Search

BackgroundWhile the clinical outcomes of immune checkpoint inhibitor (ICI) use in older adults with advanced-stage non-small cell lung cancer (NSCLC) have been described, the role of ICI use continued beyond disease progression (BDP) remains to be well defined for this population. This retrospective single-center study explored the clinical outcomes of continuing ICIs BDP among older adult patients with advanced NSCLC.MethodsUsing MD Anderson’s Gemini Lung Cancer database, we retrospectively reviewed the clinical outcomes of older adults (≥70 years) diagnosed with advanced-stage NSCLC treated with anti-PD-(L)1 monotherapy from March 2015 through April 2019 to correlate clinicopathologic features with clinical outcomes. Clinical therapy responses were evaluated by Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1). Toxicities were assessed using Common Terminology Criteria for Adverse Events (CTCAE), version 5. Patients treated BDP were defined as individuals receiving ICIs for ≥8 weeks prior to documentation of progression who subsequently remained on ICIs for ≥6 weeks.ResultsOf the 159 older adults meeting the inclusion criteria, 33 (21%) received ICIs BDP (64% male, median age 74.9 years (70.1–82.0) at the start of ICI treatment, 3 received first-line ICI therapy). Most patients were former (85%) or current (6%) smokers. 79% had adenocarcinoma histology. The median duration of immunotherapy continued BDP was 7.1 months (95% CI: 3.0–8.2). After a follow-up of 30.1 months, the median overall survival (mOS) was 31.5 months (95% CI: 16.5-not reached). Eight (24%) received local consolidative radiotherapy with a median duration of ICI BDP of 8.2 months (95% CI: 1.9–13.3). Twenty-five (76%) did not receive local consolidative therapy and achieved a median duration of ICI BDP of 4.1 months (95% CI: 2.3–7.8). Six (18%) exhibited pseudo-progression (i.e. delayed response to immunotherapy with decreased tumor burden on subsequent radiologic studies), with 4 achieving ”stable disease” as best response and 2 achieving a partial response. The median duration of immunotherapy continued beyond pseudo-progression was 11.7 months (95% CI: 7.1–35.7), and the mOS was 26.2 months (95% CI: 16.5–40.0). Patients treated with ICI BDP most commonly experienced fatigue (18%), pneumonitis (12%), rash (9%), and hypothyroidism (9%). Three patients (9%) experienced grade 3 or higher toxicities (one grade 3 arthralgias and two grade 3 pneumonitis).ConclusionsICI-use BDP in older adults with advanced NSCLC may benefit a subset of patients. Additionally, local consolidative therapy with radiation may offer prolonged duration of ICI treatment.AcknowledgementsSupported by the generous philanthropic contributions to The University of Texas MD Anderson Lung Moon Shot Program and the MD Anderson Cancer Center Support Grant P30 CA01667. Special acknowledgment to the GEMINI team.Ethics ApprovalThis study was approved and conducted in accordance with the institutional review board at the University of Texas MD Anderson Cancer Center; approval number (PA13-0589).

Detecting changes in spatial datasets is important for many fields such as early warning systems that monitor environmental conditions, epidemiology, crime monitoring, and automatic surveillance. The goal of the presented research is the development of methodologies for change analysis in spatial datasets. An approach to change analysis is presented that analyses how the interesting regions in one time frame differ from the interesting regions in the next time frame with respect to a given interestingness perspective. Users are allowed to define their own interestingness perspectives that are captured in form of a reward-based fitness function. A change analysis framework is proposed in which reward-based fitness functions are maximized by clustering algorithms to find interesting regions for each dataset. Next, the relationships between interesting regions are analyzed. Two approaches to analyze correspondence between interesting regions are proposed. The first approach directly compares cluster models and strongly relies on analyzing relationships between polygons that capture the scope of an interesting region. The second approach compares cluster extensions that are computed by re-clustering. Finally, a knowledge base of change predicates is provided that allows analyzing various aspects of change, and report generators that are associated with particular predicates are proposed to generate summaries of change. Moreover, in contrast to most existing research, the framework does not assume that the identity of objects in the analyzed environment is known. The proposed framework is demonstrated in a case study that analyzes changes in earthquake patterns.

This paper addresses two main challenges for clustering which require extensive human effort: selecting appropriate parameters for an arbitrary clustering algorithm and identifying alternative clusters. We propose an architecture and a concrete system MR-CLEVER for multi-run clustering that integrates active learning with clustering algorithms. The key hypothesis of this work is that better clustering results can be obtained by combining clusters that originate from multiple runs of clustering algorithms. By defining states that represent parameter settings of a clustering algorithm, the proposed architecture actively learns a state utility function. The utility of a parameter setting is assessed based on clustering run-time, quality and novelty of the obtained clusters. Furthermore, the utility function plays an important role in guiding the clustering algorithm to seek novel solutions. Cluster novelty measures are introduced for this purpose. Finally, we also contribute a cluster summarization algorithm that assembles a final clustering as a combination of high-quality clusters originating from multiple runs. Merits of our proposed system are that it is generic and therefore can be used in conjunction with different clustering algorithms, and it reduces human effort for selecting the parameters, for comparing clustering results and for assembling clustering results. We evaluate the proposed system in conjunction with a representative based clustering algorithm namely CLEVER for a challenging data mining task involving an earthquake dataset. The obtained results demonstrate that, in comparison to the best single-run clustering, multi-run clustering discovers solutions of higher quality.

Detecting changes in spatial datasets is important for many fields. In this paper, we introduce a methodology for change analysis in spatial datasets that combines contouring algorithms with supervised density estimation techniques. The methodology allows users to define their own criteria for features of interest and to identify changes in those features between two datasets. Change analysis is performed by comparing interesting regions that have been derived using contour clustering. A novel clustering algorithm called DCONTOUR is introduced for this purpose that computes contour polygons that describe the boundary of a supervised density function at a given density threshold. Relationships between old and new data are analyzed relying on polygon operations. We evaluate our methodology in a case study that analyzes changes in earthquake patterns.