Individual and Population Comparisons of Surgery and Radiotherapy Outcomes in Prostate Cancer Using Bayesian Multistate Models

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose

BACKGROUND

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity.

METHODS

We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes.

RESULTS

We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains.

CONCLUSIONS

This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.

A preliminary study on rectal dose reduction associated with hyaluronic acid implantation in brachytherapy for prostate cancer

Objectives

Hydrogel spacer (HS) was developed to reduce rectal toxicities caused by radiotherapy, but has been reported to cause major adverse events. Our institute has attempted to introduce a hyaluronic acid (HA) as an alternative spacer. This study aimed to compare rectal doses and geometric distributions between the HS and HA implantation in prostate cancer.

Methods

HS and HA were inserted in 20 and 18 patients undergoing high-dose brachytherapy, respectively. The rectum spacer volumes injected were 10 mL and 22 mL, respectively. In the treatment planning system, 13.5 Gy was administered with common catheter positions. The rectal dose indices were assessed between the spacer groups for dosimetry evaluation. Distances between the prostate and rectum and configurations of the spacers were compared.

Results

The mean doses irradiated to 0.1 and 2 mL of the rectum were 10.45 Gy and 6.71 Gy for HS, and 6.73 Gy and 4.90 Gy for HA (p<0.001). The mean minimum distances between the prostate and rectum were 1.23 cm and 1.79 cm for HS and HA, respectively (p<0.05). Geometrical configuration comparisons revealed that HA has a higher ability to expand the space than HS.

Conclusion

The rectal dose reduction ability of HA is significantly greater than that of HS, suggesting its potential as a new spacer.

Trends and ethnic differences in stroke recurrence and mortality in a biethnic population, 2000-2019: a novel application of an illness-death model

PURPOSE

To estimate temporal trends in post-stroke outcomes in Mexican Americans (MAs) and non-Hispanic whites (NHWs).

METHODS

We included first-ever ischemic strokes from a population-based study in South Texas (n = 5343, 2000-2019). We applied an illness-death model with three jointly specified Cox-type models to estimate ethnic differences and ethnic-specific temporal trends in recurrence (first stroke to recurrence), recurrence-free mortality (first stroke to death without recurrence), recurrence-affected mortality (first stroke to death with recurrence), and postrecurrence mortality (recurrence to death).

RESULTS

MAs had higher rates of postrecurrence mortality than NHWs in 2019 but lower rates in 2000. One-year risk of this outcome increased in MAs and decreased in NHWs, resulting in ethnic differences changing from -14.9% (95% CI -35.9%, -2.8%) in 2000 to 9.1% (1.7%, 18.9%) in 2018. For recurrence-free mortality, lower rates were observed in MAs until 2013. Ethnic differences in 1-year risk changed from -3.3% (95% CI -4.9%, -1.6%) in 2000 to -1.2% (-3.1%, 0.8%) in 2018. For stroke recurrence and recurrence-affected mortality, significant ethnic disparities persisted over the study period.

CONCLUSIONS

An ethnic disparity in postrecurrence mortality was newly identified, driven by the increasing trend in MAs but a decreasing trend in NHWs.

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer

Background

In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages.

Methods

We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness.

Results

We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM.

Conclusions

Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods' ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

Automated data extraction tool (DET) for external applications in radiotherapy

Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting.

METHODS AND MATERIALS

We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015-16). Extracted data included patients' referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period.

RESULTS

From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool.

CONCLUSION

We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.

Radiotherapy utilisation rates for patients with cancer as a function of age: A systematic review

INTRODUCTION

There is an increasing incidence of cancer in older people, but limited data on radiotherapy uptake, and in particular, radiotherapy utilisation (RTU) rates. The RTU rate for older adults with cancer may be lower than recommended due to lower tolerance for radiotherapy as well as additional comorbidities, reduced life expectancy and travel for treatment. Radiotherapy use must be aligned with best available, age-specific evidence to ensure older adults with cancer receive optimal benefit without harms.

