Multi-state relative survival modelling of colorectal cancer progression and mortality

Colorectal cancer recurrence and its impact on survival after curative surgery: An analysis based on multistate models

AIM

To investigate the usefulness of multistate models (MSM) for determining colorectal cancer (CRC) recurrence rate, to analyse the effect of different factors on tumour recurrence and death, and to assess the impact of recurrence for CRC prognosis.

METHODS

Observational follow-up study of incident CRC cases disease-free after curative resection in 2006-2013 (n = 994). Recurrence and mortality were analyzed with MSM, as well as covariate effects on transition probabilities.

RESULTS

Cumulative incidence of recurrence at 60 months was 13.7%. Five years after surgery, 70.3% of patients were alive and recurrence-free, and 8.4% were alive after recurrence. Recurrence has a negative impact on prognosis, with 5-year CRC-related mortality increasing from 3.8% for those who are recurrence-free 1-year after surgery to 33.6% for those with a recurrence. Advanced stage increases recurrence risk (HR = 1.53) and CRC-related mortality after recurrence (HR = 2.35). CRC-related death was associated with age in recurrence-free patients, and with comorbidity after recurrence. As expected, age≥75 years was a risk factor for non-CRC-related death with (HR = 7.76) or without recurrence (HR = 4.26), while its effect on recurrence risk was not demonstrated.

CONCLUSIONS

MSM allows detailed analysis of recurrence and mortality in CRC. Recurrence has a negative impact on prognosis. Advanced stage was a determining factor for recurrence and CRC-death after recurrence.

Exosomal circ_0084043 derived from colorectal cancer-associated fibroblasts promotes in vitro endothelial cell angiogenesis by regulating the miR-140-3p/HIF-1α/VEGF signaling axis

Background

Circular RNAs (circRNAs) hold potential as diagnostic markers for colorectal cancer (CRC); however, their functional mechanisms remain incompletely elucidated. This work investigates the clinical implications of a unique set comprising six circRNAs derived from serum in CRC. Furthermore, we delve into the role of exosomal circ_0084043, originating from colorectal cancer-associated fibroblasts (CAFs), with a specific focus on its contribution to endothelial cell angiogenesis.

Methods

The study analyzed circRNA levels in serum samples obtained from both CRC and control groups using qRT-PCR. Additionally, exosomes originating from colorectal CAFs and normal fibroblasts (NFs) were purified and confirmed by electron microscopy and Western blotting techniques. The proangiogenic effects of CAF-derived exosomal circ_0084043 were assessed in endothelial cells through proliferation, migration, and in vitro capillary tube formation assays. Gain- and loss-of-function experiments were employed to clarify the role of the circ_0084043/miR-140-3p/HIF-1α axis in endothelial cell angiogenesis, utilizing luciferase reporter assay, Western blotting, and ELISA for mechanism elucidation.

Results

The candidate circRNAs (circ_0060745, circ_001569, circ_007142, circ_0084043, Circ_BANP, and CiRS-7) exhibited notably elevated expression in CRC patient sera compared to the levels observed in healthy individuals. Except for CiRS-7, all circRNAs showed elevated expression in CRC patients with positive lymph node metastasis and advanced tumor stages. Exosomes released by colorectal CAFs augmented endothelial cell proliferation, migration, and angiogenesis by upregulating VEGF expression and secretion. Circ_0084043 was highly detected in endothelial cells treated with CAF-derived exosomes. Silencing circ_0084043 reduced VEGFA expression and diminished CAF exosome-induced endothelial cell processes, indicating its pivotal role in angiogenesis. Circ_0084043 sponges miR-140-3p, regulating HIF-1α, and a reverse relationship was also identified between miR-140-3p and VEGFA in endothelial cells. Inhibiting miR-140-3p mitigated circ_0084043 knockdown effects in CAF exosome-treated endothelial cells. Co-transfection of si-circ_0084043 and a miR-140-3p inhibitor reversed the inhibited migration and angiogenesis caused by circ_0084043 knockdown in CAF exosome-treated endothelial cells. Inhibiting miR-140-3p rescued reduced VEGFA expression due to circ_0084043 knockdown in endothelial cells exposed to CAF-derived exosomes, indicating modulation of the circ_0084043/miR-140-3p/VEGF signaling in CAF-derived exosome-induced angiogenesis.

