Treatment Practice Analysis of Intermediate or High Risk Localized Prostate Cancer - A Multi-center Study with Veterans Health Administration Data.

ICCABS(2019)

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摘要
Prostate cancer (PCa) is a heterogeneous disease. PCa is stratified into risk groups based on clinical factors such as T-stage, Gleason score, and baseline prostate-specific antigen. Treatments are selected based on these risk groups. However, we hypothesize that non-clinical factors such as the radiation therapy (RT) center may also impact treatment selection, and we investigate the impact of these factors on treatment selection practice and their adherence to recommended guidelines from the national comprehensive cancer network (NCCN). A total of 552 patients with intermediate or high-risk localized PCa related data was collected from 34 radiation therapy centers of the Veterans Health Administration (VHA), who were treated with definitive RT and with or without Androgen Deprivation Therapy (ADT) between 2010 and 2017. Patients’ clinical information is extracted by manually reviewing their medical charts. We also extracted treatment intended and treatment administered information from consult and end-of-treatment notes, respectively. The random forest classification algorithm was used to identify the impact of clinical and non-clinical factors in treatment selection, their adherence to the treatment guidelines, and treatment alteration (i.e., change in intended and administered treatments). We created models for predicting treatment intended as well as treatment administered. Our results demonstrated that non-clinical (i.e., treatment center) factors, along with clinical factors, are significant for predicting the adherence of treatment intended to the NCCN guidelines. Furthermore, the center served as an important factor for prescribing ADT; however, it is not associated with the duration of ADT and is weakly associated with treatment alterations. This presence of center-bias in treatment selection warrants further investigation on details of center-specific barriers for NCCN guideline adherence, and as well as the impact of center-bias on oncological outcomes.
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关键词
Prostate cancer, Radiation therapy, Treatment selection, Machine learning, Clinical informatics
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