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Kalasauskas D, Kosterhon M, Kurz E, Schmidt L, Altmann S, Grauhan NF, Sommer C, Othman A, Brockmann MA, Ringel F, Keric N. Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics. Sci Rep 2024; 14:20586. [PMID: 39232068 PMCID: PMC11374997 DOI: 10.1038/s41598-024-71200-0] [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: 04/11/2024] [Accepted: 08/26/2024] [Indexed: 09/06/2024] Open
Abstract
Preoperative identification of intracranial meningiomas with aggressive behaviour may help in choosing the optimal treatment strategy. Radiomics is emerging as a powerful diagnostic tool with potential applications in patient risk stratification. In this study, we aimed to compare the predictive value of conventional, semantic based and radiomic analyses to determine CNS WHO grade and early tumour relapse in intracranial meningiomas. We performed a single-centre retrospective analysis of intracranial meningiomas operated between 2007 and 2018. Recurrence within 5 years after Simpson Grade I-III resection was considered as early. Preoperative T1 CE MRI sequences were analysed conventionally by two radiologists. Additionally a semantic feature score based on systematic analysis of morphological characteristics was developed and a radiomic analysis were performed. For the radiomic model, tumour volume was extracted manually, 791 radiomic features were extracted. Eight feature selection algorithms and eight machine learning methods were used. Models were analysed using test and training datasets. In total, 226 patients were included. There were 21% CNS WHO grade 2 tumours, no CNS WHO grade 3 tumour, and 25 (11%) tumour recurrences were detected in total. In ROC analysis the best radiomic models demonstrated superior performance for determination of CNS WHO grade (AUC 0.930) and early recurrence (AUC 0.892) in comparison to the semantic feature score (AUC 0.74 and AUC 0.65) and conventional radiological analysis (AUC 0.65 and 0.54). The combination of human classifiers, semantic score and radiomic analysis did not markedly increase the model performance. Radiomic analysis is a promising tool for preoperative identification of aggressive and atypical intracranial meningiomas and could become a useful tool in the future.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany.
| | - Elena Kurz
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Leon Schmidt
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Sebastian Altmann
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Nils F Grauhan
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Clemens Sommer
- Institute of Neuropathology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Marc A Brockmann
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Florian Ringel
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Naureen Keric
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
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Cuellar-Hernández JJ, Ortega-Ruiz OR, Rodriguez-Armendariz AG, Castillo-Acevedo CD, Pérez-Ruano LA, Caro-Osorio E, Garza-Baez A. Accurate preoperative diagnosis of a Rathke cleft cyst with the aid of a novel classification for sellar cystic lesions and a diagnostic algorithm decision: Tools for differentiating cystic sellar lesions with a representative case. Surg Neurol Int 2024; 15:120. [PMID: 38741985 PMCID: PMC11090547 DOI: 10.25259/sni_59_2024] [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: 01/23/2024] [Accepted: 03/13/2024] [Indexed: 05/16/2024] Open
Abstract
Background Rathke's cleft cyst (RCC) is a benign lesion in the sellar and suprasellar compartments. Similarly, pituitary adenomas can present with cystic morphology, making it a differential diagnosis when evaluating a patient with a cystic lesion in the sellar region. Surgical goals differ between RCCs and pituitary adenomas as the first can achieve remission of symptoms with cyst decompression in contrast to pituitary adenomas where complete resection would be the main goal. Imaging analysis alone may not be sufficient to define a preoperative surgical plan. The combination of imaging and conjoined use of validated tools may provide valuable insights to the clinician when defining a surgical approach. Case Description We present a case of a 27-year-old male with a 3-month history of visual disturbances and headaches. Magnetic resonance imaging showed a cystic lesion in the sellar compartment with compression of nearby structures. The authors were able to accurately diagnose this sellar lesion as an RCC with the conjoined aid of two classifications proposed in the literature. Cyst evacuation was performed with relief of symptoms and improved visual outcomes at follow-up. Conclusion While cystic adenomas can require total resection for cure, RCCs can show marked improvement with partial resection and evacuation of its contents. An accurate preoperative diagnosis can lead the surgeon to opt for the best surgical approach.
