Using Dual-source Photon-counting Detector CT to Simultaneously Quantify Fat and Iron Content: A Phantom Study.

RATIONALE AND OBJECTIVES: To evaluate the feasibility of using photon-counting detector computed tomography (PCD CT) to simultaneously quantify fat and iron content MATERIALS AND METHODS: Phantoms with pure fat, pure iron and fat-iron deposition were scanned by two tube voltages (120 and 140 kV) and two image quality (IQ) settings (80 and 145). Using an iron-specific three-material decomposition algorithm, virtual noniron (VNI) and virtual iron content (VIC) images were generated at quantum iterative reconstruction (QIR) strength levels 1-4. RESULTS: Significant linear correlations were observed between known fat content (FC) and VNI for pure fat phantoms (r = 0.981-0.999, p < 0.001) and between known iron content (IC) and VIC for pure iron phantoms (r = 0.897-0.975, p < 0.001). In fat-iron phantoms, the measurement for fat content of 5-30% demonstrated good linearity between FC and VNI (r = 0.919-0.990, p < 0.001), and VNI were not affected by 75, 150, and 225 µmol/g iron overload (p = 0.174-0.519). The measurement for iron demonstrated a linear range of 75-225 µmol/g between IC and VIC (r = 0.961-0.994, p < 0.001) and VIC was not confounded by the coexisting 5%, 20%, and 30% fat deposition (p = 0.943-0.999). The Bland-Altman of fat and iron measurements were not significantly different at varying tube voltages and IQ settings (all p > 0.05). No significant difference in VNI and VIC at QIR 1-4. CONCLUSION: PCD CT can accurately and simultaneously quantify fat and iron, including scan parameters with lower radiation dose.

CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis.

BACKGROUND: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. METHODS: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. RESULTS: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. CONCLUSIONS: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.

Quantification of bone quality and distribution of the proximal humerus with dual-energy computed tomography.

Background: The proximal humerus is a common site of osteoporotic fractures, and bone quality is a predictor of surgical reduction quality. Dual-energy computed tomography (DECT) is assuming an increasingly important role in the quantification of bone mineral density (BMD) due it is ability to perform three-material decomposition. We aimed to analyze the bone quality and distribution of the proximal humerus with DECT quantitatively. Methods: Sixty-five consecutive patients (average age 49.5±15.2 years; male: female ratio 32:33) without proximal humerus fractures who had undergone DECT were retrospectively selected. The humeral head was divided into 4 regions on a cross section in the medial plane between the greater tuberosity and the surgical neck. The quantitative parameters, including virtual noncalcium (VNCa) value, computed tomography value of calcium (CaCT), computed tomography value of mixed-energy images (regular CT value) (rCT), and relative calcium density (rCaD), were measured. The correlations between the quantitative parameters and age and body mass index (BMI) were analyzed, and the correlations of age, sex, BMI, region of the humeral head, and VNCa value on CaCT were evaluated. Results: The differences in CaCT, rCT, and rCaD between the 4 regions of proximal humerus were statistically significant (P<0.001), while the difference in VNCa values was not (P=0.688). The calcium concentration (CaCT and rCaD) was the densest in the posteromedial zone. The differences of CaCT, rCT, and rCaD between males and females in the 4 regions of proximal humerus were statistically significant (P<0.05), while those of the posterolateral zone were not (rCT; P>0.05). The differences in VNCa values between males and females were also not significant (P>0.05). Multivariable linear regression analysis indicated that sex, age, BMI, regions, and VNCa were significant (P<0.05) predictors of the CaCT value. Conclusions: The concentration of calcium was the densest in the posteromedial region of proximal humerus, and the VNCa value of DECT may be used for quantifying the BMD of the proximal humerus.

CT-Based Radiomic Score: A Risk Stratifier in Far-Advanced Gastric Cancer Patients.

