首页|RWTH Aachen University Reports Findings in Prostate Cancer (Multicentric 68Ga-PS MA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer)
RWTH Aachen University Reports Findings in Prostate Cancer (Multicentric 68Ga-PS MA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Prostate Ca ncer is the subject of a report. According to news reporting originating in Aach en, Germany, by NewsRx journalists, research stated, “The treatment with Lutetiu m PSMA (Lu-PSMA) in patients with metastatic castration-resistant prostate cance r (mCRPC) has recently been approved by the FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and i dentification of short- and long-term survivors after treatment could help tailo r mCRPC diagnosis and treatment accordingly.” The news reporters obtained a quote from the research from RWTH Aachen Universit y, “The aim of this study is to investigate the value of radiomic parameters ext racted from pretreatment Ga-PSMA PET images for the prediction of treatment resp onse. A total of 45 mCRPC patients treated with Lu-PSMA- 617 from two university hospital centers were retrospectively reviewed for this study. Radiomic features were extracted from the volumetric segmentations of metastases in the bone. A r andom forest model was trained and validated to predict treatment response based on age and conventionally used PET parameters, radiomic features and combinatio ns thereof. Further, overall survival was predicted by using the identified radi omic signature and compared to a Cox regression model based on age and PET param eters. The machine learning model based on a combined radiomic signature of thre e features and patient age achieved an AUC of 0.82 in 5-fold cross-validation an d outperformed models based on age and PET parameters or radiomic features (AUC, 0.75 and 0.76, respectively). A Cox regression model based on this radiomic sig nature showed the best performance to predict overall survival (C-index, 0.67). Our results demonstrate that a machine learning model to predict response to Lu- PSMA treatment based on a combination of radiomics and patient age outperforms a model based on age and PET parameters.”
AachenGermanyEuropeCancerCyborgsEmerging TechnologiesHealth and MedicineMachine LearningOncologyProsta te CancerProstatic NeoplasmsTherapy
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