Artificial intelligence and skin cancer.

Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.

Hepatic Plexus Nerve Block for Microwave Ablation of Hepatic Tumors.

Seven patients underwent microwave ablation of hepatic tumors; during ablation, a hepatic nerve plexus block was used for pain control. The mean visual analog scale (VAS) score for pain (scale, 0-10) was 0.3 ± 0.5 (SD) at baseline and 2.5 ± 1.4, 2.6 ± 1.4, and 2.3 ± 0.9 at 1, 5, and 10 minutes during ablation. Two patients reported a VAS score of 4 or greater during ablation, which improved in both patients to a VAS score of 3 after one rescue sedation dose. The remaining patients required no additional sedation. No major complication occurred. No patient required conversion to general anesthesia.

Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, sex and vital status of patients.

Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer classification systems is that, even for tumors affecting a single organ, there is significant molecular heterogeneity. Precise molecular classification of tumors could be beneficial in personalizing patients' therapy and predicting prognosis. To this end, here we propose to use molecular signatures to further refine cancer classification. Molecular signatures are collections of genes characterizing particular cell types, tissues or disease. Signatures can be used to interpret expression profiles from heterogeneous samples. Large collections of gene signatures have previously been cataloged in the MSigDB database. We have developed a web-based Signature Visualization Tool (SaVanT) to display signature scores in user-generated expression data. Here we have undertaken a systematic analysis of correlations between inflammatory signatures and cancer samples, to test whether inflammation can differentiate cancer types. Inflammatory response signatures were obtained from MsigDB and SaVanT and a signature score was computed for samples associated with 7 different cancer types. We first identified types of cancers that had high inflammation levels as measured by these signatures. The correlation between signature scores and metadata of these patients (sex, age at initial cancer diagnosis, cancer stage, and vital status) was then computed. We sought to evaluate correlations between inflammation with other clinical parameters and identified four cancer types that had statistically significant association (p-value < 0.05) with at least one clinical characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), and uveal melanoma (UVM). These results may allow future studies to use these approaches to further refine cancer subtyping and ultimately treatment.

An Introduction to the Performance of Immunohistochemistry.

Immunohistochemistry (IHC) is a powerful technique that exploits the specific binding between an antibody and antigen to detect and localize specific antigens in cells and tissue, most commonly detected and examined with the light microscope. A standard tool in many fields in the research setting, IHC has become an essential ancillary technique in clinical diagnostics in anatomic pathology (Lin F, Chen Z. Arch Pathol Lab Med 138:1564-1577, 2014) with the advent of antigen retrieval methods allowing it to be performed conveniently on formalin fixed paraffin embedded (FFPE) tissue (Taylor CR, Shi S-R, Barr NJ. Techniques of immunohistochemistry: principles, pitfalls, and standardization. In: Dabbs DJ (ed) Diagnostic immunohistochemistry: theranostic and genomic applications, 3rd edn. Saunders, Philadelphia, 2010; Shi SR, Key ME, Kalra KL. J Histochem Cytochem 39:741-748, 1991) and automated methods for high volume processing with reproducibility (Prichard J, Hicks D, Hammond E. Automated immunohistochemistry overview. In: Fan L, Jeffrey P (eds) Handbook of practical immunohistochemistry: frequently asked questions, 2nd edn. Springer, New York, 2015). IHC is frequently utilized to assist in the classification of neoplasms, determination of a metastatic tumor's site of origin and detection of tiny foci of tumor cells inconspicuous on routine hematoxylin and eosin (H&E) staining. Furthermore, it is increasingly being used to provide predictive and prognostic information, such as in testing for HER2 amplification in breast cancer (Wolff AC, Hammond MEH, Hicks DG et al. Arch Pathol Lab Med 138:241-256, 2014) in addition to serving as surrogate markers for molecular alterations in neoplasms, including IDH1 and ATRX mutations in brain tumors (Appin CL, Brat DJ. Mol Aspects Med. 45:87-96, 2015). In this chapter we describe the basic methods of immunohistochemical staining which has become an essential tool in the daily practice of anatomic pathology worldwide.