The increasing number of long-term survivors of pediatric brain tumors requires us to incorporate the most recent knowledge derived from cognitive neuroscience into their oncological treatment. As the lesion itself, as well as each treatment, can cause specific neural damage, the long-term neurocognitive outcomes are highly complex and challenging to assess. The number of neurocognitive studies in this population grows exponentially worldwide, motivating modern neuroscience to provide guidance in follow-up before, during and after treatment. In this review, we provide an overview of structural and functional brain connectomes and their role in the neuropsychological outcomes of specific brain tumor types. Based on this information, we propose a theoretical neuroscientific framework to apply appropriate neuropsychological and imaging follow-up for future clinical care and rehabilitation trials.
Child ; Humans ; Brain/diagnostic imaging* ; Brain Neoplasms/complications* ; Cognitive Dysfunction ; Connectome ; Neurosciences

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
Humans ; Algorithms ; Brain/diagnostic imaging* ; Brain Neoplasms/diagnostic imaging* ; Recognition, Psychology

OBJECTIVE: To summarize and analyze the clinical characteristics of children with basal ganglia germinoma and to improve the level of early clinical diagnosis. METHODS: The clinical data of children diagnosed with basal ganglia germinoma admitted to the Pediatric Surgery Ward of Peking University First Hospital from January 2013 to December 2020 were retrospectively analyzed, and descriptive statistics were used to analyze the clinical characteristics of children with basal ganglia germinoma. RESULTS: A total of 30 patients were included in the study, 28 were male, 2 were female, the mean age at onset was (9.7±2.2) years, the median disease duration was 7 months, 27 had unilateral disease, and 3 had bilateral disease. The clinical manifestations were decreased limb muscle strength, cognitive function disorders, polydipsia, precocious puberty, intracranial hypertension, dysphonia and swallowing dysfunction. The serum and cerebrospinal fluid tumor marker alpha-fetoprotein (AFP) were normal in the 30 patients, and the serum and cerebrospinal fluid tumor marker β-human chorionic gonadotropin (β-HCG) were normal in 8 patients.The serum β-HCG was normal in 11 patients but the cerebrospinal fluid β-HCG was slightly elevated, and the serum and cerebrospinal fluid β-HCG were slightly elevated in 11 patients. A total of 33 lesions with irregular shapes were found by imaging examination, including 15 (45.5%) patchy lesions, 10 (30.3%) patchy lesions, and 8 (24.2%) round-like high-density lesions. Tumors showed obvious high-density shadows on computed tomography (CT) scan. Magnetic resonance imaging (MRI) scan of the tumors showed low or isointensity on T1WI and isointensity on T2WI, accompanied by mild peritumoral edema, hemispheric atrophy, cerebral peduncle atrophy, calcification, cystic degeneration, ventricular dilatation and wallerian degeneration. On contrast-enhanced scans, the tumor showed no enhancement or heterogeneous enhancement. CONCLUSION The main age of onset of germ cell tumors in the basal ganglia in children is about 10 years old, and males are absolutely dominant. The clinical features and imaging manifestations have certain characteristics. With both combined, the early diagnosis of germ cell tumors in the basal ganglia can be improved.
Atrophy/pathology* ; Basal Ganglia/pathology* ; Biomarkers, Tumor ; Brain Neoplasms/diagnostic imaging* ; Child ; Chorionic Gonadotropin, beta Subunit, Human ; Female ; Germinoma/pathology* ; Humans ; Magnetic Resonance Imaging ; Male ; Neoplasms, Germ Cell and Embryonal ; Retrospective Studies

Intracranial intradural chondroma is a rare disorder,the imaging findings of which have been rarely reported.The current study reported a case of intracranial extra-cerebral chondroma and described the detailed CT and magnetic resonance imaging findings,which would provide valuable imaging evidence for the diagnosis of intracranial extra-cerebral chondroma.
Brain Neoplasms/diagnostic imaging* ; Chondroma/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve ( CONCLUSIONS The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.
Brain Neoplasms/diagnostic imaging* ; Glioma/diagnostic imaging* ; Humans ; Logistic Models ; Magnetic Resonance Imaging ; ROC Curve ; Retrospective Studies

