Virtual autopsy: Machine Learning and AI provide new opportunities for investigating minimal tumor burden and therapy resistance by cancer patients

摘要

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. 1 The well-established textbook scenario describes a patient dying from cancer by the tumor mass compressing surrounding tissue (e.g. brain tumors), or destroying surrounding tissue resulting in organ failure (e.g. multi-metastatic diseases), or destroying blood vessels causing lethal bleeding. Furthermore, patients may die from tumor-induced cachexia, which is a consequence of the tumor’s interference with the body’s energy homeostasis. However there are some instances when it is difficult to explain a tumor-related death, particularly when a patient dies of head or neck cancer. For example, when major tumor mass is not detectable, there are no signs of cachexia, or evidence of an immediate consequence of chemotherapy or radiotherapy. When patients might die exhibiting a small metastasis in the lung or a small local tumor, it is concluded that they died of the cancer because there is no other apparent cause of death (e.g. cardiac disease). This is concluded despite none of the classical cancer-related causes of death being established. In view of this therefore is an unexplained mechanism of how a tumor can kill a patient. 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. 1 Machine learning methods will also be of great assistance too, particularly for potential in-silico modelling. 2 , 3 This would save time and effort; enabling what is not currently feasible in a wet-lab. Moreover, the principles of machine learning image analysis would enhance virtopsy. 1 The enormous practical success of Machine Learning and Artificial Intelligence (AI) has led to more evidence-based decision-making in the medical domain. 4 A very recent example with deep learning models demonstrated impressive results: 5 the authors utilized a GoogleNet Inception v3 CNN architecture for the classification of skin lesions, using only pixels and disease labels as inputs. They pre-trained their network with 1.28 million images (1,000 object categories), and trained it on 129,450 clinical images, consisting of 2,032 different diseases. The performance was tested against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. 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