AI Medical Compendium Journal:
Future oncology (London, England)

Showing 1 to 10 of 14 articles

The present and future of lung cancer screening: latest evidence.

Future oncology (London, England)
Lung cancer is the leading cause of cancer-related mortality worldwide. Early lung cancer detection improves lung cancer-related mortality and survival. This report summarizes presentations and panel discussions from a webinar, "The Present and Futur...

Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

Future oncology (London, England)
PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgki...

Using bioinformatics and artificial intelligence to map the cyclin-dependent kinase 4/6 inhibitor biomarker landscape in breast cancer.

Future oncology (London, England)
A cyclin-dependent kinase 4/6 (CDK4/6) inhibitor combined with endocrine therapy is the standard-of-care for patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer. However, not all patients r...

Cancer cytogenetics in the era of artificial intelligence: shaping the future of chromosome analysis.

Future oncology (London, England)
Artificial intelligence (AI) has rapidly advanced in the past years, particularly in medicine for improved diagnostics. In clinical cytogenetics, AI is becoming crucial for analyzing chromosomal abnormalities and improving precision. However, existin...

The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors.

Future oncology (London, England)
The RAISE project assessed whether deep learning could improve early progression-free survival (PFS) prediction in patients with neuroendocrine tumors. Deep learning models extracted features from CT scans from patients in CLARINET (NCT00353496) (n...

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Future oncology (London, England)
To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neura...

Uncovering cancer's secrets: could liquid biopsies and artificial intelligence hold the answers?

Future oncology (London, England)
Tweetable abstract How are #liquidbiopsies and #AI pushing us closer to the goal of early cancer detection for all? Plus, what more needs to be done to truly deciphers cancer's secrets? Find out in this editorial by @JadeParkerB.

Unsupervised learning of cross-modal mappings in multi-omics data for survival stratification of gastric cancer.

Future oncology (London, England)
This study presents a survival stratification model based on multi-omics integration using bidirectional deep neural networks (BiDNNs) in gastric cancer. Based on the survival-related representation features yielded by BiDNNs through integrating tr...

Building a predictive model to assist in the diagnosis of cervical cancer.

Future oncology (London, England)
Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Thi...