Objective To evaluate the application of two-dimensional magnetization-prepared true fast imaging with steady-state precession(2D-MP-TrueFISP)sequence in brain tumor enhancement.Methods In this study,60 cases of brain tumor patients who underwent enhanced magnetic resonance imaging of brain were scanned with 2D-MP-TrueFISP/two-dimensional spoiled gradient-recalled echo(2D-SPGR)before and after enhancement.The scores of lesions on the images of 2D-MP-TrueFISP/2D-SPGR were compared.At the same level of 2D-SPGE and 2D-MP-TrueFISP,the signal intensities(SIs)of lesions,white matter,and cerebrospinal fluid were measured before and after enhancement,and the contrast ratios(CRs)of lesions were calculated.The CRs before and after 2D-SPGR/2D-MP-TrueFISP enhancement and those between 2D-SPGR and 2D-MP-TrueFISP after enhancement were compared.Results The scores of lesions after 2D-MP-TrueFISP/2D-SPGR T1WI enhancement were 9.0(9.0,9.0)and 7.0(6.0,7.0),respectively,with significant difference(
Brain ; Brain Neoplasms/diagnostic imaging* ; Humans ; Image Enhancement ; Imaging, Three-Dimensional ; Magnetic Resonance Imaging

Cerebral metastases are the most common intracranial tumors in adults,with an increasing incidence in recent years.Radiomics can quantitatively analyze and process medical images to guide clinical practice.In recent years,CT and MRI-based radiomics has been gradually applied to the precise diagnosis and treatment of cerebral metastases,such as the precise detection and segmentation of tumors,the differential diagnosis with other cerebral tumors,the identification of primary tumors,the evaluation of treatment efficacy,and the prediction of prognosis.This article reviews the advances in radiomics of cerebral metastases.
Brain Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Humans ; Magnetic Resonance Imaging ; Prognosis ; Supratentorial Neoplasms

We report a case of papillary tumor in the pineal region.The imaging findings mainly included:(1)slight high density on CT images;(2)short T1 signal,cystic changes,and evident enhancement on magnetic resonance imaging.
Brain Neoplasms ; Humans ; Magnetic Resonance Imaging ; Pineal Gland/diagnostic imaging* ; Pinealoma/diagnostic imaging*

Objective To investigate the clinicopathological features and immunohistochemical phenotypes of brain metastatic carcinoma in Tibetan patients. Methods The clinical and pathological data of all patients with brain metastases from 2014 to 2020 in Tibet Autonomous Region People's Hospital were retrospectively analyzed,including 13 cases of brain metastatic carcinoma.All cases were diagnosed and classified by immunohistochemical staining. Results 13 cases(9 males and 4 females)of brain metastatic carcinoma,aged 26-62 years old,present with headache,dizziness,nausea and vomiting clinically.Four patients had a medical history of tumor,and among the 9 patients with no history of tumor,7 present space occupying lesions in both the brain and other organs.Imaging data could be found in 10 cases,including 4 cases of single lesion and 6 cases of multiple lesions.Primary tumors were identified in 11 cases(8 located in the lung,including 4 cases of adenocarcinoma,3 cases of small cell carcinoma,and 1 case of squamous cell carcinoma;1 case of urothelial carcinoma of the renal pelvis;1 case of thyroid papillary carcinoma;1 case of uterine choriocarcinoma),whereas the primary tumor was unknown for the other 2 cases(1 case of small cell carcinoma and 1 case of adenocarcinoma). Conclusions Brain metastatic carcinoma are more common among middle-aged and elderly people in Tibet.Most of the cases have no history of tumor,with the initial site at the brain metastatic lesions.The most common primary site is the lung,and the primary site of some cases is unknown.Multiple lesions are common in brain metastatic carcinoma,especially in the cerebral hemisphere.For older patients with multiple brain space occupying lesions,the possibility of brain metastatic carcinoma increases.
Adult ; Aged ; Brain ; Brain Neoplasms/diagnostic imaging* ; Carcinoma, Transitional Cell ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Thyroid Neoplasms ; Tibet ; Urinary Bladder Neoplasms

Glioma is the most common malignant brain tumor and classification of low grade glioma (LGG) and high grade glioma (HGG) is an important reference of making decisions on patient treatment options and prognosis. This work is largely done manually by pathologist based on an examination of whole slide image (WSI), which is arduous and heavily dependent on doctors' experience. In the World Health Organization (WHO) criteria, grade of glioma is closely related to hypercellularity, nuclear atypia and necrosis. Inspired by this, this paper designed and extracted cell density and atypia features to classify LGG and HGG. First, regions of interest (ROI) were located by analyzing cell density and global density features were extracted as well. Second, local density and atypia features were extracted in ROI. Third, balanced support vector machine (SVM) classifier was trained and tested using 10 selected features. The area under the curve (AUC) and accuracy (ACC) of 5-fold cross validation were 0.92 ± 0.01 and 0.82 ± 0.01 respectively. The results demonstrate that the proposed method of locating ROI is effective and the designed features of density and atypia can be used to predict glioma grade accurately, which can provide reliable basis for clinical diagnosis.
Brain Neoplasms/diagnostic imaging* ; Glioma/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging ; Neoplasm Grading ; Support Vector Machine