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Hu G, Ye C, Zhong M, Lei C, Qin J, Wang L. IVIM parameters mapping with artificial neural network based on mean deviation prior. Med Phys 2024. [PMID: 39241221 DOI: 10.1002/mp.17383] [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: 01/17/2024] [Revised: 07/29/2024] [Accepted: 08/12/2024] [Indexed: 09/08/2024] Open
Abstract
BACKGROUND The diffusion and perfusion parameters derived from intravoxel incoherent motion (IVIM) imaging provide promising biomarkers for noninvasively quantifying and managing various diseases. Nevertheless, due to the distribution gap between simulated and real datasets, the out-of-distribution (OOD) problem occurred in supervised learning-based methods degrades their performance and hinders their real applications. PURPOSE To address the OOD problem in supervised methods and to further improve the accuracy and stability of IVIM parameter estimation, this work proposes a novel learning framework called IterANN, based on mean deviation prior (MDP) between training and estimated IVIM parameters on the test set. METHODS Specifically, MDP indicates that the mean of the estimated IVIM parameters always locates between the mean of IVIM parameters in the test and train sets. In IterANN, we adopt a very simple artificial neural network (ANN) architecture of two hidden layers with 12 neurons per hidden layer, an input layer containing the signals acquired at multiple b-values and an output layer composed of three IVIM parameters ( D $D$ , F $F$ andD S t a r $DStar$ ). Inspired by MDP, the distribution of IVIM parameters in the training set (simulated data) is iteratively updated so that their mean gradually approaches the predicted values of the real data. This aims to achieve a strong correlation between the simulated data and the real data. To validate the effectiveness of IterANN, we compare it with several methods on both simulation and real acquisition datasets, including 21 healthy and 3 tumor subjects, in terms of residual errors of IVIM parameters or DW signals, the coefficients of variation (CV) of IVIM parameters, and the parameter contrast-to-noise ratio (PCNR) between normal and tumor tissues. RESULTS On two simulation datasets, the proposed IterANN achieves the lowest residual error in IVIM parameters, especially in the case of low signal-to-noise ratio (SNR = 10), the residual error of D $D$ , F $F$ andD S t a r $DStar$ is decreased by15.82 % / 14.92 % , 81.19 % / 74.04 % , 50.77 % / 1.549 % $15.82\%/14.92\%, 81.19\%/74.04\%, 50.77\%/1.549\%$ (Gaussian distribution /realistic distribution) respectively comparing to the suboptimal method. On real dataset, the IterANN achieves the highest PCNR when comparing the normal and tumor regions. Additionally, the proposed IterANN demonstrated better stability, with its CV being significantly lower than that of other methods in the vast majority of cases (p < 0.01 $p<0.01$ , paired-sample Student's t-test). CONCLUSIONS The superior performance of IterANN demonstrates that updating the distribution of the train set based on MDP can effectively solve the OOD problem, which allows us not only to improve the accuracy and stability of the estimated IVIM parameters, but also to increase the potential of IVIM in disease diagnosis.
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Affiliation(s)
- Guodong Hu
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Chen Ye
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Ming Zhong
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guiyang, China
| | - Chao Lei
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Junpeng Qin
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Lihui Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
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Yang D, Ren Y, Wang C. Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer. Magn Reson Imaging 2024; 111:210-216. [PMID: 38777242 DOI: 10.1016/j.mri.2024.05.010] [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: 03/20/2024] [Revised: 05/18/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. MATERIALS AND METHODS A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. RESULTS The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = -0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (-) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. CONCLUSION The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.
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Affiliation(s)
- Dan Yang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China.
| | - Yike Ren
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Chunhong Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
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Han T, Liu X, Sun J, Long C, Jiang J, Zhou F, Zhao Z, Zhang B, Jing M, Deng L, Zhang Y, Zhou J. T2-Weighted Imaging and Apparent Diffusion Coefficient Histogram Parameters Predict Meningioma Consistency. Acad Radiol 2024; 31:2511-2520. [PMID: 38155025 DOI: 10.1016/j.acra.2023.12.014] [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: 10/23/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/30/2023]
Abstract
RATIONALE AND OBJECTIVES Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency. MATERIALS AND METHODS We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann-Whitney's U test, or independent samples t-test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters. RESULTS Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADCmean, ADCp1, ADCp10, and ADCp50 among ADC histogram parameters, and T2mean, T2p1, T2p10, T2p50, T2p90, and T2p99 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P < 0.05). We found that all combined variables (combinedall) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804-0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combinedT2 is the most beneficial for predicting meningioma consistency. CONCLUSION CombinedT2 demonstrated better predictive performance for meningioma consistency than combinedADC. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Jiachen Sun
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Changyou Long
- Image Center of Affiliated Hospital of Qinghai University, Xining 810001, China (C.L.)
| | - Jian Jiang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Fengyu Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Zhiyong Zhao
- Department of Neurosurgery, The Second Hospital of Lanzhou University, Lanzhou 730000, China (Z.Z.)
