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Zhou Y, Zhan Y, Zhao J, Zhong L, Tan Y, Zeng W, Zeng Q, Gong M, Li A, Gong L, Liu L. CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study. Acad Radiol 2024; 31:2859-2871. [PMID: 38302388 DOI: 10.1016/j.acra.2024.01.008] [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: 12/07/2023] [Revised: 01/01/2024] [Accepted: 01/04/2024] [Indexed: 02/03/2024]
Abstract
RATIONALE AND OBJECTIVES Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs). MATERIALS AND METHODS The present study included 201 patients with PPGLs from three hospitals (training set: n = 125; external validation set: n = 45; external test set: n = 31). Patients were divided into low-risk and high-risk groups using a staging system for adrenal pheochromocytoma and paraganglioma (GAPP). We extracted and selected CT radiomics features, and built radiomics models using support vector machines (SVM), k-nearest neighbors, random forests, and multilayer perceptrons. Using receiver operating characteristic curve analysis to select the optimal radiomics model, a combined model was built using the output of the optimal radiomics model and clinical radiological features, and its accuracy and clinical applicability were evaluated using calibration curves and clinical decision curve analysis (DCA). RESULTS Finally, 13 radiomics features were selected to construct machine learning models. In the radiomics model, the SVM model demonstrated higher accuracy and stability, with an AUC value of 0.915 in the training set, 0.846 in external validation set, and 0.857 in external test set. Combining the outputs of SVM models with two clinical radiological features, a combined model constructed has demonstrated optimal risk stratification ability for PPGLs with an AUC of 0.926 for the training set, 0.883 for the external validation set, and 0.899 for the external test set. The calibration curve and DCA show good calibration accuracy and clinical effectiveness for the combined model. CONCLUSION Combined model that integrates radiomics and clinical radiological features can discriminate the risk stratification of PPGLs.
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Affiliation(s)
- Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China; The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China; Jiangxi Clinical Research Center for Cancer, Nanchang, China
| | - Yuan Zhan
- Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Jinhong Zhao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Linhua Zhong
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Yongming Tan
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Wei Zeng
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Qiao Zeng
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Mingxian Gong
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Aihua Li
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China; The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China; Jiangxi Clinical Research Center for Cancer, Nanchang, China.
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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Zhao J, Zhan Y, Zhou Y, Yang Z, Xiong X, Ye Y, Yao B, Xu S, Peng Y, Xiao X, Zeng X, Zuo M, Dai X, Gong L. CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study. Abdom Radiol (NY) 2024; 49:1569-1583. [PMID: 38587628 DOI: 10.1007/s00261-024-04279-8] [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: 01/03/2024] [Revised: 02/29/2024] [Accepted: 03/05/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVES The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. METHODS A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value. RESULTS 3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy. CONCLUSION The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.
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Affiliation(s)
- Jinhong Zhao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Yuan Zhan
- Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi, 330029, China
| | - Zhili Yang
- Department of Ultrasound, Jiangxi Maternal and Child Health Hospital, Nanchang, Jiangxi, 330038, China
| | - Xiaoling Xiong
- Cancer Center Office, Jiangxi Cancer Hospital, Nanchang, Jiangxi, 330029, China
| | - Yinquan Ye
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Bin Yao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Shiguo Xu
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Yun Peng
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Xiaoyi Xiao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Minjing Zuo
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Xijian Dai
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China.
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Wang MY, Jia CG, Xu HQ, Xu CS, Li X, Wei W, Chen JC. Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients. Curr Med Sci 2023; 43:336-343. [PMID: 37059936 PMCID: PMC10103675 DOI: 10.1007/s11596-023-2713-x] [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: 11/15/2022] [Accepted: 01/14/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neuroma. METHODS A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included. Clinical data and raw features from four MRI sequences (T1-weighted, T2-weighted, T1-weighted contrast enhancement, and T2-weighted-Flair images) were analyzed. Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features. Nomogram, machine learning, and convolutional neural network (CNN) models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate model performance. A total of 1050 radiomic parameters were extracted, from which 13 radiomic and 3 clinical features were selected. RESULTS The CNN model performed best among all prediction models in the test set with an area under the curve (AUC) of 0.89 (95% CI, 0.84-0.91). CONCLUSION CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma. As such, CNN modeling may serve as a potential decision-making tool for neurosurgery.
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Affiliation(s)
- Meng-Yang Wang
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Chen-Guang Jia
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Huan-Qing Xu
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, China
| | - Cheng-Shi Xu
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Xiang Li
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Wei Wei
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
| | - Jin-Cao Chen
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
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Itoyama T, Nakaura T, Hamasaki T, Takezaki T, Uentani H, Hirai T, Mukasa A. Whole Tumor Radiomics Analysis for Risk Factors Associated With Rapid Growth of Vestibular Schwannoma in Contrast-Enhanced T1-Weighted Images. World Neurosurg 2022; 166:e572-e582. [PMID: 35863640 DOI: 10.1016/j.wneu.2022.07.058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To investigate the features associated with rapid growth of vestibular schwannoma using radiomics analysis on magnetic resonance imaging (MRI) together with clinical factors. METHODS From August 2005 to February 2019, 67 patients with vestibular schwannoma underwent contrast-enhanced T1-weighted MRI at least twice as part of their diagnosis. After excluding 3 cases with an extremely short follow-up period of 15 days or less, 64 patients were finally enrolled in this study. Ninety-three texture features were extracted from the tumor image data using 3D Slicer software (http://www.slicer.org/). We determined the texture features that significantly affected maximal tumor diameter growth of more than 2 mm/year using Random Forest and Bounty. We also analyzed age and tumor size as clinical factors. We calculated the areas under the curve (AUCs) using receiver operating characteristic analysis for prediction models using texture, clinical, and mixed factors by Random Forest and 5-fold cross-validation. RESULTS Two texture features, low minimum signal and high inverse difference moment normalized (Idmn), were significantly associated with rapid growth of vestibular schwannoma. The mixed model of texture features and clinical factors offered the highest AUC (0.69), followed by the pure texture (0.67), and pure clinical (0.63) models. The minimum signal was the most important variable followed by tumor size, Idmn, and age. CONCLUSIONS Our radiomics analysis found that texture features were significantly associated with the rapid growth of vestibular schwannoma in contrast-enhanced T1-weighted images. The mixed model offered a higher diagnostic performance than the pure texture or clinical models.
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Affiliation(s)
- Takashi Itoyama
- Department of Neurosurgery, Kumamoto University Hospital, Kumamoto, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Tadashi Hamasaki
- Department of Neurosurgery, Kumamoto University Hospital, Kumamoto, Japan.
| | - Tatsuya Takezaki
- Department of Neurosurgery, Kumamoto University Hospital, Kumamoto, Japan
| | - Hiroyuki Uentani
- Department of Diagnostic Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Kumamoto University Hospital, Kumamoto, Japan
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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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