MATERIALS AND METHODS

A systematic review was conducted to synthesise the published data on the actual RTU rate for patients with cancer as a function of age. MEDLINE and EMBASE were systematically searched to identify relevant population-based and hospital-based cohort studies on radiotherapy utilisation for all age groups, published in English, from 1 January 1990 to 1 July 2020. We focused on the following common cancers in older adults for which radiotherapy is recommended: breast, prostate, lung, rectal cancer, glioblastoma multiforme (GBM), and cervical cancer. Age-specific radiotherapy utilisation data were extracted and analysed as a narrative synthesis.

RESULTS

From 2606 studies screened, 75 cohort and population-based studies were identified with age-specific radiotherapy utilisation data. The total number of patients in the 75 studies was 4,792,138. The RTU rate decreased with increasing age for all tumour sites analysed, except for patients receiving curative radiotherapy as definitive treatment for prostate or cervical cancer. This reduction with increasing age was demonstrated in both palliative and curative settings.

DISCUSSION

There is a global reduction in radiotherapy utilisation with increasing age for most tumour sites. The reduction in delivery of radiotherapy warrants further examination and evidence-based guidelines specific to this population.

Multistate models for the natural history of cancer progression

BACKGROUND

Multistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies.

METHODS

We introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state.

RESULTS

In the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening.

CONCLUSIONS

Multistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.

Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study

Simple Summary

We describe a risk profile reconstruction model for cancer-specific survival estimation for continuous time points after urologic cancer diagnosis. We used artificial intelligence (AI)-based algorithms, a national cancer registry data, and accessible clinical parameters for the risk-profile reconstruction. We derived a risk stratification model and estimated the minimum follow-up duration and the likelihood for risk stability in prostate, kidney, and testicular cancers. The estimated follow-up duration was in alignment with recognized clinical guidelines for these cancers. Moreover, the estimated follow-up duration was differed by the cancer origin and the disease dissemination status. Overall, the reconstruction of the population’s risk profile for the cancer-specific prognostic score estimation is feasible using AI and has potential application in clinical settings to improve risk stratification and surveillance management.

Abstract

Background: Prognostication is essential to determine the risk profile of patients with urologic cancers. Methods: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan–Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. Results: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795–0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. Conclusions: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

High Keratin-7 Expression in Benign Peri-Tumoral Prostatic Glands Is Predictive of Bone Metastasis Onset and Prostate Cancer-Specific Mortality

BACKGROUND

New predictive biomarkers are needed to accurately predict metastasis-free survival (MFS) and cancer-specific survival (CSS) in localized prostate cancer (PC). Keratin-7 (KRT7) overexpression has been associated with poor prognosis in several cancers and is described as a novel prostate progenitor marker in the mouse prostate.

METHODS

KRT7 expression was evaluated in prostatic cell lines and in human tissue by immunohistochemistry (IHC, on advanced PC, n = 91) and immunofluorescence (IF, on localized PC, n = 285). The KRT7 mean fluorescence intensity (MFI) was quantified in different compartments by digital analysis and correlated to clinical endpoints in the localized PC cohort.

RESULTS

KRT7 is expressed in prostatic cell lines and found in the basal and supra-basal compartment from healthy prostatic glands and benign peri-tumoral glands from localized PC. The KRT7 staining is lost in luminal cells from localized tumors and found as an aberrant sporadic staining (2.2%) in advanced PC. In the localized PC cohort, high KRT7 MFI above the 80th percentile in the basal compartment was significantly and independently correlated with MFS and CSS, and with hypertrophic basal cell phenotype.

CONCLUSION

High KRT7 expression in benign glands is an independent biomarker of MFS and CSS, and its expression is lost in tumoral cells. These results require further validation on larger cohorts.

Somatic driver mutation prevalence in 1844 prostate cancers identifies ZNRF3 loss as a predictor of metastatic relapse

Driver gene mutations that are more prevalent in metastatic, castration-resistant prostate cancer (mCRPC) than localized disease represent candidate prognostic biomarkers. We analyze 1,844 localized (1,289) or mCRPC (555) tumors and quantify the prevalence of 113 somatic driver single nucleotide variants (SNVs), copy number aberrations (CNAs), and structural variants (SVs) in each state. One-third are significantly more prevalent in mCRPC than expected while a quarter are less prevalent. Mutations in AR and its enhancer are more prevalent in mCRPC, as are those in TP53, MYC, ZNRF3 and PRKDC. ZNRF3 loss is associated with decreased ZNRF3 mRNA abundance, WNT, cell cycle & PRC1/2 activity, and genomic instability. ZNRF3 loss, RNA downregulation and hypermethylation are prognostic of metastasis and overall survival, independent of clinical and pathologic indices. These data demonstrate a strategy for identifying biomarkers of localized cancer aggression, with ZNRF3 loss as a predictor of metastasis in prostate cancer.