OBJECTIVES: To prolong the survival, the value of a computed tomography-based radiomic score (RS) in stratifying survival and guiding personalized chemotherapy strategies in far-advanced gastric cancer (FGC) was investigated. MATERIALS AND METHODS: This retrospective multicenter study enrolled 283 FGC patients (cT4a/bNxM0-1) from three centers. Patients from one center were randomly divided into the training (n = 166) and internal validation (n = 83) cohorts, whereas the external validation cohort (n = 34) consisted of patients from the two other centers. The RS was calculated for each patient to predict progression-free survival (PFS). Features from the primary tumor and main metastasis (peritoneum, liver, and lymph node) were integrated in the training cohort and then validated for its ability to stratify PFS and overall survival (OS) in the validation cohort. The association between the RS and efficacy of neoadjuvant intraperitoneal and systemic (NIPS) therapy was also explored. RESULTS: The RS demonstrated a favorable prognostic ability to predict PFS in all cohorts (training: C-index 0.83, 95% confidence interval [CI]: 0.788-0.872; internal validation: C-index 0.75, 95% CI: 0.682-0.818; external validation: C-index 0.76, 95% CI: 0.669-0.851; all p < 0.05), as well as an excellent ability to stratify the PFS and OS in both the whole population and metastatic subgroups (p < 0.05). Patients with a low score were more likely to undergo surgery after perioperative chemotherapy (p < 0.05). Furthermore, only high-scoring patients with peritoneal metastasis benefited from NIPS. CONCLUSION: The RS may be an effective risk stratifier for the outcomes of FGC patients and may be used to select patients who can benefit from NIPS therapy.

Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study.

BACKGROUND: A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC). PURPOSE: To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value. MATERIAL AND METHODS: A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan-Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS. RESULTS: Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort (P<0.05). CONCLUSION: Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.

Prognostic value of preoperative CT features for disease-free survival in patients with primary gastric gastrointestinal stromal tumors after resection.

PURPOSE: Tumor size is an important prognostic factor without consideration of the necrotic and cystic components within tumor for patients with gastrointestinal stromal tumors (GISTs). We aimed to extract the enhancing viable component from the tumor using computed tomography (CT) post-processing software and evaluate the value of preoperative CT features for predicting the disease-free survival (DFS) after curative resection for patients with primary gastric GISTs. METHODS: 132 Patients with primary gastric GISTs who underwent preoperative contrast-enhanced CT and curative resection were retrospectively analyzed. We used a certain CT attenuation of 30 HU to extract the enhancing tissue component from the tumor. Enhancing tissue volume and other CT features were assessed on venous-phase images. We evaluated the value of preoperative CT features for predicting the DFS after surgery. Univariate and multivariate Cox regression analyses were performed to find the independent risk factor for predicting the DFS. RESULTS: Of the 132 patients, 68 were males and 64 were females, with a mean age of 61 years. The median follow-up duration was 60 months, and 28 patients experienced disease recurrence and distant metastasis during the follow-up period. Serosal invasion (p < 0.001; HR = 5.277) and enhancing tissue volume (p = 0.005; HR = 1.447) were the independent risk factors for predicting the DFS after curative resection for patients with primary gastric GISTs. CONCLUSION: Preoperative contrast-enhanced CT could be useful for predicting the DFS after the surgery of gastric GISTs, and serosal invasion and enhancing tissue volume were the independent risk factors.

Porous Polymer Cubosomes with Ordered Single Primitive Bicontinuous Architecture and Their Sodium-Iodine Batteries.

