1
|
Xue H, Lu H, Wang Y, Li N, Wang G. MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging. PLoS One 2024; 19:e0304419. [PMID: 38820482 PMCID: PMC11142489 DOI: 10.1371/journal.pone.0304419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 05/12/2024] [Indexed: 06/02/2024] Open
Abstract
In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.
Collapse
Affiliation(s)
- Han Xue
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Huimin Lu
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Yilong Wang
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
- The First Hospital of Jilin University, Changchun, Jilin, China
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, China
| | - Niya Li
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Guizeng Wang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| |
Collapse
|
2
|
Wang J, Huang Z, Zhou J. Radiomics Model for Predicting FOXP3 Expression Level and Survival in Clear Cell Renal Carcinoma. Acad Radiol 2024; 31:1447-1459. [PMID: 37940428 DOI: 10.1016/j.acra.2023.10.008] [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: 08/12/2023] [Revised: 10/01/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
RATIONALE AND OBJECTIVES We aimed to evaluate the predictive significance of forkhead box protein 3 (FOXP3) expression levels among individuals with clear cell renal carcinoma (ccRCC) and establish a radiomics model for predicting FOXP3 expression. MATERIALS AND METHODS 430 patients with ccRCC were included in the gene-based prognostic analyses; 100 samples were used for radiomics feature generation, model development, and evaluation. A gradient boosting machine was employed to model the selected radiomics features. The developed model generated radiomics scores (RS) that predicted FOXP3 expression. The FOXP3 prognostic model combining imaging features was applied for survival and clinical indicator correlation analyses. RESULTS FOXP3 was highly expressed in patients with ccRCC and served as an independent predictive marker (hazard ratio [HR]=2.357, 95% CI [confidence interval]: 1.582-3.511, p < 0.001). The radiomics model formed by three radiomics characteristics was identified as a strong prognostic indicator of overall survival (OS). The predictive power of the model was commendable (areas under the curve: 0.835 and 0.809 for training and validation sets, respectively). Significant between-group variations in RS distribution were identified, as indicated by gene expression levels (p < 0.05). Disparities were observed in pathological stage, pharmaceutical therapy, and neoplasm status between low and high RS cohorts (p < 0.001). Kaplan-Meier curves revealed a significant correlation between increased RS and decreased OS (p = 0.001), which was also observed in the multivariate analyses (HR=3.411, 95% CI: 1.039-11.196, p = 0.043). CONCLUSION Prognostic outcome of ccRCC is closely linked to FOXP3 expression level. Computed tomography-based radiomics shows promise for prognostic prediction in ccRCC.
Collapse
Affiliation(s)
- Jie Wang
- Department of Radiotherapy, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China (J.W., Z.H., J.Z.)
| | - Zaijie Huang
- Department of Radiotherapy, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China (J.W., Z.H., J.Z.)
| | - Jumei Zhou
- Department of Radiotherapy, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China (J.W., Z.H., J.Z.).
| |
Collapse
|
3
|
Battalapalli D, Vidyadharan S, Prabhakar Rao BVVSN, Yogeeswari P, Kesavadas C, Rajagopalan V. Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning. Front Physiol 2023; 14:1201617. [PMID: 37528895 PMCID: PMC10390093 DOI: 10.3389/fphys.2023.1201617] [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: 04/06/2023] [Accepted: 06/28/2023] [Indexed: 08/03/2023] Open
Abstract
Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.
Collapse
Affiliation(s)
- Dheerendranath Battalapalli
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India
| | - Sreejith Vidyadharan
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India
| | - B. V. V. S. N. Prabhakar Rao
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India
| | - P. Yogeeswari
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India
| | - C. Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Venkateswaran Rajagopalan
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India
| |
Collapse
|
4
|
Barge P, Oevermann A, Maiolini A, Durand A. Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis. Vet Radiol Ultrasound 2023. [PMID: 37133981 DOI: 10.1111/vru.13242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 05/04/2023] Open
Abstract
Conventional MRI features of canine gliomas subtypes and grades significantly overlap. Texture analysis (TA) quantifies image texture based on spatial arrangement of pixel intensities. Machine learning (ML) models based on MRI-TA demonstrate high accuracy in predicting brain tumor types and grades in human medicine. The aim of this retrospective, diagnostic accuracy study was to investigate the accuracy of ML-based MRI-TA in predicting canine gliomas histologic types and grades. Dogs with histopathological diagnosis of intracranial glioma and available brain MRI were included. Tumors were manually segmented across their entire volume in enhancing part, non-enhancing part, and peri-tumoral vasogenic edema in T2-weighted (T2w), T1-weighted (T1w), FLAIR, and T1w postcontrast sequences. Texture features were extracted and fed into three ML classifiers. Classifiers' performance was assessed using a leave-one-out cross-validation approach. Multiclass and binary models were built to predict histologic types (oligodendroglioma vs. astrocytoma vs. oligoastrocytoma) and grades (high vs. low), respectively. Thirty-eight dogs with a total of 40 masses were included. Machine learning classifiers had an average accuracy of 77% for discriminating tumor types and of 75.6% for predicting high-grade gliomas. The support vector machine classifier had an accuracy of up to 94% for predicting tumor types and up to 87% for predicting high-grade gliomas. The most discriminative texture features of tumor types and grades appeared related to the peri-tumoral edema in T1w images and to the non-enhancing part of the tumor in T2w images, respectively. In conclusion, ML-based MRI-TA has the potential to discriminate intracranial canine gliomas types and grades.
