View Presentation Video

Peng Wang^1,2, Jinlong He¹, Qiong Wu¹, Shenghui Xie¹, Lixin Weng², Shaoyu Wang³, Huapeng Zhang³, Yang Song³, and Yang Gao^1
1Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China, ²Inner Mongolia Medical University, Hohhot, China, ³Siemens Healthineers, Shanghai, China

Keywords: Tumors, Diffusion/other diffusion imaging techniques

Application of MRI diffusion models can aid in the formulation of logical treatment strategies by non-invasively predicting gliomas pathogenic grade. However, it appears that the thorough comparison of diffusion models has not been addressed or has only been partially addressed. The objective of this study was to compare the potential clinical uses of four radiomics-based diffusion prediction models for predicting adult-type diffuse gliomas grade. According to the findings, the relatively simple model (i.e. diffusion tensor imaging) had the same potential for clinical use than the more sophisticated diffusion models. This suggests that additional point-to-point research is required.

Ruili Wei¹, Xinrui Pang¹, Ye Wang¹, Fangrong Liang¹, Yongzhou Xu², and Ruimeng Yang^1
1Department of Radiology, Guangzhou First People's Hospital, Guangzhou, China, ²Philips Healthcare, Guangzhou, China

Keywords: Tumors, Radiomics

 

Juan Yu¹, Yujian Liu², kan Deng³, Fan Lin⁴, Guanghui Yue², Jie Du², and Liangping Luo^5
1Radiology Department, Shenzhen Second People's Hospital, Shenzhen, China, ²Shenzhen University, Shenzhen, China, ³Philips Healthcare, Guangzhou, China, ⁴Shenzhen Second People's Hospital, Shenzhen, China, ⁵The First Affiliated Hospital of Jinan University, Guangzhou, China

Keywords: Tumors, Tumor, meningioma

The new guidelines suggest that brain invasion is not a unique feature of malignant meningiomas, but may be a pathologic condition of benign meningiomas with a potential risk of recurrence. However, maximum preservation of normal brain tissue is essential for surgery. Accurate preoperative MRI evaluation of meningioma brain invasion is expected to solve the dilemma faced by neurosurgeons.

Caiqiang Xue¹, Qing Zhou¹, and Junlin Zhou^1
1Lanzhou University Second Hospital, Lanzhou, China

Keywords: Tumors, Tumor

CD8+ T cell infiltration in tumors is a powerful predictor of the clinical and postoperative prognosis of GBM patients. Immunohistochemical staining was used to assess tumor-infiltrating CD8+ T cell expression in patient-derived tumor tissue samples. Histogram analysis of GBM was performed using Firevoxel software. Among the T1C histogram features, the CV, mean, 5th, 10th, 25th, and 50th percentiles were correlated with the levels of CD8+ T cells. The ROC curve analysis revealed that the CV had the highest AUC value (0.783). Histogram analysis is an efficient non-invasive imaging modality for the prediction of tumor-infiltrating CD8+ T cells in glioblastoma.

View Presentation Video

Liu Guo LI¹, Jun Zhang¹, and Lin Ma^1
1Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing, China

Keywords: Tumors, Nervous system, Lymphoma

Prognostic prediction based on clinical data and laboratory tests are facing reliability challenges for immunocompetent primary central nervous system lymphoma (PCNSL) . In this study, we attempted to explore new prognostic biomarkers based on MRI-radiomics features to predict the outcomes in PCNSL. Our results showed that radio-score based on the ADC map and ASL was independent risk factors in OS and PFS predictions. Meanwhile adding radiomics features improved the model performance compared with traditional ones among which model based on ASL had better performance than ADC. So radiomics features can effectively classify different prognosis and improve the model performance in PCNSL.

View Presentation Video

Haimei Cao¹, Zhousan Huang¹, Ruowei Qiu¹, Zhiyong Li², Kan Deng³, Jay J Pillai⁴, Guanglong Huang², Yikai Xu¹, Jun Hua^5,6, and Yuankui Wu^1
1Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China, ²Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China, ³Philips Healthcare, Guangzhou, China, ⁴Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵Neurosection, Division of MRI Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁶F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Keywords: Tumors, Perfusion, enhancing non-measurable disease (NMD)

Early prediction of disease progression is of potential clinical significance for the management of high-grade glioma (HGG) patients. We investigated the value of histogram models based on volume transfer constant (K^trans) between the plasma and extravascular extracellular space and extravascular volume (Ve) in predicting the progression of enhancing non-measurable diseases (NMD) of HGG after chemoradiotherapy. Our results showed that histogram models based on K^trans and Ve can accurately predict the progression of enhancing NMD of HGG following chemoradiotherapy, and combining K^trans and Ve helps improve the prediction performance.