Impact of statin use on overall and time to biochemical failure following radical prostatectomy or radiation therapy

OBJECTIVES

To assess the impact of statin use on overall and time to biochemical failure following primary treatment of localized prostate cancer (PCa).

SUBJECTS/PATIENTS AND METHODS

1581 patients undergoing radical prostatectomy (RP) or radiation therapy (RT) for primary treatment of PCa between July 2007 and January 2020 were evaluated for statin use, demographic/oncologic characteristics, and biochemical outcomes. Rate of biochemical failure (BF) was assessed overall and at 1, 3, and 5 years; time to BF was estimated with Kaplan-Meier. Logistic and linear regression were used to control for treatment modality and disease characteristics.

RESULTS

The average age was 63.0 ± 7.5 years and median pre-treatment PSA was 6.55 (IQR 4.94). 1473 (93.2%) and 108 (6.8%) underwent RP and RT, respectively. RP patients were younger, had lower pre-PSA, lower BMI, and lower risk disease. At 3.4 ± 2.7 years follow-up, 323 (20.4%) experienced BF. When stratified by statin use, BF overall and within 1, 3, and 5 years were not different. Time to BF, was lower in patients using statins (1.8 ± 1.9 years vs. 2.4 ± 2.6 years; p = 0.016). These results persisted in multivariate analysis, wherein statin use was not associated with BF but was associated with a shorter time to BF.

CONCLUSION

Overall, statin use was not associated with a reduced risk of BF in RP or RT patients. However, for patients with BF, statin use was associated with a decreased time to BF. Future investigations are warranted to further elucidate the impact of statin use on PCa recurrence.

Impact on quality of life 3 years after diagnosis of prostate cancer patients below 75 at diagnosis: an observational case-control study

BACKGROUND

Prostate cancer patients are known to suffer from poor sexual and urinary long-term side-effects following treatment, potentially impacting quality of life. The purpose of our study was to compare health-related quality of life at 3 years between prostate cancer patients and healthy controls according to key life-style characteristics. Secondary objectives were to compare urological dysfunction, sexual function, anxiety and depression.

METHODS

Multicentric, case-control, observational prospective, open, follow-up study including 819 prostate cancer patients < 75 years old from the EPICAP cohort, newly diagnosed from 1 December 2011 to 31 March 2014 and 879 healthy controls. Participants were excluded if they experienced a relapse. Controls from the same geographical region were age-matched and were excluded if they were diagnosed with prostate cancer. Patients received one of the following treatments: active surveillance (AS), radical prostatectomy (RP), external beam radiotherapy (EBRT), High-intensity Focused Ultrasound (HIFU), chemotherapy (CT), or androgen deprivation therapy (ADT) as appropriate. The primary outcome was the quality of life as evaluated by the QLQ-C30 questionnaire. Scores were analyzed by multivariate analysis to adjust for predefined socio-demographic confounding effects.

RESULTS

In total, 564 participants were included (mean age 67.9 years): 376 patients and 188 controls. Treatment breakdown was: 258 underwent RP, 90 received EBRT, 52 brachytherapy or HIFU, 15 CT, 26 ADT and 61 AS. There was no difference in median global quality of life between patients and controls (94.87 vs 94.15, p = 0.71). Multivariate analysis showed poorer social functioning in patients (24.3% vs. 16.3%, p = 0.0209), more dyspnea (22% vs. 12.4%, p = 0.0078), and yet less current pain (23% vs 33%, p = 0.0151).

CONCLUSIONS

Global health status score at 3 years after diagnosis was similar between patients and controls, though patients showed a significantly worse social functioning. Prostate cancer diagnosis per se does not seem to impact the quality of life of patients < 75 years at diagnosis. However, the therapeutic option that will be chosen following diagnosis should be carefully discussed with the medical staff in terms of benefit-risk ratios as it could have a long-term impact on urinary or erectile dysfunction.