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.).
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Zhao Z, Zhang J, Yuan S, Zhang H, Yin H, Wang G, Pan Y, Li Q. The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression. Neurosurg Rev 2024; 47:235. [PMID: 38795181 DOI: 10.1007/s10143-024-02482-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 05/17/2024] [Accepted: 05/21/2024] [Indexed: 05/27/2024]
Abstract
PURPOSE This study investigated the value of whole tumor apparent diffusion coefficient (ADC) histogram parameters and magnetic resonance imaging (MRI) semantic features in predicting meningioma progesterone receptor (PR) expression. MATERIALS AND METHODS The imaging, pathological, and clinical data of 53 patients with PR-negative meningiomas and 52 patients with PR-positive meningiomas were retrospectively reviewed. The whole tumor was outlined using Firevoxel software, and the ADC histogram parameters were calculated. The differences in ADC histogram parameters and MRI semantic features were compared between the two groups. The predictive values of parameters for PR expression were assessed using receiver operating characteristic curves. The correlation between whole-tumor ADC histogram parameters and PR expression in meningiomas was also analyzed. RESULTS Grading was able to predict the PR expression in meningiomas (p = 0.012), though the semantic features of MRI were not (all p > 0.05). The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were able to predict meningioma PR expression (all p < 0.05). The predictive performance of the combined histogram parameters improved, and the combination of grade and histogram parameters provided the optimal predictive value, with an area under the curve of 0.849 (95%CI: 0.766-0.911) and sensitivity, specificity, ACC, PPV, and NPV of 73.08%, 81.13%, 77.14%, 79.20%, and 75.40%, respectively. The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were positively correlated with PR expression (all p < 0.05). CONCLUSION Whole tumor ADC histogram parameters have additional clinical value in predicting PR expression in meningiomas.
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Affiliation(s)
- Zhiyong Zhao
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Jinglong Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Shuai Yuan
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - He Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Hang Yin
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Gang Wang
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Yawen Pan
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China.
| | - Qiang Li
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China.
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Liu X, Han T, Wang Y, Liu H, Sun Q, Xue C, Deng J, Li S, Zhou J. Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas. Neuroradiology 2024; 66:531-541. [PMID: 38400953 DOI: 10.1007/s00234-024-03319-w] [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: 12/12/2023] [Accepted: 02/20/2024] [Indexed: 02/26/2024]
Abstract
PURPOSE To investigate the value of histogram analysis of postcontrast T1-weighted (T1C) and apparent diffusion coefficient (ADC) images in predicting the grade and proliferative activity of adult intracranial ependymomas. METHODS Forty-seven adult intracranial ependymomas were enrolled and underwent histogram parameters extraction (including minimum, maximum, mean, 1st percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy of T1C and ADC) using FireVoxel software. Differences in histogram parameters between grade 2 and grade 3 adult intracranial ependymomas were compared. Receiver operating characteristic curves and logistic regression analyses were conducted to evaluate the diagnostic performance. Spearman's correlation analysis was used to evaluate the relationship between histogram parameters and Ki-67 proliferation index. RESULTS Grade 3 intracranial ependymomas group showed significantly higher Perc.95, Perc.99, SD, variance, CV, and entropy of T1C; lower minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50 of ADC; and higher CV and entropy of ADC than grade 2 intracranial ependymomas group (all p < 0.05). Entropy (T1C) and Perc.10 (ADC) had a higher diagnostic performance with AUCs of 0.805 and 0.827 among the histogram parameters of T1C and ADC, respectively. The diagnostic performance was improved by combining entropy (T1C) and Perc.10 (ADC), with an AUC of 0.857. Significant correlations were observed between significant histogram parameters of T1C (r = 0.296-0.417, p = 0.001-0.044) and ADC (r = -0.428-0.395, p = 0.003-0.038). CONCLUSION Whole-tumor histogram analysis of T1C and ADC may be a promising approach for predicting the grade and proliferative activity of adult intracranial ependymomas.
