Machine learning techniques in cancer prognostic modeling and performance assessment

Yiyi Chen, Jess A. Millar

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Prognostic models for disease occurrence, tumor progression and survival are abundant for most types of cancers. Physicians and cancer patients are utilizing these models to make informed treatment decisions and corresponding arrangements. However, not all cancer prognostic models are built and validated rigorously. Some are more useful and reliable than others. In this chapter, we briefly introduce some popular machine learning methods for constructing cancer prognostic models, and discuss pros and cons of each. We also introduce the commonly used discrimination and calibration metrics for assessing predictive performance and validating the prognostic models. In the end, we outline several challenges of using prognostic models in the real world for clinical decision-making support, and propose related suggestions.

Original languageEnglish (US)
Title of host publicationFrontiers of Biostatistical Methods and Applications in Clinical Oncology
PublisherSpringer Singapore
Pages193-230
Number of pages38
ISBN (Electronic)9789811001260
ISBN (Print)9789811001246
DOIs
StatePublished - Oct 3 2017

Fingerprint

Performance Assessment
performance assessment
Cancer
Machine Learning
cancer
Modeling
learning
Neoplasms
Clinical Decision Support Systems
Model
Calibration
learning method
Progression
Discrimination
Tumor
Arrangement
Physicians
discrimination
Decision Making
physician

Keywords

  • Cancer prediction
  • Machine learning
  • Prognostic model
  • Validation

ASJC Scopus subject areas

  • Medicine(all)
  • Mathematics(all)
  • Social Sciences(all)

Cite this

Chen, Y., & Millar, J. A. (2017). Machine learning techniques in cancer prognostic modeling and performance assessment. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (pp. 193-230). Springer Singapore. https://doi.org/10.1007/978-981-10-0126-0_13

Machine learning techniques in cancer prognostic modeling and performance assessment. / Chen, Yiyi; Millar, Jess A.

Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore, 2017. p. 193-230.

Research output: Chapter in Book/Report/Conference proceedingChapter

Chen, Y & Millar, JA 2017, Machine learning techniques in cancer prognostic modeling and performance assessment. in Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore, pp. 193-230. https://doi.org/10.1007/978-981-10-0126-0_13
Chen Y, Millar JA. Machine learning techniques in cancer prognostic modeling and performance assessment. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore. 2017. p. 193-230 https://doi.org/10.1007/978-981-10-0126-0_13
Chen, Yiyi ; Millar, Jess A. / Machine learning techniques in cancer prognostic modeling and performance assessment. Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore, 2017. pp. 193-230
@inbook{c837ab0283fd4d679e4d0b40f4c0c3db,
title = "Machine learning techniques in cancer prognostic modeling and performance assessment",
abstract = "Prognostic models for disease occurrence, tumor progression and survival are abundant for most types of cancers. Physicians and cancer patients are utilizing these models to make informed treatment decisions and corresponding arrangements. However, not all cancer prognostic models are built and validated rigorously. Some are more useful and reliable than others. In this chapter, we briefly introduce some popular machine learning methods for constructing cancer prognostic models, and discuss pros and cons of each. We also introduce the commonly used discrimination and calibration metrics for assessing predictive performance and validating the prognostic models. In the end, we outline several challenges of using prognostic models in the real world for clinical decision-making support, and propose related suggestions.",
keywords = "Cancer prediction, Machine learning, Prognostic model, Validation",
author = "Yiyi Chen and Millar, {Jess A.}",
year = "2017",
month = "10",
day = "3",
doi = "10.1007/978-981-10-0126-0_13",
language = "English (US)",
isbn = "9789811001246",
pages = "193--230",
booktitle = "Frontiers of Biostatistical Methods and Applications in Clinical Oncology",
publisher = "Springer Singapore",

}

TY - CHAP

T1 - Machine learning techniques in cancer prognostic modeling and performance assessment

AU - Chen, Yiyi

AU - Millar, Jess A.

PY - 2017/10/3

Y1 - 2017/10/3

N2 - Prognostic models for disease occurrence, tumor progression and survival are abundant for most types of cancers. Physicians and cancer patients are utilizing these models to make informed treatment decisions and corresponding arrangements. However, not all cancer prognostic models are built and validated rigorously. Some are more useful and reliable than others. In this chapter, we briefly introduce some popular machine learning methods for constructing cancer prognostic models, and discuss pros and cons of each. We also introduce the commonly used discrimination and calibration metrics for assessing predictive performance and validating the prognostic models. In the end, we outline several challenges of using prognostic models in the real world for clinical decision-making support, and propose related suggestions.

AB - Prognostic models for disease occurrence, tumor progression and survival are abundant for most types of cancers. Physicians and cancer patients are utilizing these models to make informed treatment decisions and corresponding arrangements. However, not all cancer prognostic models are built and validated rigorously. Some are more useful and reliable than others. In this chapter, we briefly introduce some popular machine learning methods for constructing cancer prognostic models, and discuss pros and cons of each. We also introduce the commonly used discrimination and calibration metrics for assessing predictive performance and validating the prognostic models. In the end, we outline several challenges of using prognostic models in the real world for clinical decision-making support, and propose related suggestions.

KW - Cancer prediction

KW - Machine learning

KW - Prognostic model

KW - Validation

UR - http://www.scopus.com/inward/record.url?scp=85042635895&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042635895&partnerID=8YFLogxK

U2 - 10.1007/978-981-10-0126-0_13

DO - 10.1007/978-981-10-0126-0_13

M3 - Chapter

AN - SCOPUS:85042635895

SN - 9789811001246

SP - 193

EP - 230

BT - Frontiers of Biostatistical Methods and Applications in Clinical Oncology

PB - Springer Singapore

ER -