Bicontinuous porous materials, which possess 3D interconnected pore channels facilitating a smooth mass transport, have attracted much interest in the fields of energy and catalysis. However, their synthesis remains very challenging. We report a general approach, using polymer cubosomes as the template, for the controllable synthesis of bicontinuous porous polymers with an ordered single primitive (SP) cubic structure, including polypyrrole (SP-PPy), poly-m-phenylenediamine (SP-PmPD), and polydopamine (SP-PDA). Specifically, the resultant SP-PPy had a unit cell parameter of 99 nm, pore diameter of 45 nm, and specific surface area of approximately 60 m2·g-1. As a proof of concept, the I2-adsorbed SP-PPy was employed as the cathode materials of newly emerged Na-I2 batteries, which delivered a record-high specific capacity (235 mA·h·g-1 at 0.5 C), excellent rate capability, and cycling stability (with a low capacity decay of 0.12% per cycle within 400 cycles at 1 C). The advantageous contributions of the bicontinuous structure and I3- adsorption mechanism of SP-PPy were revealed by a combination of ion diffusion experiments and theoretical calculations. This study opens a new avenue for the synthesis of porous polymers with new topologies, broadens the spectrum of bicontinuous-structured materials, and also develops a novel potential application for porous polymers.

CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy.

Objective: The aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy. Materials and Methods: A total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated. Results: There were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.76~0.90), 0.77 (0.63~0.91), 0.78 (0.66~0.89), and 0.72 (0.66~0.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010). Conclusion: Radiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.

Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy.

BACKGROUND: To investigate the prognostic value of dual-energy CT (DECT) based radiomics to predict disease-free survival (DFS) and overall survival (OS) for patients with advanced gastric cancer (AGC) after neoadjuvant chemotherapy (NAC). METHODS: From January 2014 to December 2018, a total of 156 AGC patients were enrolled and randomly allocated into a training cohort and a testing cohort at a ratio of 2:1. Volume of interest of primary tumor was delineated on eight image series. Four feature sets derived from pre-NAC and delta radiomics were generated for each survival arm. Random survival forest was used for generating the optimal radiomics signature (RS). Statistical metrics for model evaluation included Harrell's concordance index (C-index) and the average cumulative/dynamic AUC throughout follow-up. A clinical model and a combined Rad-clinical model were built for comparison. RESULTS: The pre-IU (derived from iodine uptake images before NAC) RS performed best for DFS and OS in the testing cohort (C-indices, 0.784 and 0.698; the average cumulative/dynamic AUCs, 0.80 and 0.77). When compared with the clinical model, the radiomics model had significantly higher C-index to predict DFS in the testing cohort (0.784 vs. 0.635, p < 0.001), but no statistical difference was found for OS (0.698 vs. 0.680, p = 0.473). The combined Rad-clinical models showed improved performance in the testing cohort, with C-indices of 0.810 and 0.710 for DFS and OS, respectively. CONCLUSION: DECT-derived radiomics serves as a promising non-invasive biomarker to predict survival for AGC patients after NAC, providing an opportunity for transforming proper treatment.

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma.

BACKGROUND: To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma. METHODS: Patients with >2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS. RESULTS: In the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI: 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p < 0.01). CONCLUSION: The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.

Utility of preoperative computed tomography features in predicting the Ki-67 labeling index of gastric gastrointestinal stromal tumors.

PURPOSE: To evaluate the value of preoperative computed tomography (CT) features including morphologic and quantitative features for predicting the Ki-67 labeling index (Ki-67LI) of gastric gastrointestinal stromal tumors (GISTs). METHODS: We retrospectively included 167 patients with gastric GISTs who underwent preoperative contrast-enhanced CT. We assessed the morphologic features of preoperative CT images and the quantitative features including the maximum diameter of tumor, total tumor volume, mean total tumor CT value, necrosis volume, necrosis volume ratio, enhanced tissue volume, and mean CT value of enhanced tissue. Potential predictive parameters to distinguish the high-level Ki-67LI group (>4%, n = 125) from the low-level Ki-67LI group (≤4%, n = 42) were compared and subsequently determined in multivariable logistic regression analysis. RESULTS: Growth pattern (p = 0.036), shape (p = 0.000), maximum diameter (p = 0.018), total tumor volume (p = 0.021), mean total tumor CT value (p = 0.009), necrosis volume (p = 0.006), necrosis volume ratio (p = 0.000), enhanced tissue volume (p = 0.027), and mean CT value of enhanced tissue (p = 0.004) were significantly different between the two groups. Multivariate logistic regression analysis indicated that lobulated/irregular shape (odds ratio [OR] = 3.817; p = 0.000) and high necrosis volume ratio (OR = 1.935; p = 0.024) were independent factors of high-level Ki-67LI. CONCLUSIONS: Higher necrosis volume ratio in combination with lobulated/irregular shape could potentially predict high expression of Ki-67LI for gastric GISTs.

