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Zhu Y, Wang J, Xue C, Zhai X, Xiao C, Lu T. Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00671-8. [PMID: 39322536 DOI: 10.1016/j.acra.2024.09.021] [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: 05/31/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 09/27/2024]
Abstract
RATIONALE AND OBJECTIVES Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pathological prediction outcomes by combining habitat analysis with deep learning. MATERIALS AND METHODS 387 cases of primary glioma from three hospitals were collected, along with their T1 contrast-enhanced and T2-weighted MR sequences, pathological reports and clinical histories. The training set consisted of 264 patients, 82 patients composed the test set, and 41 patients were used as the validation set for hyperparameter tuning and optimal model selection. All groups were sourced from different centers. Through radiomics, deep learning, habitat analysis and combined analysis, we extracted imaging features separately and jointly modeled them with clinical features. We identified the optimal models for predicting glioma grades, Ki67 expression levels, P53 mutation and IDH1 mutation. RESULTS Using a LightGBM model with DenseNet161 features based on habitat subregions, the best tumor grade prediction model was achieved. A LightGBM model with ResNet50 features based on habitat subregions yielded the best Ki67 expression level prediction model. An SVM model with Radiomics and Inception_v3 features provided the best prediction of P53 mutation. The best model for predicting IDH1 mutation was achieved by an MLP model with Radiomics features based on habitat subregions. Clinical features might be potentially helpful for the prediction with relatively weak evidence. CONCLUSION Habitat+Deep Learning feature extraction methods were optimal for predicting grades and Ki67 levels. Deep Learning is optimal for predicting P53 mutation, while the combination of Habitat+ Radiomics models yielded the best prediction for IDH1 mutation.
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Affiliation(s)
- Yunyang Zhu
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China (Y.Z., J.W., T.L.)
| | - Jing Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China (Y.Z., J.W., T.L.)
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China (C.X., C.X.)
| | - Xiaoyang Zhai
- The First Affiliated Hospital of Xinxiang University, Xinxiang, China (X.Z.)
| | - Chaoyong Xiao
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China (C.X., C.X.)
| | - Ting Lu
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China (Y.Z., J.W., T.L.).
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Yoshimura H, Kawahara D, Saito A, Ozawa S, Nagata Y. Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN. Phys Eng Sci Med 2024; 47:1227-1243. [PMID: 38884673 PMCID: PMC11408565 DOI: 10.1007/s13246-024-01443-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 05/14/2024] [Indexed: 06/18/2024]
Abstract
To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.
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Affiliation(s)
- Hisanori Yoshimura
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Department of Radiology, National Hospital Organization Kure Medical Center, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
| | - Akito Saito
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Shuichi Ozawa
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
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Akade E, Aslani F, Verdi K, Bahadoram M, Kaydani GA. Diagnosis of choroid plexus papilloma: Current perspectives and future directions. CANCER PATHOGENESIS AND THERAPY 2024; 2:173-179. [PMID: 39027146 PMCID: PMC11252511 DOI: 10.1016/j.cpt.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 07/20/2024]
Abstract
Choroid plexus papilloma (CPP) is a rare, slow-growing, and typically benign brain tumor that predominantly affects children. CPP is characterized by well-defined circular or lobulated masses in the ventricles, leading to symptoms related to increased intracranial pressure and hydrocephalus. CPP diagnosis relies on a combination of clinical presentation, imaging findings, and histological examination. The World Health Organization (WHO) classification categorizes choroid plexus tumors into CPP (Grade І), atypical CPP (aCPP, Grade II), and choroid plexus carcinoma (CPC, Grade III). This article reviewed current diagnostics modalities and explored the emergence of new diagnostic methods for CPP. Research on molecular markers and genetic alterations associated with CPP is ongoing, and some potential markers have been identified. These results offered insights into potential therapeutic targets and personalized treatment approaches for CPP. Advancements in radiomics and liquid biopsy hold promise for improving diagnostic accuracy and monitoring treatment outcomes for choroid plexus tumors. Radiomics can provide quantitative data from imaging studies, whereas liquid biopsy can analyze tumor-derived genetic material and molecular markers from body fluids, such as cerebrospinal fluid (CSF) and blood. The rapidly evolving fields of molecular and genetic research and novel diagnostic methods require continuous updates and advancements before their application in clinical practice. We hope that these advancements will lead to earlier and more precise diagnoses, better treatment options, and improved outcomes in patients with CPP and other brain tumors.