View Presentation Video

Clement Debacker^1,2, Geoffroy Pouliquen^1,2, Sylvain Charron², Anna Fayolle^1,2, Valentin H. Prevost³, Wolter de Graaf⁴, Alexandre Roux^2,5, Johan Pallud^2,5, and Catherine Oppenheim^1,2
1Radiology department, GHU Paris Psychiatrie et Neurosciences, Site Sainte-Anne, Paris, France, ²IMA-BRAIN, Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Paris, France, ³Canon Medical Systems Corporation, Tochigi, Japan, ⁴Canon Medical Systems Europe, Zoetermeer, Netherlands, ⁵Neuro-surgery department, GHU Paris Psychiatrie et Neurosciences, Site Sainte-Anne, Paris, France

Keywords: Tumors, Tumor

Our work is the clinical validation of a deep-learning algorithm (DLR) used to denoise MR images on quantitative MR biomarkers. Since it has been trained on including T1- and T2-weighted conventional images, in healthy volunteers, its effects on multiparametric quantitative MRI in patients, are uncertain. It could potentially improve brain tumors characterization by providing quantitative biomarkers under clinical time-constraints.

View Presentation Video

Zongye Li¹, Yijie Yang², Yue Zhang¹, Yanhong Lin², Xiao Wang¹, Yuchuan Zhuang³, Qinqin Yang², Eryuan Gao¹, Yanan Ren¹, Yong Zhang¹, Shuhui Cai², Zhong Chen², Congbo Cai², Jingliang Cheng¹, and Jianfeng Bao^1
1Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²Department of Electronic Science, Xiamen University, Xiamen, China, ³Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Keywords: Tumors, Relaxometry

There is an immediate need for neurosurgeons to be able to identify certain molecular markers’ status of meningioma preoperatively, such as progesterone receptors and S100. In this study, we explored the use of histogram analysis of T2 maps and ADC maps in predicting such molecular markers. T2 maps and ADC maps could serve as useful tools for predicting PR and S100 status of meningiomas. Similar studies may be extended to investigate other pathological markers.

View Presentation Video

Seyyed Ali Hosseini^1,2, Elahe Hosseini³, Isaac Shiri⁴, Ghasem Hajianfar⁵, Stijn Servaes^1,2, Pedro Rosa-Neto^1,2, Laiz Godoy⁶, Stephen Bagley⁷, MacLean Nasrallah⁸, Donald M O’Rourke⁹, Suyash Mohan⁶, and SANJEEV CHAWLA^6
1Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada, ²Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital,McGill University, Montreal, QC, Canada, ³Electrical and Computer Engineering, Kharazmi University, Tehran, Iran (Islamic Republic of), ⁴Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland, ⁵Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran (Islamic Republic of), ⁶Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ⁷Hematology-Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ⁸Clinical Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ⁹Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States

Keywords: Tumors, Radiomics

Patients (n=57) with isocitrate dehydrogenase (IDH) mutant grade-4 astrocytomas and IDH wild-type glioblastomas underwent anatomical imaging (post-contrast T1 and T2-FLAIR) on 3T magnet.  Neoplasms were segmented into 5 ROIs and 105 radiomics features were extracted from each ROI.  Features were subsequently selected using various algorithms. Patients were divided into two groups (50%-training and 50%-testing).   A GAN-based deep learning algorithm was used to generate 1000 synthesized data-sets and four distinct deep-learning modules were implemented. The best model for differentiating two genotypes of neoplasms was obtained from core tumor regions by using K-best feature selection and Ensembled algorithm with high diagnostic performance.

View Presentation Video

Xiaorui Su¹, Xibiao Yang¹, Shuang Li¹, Hanbing Shao¹, Yukun Liu², and Qiang Yue^1
1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, ²West China School of Medicine &West China Hospital, Chengdu, China

Keywords: Tumors, Radiomics

To explore biological interpretation of prognosis-related radiomic signatures in diffuse midline gliomas, forty-one cases with paired MRI and RNA sequencing were screened in the public database TCGA-TCIA. We extracted radiomics features from preoperative MRI and divided these patients into high- and low-risk groups. Differential and enrichment analysis showed that high-expression genes of the high-risk group were mainly activated and involved in biological processes such as axon extension, neurotransmitter signaling, and cell connection. Moreover, typical driver molecular changes known in DMG were observed. It suggested that biological interpretation of prognosis-related radiomic signatures could be achieved by integrating MRI and RNA data

View Presentation Video

Shao ru zhang¹, Zhi qiang Chen², Xiao hua Chen¹, Zhuo Wang¹, Yun shu Zhou¹, Shi li Liu¹, Ruo di Zhang¹, Yu hui Xiong³, and Bing Chen^4
1Clinical medicine school of Ningxia Medical University, Yinchuan, China, ²Department of Radiology ,the First Hospital Affiliated to Hainan Medical College, Haikou, China, ³GE Healthcare, Beijing, China, ⁴Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China

Keywords: Tumors, Radiomics

This study aims to explore the value of multi-parametric MRI-based radiomics model for non-invasively predicting IDH1 mutation in glioma. It was shown that among various single-sequence radiomics models, the contrast-enhanced T1-weighted image radiomics model should be considered as an optimal model in predicting IDH1 mutation, while the combined model based on three sequences could further improve the predicting performance.

