The prognostic value of TCF1+CD8+T in primary small cell carcinoma of the esophagus.

TCF1+CD8+T cells are reported to exhibit stem-like properties with the ability to self-renew and differentiate into terminal effector T cells (TCF1-CD8+T cells) to enhance antitumor response. Previous studies indicated that TCF1+CD8+ tumor-infiltrating lymphocytes (TILs) are related to response to immunotherapy. However, their role in predicting prognosis for patients with primary small cell carcinoma of the esophagus (PSCCE) remains unclear. In this study, the expression of TCF1+CD8+T was analyzed by multiplex fluorescence immunohistochemistry in tumor tissues of 79 patients with PSCCE. High infiltration of TCF1+CD8+T cells had longer overall survival (OS) than low infiltration (P = .009, hazard ratio [HR] = 0.506). High TCF1+CD8/CD8 ratio (>21%) showed superior OS compared with low ratio (≤21%) (P < .001, HR = 0.394). In the validation set (n = 20), the prognostic value of TCF1+CD8+T cells on OS was also verified. TCF1+CD8+T cells are strong prognostic predictors.

Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model.

Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT-CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement-random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT-CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer-aided diagnosis of acute lymphoblastic leukemia.