Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

Esthesioneuroblastoma

Esthesioneuroblastomas (ENB) are rare malignant tumours of neuroepithelial origin arising from the olfactory mucosa. Not uncommonly they metastasize to regional lymph nodes and distant organs. Commonly they have a long and insidious course and the diagnosis requires a high index of suspicion. Since this tumour was first described, many grading and staging systems have been proposed but still there is some controversy on which correlates best with tumour prognosis. The classic treatment for ENB is craniofacial resection with the goal of gross total resection and achievement of tumour-free margins. The enormous advances in endoscopic techniques over the last two decades means this now has comparable results in achieving tumour-free margins, especially for low stage tumours. The role of preoperative and postoperative radiation has been well established in the treatment of this tumour. Controversy exists regarding the role of neck dissection or elective neck radiation as standard therapeutic modalities.

Overview Tumour Disease and Oncologic Therapy During Pregnancy

Newly diagnosed cancer in pregnancy is an issue with increasing importance, because fulfilling the wish to have a child is shifted later in life. Tumour disease is diagnosed in around 0.1% of all pregnancies in Germany. Most common tumour entities involve the breast, the cervix and the hematopoietic system. As tumour prognosis is not negatively affected by pregnancy and gestation does not disclude oncologic therapy it is important to plan treatment immediately. Oncologic surgery preserving the uterus is not at all limited. Chemotherapy with anthracyclines, taxanes or platinum derivates is possible from the second trimenon on. Only radiotherapy should be avoided during pregnancy. Before initiating therapy, fertility preserving methods like cryopreservation of oocytes and/or ovarian tissue should be offered to the patient. Preterm birth is more common in women with a first diagnosis of cancer during pregnancy. Other negative effects on the child’s future are not yet known. Treatment of pregnant cancer patients should be handled by experienced centres with a multi-professional team.