CoChat
Sign Up
Cited by 3,338AI in cancer detectionGene expression and cancer classificationArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.

Machine learning applications in cancer prognosis and prediction

Open access
Kourou, Konstantina, Exarchos, Themis P., Exarchos, Konstantinos P., Karamouzis, Michalis V., Fotiadis, Dimitrios I.·2015·Computational and Structural Biotechnology Journal
DOI 10.1016/j.csbj.2014.11.005PMC PMC4348437
Open this paper in CoChat — free account

Abstract

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.csbj.2014.11.005

Cites

72 of 72 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Classification task in supervised learning. Tumors are represented as X and classified as benign or malignant. The circled examples depict those tumors that have been misclassified.
Fig. 1 Classification task in supervised learning. Tumors are represented as X and classified as benign or malignant. The circled examples depict those tumors that have been misclassified.CC BY · PubMed Central
Fig. 2 An indicative ROC curve of two classifiers: (a) Random Guess classifier (red curve) and (b) A classifier providing more robust predictions (blue dotted curve).
Fig. 2 An indicative ROC curve of two classifiers: (a) Random Guess classifier (red curve) and (b) A classifier providing more robust predictions (blue dotted curve).CC BY · PubMed Central
Fig. 3 An illustration of the ANN structure. The arrows connect the output of one node to the input of another.
Fig. 3 An illustration of the ANN structure. The arrows connect the output of one node to the input of another.CC BY · PubMed Central
Fig. 4 An illustration of a DT showing the tree structure. Each variable (X, Y, Z) is represented by a circle and the decision outcomes by squares (Class A, Class B). T(1–3) represents the thresholds (classification rules) in order to successfully classify each variable to a class label.
Fig. 4 An illustration of a DT showing the tree structure. Each variable (X, Y, Z) is represented by a circle and the decision outcomes by squares (Class A, Class B). T(1–3) represents the thresholds (classification rules) in order to successfully classify each variable to a class label.CC BY · PubMed Central
Fig. 5 A simplified illustration of a linear SVM classification of the input data. Figure was reproduced from the ML lectures of [21]. Tumors are classified according to their size and the patient's age. The depicted arrows display the misclassified tumors.
Fig. 5 A simplified illustration of a linear SVM classification of the input data. Figure was reproduced from the ML lectures of [21]. Tumors are classified according to their size and the patient's age. The depicted arrows display the misclassified tumors.CC BY · PubMed Central
Fig. 6 An illustration of a BN. Nodes (A–D) represent a set of random variables across with their conditional probabilities which are calculated in each table.
Fig. 6 An illustration of a BN. Nodes (A–D) represent a set of random variables across with their conditional probabilities which are calculated in each table.CC BY · PubMed Central
Fig. 7 Distribution of published studies, within the last 5 years, that employ ML techniques for cancer prediction.
Fig. 7 Distribution of published studies, within the last 5 years, that employ ML techniques for cancer prediction.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 72 references

Research works printed but not linked: 5

  1. Bishop C.M.: Pattern recognition and machine learning. New York: Springer 2006.
  2. Mitchell T.M.: The discipline of machine learning: Carnegie Mellon University. : Carnegie Mellon University, School of Computer Science, Machine Learning Department 2006.
  3. Platt J.C., Cristianini N. and Shawe-Taylor J.: Large margin DAGs for multiclass classification. : 1999: 547.
  4. Adams S.: Is Coursera the beginning of the end for traditional higher education?. : Higher Education 2012.
  5. Howlader N., Noone A., Krapcho M., Garshell J., Neyman N. and Aletkruse S.: SEER Cancer Statistics Review, 1975–2010, [Online] National Cancer Institute. Bethesda, MD: National Cancer Institute 2013. [Online].

Cited by

Sorted by citations

Items 301–400 of 1,006

Related

See all

Metadata

Authors
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I.
Affiliations
  • University of Ioannina (GR)
  • National and Kapodistrian University of Athens (GR)
Corresponding author
Fotiadis, Dimitrios I.
ORCID
Venue
Computational and Structural Biotechnology Journal
Publisher
American Association for the Advancement of Science (AAAS)
Published
2014-11-15
Volume
13
Pages
8-17
ISSN
2001-0370 (ISSN-L)
DOI
10.1016/j.csbj.2014.11.005
PMC
PMC4348437
OpenAlex
W2111547563
Cited by
3,338
References
72
Citations by year
2026: 302 · 2025: 406 · 2024: 401 · 2023: 405 · 2022: 393 · 2021: 433 · 2020: 425 · 2019: 274 · 2018: 158 · 2017: 80 · 2016: 49 · 2015: 11 · 2014: 1
FWCI
4.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
funded by the project NEOMARK (FP7-ICT 224483)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
cancer prognosis, cancer risk prediction, prediction model, artificial neural networks, Bayesian networks, support vector machine, decision tree, supervised learning, clinical decision support, tumor heterogeneity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review