IP2 – ATLANTA is launched!
IP2 – ATLANTA is launched! ATLANTA is a phase II randomised controlled trial that will explore sequential multi-modal treatment using systemic therapy, local physical cytoreduction and metastasis directed therapy in men with newly diagnosed metastatic prostate cancer against a comparator of standard of care alone.
All men with new histologically diagnosed hormone sensitive metastatic prostate cancer, within three months of commencing androgen deprivation therapy (ADT), and of performance status 0 to 2 are eligible. No upper limit on metastatic burden will apply, although men must be fit to undergo all trial interventions at point of randomisation.
Men will be randomised to: Control (Standard of Care) OR Intervention 1 (Minimally Invasive Ablative Therapy [MIAT] +/- pelvic lymph node dissection [PLND]) OR Intervention 2 (Local Radiotherapy +/- Lymph Nodes OR Radical Prostatectomy +/- PLND). Randomisation stratified by metastatic burden (CHAARTED definition), intent to treat pelvic lymph nodes, intent to treat metastasis and intent to commence chemotherapy.
Systemic therapy in all arms includes ADT +/- Docetaxel. Radical prostatectomy will be with or without PLND. Local radiotherapy will be 60Gy/20Fr OR 74-78Gy in 2Gy per fraction over a minimum of 27 days, with or without simultaneous nodal radiotherapy. MIAT will be cryotherapy or focal HIFU. Men in both intervention arms will be eligible for metastasis directed therapy in the form of stereotactic ablative radiation (SABR) or surgery.
Men will be recruited over a two year period and followed up for a minimum of two years. Primary outcome will be progression free survival (PFS). ATLANTA is commencing in 17 UK trial centres with a target recruitment of 80 patients in the internal pilot, rising to 918 patients in full phase across 30 UK trial centres from November 2019.
ATLANTA is entirely charity funded (Wellcome Trust) and available on the NIHR CRN portfolio. Follow-up trial visits are not in excess of routine practice and extra burden is minimal. If you would like to join the main phase of ATLANTA as a site, please contact Mr Martin J. Connor ([email protected]) www.imperialprostate.org.uk/ATLANTA.
Prof. Hashim U. Ahmed (ATLANTA PI & CI),
Mr. Martin J. Connor (ATLANTA Doctoral Clinical Research Fellow)
Mr. Taimur T. Shah (Urology SpR & Research Fellow)
ATLANTA Surgeons Board: Mr Mathias Winkler, Mr Tim Dudderidge, Prof. Chris Eden, Mr Paul Cathcart, Prof. Naeem Soomro, Mr Adel Makar
ATLANTA Radiotherapy Board: Prof. John Staffurth, Dr. Alison Falconer, Dr. Stephen Mangar, Dr Olivia Naismith, RTTQA team
ATLANTA MIAT Board: Prof. Hashim U. Ahmed, Mr Stuart McCracken, Mr Raj Nigam, Mr Tim Dudderidge, Prof Iqbal Shergill
ATLANTA SABR Board: Dr Vincent Khoo, RTTQA team
ATLANTA Medical Oncologists: Dr. Naveed Sarwar, Dr Michael Gonzalez
ATLANTA Trial Sites: Imperial College Healthcare NHS Trust, The Royal Marsden Hospital, Guy’s & St Thomas’ NHS Foundation Trust, London North West Healthcare NHS Trust, Royal Surrey County (Guildford) Hospital, University Hospital Southampton, Clatterbridge Cancer Centre & Arrowe Park Hospital, Newcastle Freeman Hospital, King’s Lynn (Cambridge), Norfolk & Norwich (Cambridge), Sunderland Royal Hospital, Frimley Park Hospital, Royal Devon and Exeter Hospital, Wrexham Park Hospital, West Middlesex University Hospital, Royal United Hospital Bath, Betsi Calderwar Health Board, Lister Hospital, Hampshire (Basingstoke) Hospitals, University Hospital Coventry, Worcestershire Royal Hospital.
Trial Sponsor: Imperial College London
Trial Funder: Wellcome Trust
ClinicalTrials.gov Identifier: NCT03763253
Article of the Month: Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy
Every month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.
In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.
If you only have time to read one article this week, it should be this one.
Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy
Nathan C. Wong , Cameron Lam, Lisa Patterson and Bobby Shayegan
Division of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada
Visual abstract created Rebecca Fisher @beckybeckyfishAbstract
Objectives
To train and compare machine‐learning algorithms with traditional regression analysis for the prediction of early biochemical recurrence after robot‐assisted prostatectomy.
Patients and Methods
A prospectively collected dataset of 338 patients who underwent robot‐assisted prostatectomy for localized prostate cancer was examined. We used three supervised machine‐learning algorithms and 19 different training variables (demographic, clinical, imaging and operative data) in a hypothesis‐free manner to build models that could predict patients with biochemical recurrence at 1 year. We also performed traditional Cox regression analysis for comparison.
= 0.686) and with a univariate regression model (AUC = 0.865).
Results
K‐nearest neighbour, logistic regression and random forest classifier were used as machine‐learning models. Classic Cox regression analysis had an area under the curve (AUC) of 0.865 for the prediction of biochemical recurrence. All three of our machine‐learning models (K‐nearest neighbour (AUC 0.903), random forest tree (AUC 0.924) and logistic regression (AUC 0.940) outperformed the conventional statistical regression model. Accuracy prediction scores for K‐nearest neighbour, random forest tree and logistic regression were 0.976, 0.953 and 0.976, respectively.
