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.
Highlights
Breast cancer remains the most prevalent cancer among women
It was suggested that CA 15– 3 and CEA can be considered complementary in detecting recurrence of breast cancer
The above classifications were based on microscopic examination of hematoxylin and eosin (H&E)-stained specimens and immunohistochemical staining following the current WHO classification
Summary
Breast cancer remains the most prevalent cancer among women. Biennial mammography has been highly recommended for women 50 to 74 years old for early detection of this disease. Supplemental screening with US for women with intermediate risk and dense breasts is an option to increase cancer detection. The mammographic sensitivity for breast cancer in women with very dense breasts is 47.6% and increased to 76.1% with US screening [2]. CEA can be analysed to screen for breast cancer It lacks disease sensitivity and specificity, cannot be used for screening a subpopulation with high risk for malignancies, a general asymptomatic population, or for independently diagnosing cancer. It was suggested that CA 15– 3 and CEA can be considered complementary in detecting recurrence of breast cancer Their sensitivity is low and independent of the majority of the prognostic parameters that may be considered before relapse [4]
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