Jun Zhao¹, Tiejun Gan², Xiaoai Ke¹, Wanjun Hu², Qing Zhou¹, Jing Zhang², and Junlin Zhou^1
1Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China, ²Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China

Keywords: Tumors, Radiomics, Multimodal,Quantitative Imaging

In this study, radiomic based predictive models using MRI can non-invasively assess Tert genotype of Oligodendrogliomas(ODs).Compared With histopathological assessment,radiomic based predictive models has its unique advantages in assessing Tert genotype of ODs. such as noninvasive, more comprehensive information about tumor heterogeneity and repeatable.Furthermore,radiomic based predictive models can clearly and directly show Tert genotype of ODs.Therefore, it is a potential alternative to invasive biopsy.

View Presentation Video

Minkook Seo¹, Yangsean Choi¹, Youn Soo Lee², Kook-Jin Ahn¹, Bum-soo Kim¹, Jae-Sung Park³, and Sin-soo Jeon^3
1Radiology, Seoul St. Mary's Hospital, Seoul, Korea, Republic of, ²Hospital Pathology, Seoul St. Mary's Hospital, Seoul, Korea, Republic of, ³Neurosurgery, Seoul St. Mary's Hospital, Seoul, Korea, Republic of

Keywords: Tumors, Tumor, Glioma

Diagnostic performances differentiating low-grade (LGGs) from high-grade gliomas (HGGs) were compared between the four advanced techniques of multiparametric MRI—DSC, DCE, MRS and DWI. Sixty-four patients with pathologically confirmed glioma underwent preoperative multiparametric MRI with the four techniques, followed by histogram analysis of the tumors. HGGs showed significantly higher K^trans, rCBV, and Cho/Cr than LGGs. The AUROC of the 95th percentile K^trans was 0.83, being the most helpful parameter, followed by 95th percentile rCBV (0.72). Significant correlations were observed between the 95th percentile rCBV and K^trans. Our findings demonstrate higher diagnostic utility of DCE and DSC MRI in glioma grading.

Tao Han¹, Xianwang Liu¹, and Junlin Zhou^1
1Lanzhou University Second Hospital, LanZhou, China

Keywords: Tumors, Machine Learning/Artificial Intelligence

This study evaluated the feasibility of noninvasive preoperative differentiation fibroblastic meningioma (FM) from non-fibroblastic meningiomas (nFM) based a clini-radiomics model. A total of thirteen radiomics features were included after Selectpercentile and Lasso feature screening. Our results showed that Random forest is the most efficient among the six radiomics models in differentiating FM from nFM, and the diagnostic efficacy of clini-radiomics models in training and validation group is further improved. Therefore, we believe that the clini-radiomics model is of great value in noninvasive preoperative differentiation of FM and nFM and contributes to the selection of individualized treatment options for meningioma patients.

View Presentation Video

Chien-Yi Liao¹, Cheng-Chia Lee², Huai-Che Yang², Wen-Yuh Chung², Hsiu-Mei Wu³, Wan-Yuo Guo³, Ren-Shyan Liu⁴, and Chia-Feng Lu^1
1National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei, Taiwan, ²Department of Neurosurgery, Neurological Institute, Taipei Veteran General Hospital, Taipei, Taiwan, Taipei, Taiwan, ³Department of Radiology, Taipei Veteran General Hospital, Taipei, Taiwan, Taipei, Taiwan, ⁴Department of Medical Imaging, Cheng-Hsin General Hospital, Taipei, Taiwan, Taipei, Taiwan

Keywords: Tumors, Radiotherapy

Gamma Knife radiosurgery (GKRS) is a first-line treatment for brain metastases (BMs) from non-small cell lung cancer (NSCLC). The convolutedness, leakiness and disorganized structure of the peritumoral vasculature have been suggested to be related to the treatment resistance of the tumor. In this study, radiomic features that measure the morphology and spatial organization of peritumoral vasculature were extracted from pre-GKRS MRI. These features were applied to predict overall survival after GKRS in NSCLC-BM patients. We suggested that peritumoral vasculature radiomics could facilitate patient management by identifying potential benefits of GKRS.