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer.

OBJECTIVE: To develop and validate a dual-energy computed tomography (DECT) derived radiomics model to predict peritoneal metastasis (PM) in patients with gastric cancer (GC). METHODS: This retrospective study recruited 239 GC (non-PM = 174, PM = 65) patients with histopathological confirmation for peritoneal status from January 2015 to December 2019. All patients were randomly divided into a training cohort (n = 160) and a testing cohort (n = 79). Standardized iodine-uptake (IU) images and 120-kV-equivalent mixed images (simulating conventional CT images) from portal-venous and delayed phases were used for analysis. Two regions of interest (ROIs) including the peritoneal area and the primary tumor were independently delineated. Subsequently, 1691 and 1226 radiomics features were extracted from the peritoneal area and the primary tumor from IU and mixed images on each phase. Boruta and Spearman correlation analysis were used for feature selection. Three radiomics models were established, including the R_IU model for IU images, the R_MIX model for mixed images and the combined radiomics model (the R_comb model). Random forest was used to tune the optimal radiomics model. The performance of the clinical model and human experts to assess PM was also recorded. RESULTS: Fourteen and three radiomics features with low redundancy and high importance were extracted from the IU and mixed images, respectively. The R_IU model showed significantly better performance to predict PM than the R_MIX model in the training cohort (AUC, 0.981 vs. 0.917, p = 0.034). No improvement was observed in the R_comb model (AUC = 0.967). The R_IU model was the optimal radiomics model which showed no overfitting in the testing cohort (AUC = 0.967, p = 0.528). The R_IU model demonstrated significantly higher predictive value on peritoneal status than the clinical model and human experts in the testing cohort (AUC, 0.785, p = 0.005; AUC, 0.732, p <0.001, respectively). CONCLUSION: DECT derived radiomics could serve as a non-invasive and easy-to-use biomarker to preoperatively predict PM for GC, providing opportunity for those patients to tailor appropriate treatment.

Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics.

PURPOSE: To develop a machine learning-derived radiomics approach to simultaneously discriminate epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene (KRAS), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and tumor protein 53 (TP53) genetic mutations in patients with non-small cell lung cancer (NSCLC). METHODS: This study included consecutive patients from April 2018 to June 2020 who had histologically confirmed NSCLC, and underwent pre-surgical contrast-enhanced CT and post-surgical next-generation sequencing (NGS) tests to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations. A dedicated radiomics analysis package extracted 1672 radiomic features in three dimensions. Discriminative models were established using the least absolute shrinkage and selection operator to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations, based on radiomic features and relevant clinical factors. RESULTS: In 134 patients (63.6 ± 8.9 years), the 20 most relevant radiomic features (13 for KRAS) to mutations were selected to construct models. The areas under the curve (AUCs) of the combined model (radiomic features and relevant clinical factors) for discriminating EGFR, KRAS, ERBB2, and TP53 mutations were 0.78 (95% CI: 0.70-0.86), 0.81 (0.69-0.93), 0.87 (0.78-0.95), and 0.84 (0.78-0.91), respectively. In particular, the specificity to exclude EGFR mutations was 0.96 (0.87-0.99). The sensitivity to determine KRAS, ERBB2, and TP53 mutations ranged from 0.82 (0.69-90) to 0.92 (0.62-0.99). CONCLUSIONS: Machine learning-derived 3D radiomics can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations in patients with NSCLC. This noninvasive and low-cost approach may be helpful in screening patients before invasive sampling and NGS testing.

