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.
Objective To improve esophageal lymph node staging and investgate an ideal esophageal lymph node metastasis staging method. Methods The clinical pathological data and followup data of the 236patients who had undergone thoracic esophagectomy with at least 6 lymph nodes (LN) removed from January 1985 to December 1989 were analyzed retrospectively. Cox proportional hazard model was used to screen risk factors, and Logrank test was applied to perform survival analysis according to lymph node metastasis staging (number, distance and extent). Results The 10-year follow-up rate was 92.3%（218/236）. The overall 1-year, 5-year and 10-year survival rates were 80.2%, 43.1% and 34.2% respectively. One hundred and twelve (47.4%) patients had LN metastasis, and their 5-year survival rates were lower than that of patients without LN metastasis (14.8% vs. 66.6%; χ2=77.18, P=0.000). Cox regression analysis showed that besides depth of invasion, differentiation grade and LN metastasis, the number, distance and extent of LN metastasis were the independent risk factors which could influence prognosis. A further analysis was given via univariate Logrank test. When grouped according to the number of LN metastasis, there were significant differences in overall survival rates (χ2=96.00,P=0.000), but no significant difference was found in survival rates between N2 and N3 group(Pgt;0.05). When grouped according to the distance of LN metastasis, there were significant differences in overall survival rates (χ2=79.29, P=0.000), but no significant difference was found in survival rates among S1, S2 and S3 group(Pgt;0.05). When grouped according to the extent of LN metastasis (0, 1, and ≥2 fields), there were significant differences in overall survival rates (χ2=87.47, P=0.000), and so were the survival rates among groups (χ2=5.14, P=0.023). Conclusion Revising the current Nclassification of TNM staging of esophageal cancer according to the extent of LN metastasis(0, 1, and ≥2 fields) is more reasonable, and can reflect the prognosis of patients with esophageal cancer after esophagectomy better.
ObjectiveTo evaluate the methodological bias and the reliability of the conclusions of systematic reviews (SRs) about traditional Chinese medicine for essential hypertension. MethodsWe comprehensively searched PubMed, EMbase, The Cochrane library (Issue 4, 2014), CBM, CNKI and WanFang Data to collect SRs of traditional Chinese medicine for essential hypertension from the establishment time of databases to April 30th, 2014. The AMSTAR tool was applied for methodological quality assessment of included studies, and the GRADE system was applied for evidence quality assessment of included outcomes of SRs. ResultsA total of 12 SRs involving 31 outcomes were included, of which 11 SRs focused on the comparison of therapeutic effects between traditional Chinese medicine combined with western medicine and western medicine alone. Nine SRs adopted Jadad tool to assess methodological quality of included original studies. The results of assessment using AMSTAR showed that, among 11 items, there were the most problems concerning Item 1 "Was an 'a prior' design provided?" (none of the 12 SRs provided it); followed by Item 11 "Were potential conflict of interest included?" (nine SRs didn't described it), and Item 6 "Were the characteristics of included studies provided" (six SRs didn't provided it). The results of grading showed that, 29 outcomes were graded as "low" or "very low" quality. The main factors contributed to downgrading evidence quality were limitations (31 outcomes), followed by imprecision (12 outcomes), and inconsistency (13 outcomes). ConclusionCurrently, the methodological quality of SRs about traditional Chinese medicine for essential hypertension was poor on the whole, with low quality of evidence as well as lack of enough attention to the end outcomes of patients with essential hypertension. Thus, physicians should apply the evidence to make decision about traditional Chinese medicine for essential hypertension with caution in clinical practice.
ObjectiveTo investigate value of MSCT imaging on differentiating low grade pancreatic neuroendo-crine neoplasms (pNENs) from non-low grade pNENs. MethodThe clinical and CT data of 32 patients with pNENs,who were confirmed by pathological diagnosis from January 2014 to August 2015,were collected and analyzed retrospec-tively. ResultsThere were 15 patients with grade 1 in the low grade pNENs group,there were 11 patients with grade 2 and 6 patients with grade 3 in the non-low grade pNENs group.Compared with the low grade pNENs,the non-low grade pNENs had the larger diameter of the tumor (P=0.007),irregular tumor shape (P=0.006),obscure tumor margin (P=0.003),peripancreatic tissue or vascular invasion (P=0.036),lymphadenopathy (P=0.003),distant metastasis (P=0.019),lower absolute enhancement of tumor at the arterial (P=0.003) and the relative enhancement of tumor at the arterial (P=0.013). ConclusionThe analysis of MSCT features might help for differentiating low grade pNENs from non-low grade pNENs,so that more timely selection of appropriate treatment strategies would be made.
