ObjectiveTo construct a multimodal imaging radiomics model based on enhanced CT features to predict tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (NCRT). MethodsA retrospective analysis was conducted on the Database from Colorectal Cancer (DACCA) at West China Hospital of Sichuan University, including 199 LARC patients treated from October 2016 to October 2023. All patients underwent total mesorectal excision after NCRT. Clinical pathological information was collected, and radiomics features were extracted from CT images prior to NCRT. Python 3.13.0 was used for feature dimension reduction, and univariate logistic regression (LR) along with Lasso regression with 5-fold cross-validation were applied to select radiomics features. Patients were randomly divided into training and testing sets at a ratio of 7∶3 for machine learning and joint model construction. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). Receiver operating characteristic curve (ROC), confusion matrices, and clinical decision curves (DCA) were plotted to assess the model’s performance. ResultsAmong the 199 patients, 155 (77.89%) had poor therapeutic outcomes, while 44 (22.11%) had good outcomes. Univariate LR and Lasso regression identified 8 clinical pathological features and 5 radiomic features, including 1 shape feature, 2 first-order statistical features, and 2 texture features. LR, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models were 0.99, 0.98, 1.00, and 1.00, respectively, with accuracy rates of 0.94, 0.93, 1.00, and 1.00, sensitivity rates of 0.98, 1.00, 1.00, and 1.00, and specificity rates of 0.80, 0.67, 1.00, and 1.00, respectively. In the testing set, the AUC values of 4 models were 0.97, 0.92, 0.96, and 0.95, with accuracy rates of 0.87, 0.87, 0.88, and 0.90, sensitivity rates of 1.00, 1.00, 1.00, and 0.95, and specificity rates of 0.50, 0.50, 0.56, and 0.75. Among the models, the XGBoost model had the best performance, with the highest accuracy and specificity rates. DCA indicated clinical benefits for all 4 models. ConclusionsThe multimodal imaging radiomics model based on enhanced CT has good clinical application value in predicting the efficacy of NCRT in LARC. It can accurately predict good and poor therapeutic outcomes, providing personalized clinical surgical interventions.
ObjectiveTo analyze the protein expression changes in the retina of non-arteritic anterior ischemic optic neuropathy (NAION) in rats.MethodsThe rat NAION (rNAION) model was established by Rose Bengal and laser. Twenty Sprague-Dawley rats were randomly divided into 4 groups, the normal control group, the laser control group, the RB injection control group, and the rNAION model group, with 5 rats in each group. The right eye was used as the experimental eye. The retina was dissected at the third day after modeling. Enzyme digestion method was used for sample preparation and data collection was performed in a non-dependent collection mode. The data were quantitatively analyzed by SWATH quantitative mass spectrometry, searching for differential proteins and performing function and pathway analysis.ResultsCompared with the other three control groups, a total of 184 differential proteins were detected in the rNAION group (expression fold greater than 1.5 times and P＜0.05), including 99 up-regulated proteins and 85 down-regulated proteins. The expressions of glial fibrillary acidic protein, guanine nucleotide binding protein 4, laminin 1, 14-3-3γ protein YWHAG were increased. Whereas the expressions of Leucine-rich glioma-inactivated protein 1, secretory carrier-associated membrane protein 5, and Clathrin coat assembly protein AP180 were decreased. The differential proteins are mainly involved in biological processes such as nerve growth, energy metabolism, vesicle-mediated transport, the regulation of synaptic plasticity, apoptosis and inflammation. Pathway enrichment analysis showed that PI3K-Akt signaling pathway and complement and thrombin reaction pathway was related to the disease.ConclusionThe protein expressions of energy metabolism, nerve growth, synaptic vesicle transport and PI3K-Akt signaling pathway can regulate the neuronal regeneration and apoptosis in NAION.
