1
|
Silva Santana L, Borges Camargo Diniz J, Mothé Glioche Gasparri L, Buccaran Canto A, Batista Dos Reis S, Santana Neville Ribeiro I, Gadelha Figueiredo E, Paulo Mota Telles J. Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis. World Neurosurg 2024; 186:204-218.e2. [PMID: 38580093 DOI: 10.1016/j.wneu.2024.03.152] [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: 01/21/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types. METHODS A systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity. RESULTS Fifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI]: 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI: 0.85-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI: 0.97-1.00) and 0.94, (95% CI: 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI: 0.83-0.93) and 0.87 (95% CI: 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications: sensitivity of 0.99 (95% CI: 0.99-1.00) and specificity of 0.99 (95% CI: 0.98-1.00). CONCLUSIONS ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.
Collapse
Affiliation(s)
| | | | | | | | | | - Iuri Santana Neville Ribeiro
- Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Eberval Gadelha Figueiredo
- Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - João Paulo Mota Telles
- Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
| |
Collapse
|
2
|
Joy A, Lin M, Joines M, Saucedo A, Lee-Felker S, Baker J, Chien A, Emir U, Macey PM, Thomas MA. Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging. Metabolites 2023; 13:835. [PMID: 37512542 PMCID: PMC10385820 DOI: 10.3390/metabo13070835] [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: 05/05/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl-fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.
Collapse
Affiliation(s)
- Ajin Joy
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Marlene Lin
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Melissa Joines
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Andres Saucedo
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
- Physics and Biology in Medicine-Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Stephanie Lee-Felker
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Jennifer Baker
- Surgery, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Aichi Chien
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Uzay Emir
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA;
| | - Paul M. Macey
- School of Nursing, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - M. Albert Thomas
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
- Physics and Biology in Medicine-Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA 90095, USA
- BioEngineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| |
Collapse
|
3
|
Yano H, Miwa K, Nakayama N, Maruyama T, Ohe N, Ikuta S, Ikegame Y, Yamada T, Takei H, Owashi E, Ohmura K, Yokoyama K, Kumagai M, Muragaki Y, Iwama T, Shinoda J. Differentiation of astrocytoma between grades II and III using a combination of methionine positron emission tomography and magnetic resonance spectroscopy. World Neurosurg X 2023; 19:100193. [PMID: 37123626 PMCID: PMC10141501 DOI: 10.1016/j.wnsx.2023.100193] [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: 10/05/2022] [Accepted: 04/04/2023] [Indexed: 05/02/2023] Open
Abstract
Objective This study aimed to establish a method for differentiating between grades II and III astrocytomas using preoperative imaging. Methods We retrospectively analyzed astrocytic tumors, including 18 grade II astrocytomas (isocitrate dehydrogenase (IDH)-mutant: IDH-wildtype = 8:10) and 56 grade III anaplastic astrocytomas (37:19). We recorded the maximum methionine (MET) uptake ratios (tumor-to-normal: T/N) on positron emission tomography (PET) and three MRS peak ratios: choline (Cho)/creatine (Cr), N-acetyl aspartate (NAA)/Cr, and Cho/NAA, between June 2015 and June 2020. We then evaluated the cut-off values to differentiate between grades II and III. We compared the grading results between contrast enhancement effects on MR and combinational diagnostic methods (CDM) on a scatter chart using the cutoff values of the T/N ratio and MRS parameters. Results The IDH-mutant group showed significant differences in the Cho/NAA ratio between grades II and III using univariate analysis; however, multiple regression analysis results negated this. The IDH-wildtype group showed no significant differences between the groups. Contrast enhancement effects also showed no significant differences in IDH status. Accordingly, regardless of the IDH status, no statistically independent factors differentiated between grades II and III. However, CDMs showed higher sensitivity and negative predictive value in distinguishing them than MRI contrast examinations for both IDH statuses. We demonstrated a significantly higher diagnostic rate of grade III than of grade II with CDM, which was more striking in the IDH-mutant group than in the wild-type group. Conclusions CDM could be valuable in differentiating between grade II and III astrocytic tumors.
Collapse
Affiliation(s)
- Hirohito Yano
- Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan
- Department of Clinical Brain Science, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
- Corresponding author. Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan.
| | - Kazuhiro Miwa
- Department of Neurosurgery, Central Japan International Medical Center, 1-1 Kenkou-no-machi, Minokamo City, 505-8510, Japan
| | - Noriyuki Nakayama
- Department of Neurosurgery, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
| | - Takashi Maruyama
- Department of Neurosurgery, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Naoyuki Ohe
- Department of Neurosurgery, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
| | - Soko Ikuta
- Department of Neurosurgery, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Yuka Ikegame
- Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan
- Department of Clinical Brain Science, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
| | - Tetsuya Yamada
- Department of Neurosurgery, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
| | - Hiroaki Takei
- Department of Neurosurgery, Central Japan International Medical Center, 1-1 Kenkou-no-machi, Minokamo City, 505-8510, Japan
| | - Etsuko Owashi
- Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan
| | - Kazufumi Ohmura
- Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan
| | - Kazutoshi Yokoyama
- Department of Neurosurgery, Central Japan International Medical Center, 1-1 Kenkou-no-machi, Minokamo City, 505-8510, Japan
| | - Morio Kumagai
- Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan
| | - Yoshihiro Muragaki
- Department of Neurosurgery, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Toru Iwama
- Department of Neurosurgery, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
| | - Jun Shinoda
- Department of Neurosurgery and Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Chubu Neurorehabilitation Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, 505-0034, Japan
- Department of Clinical Brain Science, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, 501-1194, Japan
| |
Collapse
|
4
|
Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol 2022; 12:892056. [PMID: 35965542 PMCID: PMC9363668 DOI: 10.3389/fonc.2022.892056] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.
