curve (AUC) of 0.81 (95% CI: 0.71, 0.91) in the test set. The AUC of the radiomics model was significantly higher than that of aPH (0.81 vs. 0.65) but there was no significant difference compared to aPV (0.81 vs. 0.78, P = 0.58). In DCA analysis, the radiomics signature showed higher net benefit over the aPV and aPH models. DATA CONCLUSIONS The MRI radiomics model has potential to assess HPG axis activation status noninvasively, potentially providing valuable information in the diagnosis of patients with pathological puberty onset. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Dong Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, Hubei, China
| | | | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yakun Li
- Department of Endocrinology and Metabolism, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qin Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jianjian Cai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sikang Gao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guojun Ding
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunyun Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiran Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Maroufi SF, Doğruel Y, Pour-Rashidi A, Kohli GS, Parker CT, Uchida T, Asfour MZ, Martin C, Nizzola M, De Bonis A, Tawfik-Helika M, Tavallai A, Cohen-Gadol AA, Palmisciano P. Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review. Pituitary 2024; 27:91-128. [PMID: 38183582 DOI: 10.1007/s11102-023-01369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/08/2024]
Abstract
PURPOSE Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations. METHODS PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies. RESULTS Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately. CONCLUSION AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.
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Affiliation(s)
- Seyed Farzad Maroufi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Yücel Doğruel
- Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul, Turkey
| | - Ahmad Pour-Rashidi
- Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Gurkirat S Kohli
- Department of Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Tatsuya Uchida
- Department of Neurosurgery, Stanford University, Palo Alto, CA, USA
| | - Mohamed Z Asfour
- Department of Neurosurgery, Nasser Institute for Research and Treatment Hospital, Cairo, Egypt
| | - Clara Martin
- Department of Neurosurgery, Hospital de Alta Complejidad en Red "El Cruce", Florencio Varela, Buenos Aires, Argentina
| | | | - Alessandro De Bonis
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Amin Tavallai
- Department of Pediatric Neurosurgery, Children's Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Paolo Palmisciano
- Department of Neurological Surgery, University of California, Davis, 4860 Y Street, Suite 3740, Sacramento, CA, 95817, USA.
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Bioletto F, Prencipe N, Berton AM, Aversa LS, Cuboni D, Varaldo E, Gasco V, Ghigo E, Grottoli S. Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives. J Clin Med 2024; 13:336. [PMID: 38256471 PMCID: PMC10816809 DOI: 10.3390/jcm13020336] [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: 11/27/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Silvia Grottoli
- Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (F.B.); (N.P.); (A.M.B.); (L.S.A.); (D.C.); (E.V.); (V.G.); (E.G.)
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Zhou Z, Qian X, Hu J, Geng C, Zhang Y, Dou X, Che T, Zhu J, Dai Y. Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma. Front Oncol 2023; 13:1167328. [PMID: 37692840 PMCID: PMC10485140 DOI: 10.3389/fonc.2023.1167328] [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: 02/16/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Objective This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yongsheng Zhang
- Department of Pathology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xin Dou
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Tuanjie Che
- Key Laboratory of Functional Genomic and Molecular Diagnosis of Gansu Province, Lanzhou, Gansu, China
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
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Shen C, Liu X, Jin J, Han C, Wu L, Wu Z, Su Z, Chen X. A Novel Magnetic Resonance Imaging-Based Radiomics and Clinical Predictive Model for the Regrowth of Postoperative Residual Tumor in Non-Functioning Pituitary Neuroendocrine Tumor. