Sheng Hui, He Xin, Chen Zhiqiang, Huang Kewei, Yang Jingjing, Wei Xiaoli, Mao Minjie
Department of Experimental Research, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Department of Pharmacy, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Transl Lung Cancer Res. 2024 Mar 29;13(3):453-464. doi: 10.21037/tlcr-23-813. Epub 2024 Mar 27.
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.
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.
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, were found to be independent factors and were used to construct a nomogram, which effectively categorized PPLELC patients into a high-risk and a low-risk group, with patients in the high-risk patients demonstrating worse PFS (7.0 . 18.0 months, P<0.001) and lower overall response rates (22.2% . 52.7%, P<0.001). The nomogram showed satisfactory discrimination for PFS, with AUC values of 0.837 and 0.855 in the training and validation cohorts, respectively.
The HIBS-based nomogram could effectively predict the PFS and response of patients with PPLELC regarding immunotherapy and serve as a valuable tool for clinical decision making.
原发性肺淋巴上皮瘤样癌（PPLELC）是一种罕见但侵袭性强的恶性肿瘤。本研究旨在研究一种基于血液学指标的深度学习模型，即基于血液学指标的特征（HIBS），并提出多变量预测模型，以准确预测PPLELC的预后并评估免疫治疗的疗效。
这项回顾性研究纳入了117例接受免疫治疗的PPLELC患者，随机分为训练组（n = 82）和验证组（n = 35）。从常规实验室检查中提取了总共41项血液学特征，并利用最小绝对收缩和选择算子（LASSO）算法建立了HIBS。此外，我们通过多变量Cox回归分析，使用HIBS和临床特征开发了一个列线图。为了评估列线图的预测性能，我们使用了校准曲线并计算了曲线下的时间依赖性面积（AUC）。进行Kaplan-Meier生存分析以估计两个队列中的无进展生存期（PFS）。
所提出的HIBS包含14项血液学特征，结果显示疾病进展的患者与未进展的患者相比，HIBS评分显著更高（P<0.001）。发现包括HIBS、肿瘤-淋巴结-转移（TNM）分期、骨转移的存在和特定免疫治疗方案在内的五个预后因素是独立因素，并用于构建列线图，该列线图有效地将PPLELC患者分为高风险组和低风险组，高风险组患者的PFS较差（7.0. 18.0个月，P<0.001），总体缓解率较低（22.2%. 52.7%，P<0.001）。列线图对PFS显示出令人满意的区分能力，训练组和验证组的AUC值分别为0.837和0.855。
基于HIBS列线图可以有效地预测PPLELC患者免疫治疗的PFS和反应，并作为临床决策的有价值工具。
