Automated molecular-image cytometry and analysis in modern oncology

Abstract

Diagnostic methods for initial diagnosis and patient stratification for treatment are key to modern oncology, but many challenges remain. In developed countries, advances in early diagnosis and therapeutics have led to challenges in the sampling of sub-centimetre lesions, with repeat biopsies straining accuracy and throughput of pathological assessment. Conversely, low-income and middle-income countries face extremely limited pathology and imaging resources, large caseloads, convoluted and inefficient workflows, and a lack of specialists. Advances in material sciences, chemistry, engineering and artificial intelligence have led to the introduction of a new class of affordable image cytometers that enable automated cell phenotyping, with ongoing clinical testing indicating that these systems can alleviate existing bottlenecks and improve diagnostic efficiency. Ultimately, these diagnostic methods are likely to surpass current pathology approaches on the basis of the richness of molecular measurements and the fact that they require only scant cellular material, rather than tissue sections. As these methods can be miniaturized and are low-power, they can also be used in point-of-care settings. In this Review, we focus on new devices and approaches for the integrated analysis of scant cancer samples, particularly those obtained by fine-needle aspiration. Over the past five years, advances in materials science, chemistry, engineering and artificial intelligence have yielded a new class of affordable image cytometers for automated single-cell phenotyping of scant samples. In this Review, these technologies are introduced and their potential molecular-diagnostic roles in resource-limited oncology settings are discussed.