Conclusions

This study unveiled a distinctive signature of six serum-derived circular RNAs, indicating their potential as promising diagnostic biomarkers for CRC. Importantly, exosomal circ_0084043 originating from colorectal CAFs was identified as playing a crucial role in endothelial cell angiogenesis, exerting its influence through the modulation of the miR-140-3p/HIF-1α/VEGF signaling axis.

Cost-utility analysis of real-time artificial intelligence-assisted colonoscopy in Italy

Background and study aims Artificial intelligence (AI)-assisted colonoscopy has proven to be effective compared with colonoscopy alone in an average-risk population. We aimed to evaluate the cost-utility of GI GENIUS, the first marketed real-time AI system in an Italian high-risk population. Methods A 1-year cycle cohort Markov model was developed to simulate the disease evolution of a cohort of Italian individuals positive on fecal immunochemical test (FIT), aged 50 years, undergoing colonoscopy with or without the AI system. Adenoma or colorectal cancer (CRC) were identified according to detection rates specific for each technique. Costs were estimated from the Italian National Health Service perspective. Results Colonoscopy+AI system was dominant with respect to standard colonoscopy. The GI GENIUS system prevented 155 CRC cases (-2.7%), 77 CRC-related deaths (-2.8%), and improved quality of life (+0.027 QALY) with respect to colonoscopy alone. The increase in screening cost (+€10.50) and care for adenoma (+€3.53) was offset by the savings in cost of care for CRC (-€28.37), leading to a total savings of €14.34 per patient. Probabilistic sensitivity analysis confirmed the cost-efficacy of the AI system (almost 80% probability). Conclusions The implementation of AI detection tools in colonoscopy after patients test FIT-positive seems to be a cost-saving strategy for preventing CRC incidence and mortality.

Cost-effectiveness of Artificial Intelligence-Aided Colonoscopy for Adenoma Detection in Colon Cancer Screening

Background and Aims

Artificial intelligence-aided colonoscopy significantly improves adenoma detection. We assessed the cost-effectiveness of the GI Genius technology, an artificial intelligence-aided computer diagnosis for polyp detection (CADe), in improving colorectal cancer outcomes, adopting a Canadian health care perspective.

Methods

A Markov model with 1-year cycles and a lifetime horizon was used to estimate incremental cost-effectiveness ratio comparing CADe to conventional colonoscopy polyp detection amongst patients with a positive faecal immunochemical test. Outcomes were life years (LYs) and quality-adjusted life years (QALY) gained. The analysis applied costs associated with health care resource utilization, including procedures and follow-ups, from a provincial payer's perspective using 2022 Canadian dollars. Effectiveness and cost data were sourced from the literature and publicly available databases. Extensive probabilistic and deterministic sensitivity analyses were performed, assessing model robustness.

Results

Life years and QALY gains for the CADe and conventional colonoscopy groups were 19.144 versus 19.125 and 17.137 versus 17.113, respectively. CADe and conventional colonoscopies' overall per-case costs were $2990.74 and $3004.59, respectively. With a willingness-to-pay pre-set at $50,000/QALY, the incremental cost-effectiveness ratio was dominant for both outcomes, showing that CADe colonoscopy is cost-effective. Deterministic sensitivity analysis confirmed that the model was sensitive to the incidence risk ratio of adenoma per colonoscopy for large adenomas. Probabilistic sensitivity analysis showed that the CADe strategy was cost-effective in up to 73.4% of scenarios.

Conclusion

The addition of CADe solution to colonoscopy is a dominant, cost-effective strategy when used in faecal immunochemical test-positive patients in a Canadian health care setting.

Multi-state model for predicting ocular progression in acute Stevens-Johnson syndrome/toxic epidermal necrolysis