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Affiliation(s)
- J. Javier Cuellar-Hernández
- Department of Neurosurgery, Hospital Zambrano Hellion, TecSalud, San Pedro Garza García, Nuevo León, Mexico
- Department of Neurosurgery, National Institute of Pediatrics, Mexico City, Mexico
| | - Omar R. Ortega-Ruiz
- Department of Neurosurgery, Hospital Zambrano Hellion, TecSalud, San Pedro Garza García, Nuevo León, Mexico
| | | | | | - Luis Alejandro Pérez-Ruano
- Department of Neurosurgery, Hospital Zambrano Hellion, TecSalud, San Pedro Garza García, Nuevo León, Mexico
| | - Enrique Caro-Osorio
- Department of Neurosurgery, Hospital Zambrano Hellion, TecSalud, San Pedro Garza García, Nuevo León, Mexico
| | - Azalea Garza-Baez
- Department of Neuro-Radiology, Hospital Zambrano Hellion, TecSalud, San Pedro Garza García, Nuevo León, Mexico
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Maiuri F, Del Basso de Caro M. Update on the Diagnosis and Management of Meningiomas. Cancers (Basel) 2023; 15:3575. [PMID: 37509238 PMCID: PMC10377680 DOI: 10.3390/cancers15143575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 06/29/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
This series of five articles (one original article and four reviews) focuses on the most recent and interesting research studies on the biomolecular and radiological diagnosis and the surgical and medical management of meningiomas [...].
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Affiliation(s)
- Francesco Maiuri
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Neurosurgical Clinic, 80131 Naples, Italy
| | - Marialaura Del Basso de Caro
- Department of Advanced Biomedical Sciences, Section of Pathology, School of Medicine, University "Federico II" of Naples, 80131 Naples, Italy
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Findlay MC, Yost S, Bauer SZ, Cole KL, Henson JC, Lucke-Wold B, Mehkri Y, Abou-Al-Shaar H, Plute T, Friedman L, Richards T, Wiggins R, Karsy M. Application of Radiomics to the Differential Diagnosis of Temporal Bone Skull Base Lesions: A Pilot Study. World Neurosurg 2023; 172:e540-e554. [PMID: 36702242 DOI: 10.1016/j.wneu.2023.01.076] [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: 12/02/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023]
Abstract
BACKGROUND Temporal bone skull base pathologies represent a complex differential because they can be radiographically obscure and difficult to diagnose without biopsy. Radiomics involves the use of mathematical quantification of imaging data beyond simple intensity, size, and location to inform diagnosis and prognosis. We examined the feasibility of using radiomic parameters to help predict temporal bone tumor type. METHODS A total of 117 radiomic parameters were analyzed from 5 magnetic resonance imaging sequences (T1 without contrast, T1 with contrast, T2, fluid-attenuated inversion recovery, apparent diffusion coefficient [ADC]) for each tumor. Statistical analysis was used to delineate known primary, metastatic/secondary, and lymphoma lesions using radiomics. RESULTS The mean tumor volumes for the 14 primary, 12 secondary, and 8 lymphoma lesions were 2.98 ± 2.11, 3.28 ± 2.31, and 12.16 ± 7.1 cm3, respectively (P = 0.2). No significant differences in mean intensity values for any sequence helped distinguish tumors (P > 0.05), but 6 radiomic parameters were significantly correlated with diagnostic accuracy. Discriminant analysis using a stepwise algorithm generated a model where radiomic parameters for T1 cluster prominence, ADC dependence nonuniformity, T1 with contrast zone percentage, and ADC informational measure of correlation 2 achieved the best predictive model (P = 0.0001). These significant characteristics were often indirect measures of tumor heterogeneity on different magnetic resonance imaging sequences. CONCLUSIONS These data suggest that quantitative measures of tumor heterogeneity can be discriminatory of pathology and might be integrated into clinical workflow. Although this pilot study requires further validation, these data support the exploration of radiomics in temporal bone radiographic diagnostics.