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1525. [PMID: 37763643 PMCID: PMC10535289 DOI: 10.3390/medicina59091525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Background and Objectives: To develop a novel magnetic resonance imaging (MRI)-based radiomics-clinical risk stratification model to predict the regrowth of postoperative residual tumors in patients with non-functioning pituitary neuroendocrine tumors (NF-PitNETs). Materials and Methods: We retrospectively enrolled 114 patients diagnosed as NF-PitNET with postoperative residual tumors after the first operation, and the diameter of the tumors was greater than 10 mm. Univariate and multivariate analyses were conducted to identify independent clinical risk factors. We identified the optimal sequence to generate an appropriate radiomic score (Rscore) that combined pre- and postoperative radiomic features. Three models were established by logistic regression analysis that combined clinical risk factors and radiomic features (Model 1), single clinical risk factors (Model 2) and single radiomic features (Model 3). The models' predictive performances were evaluated using receiver operator characteristic (ROC) curve analysis and area under curve (AUC) values. A nomogram was developed and evaluated using decision curve analysis. Results: Knosp classification and preoperative tumor volume doubling time (TVDT) were high-risk factors (p < 0.05) with odds ratios (ORs) of 2.255 and 0.173. T1WI&T1CE had a higher AUC value (0.954) and generated an Rscore. Ultimately, the AUC of Model 1 {0.929 [95% Confidence interval (CI), 0.865-0.993]} was superior to Model 2 [0.811 (95% CI, 0.704-0.918)] and Model 3 [0.844 (95% CI, 0.748-0.941)] in the training set, which were 0.882 (95% CI, 0.735-1.000), 0.834 (95% CI, 0.676-0.992) and 0.763 (95% CI, 0.569-0.958) in the test set, respectively. Conclusions: We trained a novel radiomics-clinical predictive model for identifying patients with NF-PitNETs at increased risk of postoperative residual tumor regrowth. This model may help optimize individualized and stratified clinical treatment decisions.
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Affiliation(s)
- Chaodong Shen
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaoyan Liu
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Jinghao Jin
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Cheng Han
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Lihao Wu
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Zerui Wu
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Zhipeng Su
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaofang Chen
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [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: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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Luzzi S, Giotta Lucifero A, Rabski J, Kadri PAS, Al-Mefty O. The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era. Cancers (Basel) 2023; 15:cancers15082235. [PMID: 37190164 DOI: 10.3390/cancers15082235] [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: 02/20/2023] [Revised: 03/14/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023] Open
Abstract
The evolution of endoscopic trans-sphenoidal surgery raises the question of the role of transcranial surgery for pituitary tumors, particularly with the effectiveness of adjunct irradiation. This narrative review aims to redefine the current indications for the transcranial approaches for giant pituitary adenomas in the endoscopic era. A critical appraisal of the personal series of the senior author (O.A.-M.) was performed to characterize the patient factors and the tumor's pathological anatomy features that endorse a cranial approach. Traditional indications for transcranial approaches include the absent pneumatization of the sphenoid sinus; kissing/ectatic internal carotid arteries; reduced dimensions of the sella; lateral invasion of the cavernous sinus lateral to the carotid artery; dumbbell-shaped tumors caused by severe diaphragm constriction; fibrous/calcified tumor consistency; wide supra-, para-, and retrosellar extension; arterial encasement; brain invasion; coexisting cerebral aneurysms; and separate coexisting pathologies of the sphenoid sinus, especially infections. Residual/recurrent tumors and postoperative pituitary apoplexy after trans-sphenoidal surgery require individualized considerations. Transcranial approaches still have a critical role in giant and complex pituitary adenomas with wide intracranial extension, brain parenchymal involvement, and the encasement of neurovascular structures.
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Affiliation(s)
- Sabino Luzzi
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
- Neurosurgery Unit, Department of Surgical Sciences, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Alice Giotta Lucifero
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Jessica Rabski
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Paulo A S Kadri
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Medical School, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
| | - Ossama Al-Mefty
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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10
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Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence. Brain Sci 2023; 13:brainsci13030495. [PMID: 36979305 PMCID: PMC10046799 DOI: 10.3390/brainsci13030495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.
