No 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.

Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics—area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)—were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.

Background: Some E. coli strains that synthesize the toxin colibactin within the 54-kb pks island are being implicated in colorectal cancer (CRC) development. Here, the prevalence of pks + E. coli in malignant and benign colorectal tumors obtained from selected Filipino patients was compared to determine the association of pks + E. coli with CRC in this population. Methods and Results:A realtime qPCR protocol was developed to quantify uidA, clbB, clbN, and clbA genes in formalin fixed paraffin embedded colorectal tissues. The number of malignant tumors (44/62; 71%) positive for the uidA gene was not significantly different (p=0.3428) from benign (38/62; 61%) tumors. Significantly higher number of benign samples (p<0.05) were positive for all three colibactin genes (clbB, clbN, and clbA) compared with malignant samples. There was also higher prevalence of pks + E. coli among older females and in tissue samples taken from the rectum. Conclusion:Hence, pks + E. coli may not be associated with CRC development among Filipinos.

Background: 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. Methods: Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier-transform infrared (FTIR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples wherein the pathologists had discordant readings were then analyzed using the AI models described above. Results: 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% to 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% to 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 one sample (1.20%) was misclassified. Conclusion: The study provides additional proof of the ability of ATR-FTIR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.

Background: Some E. coli strains that synthesize the toxin colibactin within the 54-kb pks island are being implicated in colorectal cancer (CRC) development. Here, the prevalence of pks+ E. coli in malignant and benign colorectal tumors obtained from selected Filipino patients was compared to determine the association of pks+ E. coli with CRC in this population. Methods and Results: A realtime qPCR protocol was developed to quantify uidA, clbB, clbN, and clbA genes in formalin fixed paraffin embedded colorectal tissues. The number of malignant tumors (44/62; 71%) positive for the uidA gene was not significantly different (p=0.3428) from benign (38/62; 61%) tumors. Significantly higher number of benign samples (p<0.05) were positive for all three colibactin genes (clbB, clbN, and clbA) compared with malignant samples. There was also higher prevalence of pks+ E. coli among older females and in tissue samples taken from the rectum. Conclusion: Hence, pks+ E. coli may not be associated with CRC development among Filipinos.