3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma.

BACKGROUND: To establish a radiomic approach to identify epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients based on CT images, and to distinguish exon-19 deletion and exon-21 L858R mutation. METHODS: Two hundred sixty-three patients who underwent pre-surgical contrast-enhanced CT and molecular testing were included, and randomly divided into the training (80%) and test (20%) cohort. Tumor images were three-dimensionally segmented to extract 1,672 radiomic features. Clinical features (age, gender, and smoking history) were added to build classification models together with radiomic features. Subsequently, the top-10 most relevant features were used to establish classifiers. For the classifying tasks including EGFR mutation, exon-19 deletion, and exon-21 L858R mutation, four logistic regression models were established for each task. RESULTS: The training and test cohort consisted of 210 and 53 patients, respectively. Among the established models, the highest accuracy and sensitivity among the four models were 75.5% (61.7-86.2%) and 92.9% (76.5-99.1%) to classify EGFR mutation, respectively. The highest specificity values were 86.7% (69.3-96.2%) and 70.4% (49.8-86.3%) to classify exon-19 deletion and exon-21 L858R mutation, respectively. CONCLUSIONS: CT radiomics can sensitively identify the presence of EGFR mutation, and increase the certainty of distinguishing exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma patients. CT radiomics may become a helpful non-invasive biomarker to select EGFR mutation patients for invasive sampling.

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images.

OBJECTIVES: The aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC). METHODS: A total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman's correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared. RESULT: Three radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p<0.001) and testing cohorts (p<0.001 and p=0.034, respectively). CONCLUSIONS: Dual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.

User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine

Background In the field of microbial fermentation technology, how to optimize the fermentation conditions is of great crucial for practical applications. Here, we use artificial neural networks (ANNs) and support vector machine (SVM) to offer a series of effective optimization methods for the production of iturin A. The concentration levels of asparagine (Asn), glutamic acid (Glu) and proline (Pro) (mg/L) were set as independent variables, while the iturin A titer (U/mL) was set as dependent variable. General regression neural network (GRNN), multilayer feed-forward neural networks (MLFNs) and the SVM were developed. Comparisons were made among different ANNs and the SVM. Results The GRNN has the lowest RMS error (457.88) and the shortest training time (1 s), with a steady fluctuation during repeated experiments, whereas the MLFNs have comparatively higher RMS errors and longer training times, which have a significant fluctuation with the change of nodes. In terms of the SVM, it also has a relatively low RMS error (466.13), with a short training time (1 s). Conclusion According to the modeling results, the GRNN is considered as the most suitable ANN model for the design of the fed-batch fermentation conditions for the production of iturin A because of its high robustness and precision, and the SVM is also considered as a very suitable alternative model. Under the tolerance of 30%, the prediction accuracies of the GRNN and SVM are both 100% respectively in repeated experiments.

N-acetylcysteine induced quenching of red fluorescent oligonucleotide-stabilized silver nanoclusters and the application in pharmaceutical detection.

In this work, we reported a new, simple and sensitive method for determination of N-acetylcysteine (NAC) based on quenching of the red fluorescence of oligonuleotide-protected silver nanoculsters (Ag NCs) with the quantum yield of 68.3±0.3%. This method was successfully used for the assay of NAC granules presenting a linear range from 100 nM to 1200 nM (LOD of 50 nM) with minimal interferences from potential coexisting substances. It is for the first time that quenching performance of the thiol-containing compound was found to follow a non-linear Stern-Volmer profile, indicative of a complicated quenching mechanism with static quenching dominating, in which DNA-template of Ag NCs was partly replaced by NAC, as elucidated by spectral investigations. This study extended the analytical application of silver nanoclusters as well as provided a more insightful understanding of the quenching mechanism of thiol-compounds on the fluorescence of Ag NCs.