Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

Saba Zarean Shahraki; Mehdi Azizmohammad Looha; Pooya Mohammadi Kazaj; Mehrad Aria; Atieh Akbari; Hassan Emami; Farkhondeh Asadi; Mohammad Esmaeil Akbari

doi:10.3389/fonc.2023.1147604

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

Front Oncol. 2023 Jun 5:13:1147604. doi: 10.3389/fonc.2023.1147604. eCollection 2023.

Authors

Saba Zarean Shahraki¹, Mehdi Azizmohammad Looha², Pooya Mohammadi Kazaj³, Mehrad Aria⁴, Atieh Akbari⁵, Hassan Emami¹, Farkhondeh Asadi¹, Mohammad Esmaeil Akbari⁵

Affiliations

¹ Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
² Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
³ Geographic Information Systems Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
⁴ Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tehran, Iran.
⁵ Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Abstract

Background: Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.

Materials and methods: This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.

Results: The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.

Conclusion: This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.

Keywords: AI application in breast cancer; breast cancer molecular subtypes; breast cancer survival prediction; deep learning survival models; feature importance; survival analysis; survival prediction models; time-to-event machine learning models.