AIMC Topic: Immunotherapy

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Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction.

Scientific reports
Glioblastoma presents characteristically with an exuberant, poorly functional vasculature that causes malperfusion, hypoxia and necrosis. Despite limited clinical efficacy, anti-angiogenesis resulting in vascular normalization remains a promising the...

An Overview of Advances in Rare Cancer Diagnosis and Treatment.

International journal of molecular sciences
Cancer stands as the leading global cause of mortality, with rare cancer comprising 230 distinct subtypes characterized by infrequent incidence. Despite the inherent challenges in addressing the diagnosis and treatment of rare cancers due to their lo...

Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence.

European journal of medical research
Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to ac...

The tumor immune microenvironment in pancreatic cancer and its potential in the identification of immunotherapy biomarkers.

Expert review of molecular diagnostics
INTRODUCTION: Pancreatic cancer (PC) has an extremely poor prognosis, even with surgical resection and triplet chemotherapy treatment. Cancer immunotherapy has been recently approved for tumor-agnostic treatment with genome analysis, including in PC....

Groundwork for AI: Enforcing a benchmark for neoantigen prediction in personalized cancer immunotherapy.

Social studies of science
This article expands on recent studies of machine learning or artificial intelligence (AI) algorithms that crucially depend on benchmark datasets, often called 'ground truths.' These ground-truth datasets gather input-data and output-targets, thereby...

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Computational biology and chemistry
Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exh...

Biology-guided deep learning predicts prognosis and cancer immunotherapy response.

Nature communications
Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatmen...

Cytogenetics and genomics in pediatric acute lymphoblastic leukaemia.

Best practice & research. Clinical haematology
The last five decades have witnessed significant improvement in diagnostics, treatment and management of children with acute lymphoblastic leukaemia (ALL). These advancements have become possible through progress in our understanding of the genetic a...

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

Cell reports. Medicine
The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining...