Deng Liangna, Zhang Mingtao, Zhu Kaibo, Ren Jialiang, Zhang Peng, Zhang Yuting, Jing Mengyuan, Han Tao, Zhang Bin, Zhou Junlin
Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China.
Second Clinical School, Lanzhou University, Lanzhou 730000, China; Department of Orthopedics, Lanzhou University Second Hospital, Lanzhou 730000, China.
Acad Radiol. 2025 Jan;32(1):460-470. doi: 10.1016/j.acra.2024.07.004. Epub 2024 Aug 16.
To develop a model based on conventional CT signs and the tumor microenvironment immune types (TIMT) to predict the durable clinical benefits (DCB) of postoperative adjuvant chemotherapy in non-small cell lung cancer (NSCLC).
A total of 205 patients with NSCLC underwent preoperative CT and were divided into two groups: DCB (progression-free survival (PFS) ≥ 18 months) and non-DCB (NDCB, PFS <18 months). The density percentiles of PD-L1 and CD8 + tumor-infiltrating lymphocytes (TIL) were quantified to estimate the TIMT. Clinical characteristics and conventional CT signs were collected. Multivariate logistic regression was employed to select the most discriminating parameters, construct a predictive model, and visualize the model as a nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate prediction performance and clinical utility.
Precisely 118 patients with DCB and 87 with NDCB in NSCLC received postoperative adjuvant chemotherapy. TIMT was statistically different between the DCB and NDCB groups (P < 0.05). Clinical characteristics (neuron-specific enolase, squamous cell carcinoma antigen, Ki-76, and cM stage) and conventional CT signs (spiculation, bubble-like lucency, pleural retraction, maximum diameter, and CT value of the venous phase) varied between the four TIMT groups (P < 0.05). Furthermore, clinical characteristics (lymphocyte count [LYMPH] and cM stage) and conventional CT signs (bubble-like lucency and Pleural effusion) differed between the DCB and NDCB groups (P < 0.05). Multivariate analysis revealed that TIMT, cM stage, LYMPH, and pleural effusion were independently associated with DCB and were used to construct a nomogram. The area under the curve (AUC) of the combined model was 0.70 (95%CI: 0.64-0.76), with sensitivity and specificity of 0.73 and 0.60, respectively.
Conventional CT signs and the TIMT offer a promising approach to predicting clinical outcomes for patients treated with postoperative adjuvant chemotherapy in NSCLC.
建立一种基于传统CT征象和肿瘤微环境免疫类型（TIMT）的模型，以预测非小细胞肺癌（NSCLC）术后辅助化疗的持久临床获益（DCB）。
共有205例NSCLC患者术前行CT检查，并分为两组：DCB组（无进展生存期（PFS）≥18个月）和非DCB组（NDCB组，PFS<18个月）。对PD-L1和CD8+肿瘤浸润淋巴细胞（TIL）的密度百分位数进行量化，以评估TIMT。收集临床特征和传统CT征象。采用多因素逻辑回归选择最具鉴别力的参数，构建预测模型，并将该模型可视化为列线图。采用受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估预测性能和临床实用性。
NSCLC中恰好有118例DCB患者和87例NDCB患者接受了术后辅助化疗。DCB组和NDCB组之间的TIMT有统计学差异（P<0.05）。四个TIMT组之间的临床特征（神经元特异性烯醇化酶、鳞状细胞癌抗原、Ki-76和cM分期）和传统CT征象（毛刺征、泡状透亮影、胸膜凹陷、最大直径和静脉期CT值）有所不同（P<0.05）。此外，DCB组和NDCB组之间的临床特征（淋巴细胞计数[LYMPH]和cM分期）和传统CT征象（泡状透亮影和胸腔积液）也存在差异（P<0.05）。多因素分析显示，TIMT、cM分期、LYMPH和胸腔积液与DCB独立相关，并用于构建列线图。联合模型的曲线下面积（AUC）为0.70（95%CI：0.64-0.76），敏感性和特异性分别为0.73和0.60。
传统CT征象和TIMT为预测NSCLC术后辅助化疗患者的临床结局提供了一种有前景的方法。