Patient priority evaluation has been studied and applied abroad for a long time, which is a mature theory and widely used in practice now. This article uses the priority, patients, waiting list and criteria as keywords to search Wiley Inter Science, Web of Science, Scopus Pub Med, The Cochrane Library, Science Direct, Springer, and Jstor database (searching time is up to December 2017), to collect relevant indicators for patient admission priority evaluation. In addition, relevant citations and grey literature were searched, and experts from relevant fields in China were consulted to obtain more comprehensive research literature. On this basis, this article describes the concept of patient admission priority evaluation, and describes the meanings of the indicators and the countries of application from the three dimensions of clinical indicators, expected results, and social factors. It is considered that the research and implementation of the evaluation of the priority of patient admission has been relatively many. However, there are only a few related researches in the country and without unity. There is no systematic patient-related priority evaluation. It is necessary to use foreign mature theory research to establish a hospital admission priority evaluation system suitable for China’s national conditions.
Considering the small differences between different types in the diabetic retinopathy (DR) grading task, a retinopathy grading algorithm based on cross-layer bilinear pooling is proposed. Firstly, the input image is cropped according to the Hough circle transform (HCT), and then the image contrast is improved by the preprocessing method; then the squeeze excitation group residual network (SEResNeXt) is used as the backbone of the model, and a cross-layer bilinear pooling module is introduced for classification. Finally, a random puzzle generator is introduced in the training process for progressive training, and the center loss (CL) and focal loss (FL) methods are used to further improve the effect of the final classification. The quadratic weighted Kappa (QWK) is 90.84% in the Indian Diabetic Retinopathy Image Dataset (IDRiD), and the area under the receiver operating characteristic curve (AUC) in the Messidor-2 dataset (Messidor-2) is 88.54%. Experiments show that the algorithm proposed in this paper has a certain application value in the field of diabetic retina grading.
Objective To explore the correlation between liver volume variation of posthepatitic cirrhosis patients and the severity of the disease. Methods One hundred and eleven patients with normal livers and 74 posthepatitic cirrhosis patients underwent volume CT scan. The relation between normal liver volume and body height, body weight and body surface area was studied by linear regression and correlation method, the standard liver volume equation was deduced. The change ratio of liver volume in cirrhotic patients was calculated and compared with Child classification. Results The mean normal liver volume of Chinese adults was （1 225.15±216.23） cm3, there was a positive correlation between liver volume and body height, body weight 〔liver volume (cm3)=12.712×body weight (kg)+450.44〕 and body surface area 〔liver volume (cm3)=876.02×body surface area (m2)-297.17〕. The mean liver volume of Child A, B and C patients were （1 077.77±347.01） cm3, (1 016.35±348.60) cm3 and (805.73±208.85) cm3 respectively. The liver volume and liver volume index was significantly smaller in Child C patients than those in Child A and B patients (P＜0.05); while liver volume change ratio was higher in Child C patients (P＜0.05). Conclusion Liver volume variation of cirrhotic patients can be quantitatively assessed by 16 slices helical CT volume measurement and standard liver volume equation. The change of the liver volume is correlated with the severity of liver cirrhosis.
目的 探讨宫颈癌骨转移相关因素。 方法 回顾分析2008年6月－2011年8月收治的352例宫颈癌患者的临床资料，其中鳞癌326例，腺癌26例；临床分期Ⅰ期60例、Ⅱ期184例、Ⅲ期90例、Ⅳ期18例。比较不同期别、不同病理类型、不同组织分级患者的骨转移情况。 结果 352例宫颈癌中有18例发现骨转移，转移率为5.1%；转移时间为3～48个月，2例于骨转移后1年内死亡。鳞癌326例，骨转移率为5.2%；腺癌26例，骨转移率为3.8%。Ⅰ、Ⅱ、Ⅲ和Ⅳ期患者的骨转移率分别为0.0%、3.8%、5.6%和33.3%，晚期与早期相比有统计学意义（P＜0.05）；高、中和低分化患者骨转移率分别为3.1%、3.1%和6.3%，高分化与中分化相比，差异无统计学意义（P＞0.05），低分化与高中分化相比差异有统计学意义(P＜0.05)。 结论 宫颈癌骨转移与宫颈癌临床分期、病理类型、细胞分级密切相关。在宫颈癌的治疗过程中，做到早发现、早治疗，可提高患者的治疗效果，延长生存时间。
We elaborated the reasons why systematic reviews need to use GRADE based on a couple of specific examples. Aiming to provide references to understand and use GRADE correctly, we also answered some frequently-asked questions and concerns about GRADE as follows: a) differentiating the uses of GRADE between its application in guidelines and in systematic reviews; b) how to determine the overall quality of evidence? c) can GRADE be used to access the quality of single study or not? d) different uses of GRADE between randomized controlled trials (RCTs) and observational studies; e) weight of GRADE items; and f) factors that might influence the results of GRADE and the balance between upgrading and downgrading.
Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. ResultsA total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.