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11
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Dang Y, Wang R, Qian K, Lu J, Zhang Y. Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity. Exp Ther Med 2022; 24:685. [PMID: 36277144 PMCID: PMC9533109 DOI: 10.3892/etm.2022.11621] [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: 04/03/2022] [Accepted: 08/22/2022] [Indexed: 11/24/2022] Open
Abstract
Patients with preinvasive or invasive pulmonary ground-glass opacity (GGO) often face different clinical treatments and prognoses. The present study aimed to identify the invasiveness of pulmonary GGO by analysing clinical and radiomic features. Patients with pulmonary GGOs who were treated between January 2014 and February 2019 were included. Clinical features were collected, while radiomic features were extracted from computed tomography records using the three-dimensional Slicer software. Predictors of GGO invasiveness were selected by least absolute shrinkage and selection operator logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model. A total of 194 patients with pulmonary GGOs were included in the present study. The maximum diameter of the solid component, waveletHLL_ngtdm_Coarseness (P=0.03), waveletLHH_firstorder_Maximum (P<0.01) and waveletLLH_glrlm_LongRunEmphasis (P<0.01) were significant predictors of invasive lung GGOs. The area under the ROC curve (AUC) for the prediction models of clinical features and radiomic features was 0.755 and 0.719, respectively, whereas the AUC for the combined prediction model was 0.864 (95% CI, 0.802-0.926). Finally, a nomogram was established for individualized prediction of invasiveness. The combination of radiomic and clinical features can enable the differentiation between preinvasive and invasive GGOs. The present results can provide some basis for the best choice of treatment in patients with lung GGOs.
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Affiliation(s)
- Yutao Dang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
- Department of Thoracic Surgery, Shijingshan Hospital of Beijing City, Shijingshan Teaching Hospital of Capital Medical University, Beijing 100040, P.R. China
| | - Ruotian Wang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
| | - Kun Qian
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
| | - Yi Zhang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
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12
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Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112676. [PMID: 35681655 PMCID: PMC9179850 DOI: 10.3390/cancers14112676] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases. Abstract Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
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Chen YJ, Hsieh HP, Hung KC, Shih YJ, Lim SW, Kuo YT, Chen JH, Ko CC. Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features. Front Oncol 2022; 12:813806. [PMID: 35515108 PMCID: PMC9065347 DOI: 10.3389/fonc.2022.813806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives A subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs. Methods From June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs. Results Forty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85. Conclusions DL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.
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Affiliation(s)
- Yan-Jen Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.,Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Hsun-Ping Hsieh
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Kuo-Chuan Hung
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.,Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Yun-Ju Shih
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi Mei Medical Center, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.,Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan
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14
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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Zhang Y, Luo Y, Kong X, Wan T, Long Y, Ma J. A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years. Front Neurol 2022; 12:780628. [PMID: 35069413 PMCID: PMC8767054 DOI: 10.3389/fneur.2021.780628] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years. Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis. Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718—.860] vs. 0.739, (95% CI: 0.665–0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1. Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Wan
- School of Biomedical Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yunling Long
- Department of Biomedical Engineering, School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis. Neuroradiology 2021; 64:647-668. [PMID: 34839380 DOI: 10.1007/s00234-021-02845-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.
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Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021; 11:732196. [PMID: 34722274 PMCID: PMC8551958 DOI: 10.3389/fonc.2021.732196] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.
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Affiliation(s)
- Zhenjie Yi
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,XiangYa School of Medicine, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lifu Long
- XiangYa School of Medicine, Central South University, Changsha, China
| | - Yu Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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18
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Ko CC, Chang CH, Chen TY, Lim SW, Wu TC, Chen JH, Kuo YT. Solid tumor size for prediction of recurrence in large and giant non-functioning pituitary adenomas. Neurosurg Rev 2021; 45:1401-1411. [PMID: 34606021 PMCID: PMC8976796 DOI: 10.1007/s10143-021-01662-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/16/2021] [Accepted: 09/29/2021] [Indexed: 10/31/2022]
Abstract
A subset of large non-functioning pituitary adenomas (lNFPA) and giant non-functioning pituitary adenomas (gNFPA) undergoes early progression/recurrence (P/R) after surgery. This study revealed the clinical and image predictors of P/R in lNFPA and gNFPA, with emphasis on solid tumor size. This retrospective study investigated the preoperative MR imaging features for the prediction of P/R in lNFPA (> 3 cm) and gNFPA (> 4 cm). Only the patients with a complete preoperative brain MRI and undergone postoperative MRI follow-ups for more than 1 year were included. From November 2010 to December 2020, a total of 34 patients diagnosed with lNFPA and gNFPA were included (median follow-up time 47.6 months) in this study. A total of twenty-three (23/34, 67.6%) patients had P/R, and the median time to P/R is 25.2 months. Solid tumor diameter (STD), solid tumor volume (STV), and extent of resection are associated with P/R (p < 0.05). Multivariate analysis showed large STV is a risk factor for P/R (p < 0.05) with a hazard ratio of 30.79. The cutoff points of STD and STV for prediction of P/R are 26 mm and 7.6 cm3, with AUCs of 0.78 and 0.79 respectively. Kaplan-Meier analysis of tumor P/R trends showed that patients with larger STD and STV exhibited shorter progression-free survival (p < 0.05). For lNFPA and gNFPA, preoperative STD and STV are significant predictors of P/R. The results offer objective and valuable information for treatment planning in this subgroup.