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Affiliation(s)
| | - Samantha Yost
- School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Sawyer Z Bauer
- Reno School of Medicine, University of Nevada, Reno, Nevada, USA
| | - Kyril L Cole
- School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - J Curran Henson
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Yusuf Mehkri
- Department of Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Hussam Abou-Al-Shaar
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Tritan Plute
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Lindley Friedman
- Division of the Natural Sciences and Mathematics, Bates College, Lewiston, Maine, USA
| | - Tyler Richards
- Department of Neuroradiology, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - Richard Wiggins
- Department of Neuroradiology, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - Michael Karsy
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA.
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Chen C, Zhang T, Teng Y, Yu Y, Shu X, Zhang L, Zhao F, Xu J. Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network. Eur Radiol 2023; 33:2665-2675. [PMID: 36396792 PMCID: PMC10017618 DOI: 10.1007/s00330-022-09216-1] [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: 07/13/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 11/19/2022]
Abstract
OBJECTIVES To develop a U-Net-based deep learning model for automated segmentation of craniopharyngioma. METHODS A total number of 264 patients diagnosed with craniopharyngiomas were included in this research. Pre-treatment MRIs were collected, annotated, and used as ground truth to learn and evaluate the deep learning model. Thirty-eight patients from another institution were used for independently external testing. The proposed segmentation model was constructed based on a U-Net architecture. Dice similarity coefficients (DSCs), Hausdorff distance of 95% percentile (95HD), Jaccard value, true positive rate (TPR), and false positive rate (FPR) of each case were calculated. One-way ANOVA analysis was used to investigate if the model performance was associated with the radiological characteristics of tumors. RESULTS The proposed model showed a good performance in segmentation with average DSCs of 0.840, Jaccard of 0.734, TPR of 0.820, FPR of 0.000, and 95HD of 3.669 mm. It performed feasibly in the independent external test set, with average DSCs of 0.816, Jaccard of 0.704, TPR of 0.765, FPR of 0.000, and 95HD of 4.201 mm. Also, one-way ANOVA suggested the performance was not statistically associated with radiological characteristics, including predominantly composition (p = 0.370), lobulated shape (p = 0.353), compressed or enclosed ICA (p = 0.809), and cavernous sinus invasion (p = 0.283). CONCLUSIONS The proposed deep learning model shows promising results for the automated segmentation of craniopharyngioma. KEY POINTS • The segmentation model based on U-Net showed good performance in segmentation of craniopharyngioma. • The proposed model showed good performance regardless of the radiological characteristics of craniopharyngioma. • The model achieved feasibility in the independent external dataset obtained from another center.
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Affiliation(s)
- Chaoyue Chen
- Department of Neurosurgery, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.,Department of Radiology, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China
| | - Ting Zhang
- Department of Neurosurgery, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.,Department of Radiology, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China
| | - Yuen Teng
- Department of Neurosurgery, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.,Department of Radiology, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China
| | - Yijie Yu
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Xin Shu
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China. .,College of Computer Science, Sichuan University, Chengdu, 610041, People's Republic of China.
| | - Fumin Zhao
- Radiology Department, West China Second University Hospital, Sichuan University, No. 20, section 3, Renmin South Road, Wuhou District, Chengdu, 610041, People's Republic of China.
| | - Jianguo Xu
- Department of Neurosurgery, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China. .,Department of Radiology, Sichuan University, West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
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Machine learning–based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle. Eur Radiol 2022; 33:4259-4269. [PMID: 36547672 DOI: 10.1007/s00330-022-09319-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 10/16/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles. METHODS A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists. RESULTS The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts. CONCLUSIONS The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance. KEY POINTS • The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.
