Oncology News and Research

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AI Model MUSK Revolutionizes Cancer Prognosis and Treatment Guidance

Researchers at Stanford Medicine developed MUSK, an AI model that integrates visual and text-based medical data to improve cancer prognosis predictions. MUSK significantly outperforms standard methods in predicting survival, identifying immunotherapy candidates, and assessing recurrence risks in diverse cancer types.

8 Jan 2025

AI Bridges the Gap Between Cancer Clinical Trials and Real-World Patients

Researchers developed an AI-powered platform, TrialTranslator, to assess the real-world applicability of cancer clinical trials, helping identify which patients will benefit from treatments.

8 Jan 2025

Deep Learning Revolutionizes Cancer Biology With Insights From 50,000 Gene Profiles

Researchers introduce DeepProfile, a groundbreaking deep learning framework that deciphers gene expression data across 18 cancers, uncovering critical pathways linked to survival and treatment responses.

5 Jan 2025

The Transformative Role of AI in Oncology Data Analysis

The integration of artificial intelligence (AI) and machine learning (ML) in oncology, facilitated by advancements in large language models (LLMs) and multimodal AI systems, offers promising solutions for processing the expanding volume of patient-specific data. From image analysis to text mining in electronic health records (EHRs), these technologies are reshaping oncology research and clinical practice, though challenges such as data quality, interpretability, and regulatory compliance remain.

29 Mar 2024

Deep Learning-based Omics Analysis in Precision Medicine

Delve into the transformative fusion of tabular-to-image conversion with deep learning, particularly convolutional neural networks (CNNs), as elucidated by recent research in the Journal of Human Genetics. Explore how innovations like DeepInsight and DeepFeature are reshaping predictive modeling in precision medicine, bridging the gap between data abundance and interpretation challenges in omics analysis.

6 Mar 2024

Bridging the Gap: ChatGPT ADA for Clinical Data Analysis

Researchers investigate ChatGPT ADA, an extension of GPT-4, for developing ML models in clinical data analysis, showing comparable performance to manual methods. With transparent methodologies and robust performance across diverse clinical trials, ChatGPT ADA presents a promising tool for democratizing ML in medicine, emphasizing its potential alongside specialized training and resources.

28 Feb 2024

WSInfer: Empowering Digital Pathology with Open-Source Deep Learning

Stony Brook University and University of Edinburgh researchers introduce WSInfer, an open-source software ecosystem revolutionizing digital pathology. Enabling the sharing and reusability of deep learning models, WSInfer, with its patch-based classification and integration with QuPath, proves efficient, scalable, and user-friendly, marking a significant stride towards democratizing AI in pathology.

15 Jan 2024

Advancing Breast Cancer Diagnosis: Harnessing the Fast Learning Network Algorithm

Researchers demonstrate the efficacy of a Fast Learning Network (FLN) algorithm-based classifier for diagnosing breast cancer. The FLN algorithm achieves high accuracy, precision, recall, F-measure, and specificity in breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Breast Cancer Database (WBCD). This study highlights the potential of FLN as a reliable breast cancer diagnosing classifier, although further optimization and exploration of breast cancer stages are needed for future research.

15 Jul 2023