Brain Cancer

Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy.

Gliomas represent a highly aggressive class of tumors located in the brain. Despite the availability of multiple treatment modalities, the prognosis for patients diagnosed with glioma remains unfavorable. Therefore, further exploration of new biomarkers is crucial to enhance the prognostic assessment of glioma and to investigate more effective treatment options. In this research, we utilized multi...

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets ...

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. Th...

Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning.

BACKGROUND AND PURPOSE: To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-ne...

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation o...

Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.

OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volu...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with Lu radioligands for prostate...

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.

Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial sutures in infants, is crucial for effective treatme...

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

OBJECTIVES: To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in l...

Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental t...

Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

OBJECTIVE: Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims ...

Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model.

Environmental complaints serve as a crucial means for citizens to participate in environmental regulation, providing precise insights for real-time id...

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to ...

Machine learning-based new classification for immune infiltration of gliomas.

BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of th...

Robust brain MRI image classification with SIBOW-SVM.

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brai...

The SINFONIA project repository for AI-based algorithms and health data.

The SINFONIA project's main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental eff...

Artificial intelligence and omics in malignant gliomas.

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the...

Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation.

INTRODUCTION: - Accurate detection, segmentation, and volumetric analysis of brain lesions are essential in neuro-oncology. Artificial intelligence (A...

Optimization and correction of breast dynamic optical imaging projection data based on deep learning.

Breast cancer poses a significant health threat to women, necessitating advancements in diagnostic technologies. Breast dynamic optical imaging (DOI) ...

Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.

OBJECTIVES: To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial s...