Based on hematoma and perihematomal tissue NCCT imaging radiomics predicts early clinical outcome of conservatively treated spontaneous cerebral hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) is a very serious kind of stroke. If the outcome of patients can be accurately assessed at the early stage of disease occurrence, it will be of great significance to the patients and clinical treatment. The present study was conducted to investigate whether non-contrast computer tomography (NCCT) models of hematoma and perihematomal tissues could improve the accuracy of short-term prognosis prediction in ICH patients with conservative treatment. In this retrospective analysis, a total of 166 ICH patients with conservative treatment during hospitalization were included. Patients were randomized into a training group (N = 132) and a validation group (N = 34) in a ratio of 8:2, and the functional outcome at 90 days after clinical treatment was assessed by the modified Rankin Scale (mRS). Radiomic features of hematoma and perihematomal tissues of 5 mm, 10 mm, 15 mm were extracted from NCCT images. Clinical factors were analyzed by univariate and multivariate logistic regression to identify independent predictive factors. In the validation group, the mean area under the ROC curve (AUC) of the hematoma was 0.830, the AUC of the perihematomal tissue within 5 mm, 10 mm, 15 mm was 0.792, 0.826, 0.774, respectively, and the AUC of the combined model of hematoma and perihematomal tissue within 10 mm was 0.795. The clinical-radiomics nomogram consisting of five independent predictors and radiomics score (Rad-score) of the hematoma model were used to assess 90-day functional outcome in ICH patients with conservative treatment. Our findings found that the hematoma model had better discriminative efficacy in evaluating the early prognosis of conservatively managed ICH patients. The visual clinical-radiomics nomogram provided a more intuitive individualized risk assessment for 90-day functional outcome in ICH patients with conservative treatment. The hematoma could remain the primary therapeutic target for conservatively managed ICH patients, emphasizing the need for future clinical focus on the biological significance of the hematoma itself.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

Objectives: To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods: MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results: The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion: In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement: Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study.

OBJECTIVE: The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage. METHODS: This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model. RESULTS: The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process. CONCLUSION: Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules.

OBJECTIVES: The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS: In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS: The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION: The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT: As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.

Quantitative Modeling of the Degradation of Pesticide Residues in Wheat Flour Supply Chain.

Pesticide residues in grain products are a major issue due to their comprehensive and long-term impact on human health, and quantitative modeling on the degradation of pesticide residues facilitate the prediction of pesticide residue level with time during storage. Herein, we tried to study the effect of temperature and relative humidity on the degradation profiles of five pesticides (carbendazim, bensulfuron methyl, triazophos, chlorpyrifos, and carbosulfan) in wheat and flour and establish quantitative models for prediction purpose. Positive samples were prepared by spraying the corresponding pesticide standards of certain concentrations. Then, these positive samples were stored at different combinations of temperatures (20 °C, 30 °C, 40 °C, 50 °C) and relative humidity (50%, 60%, 70%, 80%). Samples were collected at specific time points, ground, and the pesticide residues were extracted and purified by using QuEChERS method, and then quantified by using UPLC-MS/MS. Quantitative model of pesticide residues was constructed using Minitab 17 software. Results showed that high temperature and high relative humidity accelerate the degradation of the five pesticide residues, and their degradation profiles and half-lives over temperature and relative humidity varied among pesticides. The quantitative model for pesticide degradation in the whole process from wheat to flour was constructed, with R2 above 0.817 for wheat and 0.796 for flour, respectively. The quantitative model allows the prediction of the pesticide residual level in the process from wheat to flour.

Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors.

OBJECTIVE: To develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs). METHODS: Preoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping. RESULTS: In the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review. CONCLUSION: The DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.

Testing the utility of DNA barcodes and a preliminary phylogenetic framework for Chinese freshwater mussels (Bivalvia: Unionidae) from the middle and lower Yangtze River.

The middle and lower portions of the Yangtze River basin is the most species-rich region for freshwater mussels in Asia. The management and conservation of the taxa in this region has been greatly hampered by the lack of a well-developed phylogeny and species-level taxonomic framework. In this study, we tested the utility of two mitochondrial genes commonly used as DNA barcodes: the first subunit of the cytochrome oxidase c gene (COI) and the first subunit of the NADH dehydrogenase gene (ND1) for 34 putative species representing 15 genera, and also generated phylogenetic hypotheses for Chinese unionids based on the combined dataset of the two mitochondrial genes. The results showed that both loci performed well as barcodes for species identification, but the ND1 sequences provided better resolution when compared to COI. Based on the two-locus dataset, Bayesian Inference (BI) and Maximum Likelihood (ML) phylogenetic analyses indicated 3 of the 15 genera of Chinese freshwater mussels examined were polyphyletic. Additionally, the analyses placed the 15 genera into 3 subfamilies: Unioninae (Aculamprotula, Cuneopsis, Nodularia and Schistodesmus), Gonideninae (Lamprotula, Solenaia and Ptychorhychus) and Anodontinae (Cristaria, Arconaia, Acuticosta, Lanceolaria, Anemina and Sinoanodonta). Our results contradict previous taxonomic classification that placed the genera Arconaia, Acuticosta and Lanceolaria in the Unioninae. This study represents one of the first attempts to develop a molecular phylogenetic framework for the Chinese members of the Unionidae and will provide a basis for future research on the evolution, ecology, and conservation of Chinese freshwater mussels.

