Complex situations in lung cancer: multifocal disease, oligoprogression and oligorecurrence.

With the emergence of lung cancer screening programmes and newly detected localised and multifocal disease, novel treatment compounds and multimodal treatment approaches, the treatment landscape of non-small cell lung cancer is becoming increasingly complex. In parallel, in-depth molecular analyses and clonality studies are revealing more information about tumorigenesis, potential therapeutical targets and the origin of lesions. All can play an important role in cases with multifocal disease, oligoprogression and oligorecurrence. In multifocal disease, it is essential to understand the relatedness of separate lesions for treatment decisions, because this information distinguishes separate early-stage tumours from locally advanced or metastatic cancer. Clonality studies suggest that a majority of same-histology lesions represent multiple primary tumours. With the current standard of systemic treatment, oligoprogression after an initial treatment response is a common scenario. In this state of induced oligoprogressive disease, local ablative therapy by either surgery or radiotherapy is becoming increasingly important. Another scenario involves the emergence of a limited number of metastases after radical treatment of the primary tumour, referred to as oligorecurrence, for which the use of local ablative therapy holds promise in improving survival. Our review addresses these complex situations in lung cancer by discussing current evidence, knowledge gaps and treatment recommendations.

Applications of Machine Learning in Palliative Care: A Systematic Review.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.