The T stage of esophageal cancer can be effectively predicted by muscularis propria thickness and muscularis propria + mucosa thickness under ultrasonic gastroscopy.

OBJECTIVES: The latest version of the National Comprehensive Cancer Network recommends neoadjuvant therapy followed by surgical treatment or radical chemoradiotherapy for patients with cT3N0M0. Neoadjuvant therapy can improve the prognosis of patients with locally advanced esophageal cancer. Therefore, the evaluation or prediction of T stage is particularly important because the treatment could differently affect the prognosis. Here, we establish a model to predict the T stage of patients with T2-3N0M0 to help choose the best treatment strategy. METHODS: From 1637 patents with esophageal cancer, we enrolled 48 patients and performed least absolute shrinkage and selection operator regression to screen for independent factors influencing pathological T stage. We, then, trained the decision tree to obtain the decision tree diagram and divided the T stages obtained by different methods into two categories, T2 and T3, for survival analysis. RESULTS: A total of 21 and 27 cases were predicted to be T2 and T3, respectively, under ultrasonic gastroscopy, 19 and 29 under magnetic resonance imaging, and 22 and 26 under pathological examination. Multivariate logistic regression analysis revealed that the muscularis propria thickness (MPT) (p = 0.0097) and the muscularis propria + mucosa thickness (MPMT) in the largest tumor cross-section (p = 0.0239) were independent influencing factors. We plotted a decision tree diagram with these two factors. MPT in the largest tumor cross-section >1.3 mm could be judged as pT3; if ≤1.3 mm, MPMT should be considered a thickness ≥1.7 mm could be judged as pT2 (otherwise pT3). Corresponding survival analysis was performed according to the T stage under different examination modalities. CONCLUSION: MPT in the largest tumor cross-section and MPMT in the largest tumor cross-section are independent predicting factors of pathological T stage.

A clinical nomogram for predicting tumor regression grade in esophageal squamous-cell carcinoma treated with immune neoadjuvant immunotherapy.

Background: There are various treatment options for esophageal squamous cell cancer. including surgery, peri-operative chemotherapy, and radiation. More recently, neoadjuvant immunotherapy has also been shown improve outcomes. In this study, we addressed the question, "Can we predict which patients with esophageal squamous cell cancer will benefit from neoadjuvant immunotherapy?". Methods: All patients with thoracic esophageal squamous-cell carcinoma (T2N+M0-T3-4N0/+M0) (according to the eighth edition of the National Comprehensive Cancer Network guidelines) who underwent immune neoadjuvant immunochemotherapy with programmed cell death protein 1 (PD-1) combined with paclitaxel plus cisplatin or nedaplatin in the Affiliated Cancer Hospital of Zhengzhou University, China, between November 2019 and August 2021 were included in this study. All patients underwent surgical resection. We developed a response [tumor regression grade (TRG)] prediction model using the least absolute shrinkage and selection operator (LASSO) regression incorporating factors associated with response. The accuracy of the prediction model was then validated. Results: We included 79 patients who underwent neoadjuvant immunotherapy combined with chemotherapy, aged 48-78 years (62.05±6.67), including 21 males and 58 females. There were five cases of immune-related pneumonia, of which three cases were diagnosed as immune-related pneumonia during the perioperative period, and one case of immune-related thyroid dysfunction changes. After LASSO regression, the factors that were independently associated with TRG were clinical T stage before neoadjuvant therapy, clinical N stage before neoadjuvant therapy, albumin level difference from before to after neoadjuvant therapy, white blood cell (WBC) count before neoadjuvant therapy, and T stage before surgery. We constructed a prediction model, plotted the nomogram, and verified its accuracy. Its Brier score was 0.13, its calibration slope was 0.98, and its C-index was 0.90 (95% CI: 0.82-0.97). Conclusions: Our prediction model can predict the likelihood of TRG in patients with esophageal squamous cell cancer after immunotherapy combined with neoadjuvant chemotherapy. Using this prediction model, we plan to conduct a subsequent neoadjuvant radiotherapy in patients with of TRG 2-3 patients with neoadjuvant radiotherapy.