<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Germer, Sebastian</style></author><author><style face="normal" font="default" size="100%">Rudolph, Christiane</style></author><author><style face="normal" font="default" size="100%">Katalinic, Alexander</style></author><author><style face="normal" font="default" size="100%">Rath, Natalie</style></author><author><style face="normal" font="default" size="100%">Rausch, Katharina</style></author><author><style face="normal" font="default" size="100%">Heinz Handels</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lung Cancer Survival Estimation Using Data from Seven German Cancer Registries.</style></title><secondary-title><style face="normal" font="default" size="100%">Studies in health technology and informatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stud Health Technol Inform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Germany</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Lung Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Registries</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Rate</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 May 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">327</style></volume><pages><style face="normal" font="default" size="100%">457-461</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Predicting the survival of cancer patients is of high importance for the medical community, e.g. for evaluating therapy strategies. This study is based on lung cancer data retrieved from seven German cancer registries according to the German basic oncology dataset. After data integration and preprocessing, we predicted the survival for 6, 12, 18 and 24 months respectively using a gradient boosting algorithm. To gain insight into the decision process of the models, we identified the features that have a high impact on patient survival using permutation feature importance scores as explainability metric. They show that age at diagnosis as well as the presence of distant metastases are key factors for long-term survival. The found factors can be used in a next step for multi-variate survival analysis.</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/40380489?dopt=Abstract</style></custom1></record></records></xml>