Detection of cancer metastasis: past, present and future.

The clinical importance of metastatic spread of cancer has been recognized for centuries, and melanoma has loomed large in historical descriptions of metastases, as well as the numerous mechanistic theories espoused. The "fatal black tumor" described by Hippocrates in 5000 BC that was later termed "melanose" by Rene Laennec in 1804 was recognized to have the propensity to metastasize by William Norris in 1820. And while the prognosis of melanoma was uniformly acknowledged to be dire, Samuel Cooper described surgical removal as having the potential to improve prognosis. Subsequent to this, in 1898 Herbert Snow was the first to recognize the potential clinical benefit of removing clinically normal lymph nodes at the time of initial cancer surgery. In describing "anticipatory gland excision," he noted that "it is essential to remove, whenever possible, those lymph glands which first receive the infective protoplasm, and bar its entrance into the blood, before they have undergone increase in bulk". This revolutionary concept marked the beginning of a debate that rages today: are regional lymph nodes the first stop for metastases ("incubator" hypothesis) or does their involvement serve as an indicator of aggressive disease with inherent metastatic potential ("marker" hypothesis). Is there a better way to improve prediction of disease outcome? This article attempts to address some of the resultant questions that were the subject of the session "Novel Frontiers in the Diagnosis of Cancer" at the 8th International Congress on Cancer Metastases, held in San Francisco, CA in October 2019. Some of these questions addressed include the significance of sentinel node metastasis in melanoma, and the optimal method for their pathologic analysis. The finding of circulating tumor cells in the blood may potentially supplant surgical techniques for detection of metastatic disease, and we are beginning to perfect techniques for their detection, understand how to apply the findings clinically, and develop clinical followup treatment algorithms based on these results. Finally, we will discuss the revolutionary field of machine learning and its applications in cancer diagnosis. Computer-based learning algorithms have the potential to improve efficiency and diagnostic accuracy of pathology, and can be used to develop novel predictors of prognosis, but significant challenges remain. This review will thus encompass latest concepts in the detection of cancer metastasis via the lymphatic system, the circulatory system, and the role of computers in enhancing our knowledge in this field.