Search

Item does not contain fulltext, While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improve the accuracy of prediction and quality of care. Predicting ED may help aid shared decision-making by making the advantages and disadvantages of certain treatments clear, so that a tailored treatment for an individual patient can be chosen. This study aimed to predict ED at 1-year and 2-year post-diagnosis based on patient demographics, clinical data and patient-reported outcomes (PROMs) measured at diagnosis. We used a subset of the ProZIB dataset collected by the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) that contained information on 964 localized prostate cancer cases from 69 Dutch hospitals for model training and external validation. Two models were generated using a logistic regression algorithm coupled with Recursive Feature Elimination (RFE). The first predicted ED 1 year post-diagnosis and required 10 pre-treatment variables; the second predicted ED 2 years post-diagnosis with 9 pre-treatment variables. The validation AUCs were 0.84 and 0.81 for 1 year and 2 years post-diagnosis respectively. To immediately allow patients and clinicians to use these models in the clinical decision-making process, nomograms were generated. In conclusion, we successfully developed and validated two models that predicted ED in patients with localized prostate cancer. These models will allow physicians and patients alike to make informed eviden

Item does not contain fulltext, INTRODUCTION: Urinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as "black-box" has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. METHODS: We used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. RESULTS: All models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. CONCLUSION: The outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an acce

Contains fulltext : 244248.pdf (Publisher’s version ) (Open Access), PURPOSE: External Beam Radiotherapy (EBRT) techniques dramatically changed over the years. This may have affected the risk of radiation-induced second primary cancers (SPC), due to increased irradiated low dose volumes and scatter radiation. We investigated whether patterns of SPC after EBRT have changed over the years in prostate cancer (PCa) survivors. MATERIALS AND METHODS: PCa survivors diagnosed between 1990-2014 were selected from the Netherlands Cancer Registry. Patients treated with EBRT were divided in three time periods, representing 2-dimensional Radiotherapy (RT), 3-dimensional conformal RT (3D-CRT), and the advanced RT (AdvRT) era. Standardized incidence ratios (SIR) and absolute excess risks (AER) were calculated to estimate relative and excess absolute SPC risks. Sub-hazard ratios (sHRs) were calculated to compare SPC rates between the EBRT and prostatectomy cohort. SPCs were categorized by subsite and anatomic region. RESULTS: PCa survivors who received EBRT had an increased risk of developing a solid SPC (SIR=1.08; 1.05-1.11), especially in patients aged <70 years (SIR=1.13; 1.09-1.16). Pelvic SPC risks were increased (SIR=1.28; 1.23-1.34), with no obvious differences between the three EBRT eras. Non-pelvic SPC were only significantly increased in the AdvRT era (SIR=1.08; 1.02-1.14), in particular for the 1-5 year follow-up period. Comparing the EBRT cohort to the prostatectomy cohort, again an increased pelvic SPC risk was found for all EBRT periods (sHRs= 1.61, 1.47-1.76). Increased non-pelvic SPC risks were present for all RT eras and highest for the AdvRT period (sHRs=1.17, 1.06-1.29). CONCLUSION: SPC risk in patients with EBRT is increased and remained throughout the different EBRT eras. The risk of developing a SPC outside the pelvic area changed unfavorably in the AdvRT era. Prolonged follow-up is needed to confirm this observation. Whether this is associated with increased irradiated low-dose volumes and scatter, or other changes in clinical

Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radio- therapy environment using ultrasound more appealing. (C) 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Background and Purpose To evaluate spatial differences in dose distributions of the ano-rectal wall (ARW) using dose-surface maps (DSM) between prostate cancer patients receiving intensity-modulated radiation therapy with and without implantable rectum spacer (IMRT+IRS; IMRT-IRS, respectively), and to correlate this with late gastro-intestinal (GI) toxicities using validated spatial and non-spatial normal-tissue complication probability (NTCP) models. Materials and Methods For 26 patients DSMs of the ARW were generated. From the DSMs various shape-based dose measures were calculated at different dose levels: lateral extent, longitudinal extent, and eccentricity. The contiguity of the ARW dose distribution was assessed by the contiguous-DSH (cDSH). Predicted complication rates between IMRT+IRS and IMRT-IRS plans were assessed using a spatial NTCP model and compared against a non-spatial NTCP model. Results Lateral extent, longitudinal extent and cDSH were significantly lower in IMRT+IRS than for IMRT-IRS at high-dose levels. Largest significant differences were observed for cDSH at dose levels >50Gy, followed by lateral extent at doses >57Gy, and longitudinal extent. Significant decreases (p = 0.01) in median rectal and anal NTCPs were predicted when using an IRS. Conclusions: Local-dose effects are predicted to significantly reduce by an IRS. Differences in predicted GI toxicity rates exist between spatial and non-spatial NTCP models.

BACKGROUND: Stereotactic ablative body radiotherapy (SABR) is a non-invasive alternative to surgery to control primary renal cell cancer (RCC) in patients that are medically inoperable or at high-risk of post-surgical dialysis. The objective of the FASTRACK II clinical trial is to investigate the efficacy of SABR for primary RCC. METHODS: FASTRACK II is a single arm, multi-institutional phase II study. Seventy patients will be recruited over 3 years and followed for a total of 5 years. Eligible patients must have a biopsy confirmed diagnosis of primary RCC with a single lesion within a kidney, have ECOG performance ≤2 and be medically inoperable, high risk or decline surgery. Radiotherapy treatment planning is undertaken using four dimensional CT scanning to incorporate the impact of respiratory motion. Treatment must be delivered using a conformal or intensity modulated technique including IMRT, VMAT, Cyberknife or Tomotherapy. The trial includes two alternate fractionation schedules based on tumour size: for tumours ≤4 cm in maximum diameter a single fraction of 26Gy is delivered; and for tumours > 4 cm in maximum diameter 42Gy in three fractions is delivered. The primary outcome of the study is to estimate the efficacy of SABR for primary RCC. Secondary objectives include estimating tolerability, characterising overall survival and cancer specific survival, estimating the distant failure rate, describing toxicity and renal function changes after SABR, and assessment of cost-effectiveness of SABR compared with current therapies. DISCUSSION: The present study design allows for multicentre prospective validation of the efficacy of SABR for primary RCC that has been observed from prior single institutional and retrospective series. The study also allows assessment of treatment related toxicity, overall survival, cancer specific survival, freedom from distant failure and renal function post therapy. TRIAL REGISTRATION: Clinicaltrials.gov NCT02613819 , registered Nov 2

Background and Purpose: To assess the effect of a shrinking rectal balloon implant (RBI) on the anorectal dose and complication risk during the course of prostate radiotherapy. Material/Methods: In 15 patients with localized prostate cancer an RBI was implanted. A weekly kilovolt cone-beam computed tomography (CBCT) scan was acquired to measure the dynamics of RBI volume and prostate-rectum separation. The absolute anorectal volume encompassed by the 75 Gy isodose (V75Gy) was calculated as well as the mean anorectal dose. The increase in risk of grade 2-3 late rectal bleeding (LRB) between the start and end of treatment was predicted. Results: A significant shrinkage of RBI volumes was observed, with an average volume of 70.4% of baseline at the end of the treatment. Although the prostate–rectum separation significantly decreased over time, it remained at least 1 cm. No correlation between mean anorectal dose and balloon deflation was found. No significant increase in V75Gy and increase in LRB risk over time was observed, except in one patient whose RBI had completely deflated in the third week of treatment. Conclusions: Despite significant decrease in RBI volume the high-dose rectal volume and the predicted LRB risk were unaffected due to a persistent spacing between the prostate and the anterior rectal wall.

Aim: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. Materials & methods: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org. Results: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. Conclusion: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.

Aim: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. Materials & methods: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org. Results: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. Conclusion: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.