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Affiliation(s)
- Esma'il Akade
- Department of Medical Virology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 1579461357, Iran
| | - Fereshteh Aslani
- Department of Laboratory Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 1579461357, Iran
| | - Kimia Verdi
- Department of Physiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 1579461357, Iran
| | - Mohammad Bahadoram
- Department of Neurology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 1579461357, Iran
| | - Gholam Abbas Kaydani
- Department of Laboratory Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 1579461357, Iran
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Soldatelli MD, Namdar K, Tabori U, Hawkins C, Yeom K, Khalvati F, Ertl-Wagner BB, Wagner MW. Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning. AJNR Am J Neuroradiol 2024; 45:753-760. [PMID: 38604736 PMCID: PMC11288584 DOI: 10.3174/ajnr.a8199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/16/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND PURPOSE Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molecular status of PLGNTs. MATERIALS AND METHODS In this retrospective bi-institutional study, we searched the PACS for baseline brain MRIs from children with PLGNTs. Semiautomated tumor segmentation on ADC maps was performed using the semiautomated level tracing effect tool with 3D Slicer. Clinical variables, including age, sex, and tumor location, were collected from chart review. The molecular status of tumors was derived from biopsy. Multiclass random forests were used to predict the molecular status and fine-tuned using a grid search on the validation sets. Models were evaluated using independent and unseen test sets based on the combined data, and the area under the receiver operating characteristic curve (AUC) was calculated for the prediction of 3 classes: KIAA1549-BRAF fusion, BRAF V600E mutation, and non-BRAF cohorts. Experiments were repeated 100 times using different random data splits and model initializations to ensure reproducible results. RESULTS Two hundred ninety-nine children from the first institution and 23 children from the second institution were included (53.6% male; mean, age 8.01 years; 51.8% supratentorial; 52.2% with KIAA1549-BRAF fusion). For the 3-class prediction using radiomics features only, the average test AUC was 0.74 (95% CI, 0.73-0.75), and using clinical features only, the average test AUC was 0.67 (95% CI, 0.66-0.68). The combination of both radiomics and clinical features improved the AUC to 0.77 (95% CI, 0.75-0.77). The diagnostic performance of the per-class test AUC was higher in identifying KIAA1549-BRAF fusion tumors among the other subgroups (AUC = 0.81 for the combined radiomics and clinical features versus 0.75 and 0.74 for BRAF V600E mutation and non-BRAF, respectively). CONCLUSIONS ADC values of tumor segmentations have differentiative signals that can be used for training machine learning classifiers for molecular biomarker identification of PLGNTs. ADC-based pretherapeutic differentiation of the BRAF status of PLGNTs has the potential to avoid invasive tumor biopsy and enable earlier initiation of targeted therapy.
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Affiliation(s)
- Matheus D Soldatelli
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Khashayar Namdar
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Vector Institute (K.N., F.K.), Toronto, Ontario, Canada
| | - Uri Tabori
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- The Arthur and Sonia Labatt Brain Tumour Research Centre (U.T., C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology (U.T.) The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Cynthia Hawkins
- The Arthur and Sonia Labatt Brain Tumour Research Centre (U.T., C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology (C.H.), University of Toronto, Toronto, Ontario, Canada
- Division of Pathology (C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Kristen Yeom
- Department of Radiology (K.Y.), Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California
| | - Farzad Khalvati
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Vector Institute (K.N., F.K.), Toronto, Ontario, Canada
- Department of Computer Science (F.K.), University of Toronto, Toronto, Ontario, Canada
| | - Birgit B Ertl-Wagner
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Matthias W Wagner
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Department of Diagnostic and Interventional Neuroradiology (M.W.W.), University Hospital Augsburg, Augsburg, Germany
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Kasap DNG, Mora NGN, Blömer DA, Akkurt BH, Heindel WL, Mannil M, Musigmann M. Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning. Biomedicines 2024; 12:725. [PMID: 38672080 PMCID: PMC11048271 DOI: 10.3390/biomedicines12040725] [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: 01/22/2024] [Revised: 02/24/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVES Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. MATERIAL AND METHODS In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. RESULTS Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. CONCLUSIONS Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.
