Development and validation of a radiomics nomogram for preoperative prediction of BRAF mutation status in adult patients with craniopharyngioma.

Although craniopharyngiomas are rare benign brain tumors primarily managed by surgery, they are often burdened by a poor prognosis due to tumor recurrence and long-term morbidity. In recent years, BRAF-targeted therapy has been promising, showing potential as an adjuvant or neoadjuvant approach. Therefore, we aim to develop and validate a radiomics nomogram for preoperative prediction of BRAF mutation in craniopharyngiomas.

Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.

Objective: To develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM).

Methods: In this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination.

Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.

Objectives: To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models.

Methods: A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI).

Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances.

Imaging manifestations of papillary glioneuronal tumors.

To analyse the imaging findings of papillary glioneuronal tumors (PGNTs), in order to improve the accuracy of preoperative diagnosis of this tumor. The clinical and imaging manifestations of 36 cases of PGNT confirmed by pathology were analyzed retrospectively. A total of 17 males and 19 females, averaging 22.

The clinical, radiological, and surgical characteristics of anterior perforated substance glioma: a retrospective study.

Background: To explore the clinical, radiological, and surgical characteristics of anterior perforated substance (APS) gliomas.

Methods: Twenty patients with APS glioma who were treated with surgery between March 2019 and January 2022 from Tiantan hospital were retrospectively reviewed. The clinical, histological and radiological data were collected.

Malignant transformation of desmoplastic infantile tumors in a non-infant: a case report.

Desmoplastic infantile tumors (DITs) are rare benign intracranial tumors in infants with benign biological behavior and rare malignant transformation characteristics. We present a DIT case that underwent malignant transformation and metastasis. A 6-year-old girl presented with DITs and underwent surgical resection.

Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma.

Objectives: This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM).

Methods: We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression.

The sinuous, wave-like intratumoral-wall sign is a sensitive and specific radiological biomarker for oligodendrogliomas.

Objectives: The purpose of this study was to investigate the clinical utility of the sinuous, wave-like intratumoral-wall (SWITW) sign on T2WI in diagnosing isocitrate dehydrogenase (IDH) mutant and 1p/19q codeleted (IDHmut-Codel) oligodendrogliomas, for which a relatively conservative resection strategy might be sufficient due to a better response to chemoradiotherapy and favorable prognosis.

Methods: Imaging data from consecutive adult patients with diffuse lower-grade gliomas (LGGs, histological grades 2-3) in Beijing Tiantan Hospital (December 1, 2013, to October 31, 2021, BTH set, n = 711) and the Cancer Imaging Archive (TCIA) LGGs set (n = 117) were used to develop and validate our findings. Two independent observers assessed the SWITW sign and some well-reported discriminative radiological features to establish a practical diagnostic strategy.

A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma.

Background And Objective: Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images.

Combining hyperintense FLAIR rim and radiological features in identifying IDH mutant 1p/19q non-codeleted lower-grade glioma.

Objectives: Even very small residual tumors of IDH mutant 1p/19q non-codeleted (IDHmut-Noncodel) astrocytoma could have a significantly negative impact on survival; thus, accurate preoperative diagnosis is of utmost importance to guide aggressive tumor resection strategy for this subtype. This study aimed to diagnose IDHmut-Noncodel from IDH mutant 1p/19q codeleted (IDHmut-Codel) and IDH wild-type gliomas by preoperative MRI and CT to guide surgical plan-making.

Methods: Consecutive adult patients diagnosed with diffuse lower-grade glioma (LGG, histological grade 2-3) from December 1, 2013 to December 31, 2020, were retrospectively included in this study.

Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.

[This corrects the article DOI: 10.3389/fonc.2021.

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics.

Background: Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.

Purpose: To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.

Objective: To identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM).

Materials And Methods: This retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.

Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion.

Objectives: To explore the MRI-based differential diagnosis of deep learning with data enhancement for cerebral glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and tumefactive demyelinating lesion (TDL).

Materials And Methods: This retrospective study analyzed the MRI data of 261 patients with pathologically diagnosed solitary and multiple cerebral GBM (n = 97), PCNSL (n = 92), and TDL (n = 72). The 3D segmentation model was trained to capture the lesion.

Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery.

Purpose: Craniopharyngiomas (CPs) are benign tumors, complete tumor resection is considered to be the optimal treatment. However, although histologically benign, the local invasiveness of CPs commonly contributes to incomplete resection and a poor prognosis. At present, some advocate less aggressive surgery combined with radiotherapy as a more reasonable and effective means of protecting hypothalamus function and preventing recurrence in patients with tight tumor adhesion to the hypothalamus.

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors.

Background: Brain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient.

Abnormal Interactions of the Salience Network, Central Executive Network, and Default-Mode Network in Patients With Different Cognitive Impairment Loads Caused by Leukoaraiosis.

Leukoaraiosis (LA) is associated with cognitive impairment in the older people which can be demonstrated in functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI). This study is to explore the FC changes in LA patients with different cognitive status by three network models. Fifty-three patients with LA were divided into three groups: the normal cognition (LA-NC; = 14, six males), mild cognitive impairment (LA-MCI; = 27, 13 males), and vascular dementia (LA-VD; = 12, six males), according to the Mini Mental State Exam (MMSE) and Clinical Dementia Rating (CDR).

A deep learning radiomics model for preoperative grading in meningioma.

Objectives: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.

Methods: We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep learning features were extracted by the convolutional neural network.

Evaluation of Angioarchitectural Features of Unruptured Brain Arteriovenous Malformation by Susceptibility Weighted Imaging.

Objectives: A precise assessment of angioarchitectural characteristics using noninvasive imaging is helpful for serial follow-up and weighting risk of natural history in unruptured brain arteriovenous malformation (bAVM). This study aimed to test the hypothesis that susceptibility weighted imaging (SWI) would provide an accurate evaluation of angioarchitectural features of unruptured bAVM.

Methods: A total of 81 consecutive patients with unruptured bAVM were examined.

A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model.

Diffusion tensor imaging (DTI) has been proven to be a sophisticated and useful tool for the delineation of tumors. In the present study, we investigated the predictive role of DTI compared to other magnetic resonance imaging (MRI) techniques in combination with Ki-67 labeling index in defining tumor cell infiltration in the peritumoral regions of F98 glioma-bearing rats. A total of 29 tumor-bearing Fischer rats underwent T2-weighted imaging, contrast-enhanced T1-weighted imaging, and DTI of their brain using a 7.