Gross Tumor and Intracranial Control Benefits with Fractionated Radiotherapy Compared with Stereotactic Radiosurgery for Patients with WHO Grade 2 Meningioma.

OBJECTIVE: Surgical resection is the mainstay of treatment for WHO grade 2 meningioma. Fractionated radiation therapy (RT) is frequently used after surgery, though many centers utilize stereotactic radiosurgery (SRS) for recurrence or progression. Herein, we report disease control outcomes from an institutional cohort with adjuvant fractionated RT versus salvage SRS. METHODS: We identified 32 patients from an institutional database with WHO grade 2 meningioma and residual/recurrent tumor treated with either SRS or fractionated RT. Patients were treated between 2007 and 2021 and had at least 1 year of follow-up. Kaplan-Meier estimators were used to determine gross tumor control (GTC) and intracranial control (IC). Univariate Cox proportional hazards models using biologically effective dose (BED) as a continuous parameter were used to assess for dose responses. RESULTS: With a median follow-up of 5.5 years, 13 patients (41%) received SRS to a recurrent or progressive nodule, 2 (6%) fractionated RT to a recurrent or progressive nodule, and 17 (53%) adjuvant fractionated RT following subtotal resection. Five-year GTC was higher with fractionated RT versus SRS (82% vs. 38%, P = 0.03). Five-year IC was also better with fractionated RT versus SRS (82% vs. 11%, P < 0.001). On univariate analysis, increasing BED10 was significantly associated with better GTC (P = 0.039); increasing BED3 was not (P = 0.82). CONCLUSIONS: In this patient cohort, GTC and IC were significantly higher in patients treated with adjuvant fractionated RT compared with salvage SRS. Increasing BED10 was associated with better GTC. Fractionated RT may provide a better therapeutic ratio than SRS for grade 2 meningiomas.

Patterns of failure after radiosurgery for WHO grade 1 or imaging defined meningiomas: Long-term outcomes and implications for management.

BACKGROUND: We analyzed long-term control and patterns of failure in patients with World Health Organization Grade 1 meningiomas treated with definitive or postoperative stereotactic radiosurgery at the authors' affiliated institution. METHODS: 96 patients were treated between 2004 and 2019 with definitive (n = 57) or postoperative (n = 39) stereotactic radiosurgery. Of the postoperative patients, 17 were treated adjuvantly following subtotal resection and 22 were treated as salvage at time of progression. Patients were treated to the gross tumor alone without margin or coverage of the dural tail to a median dose of 15 Gy. Median follow up was 7.4 years (inter-quartile range 4.8-11.3). Local control, marginal control, regional control, and progression-free survival were analyzed. RESULTS: Local control at 5 and 10 years was 97 % and 95 %. PFS at 5 and 10 years was 94 % and 90 % with no failures reported after 6 years. Definitive and postoperative local control were similar at 5 (95 % [82-99 %] vs. 100 %) and 10 years (92 % [82-99 %] vs. 100 %). Patients treated with postoperative SRS did not have an increased marginal failure rate (p = 0.83) and only 2/39 (5 %) experienced recurrence elsewhere in the cavity. CONCLUSIONS: Stereotactic radiosurgery targeting the gross tumor alone provides excellent local control and progression free survival in patients treated definitively and postoperatively. As in the definitive setting, patients treated postoperatively can be treated to gross tumor alone without need for additional margin or dural tail coverage.

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.

Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy.

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.

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

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.

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.

Cholesterol-lowering activity of natural mono- and sesquiterpenoid compounds in essential oils: A review and investigation of mechanisms using in silico protein-ligand docking.

Mono- and sesquiterpenoids are the main chemical constituents of essential oils. Essential oils and their constituents have received increasing attention for lipid-lowering properties in both cell and animal models. Despite the chemical diversity of essential oil compounds, the effects of many of these compounds on cholesterol metabolism are highly similar. In this report, we review the literature regarding the effects of essential oils and their terpenoid constituents on cholesterol homeostasis, and explore likely mechanisms using protein-ligand docking. We identified 98 experimental and seven clinical studies on essential oils, isolated compounds, and blends; 100 of these described improvements either in blood cholesterol levels or in sterol metabolic pathways. Our review and docking analysis confirmed two likely mechanisms common to many essential oil compounds: (1) direct agonism of peroxisome-proliferator-activated receptors, and (2) direct interaction with sterol-sensing domains, motifs found in key sterol regulatory proteins including sterol regulatory element binding protein cleavage activating protein and HMG-CoA reductase. Notably, these direct interactions lead to decreased transcription and accelerated degradation of HMG-CoA reductase. Our work suggests that terpene derivatives in essential oils have cholesterol-lowering activity and could potentially work synergistically with statins, however, further high quality studies are needed to establish their clinical efficacy.

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.