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Affiliation(s)
- Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Yuzhu Wang
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
| | - Hong Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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Li Z, Wang X, Zhang H, Yang Y, Zhang Y, Zhuang Y, Yang Q, Gao E, Ren Y, Zhang Y, Cai S, Chen Z, Cai C, Dong Y, Bao J, Cheng J. Positive Progesterone Receptor Expression in Meningioma May Increase the Transverse Relaxation: First Prospective Clinical Trial Using Single-Shot Ultrafast T 2 Mapping. Acad Radiol 2024; 31:187-198. [PMID: 37316368 DOI: 10.1016/j.acra.2023.05.012] [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: 04/06/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/16/2023]
Abstract
RATIONALE AND OBJECTIVES This project aims to investigate the diagnostic performance of multiple overlapping-echo detachment imaging (MOLED) technique-derived transverse relaxation time (T2) maps in predicting progesterone receptor (PR) and S100 expression in meningiomas. MATERIALS AND METHODS 63 meningioma patients were enrolled from October 2021 to August 2022, who underwent a complete routine magnetic resonance imaging and T2 MOLED, which can characterize the whole brain transverse relaxation time within 32 seconds in a single scan. After the surgical resection of meningiomas, the expression levels of PR and S100 were determined by an experienced pathologist using immunohistochemistry techniques. Histogram analysis was performed in tumor parenchyma based on the parametric maps. Independent t test and Mann-Whitney U test were applied for the comparison of histogram parameters between different groups, with a significance level of P < .05. Logistic regression and receiver operating characteristic (ROC) analysis with 95% confidence interval were conducted for the diagnostic efficiency evaluation. RESULTS PR-positive group had significantly elevated T2 histogram parameters (P = .001-.049) compared to the PR-negative group. The multivariate logistic regression model with T2 showed the highest area under the ROC curve (AUC) for predicting PR expression (AUC=0.818). Additionally, the multivariate model also had the best diagnostic performance for predicting meningioma S100 expression (AUC=0.768). CONCLUSION The MOLED technique-derived T2 maps can distinguish PR and S100 status in meningiomas preoperatively.
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Affiliation(s)
- Zongye Li
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Xiao Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Hongyan Zhang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China (H.Z.)
| | - Yijie Yang
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen, China (Y.Y., Q.Y., S.C., Z.C., C.C.)
| | - Yue Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Yuchuan Zhuang
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York (Y.Z.)
| | - Qinqin Yang
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen, China (Y.Y., Q.Y., S.C., Z.C., C.C.)
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Yanan Ren
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen, China (Y.Y., Q.Y., S.C., Z.C., C.C.)
| | - Zhong Chen
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen, China (Y.Y., Q.Y., S.C., Z.C., C.C.)
| | - Congbo Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen, China (Y.Y., Q.Y., S.C., Z.C., C.C.)
| | - Yanbo Dong
- Institute of Psychology, Herzen State Pedagogical University of Russia, Saint Petersburg, Russia (Y.D.)
| | - Jianfeng Bao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.)
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, No. 1, Jianshe Dong Road, Zhengzhou 450000, China (Z.L., X.W., Y.Z., E.G., Y.R., Y.Z., J.B., J.C.).
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Han T, Liu X, Jing M, Zhang Y, Zhang B, Deng L, Zhou J. ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index. Acta Radiol 2023; 64:3032-3041. [PMID: 37822165 DOI: 10.1177/02841851231205151] [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] [Indexed: 10/13/2023]
Abstract
BACKGROUND Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. PURPOSE To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). METHODS Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. RESULTS All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293). CONCLUSION ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
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Han T, Liu X, Jing M, Zhang Y, Deng L, Zhang B, Zhou J. The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence. J Cancer Res Clin Oncol 2023; 149:17427-17436. [PMID: 37878091 DOI: 10.1007/s00432-023-05463-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/05/2023] [Indexed: 10/26/2023]
Abstract
OBJECTIVE To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. MATERIALS AND METHODS Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively. RESULTS Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model. CONCLUSION This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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Fang S, Yang Y, Tao J, Yin Z, Liu Y, Duan Z, Liu W, Wang S. Intratumoral Heterogeneity of Fibrosarcoma Xenograft Models: Whole-Tumor Histogram Analysis of DWI and IVIM. Acad Radiol 2023; 30:2299-2308. [PMID: 36481126 DOI: 10.1016/j.acra.2022.11.016] [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: 07/27/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
