AIMC Topic: Tumor Microenvironment

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A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of ...

Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

The oncologist
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current meth...

Inferring Cell-type-specific Genes of Lung Cancer Based on Deep Learning.

Current gene therapy
BACKGROUND: Lung cancer is cancer with the highest incidence in the world, and there is obvious heterogeneity within its tumor. The emergence of single-cell sequencing technology allows researchers to obtain cell-type-specific expression genes at the...

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.

Acta neurochirurgica. Supplement
The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative featur...

Perspectives in pathomics in head and neck cancer.

Current opinion in oncology
PURPOSE OF REVIEW: Pathology is the cornerstone of cancer care. Pathomics, which represents the use of artificial intelligence in digital pathology, is an emerging and promising field that will revolutionize medical and surgical pathology in the comi...

Artificial Intelligence Will Not Replace Health Professionals, but the Proper Use of Artificial Intelligence Will Make Health Professionals Better.

Cancer research
Deep learning has enabled great advances to be made in cancer research with regards to diagnosis, prognosis, and treatment. The study by Wang and colleagues in this issue of develops a deep learning algorithm with the ability to digitally stain hist...

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Cell
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipelin...

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cell
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovati...