In-depth time-dependent analysis of the benefit of Allo-HSCT for elderly patients with CR1 AML: a FILO study

The benefit of allogeneic hematopoietic stem cell transplantation (Allo-HSCT) for acute myeloid leukemia (AML) patients over 60 years remains a matter of debate, notably when performed in first complete remission (CR1). In order to clarify this issue, the French Innovative Leukemia Organization (FILO) performed a 10-year real-world time-dependent analysis. The study enrolled patients between 60 and 70 years of age with AML in CR1 after intensive chemotherapy with intermediate (IR) or unfavorable (UR) risk according to the European LeukemiaNet (ELN)-2010. The impact of Allo-HSCT was analyzed through three models, respectively i) time-dependent Cox, ii) multistate for dynamic prediction and iii) super landmark. The study enrolled 369 (73%) IR and 138 (27%) UR AML patients, 203 of whom received an Allo-HSCT. Classical multivariate analysis showed that Allo-HSCT significantly improved relapse-free (RFS; Hazard Ratio/HR [95%CI]: 0.47 [0.35-0.62], p<0.001) and overall (OS; HR [95%CI]: 0.56 [0.42-0.76], p<0.001) survivals, independently of the ELN risk group. With the multistate model, the predicted 5-year probability for IR and UR patients to remain in CR1 without Allo-HSCT was 8% and 1%, respectively. Dynamic predictions confirmed that patients without Allo-HSCT continue to relapse over time. Finally, the super landmark model showed that Allo-HSCT significantly improved RFS (HR [95%CI]: 0.47 [0.36-0.62], p<0.001) and OS (HR [95%CI]: 0.54 [0.40-0.72], p<0.001). Allo-HSCT in CR1 is demonstrated here to significantly improve the outcome of fit older AML patients. Long-term RFS without Allo-HSCT is very low (<10%), supporting Allo-HSCT as being the best curative option for these patients.

Understanding landmark types in human navigation from a database perspective

<p><strong>Abstract.</strong> Delivering location based information to support human navigation in unfamiliar environments has been a long standing research topic in Location-based Service (LBS). Enriching route and direction communication with landmark information not only increases the efficiency of navigation applications but also facilitates spatial knowledge learning among users (Lovelace et al. 1999, Wunderlich &amp; Gramann 2018). Landmarks are salient geographic entities anchored at fixed positions in an environment. In wayfinding and navigation, landmarks serve for multiple purposes as and organizing concept that help people build up structured spatial knowledge representation and as navigational tools to support navigational decision-making (Sorrows &amp; Hirtle 1999). In previous work, landmarks are often conceptualized as reference points. However, in Geodatabase landmarks are not always represented as point features. Moreover, non-point features are also important references in route instructions, especially for pedestrian navigation. The signs, though often considered as point elements not only provide something about “here”, but also directional information (Gibson 2009). In this work, we attempt to provide a categorization of landmark for route communications from a database perspective. The proposed view is intended to be extended to a landmark model to generate various route communication, such as verbal instructions or symbolic visualizations.</p>

Immortal Time Bias in Observational Studies of Time-to-Event Outcomes

The objectives of this study are to illustrate the effects of immortal time bias (ITB) using an oncology outcomes database and quantify through simulations the magnitude and direction of ITB when different analytical techniques are used. A cohort of 11 626 women who received neoadjuvant chemotherapy and underwent mastectomy with pathologically positive lymph nodes were accrued from the National Cancer Database (2004-2008). Standard Cox regression, time-dependent (TD), and landmark models were used to compare overall survival in patients who did or did not receive postmastectomy radiation therapy (PMRT). Simulation studies showing ways to reduce the effect of ITB indicate that TD exposures should be included as variables in hazard-based analyses. Standard Cox regression models comparing overall survival in patients who did and did not receive PMRT showed a significant treatment effect (hazard ratio [HR]: 0.93, 95% confidence interval [CI]: 0.88-0.99). Time-dependent and landmark methods estimated no treatment effect with HR: 0.97, 95% CI: 0.92 to 1.03 and HR: 0.98, 95% CI, 0.92 to 1.04, respectively. In our simulation studies, the standard Cox regression model significantly overestimated treatment effects when no effect was present. Estimates of TD models were closest to the true treatment effect. Landmark model results were highly dependent on landmark timing. Appropriate statistical approaches that account for ITB are critical to minimize bias when examining relationships between receipt of PMRT and survival.

Design and Robust Recognition of Omnidirectional Landmarks for Indoor Mobile Robot Positioning

In this paper, we present an omnidirectional artificial landmark model and a robust artificial landmark recognition algorithm for indoor mobile robot positioning. The landmark model encodes identities with nested circles in black and white, which provides stable edge response and enables strong tolerance to various lighting conditions and perspective distortions. The corresponding positioning system uses a single upward-facing webcam as the vision sensor to capture landmarks. To address the effect of the lighting and sensing noise, the topological contour analysis is applied to detect landmarks, and the dynamic illumination adjustment is used to assist landmark recognition. Based on the landmark recognition, the absolute position of the camera in the environment is estimated using a trilateration algorithm. The landmark model and positioning system are tested with a mobile robot in a real indoor environment. The results show that the purposed technique provides autonomous indoor positioning for mobile robots with high robustness and consistency.