RATIONAL AND OBJECTIVE To explore the correlations of histogram parameters from diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) with the heterogeneous features in a nude mouse model of fibrosarcoma. MATERIALS AND METHODS A total of 44 fibrosarcoma xenograft models were established by inoculating HT-1080 cells on the right thigh of mice and subjected tumors to DWI and IVIM imaging with 3.0 T MRI. Whole-tumor histogram parameters were calculated on apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f). Heterogeneous features, including necrosis rate, cell density, Ki-67 labeling index (LI), and microvascular density (MVD) were measured. Intraclass correlation coefficients (ICC), Pearson or Spearman correlation tests, and receiver operating characteristics (ROC) were performed. RESULTS The 90th percentile, skewness and kurtosis of ADC and D histograms showed correlations with necrosis rate, and the highest correlation coefficient was found for D90th (r = 0.485). ADC and D histogram parameters showed correlations with cell density and Ki-67 LI; D90th showed the highest correlation coefficient with cell density (r = -0.504); and Dmedian showed the most significant correlation with Ki-67 LI (r = -0.525). D*skewness, D*kurtosis, D*90th, fmean, and fmedian showed correlations with MVD. ADC90th, ADCskewness, ADCkurtosis, D90th, and Dskewness showed significant differences between the low necrosis and high necrosis groups, and the combination model showed the best diagnostic ability (AUC = 0.882), with 97% sensitivity, and 72.7% specificity. CONCLUSION Whole-tumor histogram parameters of DWI and IVIM were correlated with heterogeneous features in nude murine models of fibrosarcoma.
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Affiliation(s)
- Shaobo Fang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Yanyu Yang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Juan Tao
- Department of Pathology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Zhenzhen Yin
- Department of Radiology, Suzhou Hospital of Anhui Medical University, Anhui, China
| | - Yajie Liu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Zhiqing Duan
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Wenyu Liu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Shaowu Wang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China.
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Han T, Long C, Liu X, Jing M, Zhang Y, Deng L, Zhang B, Zhou J. Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram. Neurosurg Rev 2023; 46:245. [PMID: 37718326 DOI: 10.1007/s10143-023-02155-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/21/2023] [Accepted: 09/11/2023] [Indexed: 09/19/2023]
Abstract
The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Changyou Long
- Image Center of Affiliated Hospital of Qinghai University, Xining, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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Wang G, Zhou J. The value of whole-volume apparent diffusion coefficient histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumor and transitional meningioma. Front Oncol 2023; 13:1155162. [PMID: 37260978 PMCID: PMC10228830 DOI: 10.3389/fonc.2023.1155162] [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: 01/31/2023] [Accepted: 05/04/2023] [Indexed: 06/02/2023] Open
Abstract
Purpose To investigate the value of whole-volume apparent diffusion coefficient (ADC) histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumors (SFT) from transitional meningiomas (TM), thereby assisting the establishment of the treatment protocol. Methods Preoperative diffusion-weighted imaging datasets of 24 patients with SFT and 28 patients with TM were used to extract whole-volume ADC histogram parameters, including variance, skewness, kurtosis, and mean, as well as 1st (AP1), 10th (AP10), 50th (AP50), 90th (AP90), and 99th (AP99) percentiles of ADC using MaZda software. The independent t-test or Mann-Whitney U test was used to compare the differences between ADC histogram parameters of SFT and TM. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of significant ADC histogram parameters. Spearman's correlation coefficients were calculated to evaluate correlations between these parameters and the Ki-67 expression levels. Results SFT exhibited significantly higher variance, and lower AP1 and AP10 (all P < 0.05) than TM. The best diagnostic performance was obtained by variance, with an area under the ROC curve of 0.848 (0.722-0.933). However, there was no significant difference in skewness, kurtosis, mean, or other percentiles of ADC between the two groups (all P > 0.05). Significant correlations were also observed between the Ki-67 proliferation index and variance (r = 0.519), AP1 (r = -0.425), and AP10 (r = -0.372) (all P < 0.05). Conclusion Whole-volume ADC histogram analysis is a feasible tool for non-invasive preoperative discrimination between intracranial SFT and TM, with variance being the most promising prospective parameter.