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Demircioğlu A. The effect of data resampling methods in radiomics. Sci Rep 2024; 14:2858. [PMID: 38310165 PMCID: PMC10838284 DOI: 10.1038/s41598-024-53491-5] [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: 12/12/2023] [Accepted: 02/01/2024] [Indexed: 02/05/2024] Open
Abstract
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
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Li J, Sun J, Wang N, Zhang Y. Study on the Relationship Between MRI Functional Imaging and Multiple Immunohistochemical Features of Glioma: A Noninvasive and More Precise Glioma Management. Mol Imaging 2024; 23:15353508241261583. [PMID: 38952400 PMCID: PMC11208885 DOI: 10.1177/15353508241261583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 05/09/2024] [Accepted: 05/23/2024] [Indexed: 07/03/2024] Open
Abstract
Objective To investigate the performance of diffusion-tensor imaging (DTI) and hydrogen proton magnetic resonance spectroscopy (1H-MRS) parameters in predicting the immunohistochemistry (IHC) biomarkers of glioma. Methods Patients with glioma confirmed by pathology from March 2015 to September 2019 were analyzed, the preoperative DTI and 1H-MRS images were collected, apparent diffusion coefficient (ADC) and fractional anisotropy (FA), in the lesion area were measured, the relative values relative ADC (rADC) and relative FA (rFA) were obtained by the ratio of them in the lesion area to the contralateral normal area. The peak of each metabolite in the lesion area of 1H-MRS image: N-acetylaspartate (NAA), choline (Cho), and creatine (Cr), and metabolite ratio: NAA/Cho, NAA/(Cho + Cr) were selected and calculated. The preoperative IHC data were collected including CD34, Ki-67, p53, S-100, syn, vimentin, NeuN, Nestin, and glial fibrillary acidic protein. Results One predicting parameter of DTI was screened, the rADC of the Ki-67 positive group was lower than that of the negative group. Two parameters of 1H-MRS were found to have significant reference values for glioma grades, the NAA and Cr decreased as the grade of glioma increased, moreover, Ki-67 Li was negatively correlated with NAA and Cr. Conclusion NAA and Cr have potential application value in predicting glioma grades and tumor proliferation activity. Only rADC has predictive value for Ki-67 expression among DTI parameters.
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Affiliation(s)
- Jing Li
- Department of Radiology, Tangshan Women and Children's Hospital, Tangshan, Hebei, China
| | - Jingtao Sun
- Department of Radiology, Tangshan Women and Children's Hospital, Tangshan, Hebei, China
| | - Ning Wang
- Department of Radiology and Nuclear Medicine, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yan Zhang
- Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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Yin F, Yan X, Gao R, Ren Z, Yu T, Zhao Z, Zhang G. Radiomics features from 3D-MPRAGE imaging can differentiate temporal-plus epilepsy from temporal lobe epilepsy. Epileptic Disord 2023; 25:681-689. [PMID: 37349866 DOI: 10.1002/epd2.20092] [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: 12/30/2022] [Revised: 05/15/2023] [Accepted: 06/21/2023] [Indexed: 06/24/2023]
Abstract
OBJECTIVE This study aimed to differentiate temporal-plus epilepsy (TPE) from temporal lobe epilepsy (TLE) using extraction of radiomics features from three-dimensional magnetization-prepared rapid acquisition gradient echo (3D-MPRAGE) imaging data. METHODS Data from patients with TLE or TPE who underwent epilepsy surgery between January 2019 and January 2021 were retrospectively analyzed. Thirty-three regions of interest in the affected hemisphere of each patient were defined on 3D-MPRAGE images. A total of 3531 image features were extracted from each patient. Four feature selection methods and 10 machine learning algorithms were used to build 40 differentiation models. Model performance was evaluated using receiver operating characteristic analysis. RESULTS Eighty-two patients were included for analysis, 47 with TLE and 35 with TPE. The model combining logistic regression and the relief selection method had the best performance (area under the receiver operating characteristic curve, .779; accuracy, .875; sensitivity, .800; specificity, .929; positive predictive value, .889; negative predictive value, .867). SIGNIFICANCE Radiomics analysis can differentiate TPE from TLE. The logistic regression classifier trained with radiomics features extracted from 3D-MPRAGE images had the highest accuracy and best performance.
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Affiliation(s)
- Fangzhao Yin
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Tianjin Huanhu Hospital, Tianjin, China
| | - Xiaoming Yan
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Runshi Gao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhiwei Ren
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhuoling Zhao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guojun Zhang
- Functional Neurosurgery Department, Beijing Children's Hospital, Capital Medical University, Beijing, China
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Zheng J, Dong H, Li M, Lin X, Wang C. Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images. Clinics (Sao Paulo) 2023; 78:100238. [PMID: 37354775 DOI: 10.1016/j.clinsp.2023.100238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/19/2023] [Accepted: 06/06/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. DATA AND METHODS The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. RESULTS Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. CONCLUSION A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.
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Affiliation(s)
- Jinjing Zheng
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
| | - Haibo Dong
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China.