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Zhai Y, Bai J, Xue Y, Li M, Mao W, Zhang X, Zhang Y. Development and validation of a preoperative MRI-based radiomics nomogram to predict progression-free survival in patients with clival chordomas. Front Oncol 2022; 12:996262. [PMID: 36591445 PMCID: PMC9800789 DOI: 10.3389/fonc.2022.996262] [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: 07/17/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Objectives The aim of this study was to establish and validate a MRI-based radiomics nomogram to predict progression-free survival (PFS) of clival chordoma. Methods A total of 174 patients were enrolled in the study (train cohort: 121 cases, test cohort: 53 cases). Radiomic features were extracted from multiparametric MRIs. Intraclass correlation coefficient analysis and a Lasso and Elastic-Net regularized generalized linear model were used for feature selection. Then, a nomogram was established via univariate and multivariate Cox regression analysis in the train cohort. The performance of this nomogram was assessed by area under curve (AUC) and calibration curve. Results A total of 3318 radiomic features were extracted from each patient, of which 2563 radiomic features were stable features. After feature selection, seven radiomic features were selected. Cox regression analysis revealed that 2 clinical factors (degree of resection, and presence or absence of primary chordoma) and 4 radiomic features were independent prognostic factors. The AUC of the established nomogram was 0.747, 0.807, and 0.904 for PFS prediction at 1, 3, and 5 years in the train cohort, respectively, compared with 0.582, 0.852, and 0.914 in the test cohort. Calibration and risk score stratified survival curves were satisfactory in the train and test cohort. Conclusions The presented nomogram demonstrated a favorable predictive accuracy of PFS, which provided a novel tool to predict prognosis and risk stratification. Our results suggest that radiomic analysis can effectively help neurosurgeons perform individualized evaluations of patients with clival chordomas.
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Affiliation(s)
- Yixuan Zhai
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jiwei Bai
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yake Xue
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingxuan Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenbin Mao
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuezhi Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yazhuo Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,Center of Brain Tumor, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China,China National Clinical Research Center for Neurological Diseases, Beijing, China,*Correspondence: Yazhuo Zhang,
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Shang L, Wang F, Gao Y, Zhou C, Wang J, Chen X, Chughtai AR, Pu H, Zhang G, Kong W. Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study. Front Oncol 2022; 12:1043163. [PMID: 36505817 PMCID: PMC9731806 DOI: 10.3389/fonc.2022.1043163] [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: 09/13/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022] Open
Abstract
Background This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas. Methods In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination. Results Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1. Conclusion Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.
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Affiliation(s)
- Lan Shang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Affiliated Hospital of University of Electronic Science and Technology of China, Chengdu, China,Department of Radiology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Fang Wang
- Department of Radiology, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, China
| | - Yan Gao
- Department of Radiology, The First People’s Hospital of Liangshan Yi Autonomous Prefecture, Xichang, China
| | - Chaoxin Zhou
- Department of Radiology, The First People’s Hospital of Liangshan Yi Autonomous Prefecture, Xichang, China
| | - Jian Wang
- Department of diagnostic imaging School of Computer Science, Nanjing University of Science and Technology, Nanjing, China
| | - Xinyue Chen
- Department of Diagnostic Imaging, Computed Tomography (CT) Collaboration, Siemens Healthineers, Chengdu, China
| | - Aamer Rasheed Chughtai
- Section of Thoracic Imaging, Cleveland Clinic Health System, Cleveland, OH, United States
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Affiliated Hospital of University of Electronic Science and Technology of China, Chengdu, China,Department of Radiology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,*Correspondence: Weifang Kong, ; Guojin Zhang, ; Hong Pu,
| | - Guojin Zhang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Affiliated Hospital of University of Electronic Science and Technology of China, Chengdu, China,Department of Radiology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,*Correspondence: Weifang Kong, ; Guojin Zhang, ; Hong Pu,
| | - Weifang Kong
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Affiliated Hospital of University of Electronic Science and Technology of China, Chengdu, China,Department of Radiology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,*Correspondence: Weifang Kong, ; Guojin Zhang, ; Hong Pu,
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Li N, Desiderio DM, Zhan X. The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas. MASS SPECTROMETRY REVIEWS 2022; 41:964-1013. [PMID: 34109661 DOI: 10.1002/mas.21710] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.