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Affiliation(s)
- Gang Wang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
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Yao Y, Xu Y, Liu S, Xue F, Wang B, Qin S, Sun X, He J. Predicting the grade of meningiomas by clinical-radiological features: A comparison of precontrast and postcontrast MRI. Front Oncol 2022; 12:1053089. [PMID: 36530973 PMCID: PMC9752076 DOI: 10.3389/fonc.2022.1053089] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 11/11/2022] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES Postcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningiomas. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aimed to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI. METHODS From January 2019 to October 2021, a total of 231 meningioma patients (development cohort n = 137, low grade/high grade, 85/52; external validation cohort n = 94, low-grade/high-grade, 60/34) were retrospectively included. Fourteen types of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomograms, based on full MRI and precontrast MRI. Their distinguishing performances were validated and compared using the external validation cohort. RESULTS One demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, and peritumoral edema), and one postcontrast MRI feature (absence of a dural tail sign) were independent predictive factors for high-grade meningiomas. The area under the receiver operating characteristic (ROC) curve (AUC) values of the two distinguishing models (precontrast-postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898 and in the validation cohort were 0.922 and 0.878. DeLong's test showed no statistical difference between the AUC values of the two distinguishing models (p = 0.101). CONCLUSIONS An accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.
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Affiliation(s)
- Yuan Yao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yifan Xu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Shihe Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Feng Xue
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Bao Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Shanshan Qin
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiubin Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Jingzhen He
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
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T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. Eur Radiol 2022; 33:258-269. [PMID: 35953734 DOI: 10.1007/s00330-022-09026-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/05/2022] [Accepted: 07/09/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. METHODS This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression. RESULTS High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance). CONCLUSION T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. KEY POINTS • The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.
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Yang H, Liu X, Jiang J, Zhou J. Apparent diffusion coefficient histogram analysis to preoperative evaluate intracranial solitary fibrous tumor: Relationship to Ki-67 proliferation index. Clin Neurol Neurosurg 2022; 220:107364. [PMID: 35872434 DOI: 10.1016/j.clineuro.2022.107364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/22/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To explore the value of apparent diffusion coefficient (ADC) histogram analysis in preoperative evaluating intracranial solitary fibrous tumor (SFT) and further investigate the relationship between ADC histogram parameters and the Ki-67 proliferation index. METHODS From January 2014 to March 2022, 37 patients with intracranial SFT (grade 2, n = 20; grade 3, n = 17) who underwent preoperative diffusion-weighted imaging were enrolled in this study. For each tumor, nine histogram parameters were automatically extracted and selected using MaZda software based on the axial ADC maps of the whole tumor, including mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC (Perc.01, Perc.10, Perc.50, Perc.90, Perc.99). Differences in ADC histogram parameters between grade 2 and 3 intracranial SFT were compared. Receiver operating characteristic (ROC) curves were drawn to determine the diagnostic performance, and Pearson's correlation coefficient was used to investigate the relationship between these parameters and the Ki-67 proliferation index. RESULTS The mean, Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99 were significantly lower in grade 3 than in grade 2 intracranial SFT (all P < 0.05). ROC analysis showed that these parameters can effectively distinguish between the two groups, with Perc.01 generating the best differentiation performance. Significant negative correlations were also observed between these parameters and the Ki-67 proliferation index (r = -0.436 ~ -0.522, all P < 0.05). However, there was no significant difference in variance, skewness, or kurtosis between the two groups (all P > 0.05). CONCLUSIONS ADC histogram analysis enables effective preoperative distinction of grade 2 and grade 3 intracranial SFT.
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Affiliation(s)
- Haiting Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Jian Jiang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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Tsai YT, Hung KC, Shih YJ, Lim SW, Yang CC, Kuo YT, Chen JH, Ko CC. Preoperative Apparent Diffusion Coefficient Values for Differentiation between Low and High Grade Meningiomas: An Updated Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12030630. [PMID: 35328183 PMCID: PMC8947055 DOI: 10.3390/diagnostics12030630] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 02/01/2023] Open
Abstract
The meta-analysis aimed to compare the preoperative apparent diffusion coefficient (ADC) values between low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs). Medline, Cochrane, Scopus, and Embase databases were screened up to January 2022 for studies investigating the ADC values of meningiomas. The study endpoint was the reported ADC values for LGMs and HGMs. Further subgroup analyses between 1.5T and 3T MRI scanners, ADC threshold values, ADC in different histological LGMs, and correlation coefficients (r) between ADC and Ki-67 were also performed. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS-2). A χ2-based test of homogeneity was performed using Cochran’s Q statistic and inconsistency index (I2). Twenty-five studies with a total of 1552 meningiomas (1102 LGMs and 450 HGMs) were included. The mean ADC values (×10−3 mm2/s) were 0.92 and 0.79 for LGMs and HGMs, respectively. Compared with LGMs, significantly lower mean ADC values for HGMs were observed with a pooled difference of 0.13 (p < 0.00001). The results were consistent in both 1.5T and 3T MRI scanners. For ADC threshold values, pooled sensitivity of 69%, specificity of 82%, and AUC of 0.84 are obtained for differentiation between LGMs and HGMs. The mean ADC (×10−3 mm2/s) in different histological LGMs ranged from 0.87 to 1.22. Correlation coefficients (r) of mean ADC and Ki-67 ranged from −0.29 to −0.61. Preoperative ADC values are a useful tool for differentiating between LGMs and HGMs. Results of this study provide valuable information for planning treatments in meningiomas.