摘要:目的: 金黄色葡萄球菌(金葡菌)的感染近年来已成为医院内的主要致病菌,而其耐药性也呈逐渐升高的趋势,为了解该菌在我院的感染和耐药情况,为临床合理使用抗生素提供科学依据。 方法 : 用经典生理生化鉴定方法,对各种临床标本主要来源于痰液和各种伤口脓液标本分离到的102株金葡菌进行生物学特性及药敏试验。 结果 : 从我们医院2007年5月至2009年8月所分离出来的102株金葡菌中青霉素耐药性8923%,氨苄青霉素耐药率为9385%,没有发现万古霉素耐药菌。 结论 : 除万古霉素外,耐药率较低的依次是利福平、苯唑青霉素、环丙沙星、呋喃妥因、阿米卡星、磺胺甲基异恶唑、红霉素,而青霉素G、氨苄青霉素、四环素耐药性情况非常严重,并且多重耐药,耐药性强,应引起临床的高度重视。Abstract: Objective: To analyze the bionomics and antimicrobial susceptibility of staphylococcus aureus, which was the main pathogenic bacterium with high drug tolerance in our hospital, in order to provide the rational use of antibiotics. Methods : Samples of one hundred and two staphylococcus aureus cases from sputamentum and pus were evaluated by classic physiology and biochemistry methods to test the bionomics and antimicrobial susceptibility. Results : The drug resistance rate to penicillin, penbritin and vancomycin was 8923%, 9385% and 0, separately. Conclusion : Besides vancomycin, the drug resistance rate of rifampicin, oxazocilline, ciprofloxacin, furadantin, amikacin, sulfamethoxazole and sulfamethoxazole increased one by one. The resistance to penicillin G, penbritin and tetracycline was serious, including multidrug resistant, which should be paid highly attention.
ObjectiveTo investigate the radiomics features to distinguish invasive lung adenocarcinoma with micropapillary or solid structure. MethodsA retrospective analysis was conducted on patients who received surgeries and pathologically confirmed invasive lung adenocarcinoma in our hospital from April 2016 to August 2019. The dataset was randomly divided into a training set [including a micropapillary/solid structure positive group (positive group) and a micropapillary/solid structure negative group (negative group)] and a testing set (including a positive group and a negative group) with a ratio of 7∶3. Two radiologists drew regions of interest on preoperative high-resolution CT images to extract radiomics features. Before analysis, the intraclass correlation coefficient was used to determine the stable features, and the training set data were balanced using synthetic minority oversampling technique. After mean normalization processing, further radiomics features selection was conducted using the least absolute shrinkage and selection operator algorithm, and a 5-fold cross validation was performed. Receiver operating characteristic (ROC) curves were depicted on the training and testing sets to evaluate the diagnostic performance of the radiomics model. ResultsA total of 340 patients were enrolled, including 178 males and 162 females with an average age of 60.31±6.69 years. There were 238 patients in the training set, including 120 patients in the positive group and 118 patients in the negative group. There were 102 patients in the testing set, including 52 patients in the positive group and 50 patients in the negative group. The radiomics model contained 107 features, with the final 2 features selected for the radiomics model, that is, Original_ glszm_ SizeZoneNonUniformityNormalized and Original_ shape_ SurfaceVolumeRatio. The areas under the ROC curve of the training and the testing sets of the radiomics model were 0.863 (95%CI 0.815-0.912) and 0.857 (95%CI 0.783-0.932), respectively. The sensitivity was 91.7% and 73.7%, the specificity was 78.8% and 84.0%, and the accuracy was 85.3% and 78.4%, respectively. ConclusionThere are differences in radiomics features between invasive pulmonary adenocarcinoma with or without micropapillary and solid structures, and the radiomics model is demonstrated to be with good diagnostic value.
Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus (DM), and its pathogenesis remains incompletely understood. Research has identified inflammation as a key factor in the onset and progression of DR. As a group of systemic metabolic disorders, the dysregulation of polyunsaturated fatty acid (PUFA) metabolism induced by DM is closely related to the inflammatory mechanisms in DR. Recent metabolomic studies have revealed that in various stages of DR and in diabetic animal models, most upregulated PUFAs and their derivatives act as pro-inflammatory mediators, while downregulated PUFAs and their derivatives are predominantly anti-inflammatory. In the progression of DR, some PUFAs may exert anti-inflammatory effects by inhibiting microglial activation, reducing the expression of inflammatory proteins, antagonizing the pro-inflammatory effects of arachidonic acid, and suppressing the activation of inflammasomes and the migration of neutrophils. Conversely, other PUFAs may promote inflammation through mechanisms such as the formation of pro-inflammatory mediators resembling prostaglandins, facilitating leukocyte adhesion, and inducing oxidative stress responses. PUFAs play a complex dual role in the inflammatory mechanisms of DR. A deeper understanding of these mechanisms not only aids in elucidating the pathogenesis of DR but also provides potential targets for developing new therapeutic strategies.
Radiomics transforms the medical images into minable high-throughput data, extracts the in-depth information invisible to the naked eye, in order to provide support for clinical diagnosis and treatment decision-making processes through the analysis of these data. Recently, radiomics has garnered widespread attention from researchers, with a continuously increasing number of research publications. However, there is still a lack of transparency in reporting radiomics studies. To guide the reporting of radiomics research, the CheckList for EvaluAtion of Radiomics research (CLEAR) was developed by the CLEAR working group using an expert consensus process. This checklist, which was published in May 2023, comprises 58 items and has been endorsed by the European Society of Radiology (ESR) and the European Society for Medical Imaging Informatics (EuSoMII). With authorization from the CLEAR working group, this article introduces and interprets the content of this checklist, to promote the understanding and application of CLEAR among radiomics researchers in China, and to enhance the transparency of radiomics research reporting.
ObjectiveTo establish a classification model based on knee MRI radiomics, realize automatic identification of meniscus tear, and provide reference for accurate diagnosis of meniscus injury. Methods A total of 228 patients (246 knees) with meniscus injury who were admitted between July 2018 and March 2021 were selected as the research objects. There were 146 males and 82 females; the age ranged from 9 to 76 years, with a median age of 53 years. There were 210 cases of meniscus injury in one knee and 18 cases in both knees. All the patients were confirmed by arthroscopy, among which 117 knees with meniscus tear and 129 knees with meniscus non-tear injury. The proton density weighted-spectral attenuated inversion recovery (PDW-SPAIR) sequence images of sagittal MRI were collected, and two doctors performed radiomics studies. The 246 knees were randomly divided into training group and testing group according to the ratio of 7∶3. First, ITK-SNAP3.6.0 software was used to extract the region of interest (ROI) of the meniscus and radiomic features. After retaining the radiomic features with intraclass correlation coefficient (ICC)>0.8, the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for filtering the features to establish an automatic identification model of meniscus tear. The receiver operator characteristic curve (ROC) and the corresponding area under the ROC curve (AUC) was obtained; the model performance was comprehensively evaluated by calculating the accuracy, sensitivity, and specificity. Results A total of 1 316-dimensional radiomic features were extracted from the meniscus ROI, and the ICC within the group and ICC between the groups of the 981-dimensional radiomic features were both greater than 0.80. The redundant information in the 981-dimensional radiomic features was eliminated by mRMR, and the 20-dimensional radiomic features were retained. The optimal feature subset was further selected by LASSO, and 8-dimensional radiomic features were selected. The average ICC within the group and the average ICC between the groups were 0.942 and 0.920, respectively. The AUC of the training group was 0.889±0.036 [95%CI (0.845, 0.942), P<0.001], and the accuracy, sensitivity, and specificity were 0.873, 0.869, and 0.842, respectively; the AUC of the testing group was 0.876±0.036 [95%CI (0.875, 0.984), P<0.001], and the accuracy, sensitivity, and specificity were 0.862, 0.851, and 0.845, respectively. ConclusionThe model established by the radiomics method has good automatic identification performance of meniscus tear.
ObjectiveTo summarize the application of radiomics in colorectal cancer.MethodsRelevant literatures about the therapeutic decision-making, therapeutic, and prognostic evaluation of colorectal cancer using radiomics were collected to make an review.ResultsRadiomics is of great value in preoperative stages, therapeutic, and prognostic evaluation in colorectal cancer.ConclusionRadiomics is an important part of precision medical imaging for colorectal cancer.
Lung cancer has the highest incidence and mortality rates among malignant tumors both in China and worldwide, with approximately 85% of cases being non-small cell lung cancer (NSCLC). In the diagnosis and treatment of lung cancer, conventional imaging and tissue biopsy are often limited by insufficient sensitivity or invasive risks, making it difficult to meet the demands of future precision medicine. In recent years, artificial intelligence (AI)-based radiomics and autoantibody-based liquid biopsy have developed rapidly and have become major research focuses. AI radiomics significantly improves the accuracy of traditional imaging diagnosis by autonomously learning from large-scale imaging databases. Autoantibody liquid biopsy, on the other hand, utilizes tumor-associated autoantigens and antibodies as biomarkers, offering the advantages of being non-invasive, precise, efficient, and capable of reflecting spatiotemporal tumor heterogeneity, thereby demonstrating great potential in NSCLC diagnosis and treatment. This review summarizes recent research advances in autoantibody liquid biopsy and AI radiomics for the management of lung cancer.
Objective The article introduces the present status of the application of comparative proteomics in study of tumor marker. Methods This essay review the present status and advances of the application of comparative proteomics in study of tumor marker through refer considerable literatures about proteome, proteomics and tumor marker. Results Follow the study of human genome deepening; the paradox between the finiteness of genes’ number and stability of genes’ structure and the variety of the life phenomena is more conspicuous. Then, the study of proteomics was pushed to the advancing front of life science research. The application of comparative proteomics to tumor research becomes a hot spot nowadays. Conclusion Screening tumor marker via comparative proteomics is an extremely promising research.