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Affiliation(s)
- Na Li
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
| | - Dominic M Desiderio
- The Charles B. Stout Neuroscience Mass Spectrometry Laboratory, Department of Neurology, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Xianquan Zhan
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
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Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization. Cancers (Basel) 2022; 14:cancers14112605. [PMID: 35681585 PMCID: PMC9179263 DOI: 10.3390/cancers14112605] [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: 05/05/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Meningiomas are typically benign, common extra-axial tumors of the central nervous system. Routine clinical assessment by radiologists presents some limitations regarding long-term patient outcome prediction and risk stratification. Given the exponential growth of interest in radiomics and artificial intelligence in medical imaging, numerous studies have evaluated the potential of these tools in the setting of meningioma imaging. These were aimed at the development of reliable and reproducible models based on quantitative data. Although several limitations have yet to be overcome for their routine use in clinical practice, their innovative potential is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging. Abstract Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.
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Jia X, Zhai Y, Song D, Wang Y, Wei S, Yang F, Wei X. A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment. Front Oncol 2022; 12:758622. [PMID: 35251957 PMCID: PMC8888684 DOI: 10.3389/fonc.2022.758622] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 01/21/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To construct and validate a radiomics nomogram for preoperative prediction of survival stratification in glioblastoma (GBM) patients with standard treatment according to radiomics features extracted from multiparameter magnetic resonance imaging (MRI), which could facilitate clinical decision-making. METHODS A total of 125 eligible GBM patients (53 in the short and 72 in the long survival group, separated by an overall survival of 12 months) were randomly divided into a training cohort (n = 87) and a validation cohort (n = 38). Radiomics features were extracted from the MRI of each patient. The T-test and the least absolute shrinkage and selection operator algorithm (LASSO) were used for feature selection. Next, three feature classifier models were established based on the selected features and evaluated by the area under curve (AUC). A radiomics score (Radscore) was then constructed by these features for each patient. Combined with clinical features, a radiomics nomogram was constructed with independent risk factors selected by the logistic regression model. The performance of the nomogram was assessed by AUC, calibration, discrimination, and clinical usefulness. RESULTS There were 5,216 radiomics features extracted from each patient, and 5,060 of them were stable features judged by the intraclass correlation coefficients (ICCs). 21 features were included in the construction of the radiomics score. Of three feature classifier models, support vector machines (SVM) had the best classification effect. The radiomics nomogram was constructed in the training cohort and exhibited promising calibration and discrimination with AUCs of 0.877 and 0.919 in the training and validation cohorts, respectively. The favorable decision curve analysis (DCA) indicated the clinical usefulness of the radiomics nomogram. CONCLUSIONS The presented radiomics nomogram, as a non-invasive tool, achieved satisfactory preoperative prediction of the individualized survival stratification of GBM patients.
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Affiliation(s)
- Xin Jia
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yixuan Zhai
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dixiang Song
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yiming Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuxin Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fengdong Yang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinting Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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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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Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2022; 12:752119. [PMID: 35069406 PMCID: PMC8770750 DOI: 10.3389/fneur.2021.752119] [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: 08/02/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.
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Affiliation(s)
- Caijie Qin
- Institute of Information Engineering, Sanming University, Sanming, China
| | - Wenxing Hu
- University of New South Wales, Sydney, NSW, Australia
| | - Xinsheng Wang
- School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China
| | - Xibo Ma
- CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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14
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.
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Zhao Z, Xiao D, Nie C, Zhang H, Jiang X, Jecha AR, Yan P, Zhao H. Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma. Front Oncol 2021; 11:709321. [PMID: 34307178 PMCID: PMC8300562 DOI: 10.3389/fonc.2021.709321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/18/2021] [Indexed: 11/21/2022] Open
Abstract
Background Given the similarities in clinical manifestations of cystic-solid pituitary adenomas (CS-PAs) and craniopharyngiomas (CPs), this study aims to establish and validate a nomogram based on preoperative imaging features and blood indices to differentiate between CS-PAs and CPs. Methods A departmental database was searched to identify patients who had undergone tumor resection between January 2012 and December 2020, and those diagnosed with CS-PAs or CPs by histopathology were included. Preoperative magnetic resonance imaging (MRI) features as well as blood indices were retrieved and analyzed. Radiological features were extracted from the tumor on contrast-enhanced T1 (CE-T1) weighted and T2 weighted sequences. The two independent samples t-test and principal component analysis (PCA) were used for feature selection, data dimension reduction, and radiomics signature building. Next, the radiomics signature was put in five classification models for exploring the best classifier with superior identification performance. Multivariate logistic regression analysis was then used to establish a radiomic-clinical model containing radiomics and hematological features, and the model was presented as a nomogram. The performance of the radiomics-clinical model was assessed by calibration curve, clinical effectiveness as well as internal validation. Results A total of 272 patients were included in this study: 201 with CS-PAs and 71 with CPs. These patients were randomized into training set (n=182) and test set (n=90). The radiomics signature, which consisted of 18 features after dimensionality reduction, showed superior discrimination performance in 5 different classification models. The area under the curve (AUC) values of the training set and the test set obtained by the radiomics signature are 0.92 and 0.88 in the logistic regression model, 0.90 and 0.85 in the Ridge classifier, 0.88 and 0.82 in the stochastic gradient descent (SGD) classifier, 0.78 and 0.85 in the linear support vector classification (Linear SVC), 0.93 and 0.86 in the multilayers perceptron (MLP) classifier, respectively. The predictive factors of the nomogram included radiomic signature, age, WBC count, and FIB. The nomogram showed good discrimination performance (with an AUC of 0.93 in the training set and 0.90 in the test set) and good calibration. Moreover, decision curve analysis (DCA) demonstrated satisfactory clinical effectiveness of the proposed radiomic-clinical nomogram. Conclusions A personalized nomogram containing radiomics signature and blood indices was proposed in this study. This nomogram is simple yet effective in differentiating between CS-PAs and CPs and thus can be used in routine clinical practice.
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Affiliation(s)
- Zhen Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dongdong Xiao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuansheng Nie
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Zhang
- Department of Geriatric Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Jiang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ali Rajab Jecha
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengfei Yan
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongyang Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Huang ZS, Xiao X, Li XD, Mo HZ, He WL, Deng YH, Lu LJ, Wu YK, Liu H. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma. J Magn Reson Imaging 2021; 54:1541-1550. [PMID: 34085336 DOI: 10.1002/jmri.27761] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. PURPOSE To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. STUDY TYPE Retrospective. POPULATION A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32). FIELD STRENGTH/SEQUENCE Axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w) on 3.0 T or 1.5 T magnetic resonance scanners. ASSESSMENT Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. STATISTICAL TESTS The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. RESULTS Seven texture features, three from T1 -w, two from T2 -w, and two from CET1 -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. DATA CONCLUSION The model based on radiomic features from T1 -w, T2 -w, and CET1 -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Zhou-San Huang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiang Xiao
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiao-Dan Li
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hai-Zhu Mo
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wen-Le He
- Department of Medical Imaging, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Yao-Hong Deng
- Yizhun Medical AI Co. Ltd, Beijing, China.,School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Li-Jun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuan-Kui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hao Liu
- Yizhun Medical AI Co. Ltd, Beijing, China
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Zhai Y, Song D, Yang F, Wang Y, Jia X, Wei S, Mao W, Xue Y, Wei X. Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics. Front Oncol 2021; 11:657288. [PMID: 34123812 PMCID: PMC8187861 DOI: 10.3389/fonc.2021.657288] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/12/2021] [Indexed: 12/30/2022] Open
Abstract
Objectives The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making. Methods A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada’s consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination. Results A total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts. Conclusions The presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.
