AI Medical Compendium Topic:
Neoplasms

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A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images.

Computer methods and programs in biomedicine
Previously, doctors interpreted computed tomography (CT) images based on their experience in diagnosing kidney diseases. However, with the rapid increase in CT images, such interpretations were required considerable time and effort, producing inconsi...

Artificial intelligence in cancer target identification and drug discovery.

Signal transduction and targeted therapy
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying...

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.

Science advances
Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many prev...

Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms.

PloS one
Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for cancer patients. Recently, an increasing number of patients post thei...

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy.

IEEE transactions on neural networks and learning systems
Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, ...

An integrated network representation of multiple cancer-specific data for graph-based machine learning.

NPJ systems biology and applications
Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotyp...

Swarm learning for decentralized artificial intelligence in cancer histopathology.

Nature medicine
Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obsta...

Gradient tree boosting and network propagation for the identification of pan-cancer survival networks.

STAR protocols
Cancer survival prediction is typically done with uninterpretable machine learning techniques, e.g., gradient tree boosting. Therefore, additional steps are needed to infer biological plausibility of the predictions. Here, we describe a protocol that...

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.

Clinical and molecular hepatology
Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict mole...