A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer.

This study aimed to develop an optimal radiomics model for preoperatively predicting microsatellite instability (MSI) in patients with rectal cancer (RC) based on multiparametric magnetic resonance imaging. The retrospective study included 308 RC patients who did not receive preoperative antitumor therapy, among whom 51 had MSI. Radiomics features were extracted and dimensionally reduced from T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), and T1-weighted contrast enhanced (T1CE) images for each patient, and the features of each sequence were combined. Multifactor logistic regression was used to screen the optimal feature set for each combination. Different machine learning methods were applied to construct predictive MSI status models. Relative standard deviation values were determined to evaluate model performance and select the optimal model. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate model performance. The model constructed using the k-nearest neighbor (KNN) method combined with T2WI and T1CE images performed best. The area under the curve values for prediction of MSI with this model were 0.849 (0.804-0.887), with a sensitivity of 0.784 and specificity of 0.805. The Delong test showed no significant difference in diagnostic efficacy between the KNN-derived model and the traditional logistic regression model constructed using T1WI + DWI + T1CE and T2WI + T1WI + DWI + T1CE data (P > 0.05) and the diagnostic efficiency of the KNN-derived model was slightly better than that of the traditional model. From ROC curve analysis, the KNN-derived model significantly distinguished patients at low- and high-risk of MSI with the optimal threshold of 0.2, supporting the clinical applicability of the model. The model constructed using the KNN method can be applied to noninvasively predict MSI status in RC patients before surgery based on radiomics features from T2WI and T1CE images. Thus, this method may provide a convenient and practical tool for formulating treatment strategies and optimizing individual clinical decision-making for patients with RC.

Predicting Alzheimer's progression in MCI: a DTI-based white matter network model.

OBJECTIVE: This study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients. METHODS: This study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation. RESULTS: APOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers. CONCLUSIONS: A comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Adopting the "Missile boats-Aircraft carrier" strategy via human-contact friendly oxidized starch to achieve rapid-sustainably antibacterial paperboards.

Polysaccharide-based antibacterial agents have received tremendous attention for the facile fabrication, low toxicity, and high compatibility with carbohydrate polymers. However, the antimicrobial mechanism, activity, and cytotoxicity for human-contact paperboards of oxidized starch (OST) with high carboxyl content, has not been explored. Herein, OST-27- 75 with 27- 75 wt% carboxyl contents were fabricated by H2O2 and coated on paperboards. Strikingly, OST-55 coating layer (16 g/m2) did not exfoliate from paperboard and possessed the rapid-sustainable antibacterial performance against Staphylococcus aureus and Escherichia coli. The soluble and insoluble components of OST-55 (OST55-S: OST55-IS mass ratio = 1: 2.1) presented different antimicrobial features and herein they were characterized by GC-MS, FT-IR, H-NMR, XRD, bacteriostatic activities, biofilm formation inhibition and intracellular constituent leakage to survey the antibacterial mechanism. The results revealed OST55-S displayed an amorphous structure and possessed superior antibacterial activity against S. aureus (MIC = 4 mg/mL) and E. coli (MIC = 8 mg/mL). Distinctively, OST55-S could rapidly ionize [H+] like "missile boats" from small molecule saccharides, while OST55-IS polyelectrolyte could continuously and slowly release for [H+] like an "aircraft carrier" to inhibit biofilm formation and disrupt cell structure. Eventually, the "Missile boats-Aircraft carrier" strategy provided a green methodology to fabricate polymeric antibacterial agents and expanded the use of cellulose-based materials.

Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning.

BACKGROUND: The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning. METHODS: Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves. RESULTS: Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively. CONCLUSIONS: The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.

Effect of neutralization treatment on properties of chitosan/bamboo leaf flavonoids/nano-metal oxide composite films and application of films in antioxidation of rapeseed oil.

Neutralization treatment improved the slow-release antioxidant food packaging function of chitosan (CS)/bamboo leaf flavone (BLF)/nano-metal oxides composite films. The film cast from the CS composite solution neutralized by KOH solution had good thermal stability. The elongation at break of the neutralized CS/BLF film was increased by about 5 times, which provided the possibility for its packaging application. After 24 h of soaking in different pH solutions, the unneutralized films swelled severely and even dissolved, while the neutralized films maintained the basic structure with a small degree of swelling, and the release trend of BLF conformed to the logistic function (R2 ≥ 0.9186). The films had a good ability to resist free radicals, which was related to the release amount of BLF and the pH of the solution. The antimicrobial neutralized CS/BLF/nano-ZnO film, like the nano-CuO and Fe3O4 films, were effective in inhibiting the increase in peroxide value and 2-thiobarbituric acid induced by thermal oxygen oxidation of rapeseed oil and had no toxicity to normal human gastric epithelial cells. Therefore, the neutralized CS/BLF/nano-ZnO film is likely to become an active food packaging material for oil-packed food, which can prolong the shelf life of packaged food.