TRIAL REGISTRATION

clinicaltrials.gov, NCT02854982 . Registered 4 August 2016, retrospectively registered.

Bayesian variable selection and shrinkage strategies in a complicated modelling setting with missing data: A case study using multistate models

Multistate modelling is a strategy for jointly modelling related time-to-event outcomes that can handle complicated outcome relationships, has appealing interpretations, can provide insight into different aspects of disease development and can be useful for making individualized predictions. A challenge with using multistate modelling in practice is the large number of parameters, and variable selection and shrinkage strategies are needed in order for these models to gain wider adoption. Application of existing selection and shrinkage strategies in the multistate modelling setting can be challenging due to complicated patterns of data missingness, inclusion of highly correlated predictors and hierarchical parameter relationships.

In this article, we discuss how to modify and implement several existing Bayesian variable selection and shrinkage methods in a general multistate modelling setting. We compare the performance of these methods in terms of parameter estimation and model selection in a multistate cure model of recurrence and death in patients treated for head and neck cancer. We can view this work as a case study of variable selection and shrinkage in a complicated modelling setting with missing data.

Adrenergic Blockade Promotes Maintenance of Dormancy in Prostate Cancer Through Upregulation of GAS6

Men diagnosed with localized prostate cancer can develop metastases many years after initial treatment, resulting in a poor prognosis. The purpose of this study was to investigate the mechanisms by which signaling through norepinephrine (NE) may incite relapse of quiescent prostate cancer. We used an unbiased bioinformatics pipeline to examine mechanisms for recurrence related to sympathetic signaling in the bone marrow. A transcription factor cell array identified ATF1, RAR, and E2F as key nodes in prostate cancer cells exiting quiescence through adrenergic signaling. Subsequent secretome analysis identified GAS6 as affecting activity of these three factors, leading to cell cycle reentry. GAS6 expression was downregulated in osteoblasts through activation of the cAMP pathway and was targeted in vitro and in vivo using pharmacological agents (propranolol and phentolamine). Propranolol increased expression of GAS6 by osteoblasts, and phentolamine significantly inhibited expression. Propranolol treatment was sufficient to both increase GAS6 expression in marrow osteoblasts as well as eliminate the effects of NE signaling on GAS6 expression. These results demonstrate a strong correlation between adrenergic signaling, GAS6 expression, and recurrence in prostate cancer, suggesting a novel therapeutic direction for patients at high risk of metastasis.

Primary Radical Prostatectomy or Ablative Radiotherapy as Protective Factors for Patients With mCRPC Treated With Radium-223 Dichloride: An Italian Multicenter Study

BACKGROUND

We investigated, in a real-life setting, the prognostic relevance of previous primary treatment (radical prostatectomy [RP] or external beam radiotherapy [EBRT]) on overall survival for patients with metastatic castration-resistant prostate cancer (mCRPC) treated with radium-223 (²²³Ra).

MATERIALS AND METHODS

In the present multicenter retrospective study, we enrolled 275 consecutive patients. The demographic and clinical data and mCRPC characteristics were recorded and evaluated at baseline and at the end of treatment or progression. ²²³Ra was administered according to the current label authorization until disease progression or unacceptable toxicity. We divided the whole cohort into 2 groups: those who had undergone primary radical prostatectomy or ablative radiotherapy (RP/EBRT) and those who had not received previous primary treatment (NO).

RESULTS

Of the 275 patients, 128 (46.5%) were alive and undergoing monitoring at the last follow-up examination, 103 (37.4%) had stopped treatment because of disease progression or the onset of comorbidities, and 147 (53.5%) had died during the study period. Of the 275 patients, 132 were in the RP/EBRT group (48%), of whom 93 had undergone RP and 76 had undergone ablative EBRT, and 143 patients were in the NO group (52%). The data showed a clear advantage for the patients in the RP/EBRT group compared with those in the NO group, with an estimated median survival of 18 versus 11 months, respectively (P < .001). The results from the multivariate analysis corroborated this trend, with a hazard ratio of 0.7 (P = .0443), confirming the better outcome for the RP/EBRT group.

CONCLUSIONS

Previous radical treatment provides a protective role for patients with mCRPC undergoing ²²³Ra treatment.