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 …

The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its
primary site and accounts for 3–5% of all cancers. Established targeted therapies are …

Metastatic cancer remains an almost inevitably lethal disease,–. A better understanding of
disease progression and response to therapies therefore remains of utmost importance …

Background Using visual, biological, and electronic health records data as the sole input
source, pretrained convolutional neural networks and conventional machine learning …

As data-driven science, artificial intelligence (AI) has paved a promising path toward an
evolving health system teeming with thrilling opportunities for precision oncology …

The current increase in number and diversity of targeted anticancer agents poses
challenges to the logistics and timeliness of molecular diagnostics (MolDx), resulting in …

Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid
biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets …

Cancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic
cancers where standardised diagnostic techniques fail to identify the organ where it …

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis
remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor …