[en] Objective: To evaluated the value of PET conventional parameters and texture parameters in prediction of the Kirsten rat sarcoma viral oncogene (KRAS) gene expression status in colorectal cancer (CRC) by analyzing the relationship between those parameters and KRAS gene status. Methods: From December 2012 to January 2017, ¹⁸F-fluorodeoxyglucose (FDG) PET/CT data and KRAS gene status of 58 CRC patients (40 males, ¹⁸Females, average age 56.31 years) before anti-tumor therapies were collected. The relationship between PET parameters and KRAS gene expression was analyzed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the values of PET conventional parameters and texture parameters for predicting the KRAS gene status. Spearman rank correlation and Mann-Whitney u test were used to analyze the data. Results: of 58 CRC patients, 19 (32.8%) had KRAS mutation, while 39 (67.2%) were with wild type KRAS. Among the 46 PET image parameters extracted by Chang-Gung image texture analysis (CGITA), 14 PET image parameters were selected by Spearman rank correlation (all |r_s| > 0.8), including 12 texture parameters and 2 conventional parameters (maximum standardized uptake value (SUV_max) and total lesion glycolysis (TLG)). Six PET image parameters (4 texture parameters and 2 conventional parameters) were found to be different between KRAS gene mutant group and wild group (u values: from -4.481 to -2.046, all P < 0.05). Among the 4 texture parameters, standardized uptake value (SUV) kurtosis (SUV_kur) had the best prediction value, while SUV_max was the better one for prediction in the 2 conventional parameters. When 4.27 was selected as the cut-off value for SUV_kur , the Youden index was up to the maximum as 0.35 and it yielded 0.667 on the area under curve (AUC) (95% CI: 0.517-0.816, P = 0.041) with the sensitivity of 15/19 and specificity of 56.4% (22/39), respectively. When 16.6 was selected as the cut-off value of for SUV_max , the Youden index was up to the maximum as 0.64 and the AUC on predicting the KRAS mutant was 0.865 (95% CI: 0.770-0.960, P < 0.001) with the sensitivity of 17/19 and specificity of 74.4%(29/39), respectively. The efficacy of SUV_max for predicting KRAS mutation was significantly better than that of SUV_kur (z = 3.258, P = 0.001). Conclusion: PET texture parameters and conventional parameters can be used to predict the KRAS gene status in CRC patients, and SUV_max may be the best parameter. (authors)

[en] 

Purpose

To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on ¹⁸F-FDG PET/CT and radiomic features using machine-learning methods.

Methods

A total of 199 colorectal cancer patients underwent pre-therapy diagnostic ¹⁸F-FDG PET/CT scans and CRC radical surgery. The Chang-Gung Image Texture Analysis toolbox (CGITA) was used to extract 70 PET radiomic features reflecting ¹⁸F-FDG uptake heterogeneity of tumors. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomic signature score (Rad-score). The training set was used to establish five machine-learning prediction models and the test set was used to test the efficacy of the models. The effectiveness of the models was compared by ROC analysis.

Results

The CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808-0.925) and XGBoost (AUC 0.903, 95% CI 0.855-0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by ¹⁸F-FDG PET/CT for differentiating positive and negative regional LN metastases in CRC (all p < 0.05). Good efficacy of the above two models was also achieved in the test set. We created a nomogram based on the logistic regression model that visualized the results and provided an easy-to-use method for predicting regional LN metastasis in patients with CRC.

Conclusion

In this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on ¹⁸F-FDG PET/CT and PET-based radiomic features were successfully developed and validated. Among them, the logistic regression and XGBoost models performed the best, with higher efficacy than ¹⁸F-FDG PET/CT in both the training and test sets.

[en] Highlights: •BMBIMI is an effective mixed-type inhibitor for mild steel in H₂SO₄ solution. •The inhibition efficiency increases with the increment of inhibitor concentration. •BMBIMI takes the flat mode adsorbed on the metal surface by benzimidazole ring. •The adsorption is an exothermic process and obeys Langmuir adsorption isotherm. -- Abstract: A newly benzimidazole derivative, 1-butyl-3-methyl-1H-benzimidazolium iodide (BMBIMI), has been tested as inhibitor for mild steel in 0.5 M H₂SO₄ solution via various approaches including weight loss, electrochemical measurements, scanning electron microscope (SEM), atomic force microscope (AFM) and theoretical calculations. The obtained results reveal that BMBIMI is an effective mixed-type corrosion inhibitor for mild steel, and the adsorption of BMBIMI on the mild steel surface is found to obey the Langmuir adsorption isotherm, thus the thermodynamic and kinetic parameters governing the adsorption process are calculated and discussed. Moreover, theoretical calculations give further insight into the mechanism of inhibition of BMBIMI

[en] The sluggish Ni(II)/Ni(III) redox cycle does not benefit perxymonosulfate (PMS) activation for recalcitrant pollutant degradation. To solve this problem, a heterogeneous catalyst, Cu_0.2Ni_0.8O/SBA-15 (CNS), was constructed to activate PMS for decomposing two sulfonamide antibiotics, sulfachlorpyridazine (SACP) and sulfapyridine (SAP). SACP and SAP were completely degraded over Cu_0.2Ni_0.8O/SBA-15/PMS (CNSP) after 90 min. O₂^.- was the dominant active species involved in the degradation of SACP and SAP. Structural analysis and elemental valence state observations indicated that Cu(Ⅰ) provided electrons through Cu–O–Ni bonds to realize the charge compensation for Ni(Ⅲ) in the CNSP system. Thus, the in situ Cu(I)/Cu(II) promoting the Ni(II)/Ni(III) cycle could accelerate the PMS activation. This work provides new insights into the electron transfer between transition metals and the charge compensation mechanism for PMS activation. The degradation mechanism was proposed based on the XPS results before and after the reaction, a radical quenching test, and an EPR test. Combined with the SACP and SAP degradation intermediates identified by LC-MS, we suggest that the choice of treatment process depends on the occurrence of a steric hindrance effect between the molecular structure of the degradation target and free radicals.