Conclusions
Machine‐learning techniques can produce accurate disease predictability better that traditional statistical regression. These tools may prove clinically useful for the automated prediction of patients who develop early biochemical recurrence after robot‐assisted prostatectomy. For these patients, appropriate individualized treatment options can improve outcomes and quality of life.
Article of the week: Cognitive training for technical and non‐technical skills in robotic surgery: a randomised controlled trial
Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.
In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. These are intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation. There is also a video produced by the authors.
If you only have time to read one article this week, it should be this one.
Cognitive training for technical and non‐technical skills in robotic surgery: a randomised controlled trial
Nicholas Raison* , Kamran Ahmed*, Takashige Abe*†, Oliver Brunckhorst*, Giacomo Novara‡, Nicolo Buffi§, Craig McIlhenny¶, Henk van der Poel**, Mieke van Hemelrijck††, Andrea Gavazzi‡‡ and Prokar Dasgupta*
*Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences and Medicine, Kings College London, UK, ††Division of Cancer Studies, King’s College London, UK, ¶Department of Urology, Forth Valley Royal Hospital, Larbert, UK, †Department of Urology, Hokkaido University Graduate School of Medicine, Sapporo, Japan, ‡Department of Urology, University of Padua, Padua, §Department of Urology, Humanitas Clinical and Research Centre, Rozzano, Milan, ‡‡Department of Urology, Azienda USL Toscana Centro, Florence, Italy, and **Department of Urology, Netherlands Cancer Institute, Amsterdam, The Netherlands
Visual Abstract created by Rebecca Fisher @beckybeckyfishAbstract
Objective
To investigate the effectiveness of motor imagery (MI) for technical skill and non‐technical skill (NTS) training in minimally invasive surgery (MIS).
Subjects and Methods
A single‐blind, parallel‐group randomised controlled trial was conducted at the Vattikuti Institute of Robotic Surgery, King’s College London. Novice surgeons were recruited by open invitation in 2015. After basic robotic skills training, participants underwent simple randomisation to either MI training or standard training. All participants completed a robotic urethrovesical anastomosis task within a simulated operating room. In addition to the technical task, participants were required to manage three scripted NTS scenarios. Assessment was performed by five blinded expert surgeons and a NTS expert using validated tools for evaluating technical skills [Global Evaluative Assessment of Robotic Skills (GEARS)] and NTS [Non‐Technical Skills for Surgeons (NOTSS)]. Quality of MI was assessed using a revised Movement Imagery Questionnaire (MIQ).
Results
In all, 33 participants underwent MI training and 29 underwent standard training. Interrater reliability was high, Krippendorff’s α = 0.85. After MI training, the mean (sd) GEARS score was significantly higher than after standard training, at 13.1 (3.25) vs 11.4 (2.97) (P = 0.03). There was no difference in mean NOTSS scores, at 25.8 vs 26.4 (P = 0.77). MI training was successful with significantly higher imagery scores than standard training (mean MIQ score 5.1 vs 4.5, P = 0.04).
Conclusions
Motor imagery is an effective training tool for improving technical skill in MIS even in novice participants. No beneficial effect for NTS was found.
Article of the week: Development of a side‐specific, mpMRI‐based nomogram for the prediction of extracapsular extension of PCa
Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.
In addition to the article itself, there are two accompanying editorials written by prominent members of the urological community. These are intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation. There is also a video produced by the authors.
If you only have time to read one article this week, it should be this one.
Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer
Alberto Martini*†, Akriti Gupta*, Sara C. Lewis‡, Shivaram Cumarasamy*, Kenneth G. Haines III§, Alberto Briganti†, Francesco Montorsi† and Ashutosh K. Tewari*
Departments of *Urology, ‡Radiology, §Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA and †Department of Urology, Vita-Salute San Raffaele University, Milan, Italy
Abstract
Objectives
To develop a nomogram for predicting side‐specific extracapsular extension (ECE) for planning nerve‐sparing radical prostatectomy.
Materials and Methods
We retrospectively analysed data from 561 patients who underwent robot‐assisted radical prostatectomy between February 2014 and October 2015. To develop a side‐specific predictive model, we considered the prostatic lobes separately. Four variables were included: prostate‐specific antigen; highest ipsilateral biopsy Gleason grade; highest ipsilateral percentage core involvement; and ECE on multiparametric magnetic resonance imaging (mpMRI). A multivariable logistic regression analysis was fitted to predict side‐specific ECE. A nomogram was built based on the coefficients of the logit function. Internal validation was performed using ‘leave‐one‐out’ cross‐validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit.
Results
The study population consisted of 829 side‐specific cases, after excluding negative biopsy observations (n = 293). ECE was reported on mpMRI and final pathology in 115 (14%) and 142 (17.1%) cases, respectively. Among these, mpMRI was able to predict ECE correctly in 57 (40.1%) cases. All variables in the model except highest percentage core involvement were predictors of ECE (all P ≤ 0.006). All variables were considered for inclusion in the nomogram. After internal validation, the area under the curve was 82.11%. The model demonstrated excellent calibration and improved clinical risk prediction, especially when compared with relying on mpMRI prediction of ECE alone. When retrospectively applying the nomogram‐derived probability, using a 20% threshold for performing nerve‐sparing, nine out of 14 positive surgical margins (PSMs) at the site of ECE resulted above the threshold.
Conclusion
We developed an easy‐to‐use model for the prediction of side‐specific ECE, and hope it serves as a tool for planning nerve‐sparing radical prostatectomy and in the reduction of PSM in future series.