2 research outputs found
Salvage Versus Adjuvant Radiation Therapy Following Radical Prostatectomy in Localised Prostate Cancer: A War Without a Winner
ObjectiveTo review current literature regarding the efficacy of adjuvant radiation therapy (ART) and salvage radiation therapy (SRT) following radical prostatectomy (RP) in patients with undetectable postoperative prostate-specific antigen (PSA) levels and high-risk features of prostate cancer (PCa) recurrence.MethodsSeven randomized controlled trials focused on the use of ART compared with either observation or SRT after RP that had been published in PubMed up to May 2022 were reviewed.ResultsThe use of ART following RP has been the treatment of choice over the past decade. Three RCTs comparing ART with early SRT show that SRT given as soon as biochemical recurrence (BCR) is detected is not inferior to ART while it offers the opportunity to avoid overtreatment and potential RT-related side effects. A meta-analysis summarizing the results from these trials supports these findings.ConclusionsEarly SRT may be suggested as the standard of care for patients with PCa and high-risk features for disease recurrence following RP. Nevertheless, further investigations are needed to identify those patients who will benefit from ART, particularly, in case of lymph node involvement. Moreover, some patients might avoid SRT despite reaching detectable postoperative serum PSA levels
Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study
Background: Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence in patients with localised prostate cancer. Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA). Methods: Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram [non-MRI and MRI]), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors. Findings: Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 [95% CI 0·75–0·78] and pooled AUPRC of 0·61 [0·58–0·63]). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPERA correctly predicted ssEPE in 72 (68%) of 106 cases compared with the other models (47 [44%] in the logistic regression model, none in the Sayyid model, 13 [12%] in the Soeterik non-MRI model, and five [5%] in the Soeterik MRI model). SEPERA had higher net benefit than the other models to predict ssEPE, enabling more patients to safely undergo nerve-sparing. In the algorithmic audit, no evidence of model bias was observed, with no significant difference in AUROC when stratified by race, biopsy year, age, biopsy type (systematic only vs systematic and MRI-targeted biopsy), biopsy location (academic vs community), and D'Amico risk group. According to the audit, the most common errors were false positives, particularly for older patients with high-risk disease. No aggressive tumours (ie, grade >2 or high-risk disease) were found among false negatives. Interpretation: We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy. Funding: None.SCOPUS: ar.jinfo:eu-repo/semantics/publishe