IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0262489.html
   My bibliography  Save this article

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

Author

Listed:
  • Rock Christian Tomas
  • Anthony Jay Sayat
  • Andrea Nicole Atienza
  • Jannah Lianne Danganan
  • Ma Rollene Ramos
  • Allan Fellizar
  • Kin Israel Notarte
  • Lara Mae Angeles
  • Ruth Bangaoil
  • Abegail Santillan
  • Pia Marie Albano

Abstract

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics–area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)–were averaged for comparison. The NN models were compared to six (6) machine learning models–logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)–for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C–OH C–OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample’s spectrum using NN.

Suggested Citation

  • Rock Christian Tomas & Anthony Jay Sayat & Andrea Nicole Atienza & Jannah Lianne Danganan & Ma Rollene Ramos & Allan Fellizar & Kin Israel Notarte & Lara Mae Angeles & Ruth Bangaoil & Abegail Santilla, 2022. "Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-24, January.
  • Handle: RePEc:plo:pone00:0262489
    DOI: 10.1371/journal.pone.0262489
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262489
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0262489&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0262489?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0262489. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.