Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models’ discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

三级淋巴结构（TLS）与各种癌症的良好免疫治疗反应和预后相关。尽管它们很重要，但使用 T 和 B 淋巴细胞的多重免疫组织化学 (mIHC) 染色进行定量仍然是劳动密集型的，限制了其临床实用性。为了应对这一挑战，我们从匹配的 mIHC 和 H&E 全玻片图像 (WSI) 中整理了一个数据集，并开发了用于 TLS 自动分割的深度学习模型。该模型在内部测试集上的 Dice 系数为 0.91，在外部验证集上的 Dice 系数为 0.866，交并集 (IoU) 分数分别为 0.819 和 0.787。 TLS 比率定义为分段 TLS 面积占总组织面积的比例，与癌症基因组图谱 (TCGA) 中涵盖 16 种肿瘤类型的 6140 名患者中的 B 淋巴细胞水平和CXCL13（一种与 TLS 形成相关的趋化因子）的表达相关。总体生存的预后模型表明，将 TLS 比率与 TNM 分期结合起来显着增强了模型的辨别能力，在 15 种 TCGA 肿瘤类型中的 10 种中，优于仅结合 TNM 分期的传统模型。此外，当应用于未接受过活检治疗的肿瘤样本时，较高的 TLS 比率预示着多个队列的免疫治疗反应呈阳性，包括食管鳞状细胞癌、非小细胞肺癌和胃腺癌的特异性治疗。总之，我们基于深度学习的方法为 TLS 分割和量化提供了一种自动化且可重复的方法，突显了其在预测免疫治疗反应和告知癌症预后方面的潜力。