Stretch-Induced Robust Intrinsic Antibacterial Thermoplastic Gelatin Organohydrogel for a Thermoenhanced Supercapacitor and Mono-gauge-factor Sensor.

Sustainable organohydrogel electronics have shown promise in resolving the electronic waste (e-waste) evoked by traditional chemical cross-linking hydrogels. Herein, thermoplastic-recycled gelatin/oxidized starch (OST)/glycerol/ZnCl2 organohydrogels (GOGZs) were fabricated by introducing the anionic polyelectrolyte OST and solvent exchange strategy to construct noncovalently cross-linking networks. Benefiting from the electrostatic interaction and hydrogen and coordination bonds, GOGZ possessed triple-supramolecular interactions and a continuous ion transport pathway, which resulted in excellent thermoplasticity and high ionic conductivities and mechanical and antibacterial properties. Because of the thermally induced phase transition of gelatin, GOGZ exhibited isotropic-ionic conductivity with a positive temperature coefficient and realized intrinsic affinity with the activated carbon electrode for fabricating a double-layer structure supercapacitor. These novel features significantly decreased the impedance (3.71 Ω) and facilitated the flexible supercapacitors to achieve thermoenhanced performance with 4.89 Wh kg-1 energy density and 49.2 F g-1 specific mass capacitance at 65 °C. Fantastically, the GOGZ-based stress sensor exhibited a monolinear gauge factor (R2 = 0.999) at its full-range strain (0 to 350%), and its sensitivity increased with the thermoplastic-recycled times. Consequently, this sustainable and temperature-sensitive sensor (-40 to 60 °C) could serve as health monitoring wearable devices with excellent reliability (R2 = 0.999) at tiny strain. Moreover, GOGZ could achieve efficient self-enhancement by stretch-induced alignment. The sustained weighted load, tensile strength, and elongation at break of the stretch-induced GOGZ were 6 kg/g, 2.37 MPa, and 300%, respectively. This self-enhanced feature indicated that GOGZ can be utilized as an artificial muscle. Eventually, GOGZ obtained high intrinsic antibiosis (Dinhibition circle > 25 mm) by a binding species (-COO-NH3+-) from COOH in OST and NH2 in gelatin, freezing resistance, and water retention. In summary, this study provided an effective strategy to fabricate thermoplastic-recycled organohydrogels for multifunctional sustainable electronics with novel performance.

18F-FDG-PET Radiomics Based on White Matter Predicts The Progression of Mild Cognitive Impairment to Alzheimer Disease: A Machine Learning Study.

RATIONALE AND OBJECTIVES: To build a model using white-matter radiomics features on positron-emission tomography (PET) and machine learning methods to predict progression from mild cognitive impairment (MCI) to Alzheimer disease (AD). MATERIALS AND METHODS: We analyzed the data of 341 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, of whom 102 progressed to AD during an 8-year follow-up. The patients were divided into the training (238 patients) and test groups (103 patients). PET-based radiomics features were extracted from the white matter in the training group, and dimensionally reduced to construct a psychoradiomics signature (PS), which was combined with multimodal data using machine learning methods to construct an integrated model. Model performance was evaluated using receiver operating characteristic curves in the test group. RESULTS: Clinical Dementia Rating (CDR) scores, Alzheimer's Disease Assessment Scale (ADAS) scores, and PS independently predicted MCI progression to AD on multivariate logistic regression. The areas under the curve (AUCs) of the CDR, ADAS and PS in the training and test groups were 0.683, 0.755, 0.747 and 0.737, 0.743, 0.719 respectively, and were combined using a support vector machine to construct an integrated model. The AUC of the integrated model in the training and test groups was 0.868 and 0.865, respectively (sensitivity, 0.873 and 0.839, respectively; specificity, 0.784 and 0.806, respectively). The AUCs of the integrated model significantly differed from those of other predictors in both groups (p < 0.05, Delong test). CONCLUSION: Our psych radiomics signature based on white-matter PET data predicted MCI progression to AD. The integrated model built using multimodal data and machine learning identified MCI patients at a high risk of progression to AD.

Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer.