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Affiliation(s)
- Yueh-Ting Tsai
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-T.T.); (Y.-J.S.); (C.-C.Y.); (Y.-T.K.)
| | - Kuo-Chuan Hung
- Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan;
- Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
| | - Yun-Ju Shih
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-T.T.); (Y.-J.S.); (C.-C.Y.); (Y.-T.K.)
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi Mei Medical Center, Chiali, Tainan 722, Taiwan;
- Department of Nursing, Min-Hwei College of Health Care Management, Tainan 736, Taiwan
| | - Cheng-Chun Yang
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-T.T.); (Y.-J.S.); (C.-C.Y.); (Y.-T.K.)
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-T.T.); (Y.-J.S.); (C.-C.Y.); (Y.-T.K.)
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA;
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung 824, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-T.T.); (Y.-J.S.); (C.-C.Y.); (Y.-T.K.)
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 804, Taiwan
- Correspondence:
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Yang L, Xu P, Zhang Y, Cui N, Wang M, Peng M, Gao C, Wang T. A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma. Neuroradiology 2022; 64:1373-1382. [PMID: 35037985 DOI: 10.1007/s00234-022-02894-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/04/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas. METHODS A total of 132 patients with pathologically confirmed meningiomas were consecutively enrolled (105 in the training cohort and 27 in the test cohort). Radiomics features and deep learning features were extracted from T1 weighted images (T1WI) (both axial and sagittal) and the maximum slice of the axial tumor lesion, respectively. Then, the synthetic minority oversampling technique (SMOTE) was utilized to balance the sample numbers. The optimal discriminative features were selected for model building. LightGBM algorithm was used to develop DLRM by a combination of radiomics features and deep learning features. For comparison, a radiomics model (RM) and a deep learning model (DLM) were constructed using a similar method as well. Differentiating efficacy was determined by using the receiver operating characteristic (ROC) analysis. RESULTS A total of 15 features were selected to construct the DLRM with SMOTE, which showed good discrimination performance in both the training and test cohorts. The DLRM outperformed RM and DLM for differentiating low- and high-grade meningiomas (training AUC: 0.988 vs. 0.980 vs. 0.892; test AUC: 0.935 vs. 0.918 vs. 0.718). The accuracy, sensitivity, and specificity of the DLRM with SMOTE were 0.926, 0.900, and 0.924 in the test cohort, respectively. CONCLUSION The DLRM with SMOTE based on enhanced T1WI images has favorable performance for noninvasively individualized prediction of meningioma grades, which exhibited favorable clinical usefulness superior over the radiomics features.
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Affiliation(s)
- Liping Yang
- PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Panpan Xu
- PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ying Zhang
- PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Nan Cui
- PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Menglu Wang
- PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Mengye Peng
- PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Chao Gao
- Medical Imaging Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianzuo Wang
- Medical Imaging Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
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Feng W, Gao Y, Lu XR, Xu YS, Guo ZZ, Lei JQ. Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer. Magn Reson Imaging 2021; 85:262-270. [PMID: 34740800 DOI: 10.1016/j.mri.2021.10.027] [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] [Received: 08/22/2020] [Revised: 07/26/2021] [Accepted: 10/17/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer. MATERIALS AND METHODS A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer. RESULTS Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10-3 mm2/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = -0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = -0.31, ρ = -0.34) and f 45th percentile (ρ = -0.35, ρ = -0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high. CONCLUSIONS The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.
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Affiliation(s)
- Wen Feng
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu, China; Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Ya Gao
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Xing-Ru Lu
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Yong-Sheng Xu
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Zhuan-Zhuan Guo
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, Shanxi, China
| | - Jun-Qiang Lei
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China.
