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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.

The aim of this paper is to study the completeness of L-quasi-uniform convergence spaces and L-quasi-uniform spaces. Firstly, we describe L-quasi-uniform convergence spaces as enriched categories. Then we give two kinds of completeness of L-quasi-uniform convergence spaces and show that Lawvere completeness implies Cauchy completeness. Finally, we use the Cauchy completeness of L-quasi-uniform convergence spaces to define the Cauchy completeness of L-quasi-uniform spaces, and show that Cauchy completeness is equivalent to Lawvere completeness in L-quasi-uniform spaces.

NatuAt present, radioactive seed implantation is a common treatment for prostate cancer, the TPS (treatment planning system) calculates the dose by adding the dose attributed to each source. However, the interseed attenuation effect would result in a difference between the actual dose and the calculated dose. The aim of this study was to identify the factors influencing the interseed attenuation effect.

I-125 seed sources were selected, and MC (Monte Carlo) method was used to simulate the dose distribution around seed sources. The results obtained from the linear addition of a single-source dose were compared with those obtained considering the interseed attenuation effect. The effects of the medium, source arrangement and source number on the dose were evaluated.

The MC simulation results for multiple seed sources are lower than those for linear additive doses in most areas. In different medium, the mean error caused by interseed attenuation effect is the smallest in adipose tissue (0.52%) and the largest in bone (1.41%). Taking four sources as examples, the maximum error is 9.34%, appearing in the plane where the source is located. The error decreases to 1.3% when the source is located 2 mm away from the source plane. The more scattered the sources are in space, the smaller the error will be.

A high atomic number and high-density medium will cause a high error. The area with a high error is mainly observed in the plane where the sources are located, the edge error of the source distribution area is larger.

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.