| | - Ming Li
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
| | - Xueyao Lin
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
| | - Chaochao Wang
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
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Wagner MW, Namdar K, Napoleone M, Hainc N, Amirabadi A, Fonseca A, Laughlin S, Shroff MM, Bouffet E, Hawkins C, Khalvati F, Bartels U, Ertl-Wagner BB. Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma. Can Assoc Radiol J 2023; 74:119-126. [PMID: 35768942 DOI: 10.1177/08465371221109921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Purpose: Biopsy-based assessment of H3 K27 M status helps in predicting survival, but biopsy is usually limited to unusual presentations and clinical trials. We aimed to evaluate whether radiomics can serve as prognostic marker to stratify diffuse intrinsic pontine glioma (DIPG) subsets. Methods: In this retrospective study, diagnostic brain MRIs of children with DIPG were analyzed. Radiomic features were extracted from tumor segmentations and data were split into training/testing sets (80:20). A conditional survival forest model was applied to predict progression-free survival (PFS) using training data. The trained model was validated on the test data, and concordances were calculated for PFS. Experiments were repeated 100 times using randomized versions of the respective percentage of the training/test data. Results: A total of 89 patients were identified (48 females, 53.9%). Median age at time of diagnosis was 6.64 years (range: 1-16.9 years) and median PFS was 8 months (range: 1-84 months). Molecular data were available for 26 patients (29.2%) (1 wild type, 3 K27M-H3.1, 22 K27M-H3.3). Radiomic features of FLAIR and nonenhanced T1-weighted sequences were predictive of PFS. The best FLAIR radiomics model yielded a concordance of .87 [95% CI: .86-.88] at 4 months PFS. The best T1-weighted radiomics model yielded a concordance of .82 [95% CI: .8-.84] at 4 months PFS. The best combined FLAIR + T1-weighted radiomics model yielded a concordance of .74 [95% CI: .71-.77] at 3 months PFS. The predominant predictive radiomic feature matrix was gray-level size-zone. Conclusion: MRI-based radiomics may predict progression-free survival in pediatric diffuse midline glioma/diffuse intrinsic pontine glioma.
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Affiliation(s)
- Matthias W Wagner
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada.,Department of Medical Imaging, 7938University of Toronto, Canada
| | - Khashayar Namdar
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada.,Department of Medical Imaging, 7938University of Toronto, Canada
| | - Marc Napoleone
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Nicolin Hainc
- Nicolin Hainc:Department of Neuroradiology, Clinical Neuroscience Center, 7979University Hospital Zurich,University of Zurich, Switzerland
| | - Afsaneh Amirabadi
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Adriana Fonseca
- Department of Neurooncology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Suzanne Laughlin
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada.,Department of Medical Imaging, 7938University of Toronto, Canada
| | - Manohar M Shroff
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada.,Department of Medical Imaging, 7938University of Toronto, Canada
| | - Eric Bouffet
- Department of Neurooncology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Cynthia Hawkins
- Department of Paediatric Laboratory Medicine, Division of Pathology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Farzad Khalvati
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Ute Bartels
- Department of Neurooncology, 7979The Hospital for Sick Children, Toronto, Canada
| | - Birgit B Ertl-Wagner
- Department of Diagnostic Imaging, Division of Neuroradiology, 7979The Hospital for Sick Children, Toronto, Canada.,Department of Medical Imaging, 7938University of Toronto, Canada
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11
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An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach. Eur J Radiol 2023; 158:110639. [PMID: 36463703 DOI: 10.1016/j.ejrad.2022.110639] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/05/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. METHODS In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. RESULTS The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. CONCLUSION The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.
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12
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Jiang S, Guo P, Heo HY, Zhang Y, Wu J, Jin Y, Laterra J, Eberhart CG, Lim M, Blakeley JO. Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas. NMR IN BIOMEDICINE 2023; 36:e4824. [PMID: 36057449 PMCID: PMC10502874 DOI: 10.1002/nbm.4824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/25/2022] [Accepted: 08/28/2022] [Indexed: 06/15/2023]
Abstract
The purpose of this study was to evaluate the value of amide proton transfer-weighted (APTw) MRI radiomic features for the differentiation of tumor recurrence from treatment effect in malignant gliomas. Eighty-six patients who had suspected tumor recurrence after completion of chemoradiation or radiotherapy, and who had APTw-MRI data acquired at 3 T, were retrospectively analyzed. Using a fluid-attenuated inversion recovery (FLAIR) image-based mask, radiomics analysis was applied to the processed APTw and structural MR images. A chi-square automatic interaction detector decision tree was used for classification analysis. Models with and without APTw features were built using the same strategy. Tenfold cross-validation was applied to obtain the overall classification performance of each model. Sixty patients were confirmed as having tumor recurrence, and the remainder were confirmed as having treatment effect, at median time points of 190 and 171 days after therapy, respectively. There were 525 radiomic features extracted from each of the processed APTw and structural MR images. Based on these, the APTw-based model yielded the highest accuracy (86.0%) for the differentiation of tumor recurrence from treatment effect, compared with 74.4%, 76.7%, 83.7%, and 76.7% for T1 w, T2 w, FLAIR, and Gd-T1 w, respectively. Model classification accuracy was 82.6% when using the combined structural MR images (T1 w, T2 w, FLAIR, Gd-T1 w), and increased to 89.5% when using these structural plus APTw images. The corresponding sensitivity and specificity were 85.0% and 76.9% for the combination of structural MR images, and 85.0% and 100% after adding APTw image features. Adding APTw-based radiomic features increased MRI accuracy in the assessment of the treatment response in post-treatment malignant gliomas.
