Development of a haematological indices-based nomogram for prognostic prediction and immunotherapy response assessment in primary pulmonary lymphoepithelioma-like carcinoma patients

作者：Hui Sheng, Xin He, Zhiqiang Chen, Kewei Huang, Jingjing Yang, Xiaoli Wei, Minjie Mao · 发表于：Translational Lung Cancer Research · 年份：2024 · DOI：10.21037/tlcr-23-813 · 被引用次数：5 · 研究领域：Inflammatory Biomarkers in Disease Prognosis、Viral-associated cancers and disorders、Lymphoma Diagnosis and Treatment

Background: Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare yet aggressive malignancy. This study aims to investigate a deep learning model based on hematological indices, referred to as haematological indices-based signature (HIBS), and propose multivariable predictive models for accurate prognosis prediction and assessment of therapeutic response to immunotherapy in PPLELC. Methods: This retrospective study included 117 patients with PPLELC who received immunotherapy and were randomly divided into a training (n=82) and a validation (n=35) cohort. A total of 41 hematological features were extracted from routine laboratory tests and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to establish the HIBS. Additionally, we developed a nomogram using the HIBS and clinical characteristics through multivariate Cox regression analysis. To evaluate the nomogram’s predictive performance, we used calibration curves and calculated the time-dependent area under the curve (AUC). Kaplan-Meier survival analysis was performed to estimate progression-free survival (PFS) in both cohorts. Results: The proposed HIBS comprised 14 hematological features and showed that patients who experienced disease progression had significantly higher HIBS scores compared to those who did not progress (P<0.001). Five prognostic factors, including HIBS, tumor-node-metastasis (TNM) stage, presence of bone metastasis and the specific immunotherapy regimen, w...