The complete mitogenome and phylogenetic analysis of Acrossocheilus jishouensis (Osteichthyes: Cyprinidae).

Acrossocheilus jishouensis is an endemic south China stream-dwelling cyprinid species. Its complete mitochondrial genome is 16,587 bp in length, consisting of 13 protein-coding genes, 22 tRNA genes (ranging from 67 bp in tRNACys to 76 bp in tRNALeu and tRNALys ), two rRNA genes (956 bp in 12S rRNA and 1673 bp in 16S rRNA), and one control region (942 bp). Its overall base composition is A: 31.2%, C: 27.6%, G: 16.2%, and T: 25.1%. The complete mitogenome of the Chinese barred species of Cpynidae could provide a basic data for further phylogenetics analysis.

The complete mitogenome and phylogenetic analysis of Acrossocheilus beijiangensis (osteichthyes: Cyprinidae).

Acorssocheilus beijiangensis is an endemic south China stream-dwelling cyprinid species. Its complete mitochondrial genome is 16,596 bp in length, consisting of 13 protein-coding genes, 22 tRNA genes (ranging from 67 bp in tRNACys to 76 bp in tRNALeu and tRNALys ), two rRNA genes (959 bp in 12S rRNA and 1683 bp in 16S rRNA), and one control region (937 bp). Its overall base composition is A: 31.1%, C: 27.9%, G: 16.2%, and T: 124.8%. The complete mitogenome of the Chinese barred species of Cyprinidae could provide a basic data for further phylogenetics analysis.

Complete maternal mitochondrial genome of freshwater mussel Anodonta lucida (Bivalvia: Unionidae: Anodontinae).

The taxonomy of genus Anodonta is rather ambiguous, as it has great variation on the shell shape. Anodonta lucida is an endemic species of freshwater mussel in China, characterized by shining epidermis. The complete maternal mitochondrial genome of freshwater mussel A. lucida was first determined (GenBank accession no. KF667529). The genome is 16,285 bp long with an AT content of 64.02%. All the 37 typical animal mitochondrial genes are found, including 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes. The genome also contains 24 unassigned regions, ranking from 1 to 830 bp in length, the largest of which is the putative control region (CR). The base composition of the genome is A (36.32%), G (13.01%), T (27.70%) and C (22.98%). Gene order is identical to other species of Unionidae except Gonideinae.

Isolated lithium sites supported on mesoporous silica: a novel solid strong base with high catalytic activity.

Isolated lithium sites were anchored on mesoporous silica by a molecular precursor approach at room temperature. The resultant materials exhibit ordered mesostructure, high base strength, and more importantly, a molecular-level dispersion of active sites, which are extremely desirable for catalysis and impossible to be realized by conventional methods.

Isolated Cu(I) sites supported on ß-cyclodextrin: an efficient π-complexation adsorbent for thiophene capture.

A novel π-complexation adsorbent is fabricated by grafting Cu(I)-containing molecule precursors onto ß-cyclodextrin. The adsorbent provides a molecular-level dispersion of Cu(I), which is particularly beneficial to the adsorptive removal of aromatic sulfur thiophene, and is impossible to be realized through the conventional thermal method.

Adsorptive desulfurization by copper species within confined space.

Copper species were incorporated into SBA-15 by solid-state grinding precursor with as-prepared mesoporous silica (SPA). The obtained materials (CuAS) were well-characterized by XRD, TEM, N(2) adsorption, H(2)-TPR, IR, and TG and compared with the material derived from calcined SBA-15 (CuCS). Surprisingly, CuO up to 6.7 mmol·g(-1) can be highly dispersed on SBA-15 by use of SPA strategy. Such CuO forms a smooth layer coated on the internal walls of SBA-15, which contributes to the spatial order and results in less-blocked mesopores. However, the aggregation of CuO takes place in CuCS material containing 6.7 mmol·g(-1) copper, which generates large CuO particles of 21.4 nm outside the mesopores. We reveal that the high dispersion extent of CuO is ascribed to the abundant silanols, as well as the confined space between template and silica walls provided by as-prepared SBA-15. The SPA strategy allows template removal and precursor conversion in one step, avoids the repeated calcination in conventional modification process, and saves time and energy. We also demonstrate that the CuAS material after autoreduction exhibits much better adsorptive desulfurization capacity than CuCS. Moreover, the adsorption capacity of regenerated adsorbent can be recovered completely.