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Affiliation(s)
- Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Pengfei Guo
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yi Zhang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jingpu Wu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuecen Jin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - John Laterra
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | | | - Michael Lim
- Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Neurosurgery, Stanford University, Palo Alto, California, USA
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13
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MRI based radiomics enhances prediction of neurodevelopmental outcome in very preterm neonates. Sci Rep 2022; 12:11872. [PMID: 35831452 PMCID: PMC9279296 DOI: 10.1038/s41598-022-16066-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/04/2022] [Indexed: 12/04/2022] Open
Abstract
To predict adverse neurodevelopmental outcome of very preterm neonates. A total of 166 preterm neonates born between 24–32 weeks’ gestation underwent brain MRI early in life. Radiomics features were extracted from T1- and T2- weighted images. Motor, cognitive, and language outcomes were assessed at a corrected age of 18 and 33 months and 4.5 years. Elastic Net was implemented to select the clinical and radiomic features that best predicted outcome. The area under the receiver operating characteristic (AUROC) curve was used to determine the predictive ability of each feature set. Clinical variables predicted cognitive outcome at 18 months with AUROC 0.76 and motor outcome at 4.5 years with AUROC 0.78. T1-radiomics features showed better prediction than T2-radiomics on the total motor outcome at 18 months and gross motor outcome at 33 months (AUROC: 0.81 vs 0.66 and 0.77 vs 0.7). T2-radiomics features were superior in two 4.5-year motor outcomes (AUROC: 0.78 vs 0.64 and 0.8 vs 0.57). Combining clinical parameters and radiomics features improved model performance in motor outcome at 4.5 years (AUROC: 0.84 vs 0.8). Radiomic features outperformed clinical variables for the prediction of adverse motor outcomes. Adding clinical variables to the radiomics model enhanced predictive performance.
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14
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Liu YF, Shu X, Qiao XF, Ai GY, Liu L, Liao J, Qian S, He XJ. Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer. Front Oncol 2022; 12:911426. [PMID: 35795067 PMCID: PMC9252170 DOI: 10.3389/fonc.2022.911426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 05/19/2022] [Indexed: 01/31/2023] Open
Abstract
Objective To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa). Methods A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohistochemical results between June 2016 and February 2021 was conducted. As indicated by P504s/P63 expression, the patients were divided into label 0 (atypical prostatic hyperplasia), label 1 (benign prostatic hyperplasia, BPH) and label 2 (PCa) groups. This study employed T2WI, DWI and ADC sequences to assess prostate diseases and manually segmented regions of interest (ROIs) with Artificial Intelligence Kit software for radiomics feature acquisition. Feature dimensionality reduction and selection were performed by using a mutual information algorithm. Based on screened features, P504s/P63 prediction models were established by random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), adaptive boosting (AdaBoost) and k-nearest neighbor (KNN) algorithms. The performance was evaluated by the area under the ROC curve (AUC) and accuracy. Results A total of 315 patients were enrolled. Among the 851 radiomic features, the 32 top features were derived from T2WI, in which the gray-level run length matrix (GLRLM) and gray-level cooccurrence matrix (GLCM) features accounted for the largest proportion. Among the five models, the RF algorithm performed best in general evaluations (microaverage AUC=0.920, macroaverage AUC=0.870) and provided the most accurate result in further sublabel prediction (the accuracies of label 0, 1, and 2 were 0.831, 0.831, and 0.932, respectively). In comparative sequence analyses, T2WI was the best single-sequence candidate (microaverage AUC=0.94 and macroaverage AUC=0.78). The merged datasets of T2WI, DWI, and ADC yielded optimal AUCs (microaverage AUC=0.930 and macroaverage AUC=0.900). Conclusions The radiomic-based RF classifier has the potential to be used to evaluate the presurgical P504s/P63 status and further diagnose PCa noninvasively and accurately.