This study aimed to clarify the etiologic factors predicting acute ocular progression in SJS/TEN, and identify patients who require immediate and intensive ophthalmological treatment. We previously conducted two Japanese Surveys of SJS/TEN (i.e., cases arising between 2005-2007 and between 2008-2010), and obtained the medical records, including detailed dermatological and ophthalmological findings, of 230 patients. Acute ocular severity was evaluated as none, mild, severe, and very severe. A multi-state model assuming the Markov process based on the Cox proportional hazards model was used to elucidate the specific factors affecting the acute ocular progression. Our findings revealed that of the total 230 patients, 23 (24%) of 97 cases that were mild at initial presentation worsened to severe/very severe. Acute ocular progression developed within 3 weeks from disease onset. Exposure to nonsteroidal anti-inflammatory drugs (NSAIDs) and younger patient age were found to be statistically significant for the progression of ocular severity from mild to severe/very severe [hazard ratio (HR) 3.83; 95% confidence interval (CI) 1.48 to 9.91] and none to severe/very severe [HR 0.98; 95% CI 0.97 to 0.99], respectively. The acute ocular severity score at worst-condition was found to be significantly correlated with ocular sequelae. Thus, our detailed findings on acute ocular progression revealed that in 24% of SJS/TEN cases with ocular involvement, ocular severity progresses even after initiating intensive treatment, and that in younger-age patients with a history of exposure to NSAIDs, very strict attention must be given to their ophthalmological appearances.

A multistate model incorporating estimation of excess hazards and multiple time scales

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code.

Relaxing the assumption of constant transition rates in a multi-state model in hospital epidemiology

BACKGROUND

Multi-state models are being increasingly used to capture complex disease pathways. The convenient formula of the exponential multi-state model can facilitate a quick and accessible understanding of the data. However, assuming time constant transition rates is not always plausible. On the other hand, obtaining predictions from a fitted model with time-dependent transitions can be challenging. One proposed solution is to utilise a general simulation algorithm to calculate predictions from a fitted multi-state model.

METHODS

Predictions obtained from an exponential multi-state model were compared to those obtained from two different parametric models and to non-parametric Aalen-Johansen estimates. The first comparative approach fitted a multi-state model with transition-specific distributions, chosen separately based on the Akaike Information Criterion. The second approach was a Royston-Parmar multi-state model with 4 degrees of freedom, which was chosen as a reference model flexible enough to capture complex hazard shapes. All quantities were obtained analytically for the exponential and Aalen-Johansen approaches. The transition rates for the two comparative approaches were also obtained analytically, while all other quantities were obtained from the fitted models via a general simulation algorithm. Metrics investigated were: transition probabilities, attributable mortality (AM), population attributable fraction (PAF) and expected length of stay. This work was performed on previously analysed hospital acquired infection (HAI) data. By definition, a HAI takes three days to develop and therefore selected metrics were also predicted from time 3 (delayed entry).

RESULTS

Despite clear deviations from the constant transition rates assumption, the empirical estimates of the transition probabilities were approximated reasonably well by the exponential model. However, functions of the transition probabilities, e.g. AM and PAF, were not well approximated and the comparative models offered considerable improvements for these metrics. They also provided consistent predictions with the empirical estimates in the case of delayed entry time, unlike the exponential model.

CONCLUSION

We conclude that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates. The multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data. User-friendly code is provided.

Estimation in the progressive illness-death model: A nonexhaustive review

Multistate models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so-called "illness-death" model plays a central role in the theory and practice of these models. Many time-to-event datasets from medical studies with multiple end points can be reduced to this generic structure. In these models one important goal is the modeling of transition rates but biomedical researchers are also interested in reporting interpretable results in a simple and summarized manner. These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and the sojourn time distributions. We will give a review of some of the available methods for estimating such quantities in the progressive illness-death model conditionally (or not) on covariate measures. For some of these quantities estimators based on subsampling are employed. Subsampling, also referred to as landmarking, leads to small sample sizes and usually to heavily censored data leading to estimators with higher variability. To overcome this issue estimators based on a preliminary estimation (presmoothing) of the probability of censoring may be used. Among these, the presmoothed estimators for the cumulative incidences are new. We also introduce feasible estimation methods for the cumulative incidence function conditionally on covariate measures. The proposed methods are illustrated using real data. A comparative simulation study of several estimation approaches is performed and existing software in the form of R packages is discussed.

Improving survival in end-stage renal disease: A case study

Background: With the increase of life expectancy, *On behalf of the REIN registry. end-stage renal disease (ESRD) is affecting a growing number of people. Simultaneously, renal replacement therapies (RRTs) have considerably improved patient survival. We investigated the way current RRT practices would affect patients' survival.

