The AI Settlement Generation Challenge in Minecraft : First Year Report

© 2020 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in KI - Künstliche Intelligenz. The final authenticated version is available online at: https://doi.org/10.1007/s13218-020-00635-0.This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.Peer reviewedFinal Accepted Versio

Whole tumor kinetics analysis of F-18-fluoromisonidazole dynamic PET scans of non-small cell lung cancer patients, and correlations with perfusion CT blood flow

Abstract Background To determine the relative abilities of compartment models to describe time-courses of 18F-fluoromisonidazole (FMISO) tumor uptake in patients with advanced stage non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography (dPET), and study correlations between values of the blood flow-related parameter K 1 obtained from fits of the models and an independent blood flow measure obtained from perfusion CT (pCT). NSCLC patients had a 45-min dynamic FMISO PET/CT scan followed by two static PET/CT acquisitions at 2 and 4-h post-injection. Perfusion CT scanning was then performed consisting of a 45-s cine CT. Reversible and irreversible two-, three- and four-tissue compartment models were fitted to 30 time-activity-curves (TACs) obtained for 15 whole tumor structures in 9 patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC) and leave-one-out cross-validation. The precision with which fitted model parameters estimated ground-truth uptake kinetics was determined using statistical simulation techniques. Blood flow from pCT was correlated with K 1 from PET kinetic models in addition to FMISO uptake levels. Results An irreversible three-tissue compartment model provided the best description of whole tumor FMISO uptake time-courses according to AIC, BIC, and cross-validation scores totaled across the TACs. The simulation study indicated that this model also provided more precise estimates of FMISO uptake kinetics than other two- and three-tissue models. The K 1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from pCT (Pearson r coefficient = 0.81). The correlation from the irreversible three-tissue model (r = 0.81) was stronger than that from than K 1 values obtained from fits of a two-tissue compartment model (r = 0.68), or FMISO uptake levels in static images taken at time-points from tracer injection through to 4 h later (maximum at 2 min, r = 0.70). Conclusions Time-courses of whole tumor FMISO uptake by advanced stage NSCLC are described best by an irreversible three-tissue compartment model. The K 1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from perfusion CT (r = 0.81)

Investigation of atovaquone-induced spatial changes in tumour hypoxia assessed by hypoxia PET/CT in non-small cell lung cancer patients

Background Tumour hypoxia promotes an aggressive tumour phenotype and enhances resistance to anticancer treatments. Following the recent observation that the mitochondrial inhibitor atovaquone increases tumour oxygenation in NSCLC, we sought to assess whether atovaquone affects tumour subregions differently depending on their level of hypoxia. Methods Patients with resectable NSCLC participated in the ATOM trial (NCT02628080). Cohort 1 (n = 15) received atovaquone treatment, whilst cohort 2 (n = 15) did not. Hypoxia-related metrics, including change in mean tumour-to-blood ratio, tumour hypoxic volume, and fraction of hypoxic voxels, were assessed using hypoxia PET imaging. Tumours were divided into four subregions or distance categories: edge, outer, inner, and centre, using MATLAB. Results Atovaquone-induced reduction in tumour hypoxia mostly occurred in the inner and outer tumour subregions, and to a lesser extent in the centre subregion. Atovaquone did not seem to act in the edge subregion, which was the only tumour subregion that was non-hypoxic at baseline. Notably, the most intensely hypoxic tumour voxels, and therefore the most radiobiologically resistant areas, were subject to the most pronounced decrease in hypoxia in the different subregions. Conclusions This study provides insights into the action of atovaquone in tumour subregions that help to better understand its role as a novel tumour radiosensitiser

Additional file 1: of Whole tumor kinetics analysis of 18F-fluoromisonidazole dynamic PET scans of non-small cell lung cancer patients, and correlations with perfusion CT blood flow

Table S1. TAC-by-TAC Wald-Wolfowitz runs-test results for fits of the different models (ticks indicate runs-test passes) (DOCX 38 kb

Additional file 2: of Whole tumor kinetics analysis of 18F-fluoromisonidazole dynamic PET scans of non-small cell lung cancer patients, and correlations with perfusion CT blood flow

Table S2. Individual AIC, BIC and MSEP scores for fits of the various models to each TAC. The lowest AIC, BIC, and MSEP scores have been underlined for each TAC, indicating the best model according to that measure (DOCX 46 kb

Mitochondrial inhibitor atovaquone increases tumor oxygenation and inhibits hypoxic gene expression in patients with non-small cell lung cancer

Clinical trial with omics and imaging data to study response to treatment