y. geng

Preoperative assessment of lymph node metastasis (LNM) status is the basis of individual treatment for rectal cancer (RC). However, conventional imaging methods are not accurate enough.

We collected 282 RC patients who were divided into the training dataset (n=225) and the test dataset (n=57) with an 8:2 scale. A large number of deep learning (DL) features and hand-crafted radiomics (HCR) features of primary tumors were extracted from the arterial and venous phases of the computed tomography (CT) images. Three machine learning models, including support vector machine (SVM), k-nearest neighbor (KNN),and multi-layer perceptron (MLP) were utilized to predict LNM status in RC patients. A stacking nomogram was constructed by selecting optimal machine learning models for arterial and venous phases, respectively, combined with predictive clinical features.

The stacking nomogram performed well in predicting LNM status, with an area under the curve (AUC) of 0.914 [95% confidence interval (CI): 0.874-0.953] in the training dataset, and an AUC of 0.942 (95%CI: 0.886-0.997) in the test dataset. The AUC of the stacking nomogram were higher than those of CT_reported_N_status, ASVM, and VSVM model in the training dataset (P <0.05). However, in the test dataset, although the AUC of the stacking nomogram was higher than the VSVM, the difference was not obvious (P =0.1424).

The developed deep learning radiomics stacking nomogram showed to be effective in predicting the preoperative LNM status in RC patients.

To investigate whether features of 5-mm peritumoral regions could significantly improve the predictive efficacy of a radiomics model based on solid pulmonary tumors at distinguishing lung adenocarcinoma(LAC) from granuloma(GR).

We retrospectively evaluated 167 lung tumors pathologically proven to be LAC (96) or GR (71) and divided them into training group (116) and testing (51) group. We delineated each tumor with three different measures using the tumor and its 5-mm peritumoral region. Then, we extracted 465 features from each volume of interest(VOI) and chose the optimal features to build the diagnostic models. We built four different models using different methods. Finally, we compared the performance of the four models in the test set.

The area under the curve(AUC) of each model in the test group was 0.765 (95% confidence interval(CI): 0.620–0.909), 0.797 (95%CI: 0.670–0.924), and 0.784 (95%CI: 0.647–0.920), respectively. Results of the DeLong test showed that the differences between model 2, model 3, and model 1 were not significant. Results of net reclassification improvement(NRI) showed that model 2 and model 3 had better differential diagnostic efficacy than model 1, with accuracies(ACCs) of 0.784, 0.745, and 0.686, respectively, but the differences were not significant (P>0.05). Moreover, the nomogram had good diagnostic and predictive abilities, with an AUC of 0.848 (95%CI: 0.736–0.961) and an ACC of 0.804.

Features of 5-mm peritumoral regions improved the predictive ability of the radiomics model based on the solid pulmonary tumor, but the difference was not significant.

The formation of the work of public health institutions relates primarily to the category of ensuring socioeconomic balance. When implementing a specific order of work in a healthcare institution, the formation of directed care and providing the best conditions for patients occurs. This study aimed to develop of an indicator framework for integrated monitoring of the costing process for a patient by rehabilitation medical institutions.

In this study, a system of factors of strategic changes in integrated monitoring of the costing process by medical rehabilitation institutions, as well as a price matrix and risk assessment criteria were applied to determine the internal pricing strategy by medical rehabilitation institutions.

The authors show that the functioning of institutions that contribute to the rehabilitation of citizens should be based on the principles of distributing the correct price for the treatment for each person.

The paper presents a model for the formation of this assessment and the method of its application in the management of healthcare institutions. The practical significance of the study is determined by the possibility of balanced determination of the share of the health care institution in the functioning of the general public health system. This will expand the range of services and identify the entities in need of managerial activity improvement.