Dataset Information

Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

ABSTRACT:

Background

In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence).

Methods

An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model.

Results

For recurrence ?5 years, stage 2 cancer (OR 1.67, 95% CI?=?1.31-2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI?=?1.81-3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence >?5 years (OR 7.62, 95% CI?=?1.52-38.15 vs. OR 1.75, 95% CI?=?1.32-2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively.

Conclusion

For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (>?80%).

SUBMITTER: Akrami M

PROVIDER: S-EPMC6136222 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

Akrami Majid M Arasteh Peyman P Eghbali Tannaz T Shahraki Hadi Raeisi HR Tahmasebi Sedigheh S Zangouri Vahid V Rezaianzadeh Abbas A Talei Abdolrasoul A

World journal of surgical oncology 20180912 1

<h4>Background</h4>In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence).<h4>Methods</h4>An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model.<h4>Results</h4>For recurrence < 5 years, age (OR 0.96, 95% CI = 0.95-0.97), number of pregnancies (OR 0.94, 95% CI = 0.89-0.99), family history of other cancers (OR ...[more]

PMID: 30208904

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Project description:BackgroundProstate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.MethodsWe systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.ResultsBased on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.ConclusionOverall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Dataset Information

Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

Background

Methods

Results

Conclusion

Publications

Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

Similar Datasets

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