Virtual autopsy: Machine Learning and AI provide new opportunities for investigating minimal tumor burden and therapy resistance by cancer patients
Shane O’Sullivan; Andreas Holzinger; Dominic Wichmann; Paulo Hilario Nascimento Saldiva; Mohammed Imran Sajid; Kurt Zatloukal
One advantage yet to focus on in scientific literature is the beneficial use of virtual autopsy (virtopsy) for investigating minimal tumor burden. Our hypothesis is that virtopsy assists in the understanding of therapy resistance of cancer patients or cause of death in patients with minimal tumor burden.
The majority of current cancer therapies are aimed at killing tumor cells. This is done either directly (by chemical agents or radiation), or indirectly (by depriving the tumor from nutrients, or activating and redirecting the immune response against the cancer). This variety of therapeutic approaches is reflected in modern therapies such as PDL-1 blockers, VEGF-inhibitors, tumor-vaccines or Proteasome-inhibitors. However the observation that a fair number of patients with minimal detectable tumor mass die of cancer, given that treatment options described were considered, highlights gaps in knowledge that require filling as well as development of new potential therapeutic approaches.
As a first step we propose epidemiological studies be undertaken - these are required in order to obtain quantifiable data on how many and which type of cancer patients die of cancer with minimal tumor mass. As autopsies are rarely performed on patients whose cancer has been well-characterized during the course of the disease, systematic data on this poorly-characterized cause of cancer-related death do not exist. Furthermore, the assessment of total tumor mass in the body is difficult in disseminated diseases by traditional autopsy. In cases where minimal tumor burden has caused patients to die, we need to gain more knowledge (evidence-based practice). It is the combination of autopsy, pathology and virtopsy that truly defines or examines the entire body. When dealing with a localized disease, traditional autopsy is appropriate in order to cut out, weigh and measure parts that are affected. This however, cannot be performed when dealing with a disseminated disease with many small lesions in various organ systems. Virtopsy can be a very effective intervention to quantify tumor mass. Imaging would provide very important baseline data to compare different patients’ tumor mass and to exclude other non-cancer-related causes of death.
The enormous practical success of Machine Learning and Artificial Intelligence (AI) has led to more evidence-based decision-making in the medical domain.
The results demonstrated that such deep learning models can achieve a performance even beyond human experts. However, besides being resource-intensive and data-hungry, black-box machine learning and AI approaches have one enormous disadvantage in the medical domain – they are lacking transparency. Even if we understand the mathematical theory of machine learning model it is complicated, yet impossible to get insight into the internal working of such a model. This leads to a major question – can we trust such results?
Consequently, there is growing demand in interactive machine learning advances,
Increasing legal and privacy characteristics are a massive motivation for this practice. The new European General Data Protection Regulation (GDPR and ISO/IEC 27001) entering into force on May, 25, 2018, will make black-box approaches difficult to use in any business, because they recognize that they are not able to explain why a decision has been made; this will make glass-box approaches essential
We therefore call for a collaborative effort to generate quantifiable data on tumor burden of patients who died of cancer without evidence of classical causes of cancer-related deaths. These data could lay the foundation for discovering novel mechanisms of how a cancer may interfere with body function. There is increasing evidence from metabolomic studies that tumors may markedly interfere with metabolism as demonstrated by certain metabolic signatures that correlate with disease prognosis.
In summary, this article presents and supports an add-on example of how virtopsy can propel medicine into the future, its impact, implications and application in investigating minimal tumor burden and therapy resistance by cancer patients. Great advances will be made by taking advantage of current progress in Machine Learning and Artificial Intelligence, however new approaches are needed that make use of a human-in-the-loop and above all in making transparent why and how a decision has been made.
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