Collapse
Affiliation(s)
- Pablo Barge
- Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Anna Oevermann
- Division of Neurological Sciences, Department of Clinical Research and Veterinary Public Health, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Arianna Maiolini
- Division of Clinical Neurology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Alexane Durand
- Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| |
Collapse
|
5
|
Tasci E, Zhuge Y, Camphausen K, Krauze AV. Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets. Cancers (Basel) 2022; 14:2897. [PMID: 35740563 PMCID: PMC9221277 DOI: 10.3390/cancers14122897] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 02/06/2023] Open
Abstract
Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.
Collapse
Affiliation(s)
- Erdal Tasci
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Bethesda, MD 20892, USA; (E.T.); (Y.Z.); (K.C.)
- Department of Computer Engineering, Ege University, Izmir 35100, Turkey
| | - Ying Zhuge
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Bethesda, MD 20892, USA; (E.T.); (Y.Z.); (K.C.)
| | - Kevin Camphausen
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Bethesda, MD 20892, USA; (E.T.); (Y.Z.); (K.C.)
| | - Andra V. Krauze
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Bethesda, MD 20892, USA; (E.T.); (Y.Z.); (K.C.)
| |
Collapse
|
6
|
Müther M, Jaber M, Johnson TD, Orringer DA, Stummer W. A Data-Driven Approach to Predicting 5-Aminolevulinic Acid-Induced Fluorescence and World Health Organization Grade in Newly Diagnosed Diffuse Gliomas. Neurosurgery 2022; 90:800-806. [PMID: 35285461 PMCID: PMC9067086 DOI: 10.1227/neu.0000000000001914] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 12/17/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND A growing body of evidence has revealed the potential utility of 5-aminolevulinic acid (5-ALA) as a surgical adjunct in selected lower-grade gliomas. However, a reliable means of identifying which lower-grade gliomas will fluoresce has not been established. OBJECTIVE To identify clinical and radiological factors predictive of intraoperative fluorescence in intermediate-grade gliomas. In addition, given that higher-grade gliomas are more likely to fluoresce than lower-grade gliomas, we also sought to develop a means of predicting glioma grade. METHODS We investigated a cohort of patients with grade II and grade III gliomas who received 5-ALA before resection at a single institution. Using a logistic regression-based model, we evaluated 14 clinical and molecular variables considered plausible determinants of fluorescence. We then distilled the most predictive features to develop a model for predicting both fluorescence and tumor grade. We also explored the relationship between intraoperative fluorescence and diagnostic molecular markers. RESULTS One hundered seventy-nine subjects were eligible for inclusion. Our logistic regression classifier accurately predicted intraoperative fluorescence in our cohort with 91.9% accuracy and revealed enhancement as the singular variable in determining intraoperative fluorescence. There was a direct relationship between enhancement on MRI and the likelihood of observed fluorescence. Observed fluorescence correlated with MIB-1 index but not with isocitrate dehydrogenase (IDH) status, 1p19q codeletion, or methylguanine DNA methyltransferase promoter methylation. CONCLUSION We demonstrate a strong correlation between enhancement on preoperative MRI and the likelihood of visible fluorescence during surgery in patients with intermediate-grade glioma. Our analysis provides a robust method for predicting 5-ALA-induced fluorescence in patients with grade II and grade III gliomas.
Collapse
Affiliation(s)
- Michael Müther
- Department of Neurosurgery, University Hospital Münster, Münster, Germany;
| | - Mohammed Jaber
- Department of Neurosurgery, University Hospital Münster, Münster, Germany;
| | - Timothy D. Johnson
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA;
| | | | - Walter Stummer
- Department of Neurosurgery, University Hospital Münster, Münster, Germany;
| |
Collapse
|
7
|
Zhang Y, Lin Y, Xing Z, Yao S, Cao D, Miao WB. Non-invasive assessment of heterogeneity of gliomas using diffusion and perfusion MRI: correlation with spatially co-registered PET. Acta Radiol 2022; 63:664-671. [PMID: 33858207 DOI: 10.1177/02841851211006913] [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] [Indexed: 12/24/2022]
Abstract
BACKGROUND Heterogeneity of gliomas challenges the neuronavigated biopsy and oncological therapy. Diffusion and perfusion magnetic resonance imaging (MRI) can reveal the cellular and hemodynamic heterogeneity of tumors. Integrated positron emission tomography (PET)/MRI is expected to be a non-invasive imaging approach to characterizing glioma. PURPOSE To evaluate the value of apparent diffusion coefficient (ADC), cerebral blood volume (CBV), and spatially co-registered maximal standard uptake value (SUVmax) for tissue characterization and glioma grading. MATERIAL AND METHODS Thirty-seven consecutive patients with pathologically confirmed gliomas were retrospectively investigated. The relative minimum ADC (rADCmin), relative maximal ADC (rADCmax), relative maximal rCBV (rCBVmax), the relative minimum rCBV (rCBVmin), and the corresponding relative SUVmax (rSUVmax) were measured. The paired t-test was used to compare the quantitative parameters between different regions to clarify tumor heterogeneity. Imaging parameters between WHO grade IV and grade II/III gliomas were compared by t-test. The diagnostic efficiency of multiparametric PET/MRI was analyzed by receiver operating characteristic (ROC) curve. RESULTS The values of rSUVmax were significantly different between maximal diffusion/perfusion area and minimum diffusion/perfusion area (P < 0.001/P < 0.001) within tumor. The values of rADCmin (P < 0.001), rCBVmax (P = 0.002), and corresponding rSUVmax (P = 0.001/P < 0.001) could be used for grading gliomas. The areas under the ROC curves of rSUVmax defined by rADCmin and rCBVmax were 0.89 and 0.91, respectively. CONCLUSION Diffusion and perfusion MRI can detect glioma heterogeneity with excellent molecular imaging correlations. Regions with rCBVmax suggest tissues with the highest metabolism and malignancy for guiding glioma grading and tissue sampling.
Collapse
Affiliation(s)
- Ying Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Yu Lin
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian, PR China
| | - Zhen Xing
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Shaobo Yao
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Wei-bing Miao
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| |
Collapse
|
8
|
Improving geometric P-norm-based glioma segmentation through deep convolutional autoencoder encapsulation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103232] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
9
|
Wang Z, Tang X, Wu J, Zhang Z, He K, Wu D, Chen S, Xiao X. Radiomics features based on T2-weighted fluid-attenuated inversion recovery MRI predict the expression levels of CD44 and CD133 in lower-grade gliomas. Future Oncol 2021; 18:807-819. [PMID: 34783576 DOI: 10.2217/fon-2021-1173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Objective: To verify the association between CD44 and CD133 expression levels and the prognosis of patients with lower-grade gliomas (LGGs) and constructing radiomic models to predict those two genes' expression levels before surgery. Materials & methods: Genomic data of patients with LGG and the corresponding T2-weighted fluid-attenuated inversion recovery images were downloaded from the Cancer Genome Atlas and the Cancer Imaging Archive, which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. Results & conclusion: CD44 and CD133 expression levels in LGG can significantly affect the prognosis of patients with LGG. Based on the T2-weighted fluid-attenuated inversion recovery images, the radiomic features can effectively predict the expression levels of CD44 and CD133 before surgery.
Collapse
Affiliation(s)
- Zhenhua Wang
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Xiaoping Tang
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Ji Wu
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Zhaotao Zhang
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Keng He
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Di Wu
- Department of Radiology, First Affiliated Hospital of GanNan Medical College, GanZhou, China
| | - ShiQi Chen
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Xinlan Xiao
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| |
Collapse
|
10
|
Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:8873065. [PMID: 33531882 PMCID: PMC7826202 DOI: 10.1155/2021/8873065] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/10/2020] [Accepted: 01/04/2021] [Indexed: 11/25/2022]
Abstract
Purpose This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D∗)) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group (P < 0.05). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.
Collapse
|
11
|
Mehrnahad M, Rostami S, Kimia F, Kord R, Taheri MS, Rad HS, Haghighatkhah H, Moradi A, Kord A. Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients. Neuroradiol J 2020; 33:428-436. [PMID: 32628089 DOI: 10.1177/1971400920937382] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients.Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. RESULTS Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group (P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1-99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9-92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group (P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2-100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3-95.8%). CONCLUSIONS Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.
Collapse
Affiliation(s)
- Mehrsad Mehrnahad
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran
| | - Sara Rostami
- Department of Radiology, University of Illinois College of Medicine, USA
| | - Farnaz Kimia
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran
| | - Reza Kord
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran
| | | | | | | | - Afshin Moradi
- Department of Pathology, Shahid Beheshti University of Medical Sciences, Iran
| | - Ali Kord
- Department of Radiology, University of Illinois College of Medicine, USA
| |
Collapse
|