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Affiliation(s)
- Yixuan Zhai
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dixiang Song
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fengdong Yang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yiming Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Jia
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuxin Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenbin Mao
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yake Xue
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinting Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Won SY, Park YW, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications. Eur J Radiol 2021; 138:109673. [PMID: 33774441 DOI: 10.1016/j.ejrad.2021.109673] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE To evaluate the quality of radiomics studies on meningiomas, using a radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and the Image Biomarker Standardization Initiative (IBSI). METHODS PubMed MEDLINE and Embase were searched to identify radiomics studies on meningiomas. Of 138 identified articles, 25 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and items in IBSI. RESULTS Only four studies (16 %) performed external validation. The mean RQS was 5.6 out of 36 (15.4 %), and the basic adherence rate was 26.8 %. The adherence rate was low for stating biological correlation (4%), conducting calibration statistics (12 %), multiple segmentation (16 %), and stating potential clinical utility (16 %). None of the studies conducted a test‒retest or phantom study, stated a comparison to a 'gold standard', conducted prospective studies or cost-effectivity analysis, or opened code and data to the public, resulting in low RQS. The overall adherence rate for TRIPOD was 54.1 %, with low scores for reporting the title (4%), abstract (0%), blind assessment of the outcome (8%), and explaining the sample size (0%). According to IBSI items, only 6 (24 %), 6 (24 %), and 3 (12 %) studies performed N4 bias-field correction, isovoxel resampling, and grey-level discretization, respectively. No study performed skull stripping. CONCLUSION The quality of radiomics studies for meningioma is insufficient. Acknowledgement of RQS, TRIPOD, and IBSI reporting guidelines may improve the quality of meningioma radiomics studies and enable their clinical application.
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Affiliation(s)
- So Yeon Won
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. Neuroradiology 2021; 63:1293-1304. [PMID: 33649882 PMCID: PMC8295153 DOI: 10.1007/s00234-021-02668-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02668-0.
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Zhou Y, Ma XL, Zhang T, Wang J, Zhang T, Tian R. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging 2021; 48:2904-2913. [PMID: 33547553 DOI: 10.1007/s00259-021-05220-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE This study was designed and performed to assess the ability of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics features combined with machine learning methods to differentiate between primary and metastatic lung lesions and to classify histological subtypes. Moreover, we identified the optimal machine learning method. METHODS A total of 769 patients pathologically diagnosed with primary or metastatic lung cancers were enrolled. We used the LIFEx package to extract radiological features from semiautomatically segmented PET and CT images within the same volume of interest. Patients were randomly distributed in training and validation sets. Through the evaluation of five feature selection methods and nine classification methods, discriminant models were established. The robustness of the procedure was controlled by tenfold cross-validation. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS Based on the radiomics features extracted from PET and CT images, forty-five discriminative models were established. Combined with appropriate feature selection methods, most classifiers showed excellent discriminative ability with AUCs greater than 0.75. In the differentiation between primary and metastatic lung lesions, the feature selection method gradient boosting decision tree (GBDT) combined with the classifier GBDT achieved the highest classification AUC of 0.983 in the PET dataset. In contrast, the feature selection method eXtreme gradient boosting combined with the classifier random forest (RF) achieved the highest AUC of 0.828 in the CT dataset. In the discrimination between squamous cell carcinoma and adenocarcinoma, the combination of GBDT feature selection method with GBDT classification had the highest AUC of 0.897 in the PET dataset. In contrast, the combination of the GBDT feature selection method with the RF classification had the highest AUC of 0.839 in the CT dataset. Most of the decision tree (DT)-based models were overfitted, suggesting that the classification method was not appropriate for practical application. CONCLUSION 18F-FDG PET/CT radiomics features combined with machine learning methods can distinguish between primary and metastatic lung lesions and identify histological subtypes in lung cancer. GBDT and RF were considered optimal classification methods for the PET and CT datasets, respectively, and GBDT was considered the optimal feature selection method in our analysis.
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Affiliation(s)
- Yi Zhou
- Department of Nuclear Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Xue-Lei Ma
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Ting Zhang
- West China School of Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Jian Wang
- School of Computer Science, Nanjing University of Science and Technology, No. 200, Xiaolinwei Road, Nanjing, 210094, China
| | - Tao Zhang
- West China School of Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China.
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Yin RH, Yang YC, Tang XQ, Shi HF, Duan SF, Pan CJ. Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:1149-1160. [PMID: 34657848 DOI: 10.3233/xst-210997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.
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Affiliation(s)
- Ruo-Han Yin
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - You-Chang Yang
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Xiao-Qiang Tang
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Hai-Feng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Shao-Feng Duan
- Precision Health Institution, GE Healthcare (China), Shanghai, China
| | - Chang-Jie Pan
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
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