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Affiliation(s)
- Yun-Fan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Feng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guang-Yong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Liu
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Jun Liao
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Shuang Qian
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Xiao-Jing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Xiao-Jing He,
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15
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Chen Y, Li B, Jiang Z, Li H, Dang Y, Tang C, Xia Y, Zhang H, Song B, Long L. Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer. Abdom Radiol (NY) 2022; 47:1276-1290. [PMID: 35166938 DOI: 10.1007/s00261-021-03397-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE The overexpression of aquaporin-1 (AQP1) is associated with poor prognosis in rectal cancer. This study aimed to explore the value of multi-parameter diffusion and perfusion MRI and radiomics models in predicting AQP1 high expression. METHODS This prospective study was performed from July 2019 to February 2021, which included rectal cancer participants after preoperative rectal MRI, with diffusion-weighted imaging, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE) sequences. Radiomic features were extracted from MR images, and immunohistochemical tests assessed AQP1 expression. Selected quantitative MRI and radiomic features were analyzed. Receiver operating characteristic (ROC) curves evaluated the predictive performance. The nomogram performance was evaluated by its calibration, discrimen, and clinical utility. The intraclass correlation coefficient evaluated the interobserver agreement for the MRI features. RESULTS 110 participants with the age of 60.7 ± 12.5 years been enrolled in this study. The apparent diffusion coefficient (ADC), IVIM_D, DKI_diffusivity, and DCE_Ktrans were significantly higher in participants with high AQP1 expression than in those with low expression (P < 0.05). ADC (b = 1000, 2000, and 3000 s/mm2), IVIM_D, DKI_diffusivity, and DCE_Ktrans were positively correlated (r = 0.205, 0.275, 0.37, 0.235, 0.229, and 0.227, respectively; P < 0.05), whereas DKI_Kurtosis was negatively correlated (r = - 0.22, P = 0.021) with AQP1 expression. ADC (b = 3000 s/mm2), IVIM_D, DKI_ diffusivity, DKI_Kurtosis, and DCE_Ktrans had moderate diagnostic efficiencies for high AQP1 expression (AUC = 0.715, 0.636, 0.627, 0.633, and 0.632, respectively; P < 0.05). The radiomic features had excellent predictive efficiency for high AQP1 expression (AUC = 0.967 and 0.917 for training and validation). The model-based nomogram had C-indexes of 0.932 and 0.851 for the training and validation cohorts, which indicated good fitting to the calibration curves (p > 0.05). CONCLUSION Diffusion and perfusion MRI can indicate the aquaporin-1 expression in rectal cancer, and radiomic features can enhance the predictive efficiency for high AQP1 expression. A nomogram for high aquaporin-1 expression will improve clinical decision-making.
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Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zijian Jiang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Hui Li
- Department of Anus and Intestine Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Cheng Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yuwei Xia
- Huiying Medical Technology, Beijing, 100192, China
| | | | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Gaungxi Medical University, Nanning, 530021, China.
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
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16
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Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 2022; 13:28. [PMID: 35201534 PMCID: PMC8873309 DOI: 10.1186/s13244-022-01170-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. RESULTS Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35-0.84). The features selected by different models varied largely (0.19; range 0.02-0.42), although the selected features themselves were highly correlated (0.71; range 0.4-0.92). CONCLUSIONS Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45157, Essen, Germany.
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17
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Carrete LR, Young JS, Cha S. Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas. Front Neurosci 2022; 16:787755. [PMID: 35281485 PMCID: PMC8904563 DOI: 10.3389/fnins.2022.787755] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/19/2022] [Indexed: 12/12/2022] Open
Abstract
Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.
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Affiliation(s)
- Luis R. Carrete
- University of California San Francisco School of Medicine, San Francisco, CA, United States
| | - Jacob S. Young
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Jacob S. Young,
| | - Soonmee Cha
- Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
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18
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Abstract
OBJECTIVES A critical problem in radiomic studies is the high dimensionality of the datasets, which stems from small sample sizes and many generic features extracted from the volume of interest. Therefore, feature selection methods are used, which aim to remove redundant as well as irrelevant features. Because there are many feature selection algorithms, it is key to understand their performance in the context of radiomics. MATERIALS AND METHODS A total of 29 feature selection algorithms and 10 classifiers were evaluated on 10 publicly available radiomic datasets. Feature selection methods were compared for training times, for the stability of the selected features, and for ranking, which measures the pairwise similarity of the methods. In addition, the predictive performance of the algorithms was measured by utilizing the area under the receiver operating characteristic curve of the best-performing classifier. RESULTS Feature selections differed largely in training times as well as stability and similarity. No single method was able to outperform another one consistently in predictive performance. CONCLUSION Our results indicated that simpler methods are more stable than complex ones and do not perform worse in terms of area under the receiver operating characteristic curve. Analysis of variance, least absolute shrinkage and selection operator, and minimum redundancy, maximum relevance ensemble appear to be good choices for radiomic studies in terms of predictive performance, as they outperformed most other feature selection methods.
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Zhou L, Peng H, Ji Q, Li B, Pan L, Chen F, Jiao Z, Wang Y, Huang M, Liu G, Liu Y, Li W. Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma. ANNALS OF TRANSLATIONAL MEDICINE 2022; 9:1665. [PMID: 34988174 PMCID: PMC8667089 DOI: 10.21037/atm-21-5348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/10/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.
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Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bo Li
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Lexin Pan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Yali Wang
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mengqian Huang
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Gaifen Liu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2021; 146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/09/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.
