Development and Internal Validation of a Web-based Tool to Predict Sexual, Urinary, and Bowel Function Longitudinally After Radiation Therapy, Surgery, or Observation

External validation of predictive models of sexual, urinary, bowel and hormonal function after surgery in prostate cancer subjects

BACKGROUND

In 2020, a research group published five linear longitudinal models, predict Expanded Prostate Cancer Index Composite-26 (EPIC-26) scores post-treatment for radical prostatectomy, external beam radiotherapy and active surveillance collectively in US patients with localized prostate cancer.

METHODS

Our study externally validates the five prediction models for patient reported outcomes post-surgery for localised prostate cancer. The models' calibration, fit, variance explained and discrimination (concordance-indices) were assessed. Two Australian validation cohorts 1 and 2 years post-prostatectomy were constructed, consisting of 669 and 439 subjects, respectively (750 in total). Patient reported function in five domains post-prostatectomy: sexual, bowel, hormonal, urinary incontinence and other urinary dysfunction (irritation/obstruction). Domain function was assessed using the EPIC-26 questionnaire.

RESULTS

1 year post-surgery, R2 was highest for the sexual domain (35%, SD = 0.02), lower for the bowel (21%, SD = 0.03) and hormone (15%, SD = 0.03) domains, and close to zero for urinary incontinence (1%, SD = 0.01) and irritation/obstruction (- 5%, SD = 0.04). Calibration slopes for these five models were 1.04 (SD = 0.04), 0.84 (SD = 0.06), 0.85 (SD = 0.06), 1.16 (SD = 0.13) and 0.45 (SD = 0.04), respectively. Calibration-in-the-large values were - 2.2 (SD = 0.6), 2.1 (SD = 0.01), 5.1 (SD = 0.1), 9.6 (SD = 0.9) and 4.0 (SD = 0.2), respectively. Concordance-indices were 0.73, 0.70, 0.70, 0.58 and 0.62, respectively (all had SD = 0.01). Mean absolute error and root mean square error were similar across the validation and development cohorts. The validation measures were largely similar at 2 years post-surgery.

CONCLUSIONS

The sexual, bowel and hormone domain models validated well and show promise for accurately predicting patient reported outcomes in a non-US surgical population. The urinary domain models validated poorly and may require recalibration or revision.

Prediction models of incontinence and sexual function one year after radical prostatectomy based on data from 20 164 prostate cancer patients

BACKGROUND

Incontinence and sexual dysfunction are long-lasting side effects after surgical treatment (radical prostatectomy, RP) of prostate cancer (PC). For an informed treatment decision, physicians and patients should discuss expected impairments. Therefore, this paper firstly aims to develop and validate prognostic models that predict incontinence and sexual function of PC patients one year after RP and secondly to provide an online decision making tool.

METHODS

Observational cohorts of PC patients treated between July 2016 and March 2021 in Germany were used. Models to predict functional outcomes one year after RP measured by the EPIC-26 questionnaire were developed using lasso regression, 80-20 splitting of the data set and 10-fold cross validation. To assess performance, R2, RMSE, analysis of residuals and calibration-in-the-large were applied. Final models were externally temporally validated. Additionally, percentages of functional impairment (pad use for incontinence and firmness of erection for sexual score) per score decile were calculated to be used together with the prediction models.

RESULTS

For model development and internal as well as external validation, samples of 11 355 and 8 809 patients were analysed. Results from the internal validation (incontinence: R2 = 0.12, RMSE = 25.40, sexual function: R2 = 0.23, RMSE = 21.44) were comparable with those of the external validation. Residual analysis and calibration-in-the-large showed good results. The prediction tool is freely accessible: https://nora-tabea.shinyapps.io/EPIC-26-Prediction/.

CONCLUSION

The final models showed appropriate predictive properties and can be used together with the calculated risks for specific functional impairments. Main strengths are the large study sample (> 20 000) and the inclusion of an external validation. The models incorporate meaningful and clinically available predictors ensuring an easy implementation. All predictions are displayed together with risks of frequent impairments such as pad use or erectile dysfunction such that the developed online tool provides a detailed and informative overview for clinicians as well as patients.

Bloemen- van Gurp E, Dekker A, Fijten RRR. Development and external validation of multivariate prediction models for erectile dysfunction in men with localized prostate cancer

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 evidence-based decisions about the most suitable treatment with quality of life in mind.

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

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 accessible illustration of how each feature impacts the predicted outcome. Additionally, the model's simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model's predictions is essential.

Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative

Background

Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction.

Objective

To develop and validate models to predict 12- and 24-month post-RP sexual function.

