AIMC Topic: Neoplasm Grading

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Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.

World neurosurgery
BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted i...

Redefining prostate cancer care: innovations and future directions in active surveillance.

Current opinion in urology
PURPOSE OF REVIEW: This review provides a critical analysis of recent advancements in active surveillance (AS), emphasizing updates from major international guidelines and their implications for clinical practice.

Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

BMC cancer
BACKGROUND: Biochemical recurrence (BCR) occurs in 20%-40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. Howe...

A deep ensemble learning framework for glioma segmentation and grading prediction.

Scientific reports
The segmentation and risk grade prediction of gliomas based on preoperative multimodal magnetic resonance imaging (MRI) are crucial tasks in computer-aided diagnosis. Due to the significant heterogeneity between and within tumors, existing methods ma...

An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.

The Journal of urology
PURPOSE: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) before biopsy and applied artificial intelligence models to these ...

Can we rely on machine learning algorithms as a trustworthy predictor for recurrence in high-grade glioma? A systematic review and meta-analysis.

Clinical neurology and neurosurgery
Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine lear...

Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.

Neurosurgical review
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering rece...

Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.

Scientific reports
Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC gr...

Deep Learning Enabled Scoring of Pancreatic Neuroendocrine Tumors Based on Cancer Infiltration Patterns.

Endocrine pathology
Pancreatic neuroendocrine tumors (PanNETs) are a heterogeneous group of neoplasms that include tumors with different histomorphologic characteristics that can be correlated to sub-categories with different prognoses. In addition to the WHO grading sc...

An interpretable multi-scale convolutional attention residual neural network for glioma grading with Raman spectroscopy.

Analytical methods : advancing methods and applications
Since the malignancy of gliomas varies with their grade, classifying gliomas of different grades can assist doctors in developing personalized surgical plans during surgery, thereby improving the prognosis. Raman spectroscopy is an optical method for...