首页|Hospital da Luz Reports Findings in Prostate Cancer (Clinical application of machine learning models in patients with prostate cancer before prostatectomy)
Hospital da Luz Reports Findings in Prostate Cancer (Clinical application of machine learning models in patients with prostate cancer before prostatectomy)
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NETL
NSTL
Springer Nature
New research on Oncology - Prostate Cancer is the subject of a report. According to news reporting from Lisbon, Portugal, by NewsRx journalists, research stated, “To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models. This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP).” The news correspondents obtained a quote from the research from Hospital da Luz, “Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models’ net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC). In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.”
NETL
NSTL
Springer Nature
