Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …

Even though radiomics can hold great potential for supporting clinical decision-making, its
current use is mostly limited to academic research, without applications in routine clinical …

Purpose To propose a new quality scoring tool, METhodological RadiomICs Score
(METRICS), to assess and improve research quality of radiomics studies. Methods We …

Computer-extracted tumour characteristics have been incorporated into medical imaging
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response
to treatment. Known prognostic factors for this disease include homologous recombination …

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …

Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Simple Summary Diagnosing cancer at an early stage increases the chance of performing
effective treatment in many tumour groups. Key approaches include screening patients who …