pks+ Escherichia coli more prevalent in benign than malignant colorectal tumors.

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.

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific method for diagnosing cancer. Here, Fourier transform infrared (FTIR) spectroscopy of thyroid tumors (n = 164; 76 malignant, 88 benign) was performed and five (5) neural network (NN) models were designed to discriminate the obtained spectral data. PCA-LDA was used as classical benchmark for comparison. Each NN model was evaluated using a stratified 10-fold cross-validation method to avoid overfitting, and the performance metrics-accuracy, area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NN models were able to perform excellently as classifiers, and all were able to surpass the LDA model in terms of accuracy. Among the NN models, the RNN model performed best, having an AUC of 95.29% ± 6.08%, an accuracy of 98.06% ± 2.87%, a PPV of 98.57% ± 4.52%, a NPV of 93.18% ± 7.93%, a SR value of 98.89% ± 3.51%, and a RR value of 91.25% ± 10.29%. The RNN model outperformed the LDA model for all metrics except for the AUC, NPV, and RR. In conclusion, NN-based tools were able to predict thyroid cancer based on infrared spectroscopy of tissues with a high level of diagnostic performance in comparison to the gold standard.

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples.

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.

ATR-FTIR spectroscopy as adjunct method to the microscopic examination of hematoxylin and eosin-stained tissues in diagnosing lung cancer.

Lung cancer remains the leading cause of cancer-related death worldwide. Since prognosis and treatment outcomes rely on fast and accurate diagnosis, there is a need for more cost-effective, sensitive, and specific method for lung cancer detection. Thus, this study aimed to determine the ability of ATR-FTIR in discriminating malignant from benign lung tissues and evaluate its concordance with H&E staining. Three (3) 5µm-thick sections were cut from formalin fixed paraffin embedded (FFPE) cell or tissue blocks from patients with lung lesions. The outer sections were H&E-stained and sent to two (2) pathologists to confirm the histopathologic diagnosis. The inner section was deparaffinized by standard xylene method and then subjected to ATR-FTIR analysis. Distinct spectral profiles that distinguished (p<0.05) one sample from another, called the "fingerprint region", were observed in five (5) peak patterns representing the amides, lipids, and nucleic acids. Principal component analysis and hierarchical cluster analysis evidently clustered the benign from malignant tissues. ATR-FTIR showed 97.73% sensitivity, 92.45% specificity, 94.85% accuracy, 91.49% positive predictive value and 98.00% negative predictive value in discriminating benign from malignant lung tissue. Further, strong agreement was observed between histopathologic readings and ATR-FTIR analysis. This study shows the potential of ATR-FTIR spectroscopy as a potential adjunct method to the gold standard, the microscopic examination of hematoxylin and eosin (H&E)-stained tissues, in diagnosing lung cancer.