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

Using machine learning for predicting cancer-specific mortality in bladder cancer patients undergoing radical cystectomy: a SEER-based study

BACKGROUND

Accurately assessing the prognosis of bladder cancer patients after radical cystectomy has important clinical and research implications. Current models, based on traditional statistical approaches and complex variables, have limited performance. We aimed to develop a machine learning (ML)-based prognostic model to predict 5-year cancer-specific mortality (CSM) in bladder cancer patients undergoing radical cystectomy, and compare its performance with current validated models.

METHODS

Patients were selected from the Surveillance, Epidemiology, and End Results database and the First Affiliated Hospital of Sun Yat-sen University for model construction and validation. We used univariate and multivariate Cox regression to select variables with independent prognostic significance for inclusion in the model's construction. Six ML algorithms and Cox proportional hazards regression were used to construct prediction models. Concordance index (C-index) and Brier scores were used to compare the discrimination and calibration of these models. The Shapley additive explanation method was used to explain the best-performing model. Finally, we compared this model with three existing prognostic models in urothelial carcinoma patients using C-index, area under the receiver operating characteristic curve (AUC), Brier scores, calibration curves, and decision curve analysis (DCA).

RESULTS

This study included 8,380 patients, with 6,656 in the training set, 1,664 in the internal validation set, and 60 in the external validation set. Eight features were ultimately identified to build models. The Light Gradient Boosting Machine (LightGBM) model showed the best performance in predicting 5-year CSM in bladder cancer patients undergoing radical cystectomy (internal validation: C-index = 0.723, Brier score = 0.191; external validation: C-index = 0.791, Brier score = 0.134). The lymph node density and tumor stage have the most significant impact on the prediction. In comparison with current validated models, our model also demonstrated the best discrimination and calibration (internal validation: C-index = 0.718, AUC = 0.779, Brier score = 0.191; external validation: C-index = 0.789, AUC = 0.884, Brier score = 0.137). Finally, calibration curves and DCA exhibited better predictive performance as well.

CONCLUSIONS

We successfully developed an explainable ML model for predicting 5-year CSM after radical cystectomy in bladder cancer patients, and it demonstrated better performance compared to existing models.

A Review on Calibration Methods of Cancer Simulation Models

Calibration, a critical step in the development of simulation models, involves adjusting unobservable parameters to ensure that the outcomes of the model closely align with observed target data. This process is particularly vital in cancer simulation models with a natural history component where direct data to inform natural history parameters are rarely available. This work reviews the literature of cancer simulation models with a natural history component and identifies the calibration approaches used in these models with respect to the following attributes: calibration target, goodness-of-fit (GOF) measure, parameter search algorithm, acceptance criteria, and stopping rules. After a comprehensive search of the PubMed database from 1981 to June 2023, 68 studies were included in the review. Nearly all (n=66) articles specified the calibration targets, and most articles (n=56) specified the parameter search algorithms they used, whereas goodness-of-fit metric (n=51) and acceptance criteria/stopping rule (n=45) were reported for fewer times. The most frequently used calibration targets were incidence, mortality, and prevalence, whose data sources primarily come from cancer registries and observational studies. The most used goodness-of-fit measure was weighted mean squared error. Random search has been the predominant method for parameter search, followed by grid search and Nelder-mead method. Machine learning-based algorithms, despite their fast advancement in the recent decade, has been underutilized in the cancer simulation models. More research is needed to compare different parameter search algorithms used for calibration.

Key points

This work reviewed the literature of cancer simulation models with a natural history component and identified the calibration approaches used in these models with respect to the following attributes: calibration target, goodness-of-fit (GOF) measure, parameter search algorithm, acceptance criteria, and stopping rules.Random search has been the predominant method for parameter search, followed by grid search and Nelder-mead method.Machine learning-based algorithms, despite their fast advancement in the recent decade, has been underutilized in the cancer simulation models. Furthermore, more research is needed to compare different parameter search algorithms used for calibration.

The Present and Future of Artificial Intelligence in Urological Cancer

Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

Simple Summary

Cancer is associated with significant morbimortality worldwide. Although significant advances have been made in the last few decades in terms of early detection and treatment, providing personalized care remains a challenge. Artificial intelligence (AI) has emerged as a means of improving cancer care with the use of computer science. Identification of risk factors for poor prognosis and patient profiling with AI techniques and tools is feasible and has potential application in clinical settings, including surveillance management. The goal of this study is to present an AI-based solution tool for cancer patients data analysis and improve their management by identifying clinical factors associated with relapse and survival, developing a prognostic model that identifies features associated with poor prognosis, and stratifying patients by risk.

Abstract

Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.