Jun Jiang¹, Juan Yu¹, Xiajing Liu¹, Kan Deng², Fan Lin¹, and Liangping Luo^3
1Shenzhen Second People's Hospital, Shenzhen, China, ²Philips Healthcare, Guangzhou, China, ³The First Affiliated Hospital of Jinan University, Guangzhou, China

Keywords: Tumors, MR Value

The purpose of this study is to explore the efficacy of clinical and MRI-specific features for tumor grading and the brain invasion assessment in patient with meningioma. Our results showed that the tumor-brain interface is considered as a key factor, preoperative MRI has excellent performance in diagnosing meningioma WHO grade and brain invasion.

View Presentation Video

Esra Sümer¹, O Artunç Türe², Meriç Şengöz^3,4, M Necmettin Pamir^3,4, Alp Dinçer^4,5, Koray Özduman^3,4, and Esin Ozturk-Isik^1,4
1Institute of Biomedical Engineering, Boğaziçi University, İstanbul, Turkey, ²Department of Radiation Oncology, Acıbadem University, İstanbul, Turkey, ³Department of Neurosurgery, Acıbadem University, İstanbul, Turkey, ⁴Brain Tumor Research Group, Acıbadem University, İstanbul, Turkey, ⁵Department of Radiology, Acıbadem University, İstanbul, Turkey

Keywords: Tumors, Radiomics, Gamma Knife, meningioma

Meningiomas located at and around the falx and superior sagittal sinus are known to have a more aggressive clinical behavior. Additionally, their localization and invasion characteristics make complete resection difficult, creating a need for radiosurgery. This study assessed the relationship between radiomics/clinical features of parasagittal and falx meningiomas and their recurrence-free survival after Gamma Knife. The radiomics and clinical features (age, pre-op tumor volume, coverage, and sensitivity) were analyzed together. Univariable Cox analysis associated maximum and skewness of T1 lesion, age, TV, and coverage, whereas multivariable Cox analysis associated skewness of T2 lesion, coverage, and selectivity with RFS.

View Presentation Video

Esra Sümer¹, Ayça Ersen Danyeli^2,3, M. Necmettin Pamir^3,4, Koray Özduman^3,4, Alp Dinçer^3,5, and Esin Ozturk-Isik^1,3
1Institution of Biomedical Engineering, Boğaziçi University, İstanbul, Turkey, ²Department of Medical Pathology, Acıbadem University, İstanbul, Turkey, ³Brain Tumor Research Group, Acıbadem University, İstanbul, Turkey, ⁴Department of Neurosurgery, Acıbadem University, İstanbul, Turkey, ⁵Department of Radiology, Acıbadem University, İstanbul, Turkey

Keywords: Tumors, Radiomics, astrocytoma, pathological grade, machine learning

The astrocytomas are currently graded from 2 to 4 according to World Health Organization 2021 central nervous system tumor classification. Increasing grade defines increasing malignancy. This study aims to explore the potential of radiomics features extracted from multiparametric pre-operative MRI to predict the grade of astrocytomas. For differentiation of grade 2 from grades 3&4, the FLAIR radiomics were predominantly determined by the feature selection procedure. The highest accuracy for differentiating grade 2 from grades 3&4 was 88.04±0.03% (92.37±0.04% precision and 86.76±0.04% recall).

Pei Dang¹, Xueying Huang², Xin Ge³, Lidong Wang⁴, Aijun Wang², Minglei Wang², Xuhong Yang⁵, Yuhui Xiong⁶, and Xiaodong Wang^7
1Radiology, General Hospital of Ningxia Medical University, yinchuan, China, ²General Hospital of Ningxia Medical University, Yinchuan, China, ³Lanzhou University, Lanzhou, China, ⁴Radiology, Yinchuan Hospital of Traditional Chinese Medicine, Yinchuan, China, ⁵Ningxia Medical University, Yinchuan, China, ⁶GE Healthcare MR Research, Beijing, China, ⁷Radiology, General Hospital of Ningxia Medical University, Yinchuan, China

Keywords: Tumors, Cancer, Glioblastoma; Synthetic MRI; 3D-ASL; Histogram; MGMT; Recurrence patterns

At least 10% of glioblastoma relapses occur at distant and even contralateral locations. This disseminated growth limits surgical intervention and contributes to neurologicalmorbidity.The aim of the present study was to evaluate factors predicting the recurrencepattern determined.The results revealed that the pattern of recurrence in glioblastoma patients after combined radio-chemotherapy treated are strictly correlated with age at diagnosis and MGMT methylation status.Whole-tumor histogram analysis of quantitative parameters from synthetic MRI and 3D-ASL was to evaluate the value of predicting MGMT methylation status .Combining T1_10th, T2_{10th ,}and CBF_entropy may explore as an effective strategy to predict of MGMT methylation status.