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Affiliation(s)
| | | | | | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA and Knowledge Engineering Center, Global Biomedical Technologies, Inc, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy.
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21
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Demircioğlu A. Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 2021; 12:172. [PMID: 34817740 PMCID: PMC8613324 DOI: 10.1186/s13244-021-01115-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Many studies in radiomics are using feature selection methods to identify the most predictive features. At the same time, they employ cross-validation to estimate the performance of the developed models. However, if the feature selection is performed before the cross-validation, data leakage can occur, and the results can be biased. To measure the extent of this bias, we collected ten publicly available radiomics datasets and conducted two experiments. First, the models were developed by incorrectly applying the feature selection prior to cross-validation. Then, the same experiment was conducted by applying feature selection correctly within cross-validation to each fold. The resulting models were then evaluated against each other in terms of AUC-ROC, AUC-F1, and Accuracy. RESULTS Applying the feature selection incorrectly prior to the cross-validation showed a bias of up to 0.15 in AUC-ROC, 0.29 in AUC-F1, and 0.17 in Accuracy. CONCLUSIONS Incorrect application of feature selection and cross-validation can lead to highly biased results for radiomic datasets.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
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22
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Wang Z, Tang X, Wu J, Zhang Z, He K, Wu D, Chen S, Xiao X. Radiomics features based on T2-weighted fluid-attenuated inversion recovery MRI predict the expression levels of CD44 and CD133 in lower-grade gliomas. Future Oncol 2021; 18:807-819. [PMID: 34783576 DOI: 10.2217/fon-2021-1173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Objective: To verify the association between CD44 and CD133 expression levels and the prognosis of patients with lower-grade gliomas (LGGs) and constructing radiomic models to predict those two genes' expression levels before surgery. Materials & methods: Genomic data of patients with LGG and the corresponding T2-weighted fluid-attenuated inversion recovery images were downloaded from the Cancer Genome Atlas and the Cancer Imaging Archive, which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. Results & conclusion: CD44 and CD133 expression levels in LGG can significantly affect the prognosis of patients with LGG. Based on the T2-weighted fluid-attenuated inversion recovery images, the radiomic features can effectively predict the expression levels of CD44 and CD133 before surgery.
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Affiliation(s)
- Zhenhua Wang
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Xiaoping Tang
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Ji Wu
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Zhaotao Zhang
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Keng He
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Di Wu
- Department of Radiology, First Affiliated Hospital of GanNan Medical College, GanZhou, China
| | - ShiQi Chen
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
| | - Xinlan Xiao
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
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23
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Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021; 43:792-801. [PMID: 34649914 DOI: 10.3174/ajnr.a7297] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.
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Affiliation(s)
- M Ak
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S A Toll
- Department of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan
| | - K Z Hein
- Department of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - R R Colen
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S Khatua
- Department of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota.
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24
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La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
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25
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Huang WP, Liu SY, Han YJ, Li LM, Liang P, Gao JB. Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm. Front Oncol 2021; 11:677814. [PMID: 34079766 PMCID: PMC8166224 DOI: 10.3389/fonc.2021.677814] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/27/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose It is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN. Methods Eighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong's test were mainly used for model assessment and AUC comparison. Results It retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong's test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs. Conclusion The arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively.
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Affiliation(s)
- Wen-Peng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Si-Yun Liu
- Pharmaceutical Diagnostics, General Electric Company (GE) Healthcare, Beijing, China
| | - Yi-Jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Ming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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26
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Kihira S, Tsankova NM, Bauer A, Sakai Y, Mahmoudi K, Zubizarreta N, Houldsworth J, Khan F, Salamon N, Hormigo A, Nael K. Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion. Neurooncol Adv 2021; 3:vdab051. [PMID: 34056604 PMCID: PMC8156980 DOI: 10.1093/noajnl/vdab051] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma. Methods In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed. Results From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR). Conclusion Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.
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Affiliation(s)
- Shingo Kihira
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Nadejda M Tsankova
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, California, USA
| | - Yu Sakai
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Keon Mahmoudi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Nicole Zubizarreta
- Institute for Health Care Delivery Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jane Houldsworth
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fahad Khan
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, USA
| | - Adilia Hormigo
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kambiz Nael
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, USA
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27
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Habib A, Jovanovich N, Hoppe M, Ak M, Mamindla P, R. Colen R, Zinn PO. MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift. J Clin Med 2021; 10:1411. [PMID: 33915813 PMCID: PMC8036428 DOI: 10.3390/jcm10071411] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/29/2022] Open
Abstract
Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.
