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Villamanca JJ, Hermogino LJ, Ong KD, Paguia B, Abanilla L, Lim A, Angeles LM, Espiritu B, Isais M, Tomas RC, Albano PM. Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples. APPLIED SPECTROSCOPY 2022; 76:1412-1428. [PMID: 35821580 DOI: 10.1177/00037028221116083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues (n = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, and none was misclassified. Moreover, it was able to correctly identify the histologically confirmed benign samples (n = 83) at 51.5-99.7% confidence (average = 91.64%). Of the 83 samples, 60 (72.29%) were classified with an above average confidence score, 22 (26.51%) classified below the average confidence score, and only 1 sample (1.20%) was misclassified. The study provides additional proof of the ability of attenuated total reflection (ATR) FT-IR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.
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Affiliation(s)
- John Jerald Villamanca
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Lemuel John Hermogino
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Katherine Denise Ong
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Brian Paguia
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Lorenzo Abanilla
- Department of Pathology, Divine Word Hospital, Tacloban City, Philippines
| | - Antonio Lim
- Department of Pathology, Divine Word Hospital, Tacloban City, Philippines
| | - Lara Mae Angeles
- Department of Pathology, 596481University of Santo Tomas Hospital, Manila, Philippines
| | - Bernadette Espiritu
- Department of Pathology, 603332Bulacan Medical Center, Malolos City, Philippines
| | - Maura Isais
- Department of Pathology, 603332Bulacan Medical Center, Malolos City, Philippines
- The Graduate School, 595547University of Santo Tomas, Manila, Philippines
| | - Rock Christian Tomas
- Department of Electrical Engineering, 54729University of the Philippines Los Baños, Los Baños, Philippines
| | - Pia Marie Albano
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
- Department of Pathology, Divine Word Hospital, Tacloban City, Philippines
- Research Center for the Natural and Applied Sciences, 564927University of Santo Tomas, Manila, Philippines
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