OBJECTIVES: To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model. METHODS: We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, diffusion-weighted imaging, and enhanced T1-weighted imaging of rectal cancer, followed by the dimension reduction of the features. Logistic regression, support vector machine, Bayes, K-nearest neighbor, and random forests algorithms were trained to obtain the radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each radiomics signature. The best radiomics signature was selected and combined with clinical and radiological characteristics to construct a joint model for predicting EMVI. Finally, the predictive performance of the joint model was assessed. RESULTS: The Bayes-based radiomics signature performed well in both the training set and the test set, with the AUCs of 0.744 and 0.738, sensitivities of 0.754 and 0.728, and specificities of 0.887 and 0.918, respectively. The joint model performed best in both the training set and the test set, with the AUCs of 0.839 and 0.835, sensitivities of 0.633 and 0.714, and specificities of 0.901 and 0.885, respectively. CONCLUSIONS: The joint model demonstrated the best diagnostic performance for the preoperative prediction of EMVI in patients with rectal cancer. Hence, it can be used as a key tool for clinical individualized EMVI prediction. KEY POINTS: • Radiomics features from magnetic resonance imaging can be used to predict extramural venous invasion (EMVI) in rectal cancer. • Machine learning can improve the accuracy of predicting EMVI in rectal cancer. • Radiomics can serve as a noninvasive biomarker to monitor the status of EMVI.

Comprehensive classification models based on amygdala radiomic features for Alzheimer's disease and mild cognitive impairment.

The amygdala is an important part of the medial temporal lobe and plays a pivotal role in the emotional and cognitive function. The aim of this study was to build and validate comprehensive classification models based on amygdala radiomic features for Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). For the amygdala, 3360 radiomic features were extracted from 97 AD patients, 53 aMCI patients and 45 normal controls (NCs) on the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images. We used maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) to select the features. Multivariable logistic regression analysis was performed to build three classification models (AD-NC group, AD-aMCI group, and aMCI-NC group). Finally, internal validation was assessed. After two steps of feature selection, there were 5 radiomic features remained in the AD-NC group, 16 features remained in the AD-aMCI group and the aMCI-NC group, respectively. The proposed logistic classification analysis based on amygdala radiomic features achieves an accuracy of 0.90 and an area under the ROC curve (AUC) of 0.93 for AD vs. NC classification, an accuracy of 0.81 and an AUC of 0.84 for AD vs. aMCI classification, and an accuracy of 0.75 and an AUC of 0.80 for aMCI vs. NC classification. Amygdala radiomic features might be early biomarkers for detecting microstructural brain tissue changes during the AD and aMCI course. Logistic classification analysis demonstrated the promising classification performances for clinical applications among AD, aMCI and NC groups.

Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method.

Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis. Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampi were segmented for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampi. Results: There were 385 features derived after calculation, and four features remained after feature selection from each group of data. The area under the receiver operating characteristic (ROC) curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively. Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.

Correlation Between Hippocampus MRI Radiomic Features and Resting-State Intrahippocampal Functional Connectivity in Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease with main symptoms of chronic primary memory loss and cognitive impairment. The study aim was to investigate the correlation between intrahippocampal functional connectivity (FC) and MRI radiomic features in AD. A total of 67 AD patients and 44 normal controls (NCs) were enrolled in this study. Using the seed-based method of resting-state functional MRI (rs-fMRI), the whole-brain FC with bilateral hippocampus as seed was performed, and the FC values were extracted from the bilateral hippocampus. We observed that AD patients demonstrated disruptive FC in some brain regions in the left hippocampal functional network, including right gyrus rectus, right anterior cingulate and paracingulate gyri, bilateral precuneus, bilateral angular gyrus, and bilateral middle occipital gyrus. In addition, decreased FC was detected in some brain regions in the right hippocampal functional network, including bilateral anterior cingulate and paracingulate gyri, right dorsolateral superior frontal gyrus, and right precentral gyrus. Bilateral hippocampal radiomics features were calculated and selected using the A.K. software. Finally, Pearson's correlation analyses were conducted between these selected features and the bilateral hippocampal FC values. The results suggested that two gray level run-length matrix (RLM) radiomic features and one gray level co-occurrence matrix (GLCM) radiomic feature weakly associated with FC values in the left hippocampus. However, there were no significant correlations between radiomic features and FC values in the right hippocampus. These findings present that the AD group showed abnormalities in the bilateral hippocampal functional network. This is a prospective study that revealed the weak correlation between the MRI radiomic features and the intrahippocampal FC in AD patients.