Design, setting, and participants

Using Michigan Urological Surgery Improvement Collaborative (MUSIC) registry data from 2016 to 2021, we developed dynamic, multivariate, random-forest models to predict sexual function recovery following RP. Model factors (established a priori) included baseline patient characteristics and repeated assessments of sexual satisfaction, and Expanded Prostate Cancer Index Composite 26 (EPIC-26) overall scores and sexual domain questions.

Outcome measurements and statistical analysis

We evaluated three outcomes related to sexual function: (1) the EPIC-26 sexual domain score (range 0–100); (2) the EPIC-26 sexual domain score dichotomized at ≥73 for “good” function; and (3) a dichotomized variable for erection quality at 12 and 24 months after RP. A gradient-boosting decision tree was used for the prediction models, which combines many decision trees into a single model. We evaluated the performance of our model using the root mean squared error (RMSE) and mean absolute error (MAE) for the EPIC-26 score as a continuous variable, and the area under the receiver operating characteristic curve (AUC) for the dichotomized EPIC-26 sexual domain score (SDS) and erection quality. All analyses were conducted using R v3.6.3.

Results and limitations

We identified 3983 patients at 12 months and 2494 patients at 24 months who were randomized to the derivation cohort at 12 and 24 months, respectively. Using baseline information only, our model predicted the 12-month EPIC-26 SDS with RMSE of 24 and MAE of 20. The AUC for predicting EPIC-26 SDS ≥73 (a previously published threshold) was 0.82. Our model predicted 24-month EPIC-26 SDS with RMSE of 26 and MAE of 21, and AUC for SDS ≥73 of 0.81. Inclusion of post-RP data improved the AUC to 0.91 and 0.94 at 12 and 24 months, respectively. A web tool has also been developed and is available at https://ml4lhs.shinyapps.io/askmusic_prostate_pro/.

Conclusions

Our model provides a valid way to predict sexual function recovery at 12 and 24 months after RP. With this dynamic, multivariate (multiple outcomes) model, accurate predictions can be made for decision-making and during survivorship, which may reduce decision regret.

Patient summary

Our prediction model allows patients considering prostate cancer surgery to understand their probability before and after surgery of recovering their erectile function and may reduce decision regret.

Variation across operating sites in urinary and sexual outcomes after radical prostatectomy in localized and locally advanced prostate cancer

PURPOSE

The extent of variation in urinary and sexual functional outcomes after radical prostatectomy (RPE) between prostate cancer (PC) operating sites remains unknown. Therefore, this analysis aims to compare casemix-adjusted functional outcomes (EPIC-26 scores incontinence, irritative/obstructive function and sexual function) between operating sites 12 months after RPE.

MATERIALS AND METHODS

Analysis of a cohort of 7065 men treated with RPE at 88 operating sites (prostate cancer centers, "PCCs") between 2016 and 2019. Patients completed EPIC-26 and sociodemographic information surveys at baseline and 12 months after RPE. Survey data were linked to clinical data. EPIC-26 domain scores at 12 months after RPE were adjusted for relevant confounders (including baseline domain score, clinical and sociodemographic information) using regression analysis. Differences between sites were described using minimal important differences (MIDs) and interquartile ranges (IQR). The effects of casemix adjustment on the score results were described using Cohen's d and MIDs.

RESULTS

Adjusted domain scores at 12 months varied between sites, with IQRs of 66-78 (incontinence), 89-92 (irritative/obstructive function), and 20-29 (sexual function). Changes in domain scores after casemix adjustment for sites ≥ 1 MID were noted for the incontinence domain (six sites). Cohen's d ranged between - 0.07 (incontinence) and - 0.2 (sexual function), indicating a small to medium effect of casemix adjustment.

CONCLUSIONS

Variation between sites was greatest in the incontinence and sexual function domains for RPE patients. Future research will need to identify the factors contributing to this variation.

TRIAL REGISTRY

The study is registered at the German Clinical Trial Registry ( https://www.drks.de/drks_web/ ) with the following ID: DRKS00010774.

Self-reported functional assessment after treatment for prostate cancer: 5-year results of the prospective cohort VICAN

Objective: We aimed to assess the long-term association of therapeutic strategies with urinary, sexual function and health-related quality of life (HR-QOL) for 5-year prostate cancer (PC) survivors. Materials & methods: The VICAN survey consisted of self-reported data prospectively collected, including living conditions, treatment side effects and quality of life (QOL) of cancer survivors. Results: Among the 434 PC survivors, 52.8% reported urinary incontinence (UI) and 55.8% reported erectile dysfunction (ED). Patients treated with radical prostatectomy with salvage radiotherapy reported significantly more UI (p = 0.014) and more ED (p = 0.012) compared with other strategies. UI was significantly associated with physical and mental health-related QOL (p = 0.045 and p = 0.049, respectively). Conclusion: Self-assessed functional outcomes 5 years after PC diagnosis remain poor and could have an impact on health-related QOL.

