AIMC Topic: Neoplasms

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Clinical application of AI-based PET images in oncological patients.

Seminars in cancer biology
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insuffici...

Highly Performing Automatic Detection of Structural Chromosomal Abnormalities Using Siamese Architecture.

Journal of molecular biology
The detection of structural chromosomal abnormalities (SCA) is crucial for diagnosis, prognosis and management of many genetic diseases and cancers. This detection, done by highly qualified medical experts, is tedious and time-consuming. We propose a...

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.

Cell
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniq...

Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks.

International journal of molecular sciences
Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs h...

Elective robotic partial colon and rectal resections: series of 170 consecutive robot procedures involving the Da Vinci Xi robot by a community general surgeon.

Journal of robotic surgery
Robotic colorectal procedures may overcome the disadvantages of laparoscopic surgery. While the literature has multiple studies from specialized centers, experience from general surgeons is minimal. The purpose of this case series is to review electi...

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction.

Seminars in cancer biology
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance co...

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

Journal of medical Internet research
BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite ...

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial.

JAMA network open
IMPORTANCE: Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.

International journal of environmental research and public health
A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to cl...