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Xu X. Diagnosis of hilar cholangiocarcinoma using intravoxel incoherent motion diffusion-weighted magnetic resonance imaging. Abdom Radiol (NY) 2021; 46:3159-3167. [PMID: 33660039 DOI: 10.1007/s00261-021-02997-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: 11/30/2020] [Revised: 02/02/2021] [Accepted: 02/11/2021] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To investigate the value of intravoxel incoherent motion diffusion-weighed magnetic resonance imaging (IVIM-DWI) in discriminating the pathological grades of hilar cholangiocarcinoma (HC). PATIENTS AND METHODS Thirty-seven HC patients were enrolled and received routine and advanced DWI scanning with multiple b-values. IVIM-DWI images were obtained using echo-planar imaging sequence. RESULTS The consistency of the maximum cross-sectional area ROI measuring method was higher than that of the repeated sampling ROI measuring method. ADCslow values were closely correlated with the pathological grades of HC. The degrees of biliary dilatation and MELD scores had no influence on the negative correlation between ADCslow values and the pathological degrees of HC patients. CONCLUSIONS ADCslow values could be applied in indicating the pathological grades of HC, which was independent on the extent of biliary dilatation.
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Abstract
The signal acquired in vivo using a diffusion-weighted MR imaging (DWI) sequence is influenced by blood motion in the tissue. This means that perfusion information from a DWI sequence can be obtained in addition to thermal diffusion, if the appropriate sequence parameters and postprocessing methods are applied. This is commonly regrouped under the denomination intravoxel incoherent motion (IVIM) perfusion MR imaging. Of relevance, the perfusion information acquired with IVIM is essentially local, quantitative and acquired without intravenous injection of contrast media. The aim of this work is to review the IVIM method and its clinical applications.
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Affiliation(s)
- Christian Federau
- University and ETH Zürich, Institute for Biomedical Engineering, Gloriastrasse 35, Zürich 8092, Switzerland; Ai Medical AG, Goldhaldenstr 22a, Zollikon 8702, Switzerland.
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Intravoxel incoherent motion as a tool to detect early microstructural changes in meningiomas treated with proton therapy. Neuroradiology 2021; 63:1053-1060. [PMID: 33392736 DOI: 10.1007/s00234-020-02630-6] [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: 09/21/2020] [Accepted: 12/21/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE To assess early microstructural changes of meningiomas treated with proton therapy through quantitative analysis of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) parameters. METHODS Seventeen subjects with meningiomas that were eligible for proton therapy treatment were retrospectively enrolled. Each subject underwent a magnetic resonance imaging (MRI) including DWI sequences and IVIM assessments at baseline, immediately before the 1st (t0), 10th (t10), 20th (t20), and 30th (t30) treatment fraction and at follow-up. Manual tumor contours were drawn on T2-weighted images by two expert neuroradiologists and then rigidly registered to DWI images. Median values of the apparent diffusion coefficient (ADC), true diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f) were extracted at all timepoints. Statistical analysis was performed using the pairwise Wilcoxon test. RESULTS Statistically significant differences from baseline to follow-up were found for ADC, D, and D* values, with a progressive increase in ADC and D in conjunction with a progressive decrease in D*. MRI during treatment showed statistically significant differences in D values between t0 and t20 (p = 0.03) and t0 and t30 (p = 0.02), and for ADC values between t0 and t20 (p = 0.04), t10 and t20 (p = 0.02), and t10 and t30 (p = 0.035). Subjects that showed a volume reduction greater than 15% of the baseline tumor size at follow-up showed early D changes, whereas ADC changes were not statistically significant. CONCLUSION IVIM appears to be a useful tool for detecting early microstructural changes within meningiomas treated with proton therapy and may potentially be able to predict tumor response.
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Chen X, Lin L, Wu J, Yang G, Zhong T, Du X, Chen Z, Xu G, Song Y, Xue Y, Duan Q. Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging. Acta Radiol 2020; 61:1228-1239. [PMID: 31986895 DOI: 10.1177/0284185119898656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. PURPOSE To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. MATERIAL AND METHODS A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann-Whitney U test, Kruskal-Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. RESULTS Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index (P < 0.05). CONCLUSION The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.
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Affiliation(s)
- Xiaodan Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, PR China
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Jie Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Tianjin Zhong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Xiaoqiang Du
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Zhiyong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Ganggang Xu
- Department of Management Science, University of Miami, Coral Gables, FL, USA
| | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
| | - Qing Duan
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China
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Hu J, Zhao Y, Li M, Liu J, Wang F, Weng Q, Wang X, Cao D. Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI. Eur J Radiol 2020; 131:109251. [PMID: 32916409 DOI: 10.1016/j.ejrad.2020.109251] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/25/2020] [Accepted: 08/10/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the prediction performance of radiomic models based on multiparametric MRI in predicting the meningioma grade. METHOD In all, 229 low-grade [Grade I] and 87 high-grade [Grade II/III] patients with pathologically diagnosed meningiomas were enrolled. Radiomic features from conventional MRI (cMRI), ADC maps and SWI were extracted based on the volume of entire tumor. Classification performance of different radiomic models (cMRI, ADC, SWI, cMRI + ADC, cMRI + SWI, ADC + SWI, and cMRI + ADC + SWI models) was evaluated by a nested LOOCV approach, combining the LASSO feature selection and RF classifier that was trained (1) without subsampling, and (2) with the synthetic minority over-sampling technique (SMOTE). The prediction performance of radiomic models was assessed using ROC curve and AUC of them was compared using Delong's test. RESULTS The cMRI + ADC + SWI model demonstrated the best performance without or with subsampling, which AUCs were 0.84 and 0.81, respectively. Following the cMRI + ADC + SWI model, the AUC range of the other models was 0.75-0.80 without subsampling, and was 0.71-0.79 with subsampling. Although the cMRI + ADC model and cMRI + SWI model showed higher AUCs than the cMRI model without subsampling (0.77 vs 0.80, P = 0.037 and 0.77 vs 0.80, P = 0.009, respectively), there was no significant difference among these models with subsampling (0.78 vs 0.77, P = 0.552 and 0.78 vs 0.79, P = 0.246, respectively). CONCLUSIONS Multiparametric radiomic model based on cMRI, ADC map and SWI yielded the best prediction performance in predicting the meningioma grade, which might offer potential guidance in clinical decision-making.
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Affiliation(s)
- Jianping Hu
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yijing Zhao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Mengcheng Li
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jianyi Liu
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Wang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Qiang Weng
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xingfu Wang
- Department of Pathology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
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ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation - A systematic review and meta-analysis. J Neurol Sci 2020; 415:116975. [PMID: 32535250 DOI: 10.1016/j.jns.2020.116975] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 05/27/2020] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION The aim of the present systematic review and meta-analysis was to compare the reported ADC values in different meningiomas and to analyze associations between ADC and cell count and proliferation activity in this tumor entity. METHOD MEDLINE library and SCOPUS database were screened for papers investigating ADC values of meningiomas up November 2019. The first primary endpoint of the systematic review was the reported ADC mean value of the meningioma groups. The second primary endpoint was the correlation coefficient between ADC values and proliferation index Ki 67 and cellularity. RESULTS For the discrimination analysis between benign and high grade meningioma 17 studies were suitable. There were 766 grade I tumors and 289 high grade meningiomas. The calculated mean ADC value of the benign grade I tumors was 0.93 × 10-3mm2/s [95%-Confidence interval 0.84;1.03] and the mean value of the high-grade tumors was 0.77 × 10-3mm2/s [95%-Confidence interval 0.73-0.80]. The pooled correlation coefficient between ADC and the proliferation index Ki 67 was r = -0.36 [95% CI -0.43; -0.28]. The pooled correlation coefficient between ADC and cellularity was r = -0.43 [95% CI -0.61; - 0.26]. CONCLUSION No validated ADC threshold can be recommended for distinguishing benign from high grade meningiomas. Only a moderate inverse correlation was identified between ADC values and tumor microstructure in meningiomas and, therefore, ADC might not accurately enough to predict proliferation potential and cellularity in this entity.
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Sacco S, Ballati F, Gaetani C, Lomoro P, Farina LM, Bacila A, Imparato S, Paganelli C, Buizza G, Iannalfi A, Baroni G, Valvo F, Bastianello S, Preda L. Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas. Neuroradiology 2020; 62:1441-1449. [PMID: 32583368 DOI: 10.1007/s00234-020-02476-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/10/2020] [Indexed: 01/22/2023]
Abstract
PURPOSE Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading. METHODS Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores. RESULTS Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson's chi-square; Fisher's exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia. CONCLUSION Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis. Key points • When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity. • Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma.
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Affiliation(s)
- Simone Sacco
- Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Francesco Ballati
- Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Clara Gaetani
- Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Pascal Lomoro
- Department of Radiology, Valduce Hospital, Como, Italy
| | | | - Ana Bacila
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Imparato
- Diagnostic Imaging Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100, Pavia, PV, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Alberto Iannalfi
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Francesca Valvo
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Stefano Bastianello
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Lorenzo Preda
- Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
- Diagnostic Imaging Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100, Pavia, PV, Italy.
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