IMPORTANCE Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC. OBJECTIVE To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC. DESIGN, SETTING, AND PARTICIPANTS This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated. MAIN OUTCOMES AND MEASURES The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM. RESULTS In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found. CONCLUSIONS AND RELEVANCE This study's findings suggest th...

Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.

It is feasible to use carbon nanoparticles to show SLNs in early gastric cancer. Carbon nanoparticles suspension is safe for submucosal injection.

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Carbon nanoparticles are useful for identifying DLNs in station 253 LNs around IMA in RC. It is not necessary to perform D3 lymph node dissection if there are no intra-operative DLNs metastases in RC.

Collagen in the tumor 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. 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 using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors.The nomogram showed good discrimination with area under the ROC curve (AUC)

First, a pilot study was performed to set up the optical diagnostic criteria of MPI for benign or malignant colorectal lesions in 30 patients. Then, a blinded study was conducted to compare the sensitivity, specificity, and accuracy of MPI versus CLE imaging in 79 patients. RESULTS : In the pilot study, MPI revealed regular tissue architecture and cell morphology in the normal tissue, and irregular tubular structures, and cellular and nuclear pleomorphism in the abnormal tissue. In the blinded study, compared with CLE imaging, MPI significantly improved the diagnostic sensitivity, specificity, and accuracy of the optical biopsy (89.74 % vs. 61.54 %, = 0.008; 92.5 % vs. 67.5 %, = 0.009; and 91.14 % vs. 64.56 %, = 0.001, respectively). CONCLUSIONS : MPI can provide a superior optical biopsy to that of CLE imaging for colorectal lesions.

A real-time optical biopsy, which could determine tissue histopathology, would be of extraordinary benefit to staging laparoscopy for gastric cancer with serosal invasion (T4) that requires downstage treatment. We investigated the feasibility of using multiphoton imaging to perform a real-time optical diagnosis of gastric cancer with or without serosal invasion. First, a pilot study was performed to establish the optical diagnostic features of gastric cancer with or without serosal invasion using multiphoton imaging compared with hematoxylin-eosin staining and Masson’s trichrome staining. Second, a blinded study was performed to compare the diagnostic sensitivity, specificity, and accuracy of multiphoton imaging and endoscopic ultrasonography (EUS) for T4 gastric cancer. In the pilot study, multiphoton imaging revealed collagen loss and degradation and cellular and nuclear pleomorphism in gastric cancer with serosal invasion. The collagen content in gastric cancer with or without serosal invasion was 0.36 ± 0.18 and 0.79 ± 0.16 (p < 0.001), respectively. In the blinded study, the sensitivity, specificity, and accuracy of EUS and multiphoton imaging for T4 gastric cancer were 70% and 90% (p = 0.029), 66.67% and 96.67% (p = 0.003), and 68.33% and 93.33% (p = 0.001), respectively. It is feasible to use multiphoton imaging to make a real-time optical diagnosis of gastric cancer with or without serosal invasion.