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Affiliation(s)
- Ahmed Habib
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA;
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Nicolina Jovanovich
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Meagan Hoppe
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Murat Ak
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
- Department of Diagnostic Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
| | - Priyadarshini Mamindla
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Rivka R. Colen
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
- Department of Diagnostic Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
| | - Pascal O. Zinn
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA;
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
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28
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Yan W, Shi H, He T, Chen J, Wang C, Liao A, Yang W, Wang H. Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma. Front Oncol 2021; 11:608191. [PMID: 33854961 PMCID: PMC8039367 DOI: 10.3389/fonc.2021.608191] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 03/08/2021] [Indexed: 12/26/2022] Open
Abstract
Objective In order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed. Methods 4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve. Results By designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF. Conclusion The model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.
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Affiliation(s)
- Wei Yan
- Haematology Department of Shengjing Hospital, China Medical University, Shenyang, China
| | - Hua Shi
- Haematology Department of Shengjing Hospital, China Medical University, Shenyang, China
| | - Tao He
- Neusoft Research Institute, Northeastern University, Shenyang, China
| | - Jian Chen
- Neusoft Research Institute, Northeastern University, Shenyang, China
| | - Chen Wang
- Neusoft Research Institute, Northeastern University, Shenyang, China
| | - Aijun Liao
- Haematology Department of Shengjing Hospital, China Medical University, Shenyang, China
| | - Wei Yang
- Haematology Department of Shengjing Hospital, China Medical University, Shenyang, China
| | - Huihan Wang
- Haematology Department of Shengjing Hospital, China Medical University, Shenyang, China
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29
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Wagner MW, Hainc N, Khalvati F, Namdar K, Figueiredo L, Sheng M, Laughlin S, Shroff MM, Bouffet E, Tabori U, Hawkins C, Yeom KW, Ertl-Wagner BB. Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors. AJNR Am J Neuroradiol 2021; 42:759-765. [PMID: 33574103 DOI: 10.3174/ajnr.a6998] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 10/23/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance. RESULTS The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96). CONCLUSIONS Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.
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Affiliation(s)
- M W Wagner
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - N Hainc
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.).,Department of Neuroradiology (N.H.), Zurich University Hospital, University of Zurich, Zurich, Switzerland
| | - F Khalvati
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - K Namdar
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - L Figueiredo
- Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.)
| | - M Sheng
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - S Laughlin
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - M M Shroff
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - E Bouffet
- Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.)
| | - U Tabori
- Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.)
| | - C Hawkins
- Paediatric Laboratory Medicine (C.H.), Division of Pathology, The Hospital for Sick Children and Department of Medical Imaging, University of Toronto, Ontario, Canada
| | - K W Yeom
- Department of Radiology (K.W.Y.), Stanford University School of Medicine, Lucile Packard Children's Hospital, Palo Alto, California
| | - B B Ertl-Wagner
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
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30
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Núñez LM, Romero E, Julià-Sapé M, Ledesma-Carbayo MJ, Santos A, Arús C, Candiota AP, Vellido A. Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction. Sci Rep 2020; 10:19699. [PMID: 33184423 PMCID: PMC7661707 DOI: 10.1038/s41598-020-76686-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 10/29/2020] [Indexed: 12/04/2022] Open
Abstract
Glioblastoma is the most frequent aggressive primary brain tumor amongst human adults. Its standard treatment involves chemotherapy, for which the drug temozolomide is a common choice. These are heterogeneous and variable tumors which might benefit from personalized, data-based therapy strategies, and for which there is room for improvement in therapy response follow-up, investigated with preclinical models. This study addresses a preclinical question that involves distinguishing between treated and control (untreated) mice bearing glioblastoma, using machine learning techniques, from magnetic resonance-based data in two modalities: MRI and MRSI. It aims to go beyond the comparison of methods for such discrimination to provide an analytical pipeline that could be used in subsequent human studies. This analytical pipeline is meant to be a usable and interpretable tool for the radiology expert in the hope that such interpretation helps revealing new insights about the problem itself. For that, we propose coupling source extraction-based and radiomics-based data transformations with feature selection. Special attention is paid to the generation of radiologist-friendly visual nosological representations of the analyzed tumors.
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Affiliation(s)
- Luis Miguel Núñez
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
| | - Enrique Romero
- Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Barcelona, Spain.,Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech), Barcelona, Spain
| | - Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain.,Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.,Institut de Biotecnologia i Biomedicina, Cerdanyola del Vallès, Spain
| | - María Jesús Ledesma-Carbayo
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain.,Biomedical Image Technologies Laboratory (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Andrés Santos
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain.,Biomedical Image Technologies Laboratory (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain.,Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.,Institut de Biotecnologia i Biomedicina, Cerdanyola del Vallès, Spain
| | - Ana Paula Candiota
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain.,Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.,Institut de Biotecnologia i Biomedicina, Cerdanyola del Vallès, Spain
| | - Alfredo Vellido
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain. .,Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Barcelona, Spain. .,Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech), Barcelona, Spain.
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