Methods: We used a multi-state model to represent the transitions between RRTs and the transition to death. The concept of “crude probability of death” combined with this model allowed estimating the proportions of ESRD-related and ESRD-unrelated deaths. Estimating the ESRD-related death rate requires comparing the mortality rate between ESRD patients and the general population. Predicting patients' courses through RRTs and Death states could be obtained by solving a system of Kolmogorov differential equations. The impact of practice on patient survival was quantified using the restricted mean survival time (RMST) which was compared with that of healthy subjects with same characteristics.

Results: The crude probability of ESRD-unrelated death was nearly zero in the youngest patients (18–45 years) but was a sizeable part of deaths in the oldest (≥70 years). Moreover, in the oldest patients, the proportion of expected death was higher in patient without vs. with diabetes because the former live older. In men aged 75 years at first RRT, the predicted RMSTs in patients with and without diabetes were, respectively, 61% and 69% those of comparable healthy men.

Conclusion: Using the concept of “crude probability of death” with multi-state models is feasible and useful to assess the relative benefits of various treatments in ESRD and help patient long-term management.

The continuum of breast cancer care and outcomes in the U.S. Military Health System: an analysis by benefit type and care source

PURPOSE

This study investigates transition rates between breast cancer diagnosis, recurrence, and death by insurance benefit type and care source in U.S. Military Health System (MHS).

METHODS

The MHS data repository and central cancer registry linked data were used to identify women aged 40-64 with histologically confirmed breast cancer between 2003 and 2007. Three-state continuous time Markov models were used to estimate transition rates and transition rate ratios (TRRs) by TRICARE benefit type (Prime or non-Prime) and care source (direct, purchased, or both), adjusted for demographic, tumor, and treatment variables.

RESULTS

Analyses included 2668 women with transitions from diagnosis to recurrence (n = 832), recurrence to death (n = 79), and diagnosis to death without recurrence (n = 91). Compared to women with Prime within each care source, women with non-Prime using both care sources had higher transition rates (TRR 1.47, 95% CI 1.03, 2.10). Compared to those using direct care within each benefit type, women utilizing both care sources with non-Prime had higher transition rates (TRR 1.86, 95% CI 1.11, 3.13), while women with Prime utilizing purchased care had lower transition rates (TRR 0.82, 95% CI 0.68, 0.98).

CONCLUSIONS

In the MHS, women with non-Prime benefit plans compared to Prime had higher transition rates along the breast cancer continuum among both care source users. Purchased care users had lower transition rates than direct care users among Prime beneficiaries.

IMPLICATIONS FOR CANCER SURVIVORS

Benefit plan and care source may be associated with breast cancer progression. Further research is needed to demonstrate differences in survivorship.

Applying Additive Hazards Models for Analyzing Survival in Patients with Colorectal Cancer in Fars Province, Southern Iran

Introduction:

Colorectal cancer (CRC) is a commonly fatal cancer that ranks as third worldwide and third and the fifth in Iranian women and men, respectively. There are several methods for analyzing time to event data. Additive hazards regression models take priority over the popular Cox proportional hazards model if the absolute hazard (risk) change instead of hazard ratio is of primary concern, or a proportionality assumption is not made.

Methods:

This study used data gathered from medical records of 561 colorectal cancer patients who were admitted to Namazi Hospital, Shiraz, Iran, during 2005 to 2010 and followed until December 2015. The nonparametric Aalen’s additive hazards model, semiparametric Lin and Ying’s additive hazards model and Cox proportional hazards model were applied for data analysis. The proportionality assumption for the Cox model was evaluated with a test based on the Schoenfeld residuals and for test goodness of fit in additive models, Cox-Snell residual plots were used. Analyses were performed with SAS 9.2 and R3.2 software.

Results:

The median follow-up time was 49 months. The five-year survival rate and the mean survival time after cancer diagnosis were 59.6% and 68.1±1.4 months, respectively. Multivariate analyses using Lin and Ying’s additive model and the Cox proportional model indicated that the age of diagnosis, site of tumor, stage, and proportion of positive lymph nodes, lymphovascular invasion and type of treatment were factors affecting survival of the CRC patients.

Conclusion:

Additive models are suitable alternatives to the Cox proportionality model if there is interest in evaluation of absolute hazard change, or no proportionality assumption is made.

Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers

INTRODUCTION

Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored.

MATERIALS AND METHODS

We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome.

RESULTS

Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71-0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables.

CONCLUSION

Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.