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Affiliation(s)
- Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan. .,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.
| | - Chin-Hong Chang
- Department of Neurosurgery, Chi Mei Medical Center, Tainan, Taiwan
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Te-Chang Wu
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA, USA.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
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Yang Y, Bao Y, Xie S, Tang B, Wu X, Yang L, Wu J, Ding H, Li S, Zheng S, Hong T. Identification of the Extradural and Intradural Extension of Pituitary Adenomas to the Suprasellar Region: Classification, Surgical Strategies, and Outcomes. Front Oncol 2021; 11:723513. [PMID: 34354957 PMCID: PMC8329720 DOI: 10.3389/fonc.2021.723513] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/05/2021] [Indexed: 11/13/2022] Open
Abstract
Objective Suprasellar pituitary adenomas (PAs) can be located in either extradural or intradural spaces, which impacts surgical strategies and outcomes. This study determined how to distinguish these two different types of PAs and analyzed their corresponding surgical strategies and outcomes. Methods We retrospectively analyzed 389 patients who underwent surgery for PAs with suprasellar extension between 2016 to 2020 at our center. PAs were classified into two main grades according to tumor topography and their relationships to the diaphragm sellae (DS) and DS-attached residual pituitary gland (PG). Grade 1 tumors were located extradurally and further divided into grades 1a and 1b, while grade 2 tumors were located intradurally. Results Of 389 PAs, 292 (75.1%) were surrounded by a bilayer structure formed by the DS and the residual PG and classified as grade 1a, 63 (16.2%) had lobulated or daughter tumors resulting from the thinning or absence of the residual PG and subsequently rendering the bilayer weaker were classified as Grade 1b, and the remaining 34 (8.7%) PAs that broke through the DS or traversed the diaphragmic opening and encased suprasellar neurovascular structures were classified as Grade 2. We found that the gross total removal of the suprasellar part of grade 1a, 1b, and 2 PAs decreased with grading (88.4%, 71.4%, and 61.8%, respectively). The rate of major operative complications, including cerebrospinal fluid leakage, hemorrhage, and death, increased with grading. Conclusions It is essential to identify whether PAs with suprasellar extension are located extradurally or intradurally, which depends on whether the bilayer structure is intact. PAs with an intact bilayer structure were classified as grade 1. These were extradural and usually had good surgical outcomes and lower complications. PAs with no bilayer structure surrounding them were classified as grade 2. These were intradural, connected to the cranial cavity, and had increased surgical complications and a lower rate of gross total removal. Different surgical strategies should be adopted for extradural and intradural PAs.
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Affiliation(s)
- YouQing Yang
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - YouYuan Bao
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - ShenHao Xie
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Bin Tang
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiao Wu
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Le Yang
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jie Wu
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Han Ding
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - ShaoYang Li
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - SuYue Zheng
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tao Hong
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
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Zhang Y, Luo Y, Kong X, Wan T, Long Y, Ma J. A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years. Front Neurol 2021. [PMID: 35069413 DOI: 10.3389/fneur.2021.780628/full] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023] Open
Abstract
Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years. Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis. Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718-.860] vs. 0.739, (95% CI: 0.665-0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1. Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Wan
- School of Biomedical Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yunling Long
- Department of Biomedical Engineering, School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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