Rapid microdissection of tissue sections via laser ablation.

We demonstrate a method for tissue microdissection using scanning laser ablation that is approximately two orders of magnitude faster than conventional laser capture microdissection. Our novel approach uses scanning laser optics and a slide coating under the tissue that can be excited by the laser to selectively eject regions of tissue for further processing. Tissue was dissected at 0.117 s/mm2 without reduction in yield, sequencing insert size or base quality compared with undissected tissue. From eight cases, 58-416 mm2 of tissue was obtained from one to four slides in 7-48 seconds total dissection time per case. These samples underwent exome sequencing and we found the variant allelic fraction increased in regions enriched for tumour as expected. This suggests that our ablation technique may be useful as a tool in both clinical and research labs.

Whole-slide laser microdissection for tumour enrichment.

The practical application of genome-scale technologies to precision oncology research requires flexible tissue processing strategies that can be used to differentially select both tumour and normal cell populations from formalin-fixed, paraffin-embedded tissues. As tumour sequencing scales towards clinical implementation, practical difficulties in scheduling and obtaining fresh tissue biopsies at scale, including blood samples as surrogates for matched 'normal' DNA, have focused attention on the use of formalin-preserved clinical samples collected routinely for diagnostic purposes. In practice, such samples often contain both tumour and normal cells which, if correctly partitioned, could be used to profile both tumour and normal genomes, thus identifying somatic alterations. Here we report a semi-automated method for laser microdissecting entire slide-mounted tissue sections to enrich for cells of interest with sufficient yield for whole genome and transcriptome sequencing. Using this method, we demonstrated enrichment of tumour material from mixed tumour-normal samples by up to 67%. Leveraging new methods that allow for the extraction of high-quality nucleic acids from small amounts of formalin-fixed tissues, we further showed that the method was successful in yielding sequence data of sufficient quality for use in BC Cancer's Personalized OncoGenomics (POG) program. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment.

Imaging-Based 3-Dimensional Printing for Improved Maxillofacial Presurgical Planning: A Single Center Case Series.

PURPOSE: 3-D printing is an increasingly widespread technology that allows physical models to be constructed based on cross-sectional medical imaging data. We sought to develop a pipeline for production of 3-dimensional (3-D) models for presurgical planning and assess the value of these models for surgeons and patients. METHODS: In this institutional review board-approved, single-center case series, participating surgeons identified cases for 3-D model printing, and after obtaining patient consent, a 3-D model was produced for each of the 7 participating patients based on preoperative cross-sectional imaging. Each model was given to the surgeon to use during the surgical consent discussion and preoperative planning. Patients and surgeons completed questionnaires evaluating the quality and usefulness of the models. RESULTS: The 3-D models improved surgeon confidence in their operative approach, influencing the choice of operative approach in the majority of cases. Patients and surgeons reported that the model improved patient comprehension of the surgery during the consent discussion, including risks and benefits of the surgery. Model production time was as little as 4 days, and the average per-model cost was $350. CONCLUSIONS: 3-D printed models are useful presurgical tools from both surgeon and patient perspectives. Development of local hospital-based 3-D printing capabilities enables model production with rapid turnaround and modest cost, representing a value-added service for radiologists to offer their surgical colleagues.

Fluorescence confocal endomicroscopy of the cervix: pilot study on the potential and limitations for clinical implementation.

Current diagnostic capabilities and limitations of fluorescence endomicroscopy in the cervix are assessed by qualitative and quantitative image analysis. Four cervical tissue types are investigated: normal columnar epithelium, normal and precancerous squamous epithelium, and stromal tissue. This study focuses on the perceived variability within and the subtle differences between the four tissue groups in the context of endomicroscopic in vivo pathology. Conclusions are drawn on the general ability to distinguish and diagnose tissue types, on the need for imaging depth control to enhance differentiation, and on the possible risks for diagnostic misinterpretations.