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Background Inferior mesenteric artery lymph node (station 253 node) metastasis occurs in approximately 0.3% to 13.9% of rectal cancer patients. This study examined whether carbon nanoparticles could aid in harvesting more station 253 nodes and evaluated the relationship between the number of station 253 nodes retrieved and station 253 node metastasis. Method A total of 480 consecutive rectal cancer patients were enrolled in this prospective cohort study between August 2014 and October 2018. Ninety-one patients (18.96%) received a preoperative submucosal injection of carbon nanoparticles (CN+ group), and 389 patients did not receive an injection (CN- group). The number of lymph node retrievals was analyzed, and the relevant risk factors for station 253 node metastasis were identified using univariate and multivariate analyses. Results The mean number of station 251, 252, and 253 lymph nodes and total lymph nodes retrieved in the CN+ group were higher than that retrieved in the CN- group. The percentage of patients with ≥4 station 253 nodes retrieved (54.0% vs 28.3%, P = .004) were higher in the CN+ group than in the CN- group. Retrieval of ≥4 station 253 nodes was an independent risk factor for station 253 node metastasis (OR: 2.40, 95% CI: 1.22–4.74, P = .012). Conclusion The preoperative submucosal injection of carbon nanoparticles was helpful for increasing the number of station 253 nodes harvested, and a minimum of 4 examined station 253 nodes was necessary for standard D3 lymph node dissection in rectal cancer.

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients. In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort. The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT. The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Background: Collagen in the tumour microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. Methods: The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed by multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (153 consecutive patients) was used to validate the model. Findings: The collagen signature comprised 4 collagen features significantly associated with pCR both in the two cohorts (P

The aim of this paper is to properly classify various stego images of JPEG to their own stegnographic methods (current steganographic methods, such as F5, OutGuess, Steghide, JPhide and Jsteg). Although some Multiclass Detection methods had been previously published by the authors, they all had various limitations and disadvantages. First, models of some detect methods are too complicated, and their process are too fussy. Second, the performance of some detect methods could decline when the embed rate minish. Based on re-stegnography, the detection of this paper's algorithm extracts 109-dimensions features and trains SVM(support vector machine) multiclassifiers to classify all kinds of stego images and cover images with very high precisions (approximately 100%). Not only the model is very simple, but the performance is all the same excellent when the embed rate minish.