Collapse
Affiliation(s)
- Jiaona Xu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuting Meng
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kefan Qiu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Win Topatana
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wei
- Department of Neurology, Affiliated Ningbo First Hospital, Ningbo, China
| | - Tianwen Chen
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| |
Collapse
|
5
|
Abstract
The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging-particularly when combined with liquid biopsy for screening purposes-promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.
Collapse
Affiliation(s)
- Steven P. Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Martin G. Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
6
|
Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR, Wang DZ, Zhang M. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol 2021; 11:660509. [PMID: 34150628 PMCID: PMC8212783 DOI: 10.3389/fonc.2021.660509] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/11/2021] [Indexed: 01/03/2023] Open
Abstract
Objectives To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery. Methods The retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively. Conclusion The machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.
Collapse
Affiliation(s)
- Wen Chen
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Tao Zhang
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Lin Xu
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Liang Zhao
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | | | - Liang Rui Gu
- Department of Radiology, Shanghai Sixth People's Hospital, Shanghai, China
| | - Dai Zhong Wang
- Department of Pathology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| |
Collapse
|
7
|
Brown SSG, Mak E, Zaman S. Multi-Modal Imaging in Down's Syndrome: Maximizing Utility Through Innovative Neuroimaging Approaches. Front Neurol 2021; 11:629463. [PMID: 33488507 PMCID: PMC7817620 DOI: 10.3389/fneur.2020.629463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 12/08/2020] [Indexed: 11/13/2022] Open
Abstract
In recent decades, the field of neuroimaging has experienced a surge of popularity and innovation which has led to significant advancements in the understanding of neurological disease, if not immediate clinical translation. In the case of Down's syndrome, a complex interplay of neurodevelopmental and neurodegenerative processes occur as a result of the trisomy of chromosome 21. The substantial potential impact of improved clinical intervention and the limited research under-taken to date make it a prime candidate for longitudinal neuroimaging-based study. However, as with a multitude of other multifaceted brain-based disorders, singular utilization of lone modality imaging has limited interpretability and applicability. Indeed, a present challenge facing the neuroimaging community as a whole is the methodological integration of multi-modal imaging to enhance clinical understanding. This review therefore aims to assess the current literature in Down's syndrome utilizing a multi-modal approach with regards to improvement upon consideration of a single modality. Additionally, we discuss potential avenues of future research that may effectively combine structural, functional and molecular-based imaging techniques for the significant benefit of the understanding of Down's syndrome pathology.
Collapse
Affiliation(s)
- Stephanie S. G. Brown
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
8
|
Kong Z, Jiang C, Zhang Y, Liu S, Liu D, Liu Z, Chen W, Liu P, Yang T, Lyu Y, Zhao D, You H, Wang Y, Ma W, Feng F. Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas. Front Neurol 2020; 11:551771. [PMID: 33192984 PMCID: PMC7642873 DOI: 10.3389/fneur.2020.551771] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/23/2020] [Indexed: 12/13/2022] Open
Abstract
Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed. Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950–1.000. Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.
Collapse
Affiliation(s)
- Ziren Kong
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chendan Jiang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yiwei Zhang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sirui Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Delin Liu
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zeyu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenlin Chen
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Penghao Liu
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tianrui Yang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuelei Lyu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Dachun Zhao
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenbin Ma
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
9
|
Li Y, Ge Z, Zhang Z, Shen Z, Wang Y, Zhou T, Wu R. Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8874521. [PMID: 33299467 PMCID: PMC7704182 DOI: 10.1155/2020/8874521] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 02/05/2023]
Abstract
In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (1H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel 1H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel 1H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference (p < 0.05). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from 1H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.
Collapse
Affiliation(s)
- Yan Li
- Department of Medical Imaging, The 2nd Affiliated Hospital, Shantou University Medical College, Shantou 515041, China
| | - Zuhao Ge
- Department of Computer Science, Shantou University, Shantou 515041, China
| | - Zhiyan Zhang
- Department of Medical Imaging, Huizhou Central Hospital, Huizhou 516000, China
| | - Zhiwei Shen
- Department of Medical Imaging, The 2nd Affiliated Hospital, Shantou University Medical College, Shantou 515041, China
| | - Yukai Wang
- Department of Rheumatology and Immunology, Shantou Central Hospital, Shantou 515041, China
| | - Teng Zhou
- Department of Computer Science, Shantou University, Shantou 515041, China
- Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou 515063, China
| | - Renhua Wu
- Department of Medical Imaging, The 2nd Affiliated Hospital, Shantou University Medical College, Shantou 515041, China
| |
Collapse
|