Pelvic Radiation Disease

Pelvic radiation is increasingly being used for the neoadjuvant and definitive treatment of pelvic organ malignancy. While this treatment can be highly effective, and may assist in organ sparing, it is also associated with significant toxicity and devastating adverse events that need to be considered. In broad terms, pelvic radiation disease affects both the primary target organ as well as adjacent organs and soft tissue structures, with complications that can be classified and graded according to consensus criteria. The complication grade is often modality, dose, and area dependent. The most common manifestations are proctitis, cystitis, recto-urethral fistula, ureteric stricture, and bone involvement. Toxicity can be misdiagnosed for many years, resulting in significant management delays. Complications can be difficult to prevent and challenging to treat, requiring specialized multi-disciplinary input to achieve the best possible strategy to minimize impact and improve patient quality of life.

A novel mHealth App (RyPros) for prostate cancer management: an accessibility and acceptability study

Background

Over the past decade, there has been a significant increase in research on the use of mobile health (mHealth) apps as disease management tools. However, very few apps are currently available for prostate cancer (PCa) patient management, and the available apps do not combine the needs of physicians with the requirements of patients. This study aimed to describe the development of a mHealth application for PCa survivors called RyPros, which includes dynamic visualization, intelligent reminders, and instant messaging to support decision-making regarding treatment and follow-up and test the initial accessibility and acceptability application.

Methods

The application was developed through a three-step procedure: logical structure design, application programming, and testing. Dynamic visualization, intelligent reminders, and instant messaging were the core functions of RyPros. Twenty-eight participants who had PCa were enrolled in four weeks of follow-up using the RyPros App. We initially evaluated participants' acceptance of RyPros based on their use of the app (login data, questionnaire completion) and a satisfaction survey.

Results

We successfully designed and tested the application. A total of 32 participants were enrolled, of whom 28 completed the 4-week follow-up, yielding a participation rate of 87.5%. Each participant logged on an average of 2.82 times and achieved an average of 0.89 questionnaires per week over the four weeks. Most participants (64%) liked the app, and most participants (71%) were satisfied, giving the RyPros app a rating of 4 or 5. More than half of the participants (61%) intended to use the RyPros app regularly, and the majority of participants agreed that the three core functionalities of RyPros were helpful (20/28, 71% for instant messaging; 16/28, 57% for visualization; and 18/28, 64% for reminders and assessments).

Conclusions

The mHealth application we developed for PCa survivor management provided dynamic visualization, reminders, assessments, and instant messaging to support decision-making based on multidisciplinary collaboration. PCa survivors showed high acceptance of the RyPros app.

Risk stratification for the prognosis of patients with chemoresistant urothelial cancer treated with pembrolizumab

The use of immune checkpoint inhibitors to treat urothelial carcinoma (UC) is increasing rapidly without clear guidance for validated risk stratification. This multicenter retrospective study collected clinicopathological information on 463 patients, and 11 predefined variables were analyzed to develop a multivariate model predicting overall survival (OS). The model was validated using an independent dataset of 292 patients. Patient characteristics and outcomes were well balanced between the discovery and validation cohorts, which had median OS times of 10.2 and 12.5 mo, respectively. The final validated multivariate model was defined by risk scores based on the hazard ratios (HRs) of independent prognostic factors including performance status, site of metastasis, hemoglobin levels, and the neutrophil‐to‐lymphocyte ratio. The median OS times (95% confidence intervals [CIs]) for the low‐, intermediate‐, and high‐risk groups (discovery cohort) were not yet reached (NYR) (NYR–19.1), 6.8 mo (5.8‐8.9), and 2.3 mo (1.2‐2.6), respectively. The HRs (95% CI) for OS in the low‐ and intermediate‐risk groups vs the high‐risk group were 0.07 (0.04‐0.11) and 0.23 (0.15‐0.37), respectively. The objective response rates for in the low‐, intermediate‐, and high‐risk groups were 48.3%, 28.8%, and 10.5%, respectively. These differential outcomes were well reproduced in the validation cohort and in patients who received pembrolizumab after perioperative or first‐line chemotherapy (N = 584). In conclusion, the present study developed and validated a simple prognostic model predicting the oncological outcomes of pembrolizumab‐treated patients with chemoresistant UC. The model provides useful information for external validation, patient counseling, and clinical trial design.