Lin, S.-H.; Chien, C.-H.; Chang, K.-P.; Lu, M.-F.; Chen, Y.-T.; Chu, Y.-W. SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification. Cancers2023, 15, 3690.
Lin, S.-H.; Chien, C.-H.; Chang, K.-P.; Lu, M.-F.; Chen, Y.-T.; Chu, Y.-W. SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification. Cancers 2023, 15, 3690.
Lin, S.-H.; Chien, C.-H.; Chang, K.-P.; Lu, M.-F.; Chen, Y.-T.; Chu, Y.-W. SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification. Cancers2023, 15, 3690.
Lin, S.-H.; Chien, C.-H.; Chang, K.-P.; Lu, M.-F.; Chen, Y.-T.; Chu, Y.-W. SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification. Cancers 2023, 15, 3690.
Abstract
(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions on treatment strategies or the use of palliative care for patients; (2) Methods: The gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected the RNA-seq data of breast cancer patients, a total of 1187 RNA-seq data (median age 58 years), in FPKM format from the TCGA database. Among them, 144 RNA-seq data with date of death information was selected to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to transcripts per million (TPM) to build survival prediction model SaBrcada. After normalization and dimension raising, the differential gene expression data were used for testing eight different deep learning architectures. Among them, GoogLeNet performed the best. Considering the effect of age on prognosis, we examined all ages between the lower and upper quartiles of patient age for a stratified random sampling test; (3) Results: Stratifying by age based on a cut-off of 61 years of age improved the accuracy of SaBrcada compared to previous findings, resulting in an accuracy of 0.798. We also built a free website tool to provide 5 kinds of predicted survival period information for clinician reference; (4) Conclusions: We established a breast cancer survival analysis prediction model, SaBrcada, and a website tool with the same name. Through this highly reliable survival analysis model and website tool, information on survival intervals will be provided for clinicians as part of precision medicine.
Keywords
breast cancer; deep learning; survival analysis; data dimension raising; age stratification
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
