Zhang Zaixian, Zhang Taijuan, Ding Hui, Liu Shunli, Li Zhiming, Ge Yaqiong, Yang Lei
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Radiology, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, China.
Curr Med Imaging. 2025;21:e15734056376470. doi: 10.2174/0115734056376470250718053430.
This study aimed to explore the relationship between radiomics features and anaplastic lymphoma kinase (ALK) gene mutation status in lung adenocarcinoma and to develop a radiomics nomogram for preoperative prediction of ALK mutations.
A retrospective analysis was conducted on 210 patients with histologically confirmed lung adenocarcinoma (50 ALK mutation-positive, 160 mutation-negative), divided into training (n=147) and validation (n=63) cohorts (7:3 ratio). Preoperative enhanced CT images were analyzed using ITK-SNAP for region-of-interest delineation, and radiomics features were extracted A.K. software. The least absolute shrinkage and selection operator algorithm selected features to generate a radiomics score. Multivariate logistic regression identified independent risk factors, and a radiomics nomogram combining clinical features and radiomics signatures was developed. Model performance was evaluated using AUC in both training and validation sets.
Nineteen radiomics features were selected to construct the radiomics signature. The signature achieved an AUC of 0.89 (95% CI: 0.84-0.95) in the training set and 0.79 (95% CI: 0.63-0.95) in the validation set. The radiomics nomogram demonstrated superior performance (AUC=0.80, 95% CI: 0.63-0.97) compared to the clinical model alone (AUC=0.66, 95% CI: 0.47-0.85) in the validation set. While the nomogram showed no statistically significant improvement over the radiomics signature alone (>0.05), it outperformed the clinical model significantly (<0.001 in training; =0.0337 in validation).
The radiomics nomogram integrating clinical and radiomics data demonstrated robust predictive capability for ALK mutations, highlighting the potential of non-invasive CT-based radiomics in guiding personalized treatment. However, the lack of significant difference between the nomogram and radiomics signature alone suggests limited incremental value from clinical variables in this cohort. Limitations include the retrospective design, single-center data, and class imbalance (fewer ALK-positive cases), which may affect generalizability. External validation is warranted to confirm clinical utility.
The CT-derived radiomics signature and nomogram show promise for preoperative ALK mutation prediction in lung adenocarcinoma. These tools could enhance clinical decision-making by identifying candidates for targeted therapies, though further validation is needed to optimize their application in diverse populations.
本研究旨在探讨肺腺癌的放射组学特征与间变性淋巴瘤激酶（ALK）基因突变状态之间的关系，并开发一种放射组学列线图用于术前预测ALK突变。
对210例经组织学确诊的肺腺癌患者进行回顾性分析（50例ALK突变阳性，160例突变阴性），分为训练组（n = 147）和验证组（n = 63）（7:3比例）。使用ITK-SNAP分析术前增强CT图像以进行感兴趣区域的勾画，并使用A.K.软件提取放射组学特征。采用最小绝对收缩和选择算子算法选择特征以生成放射组学评分。多因素逻辑回归确定独立危险因素，并开发结合临床特征和放射组学特征的放射组学列线图。在训练集和验证集中使用AUC评估模型性能。
选择19个放射组学特征构建放射组学特征。该特征在训练集中的AUC为0.89（95%CI：0.84 - 0.95），在验证集中为0.79（95%CI：0.63 - 0.95）。在验证集中，放射组学列线图显示出优于单独临床模型的性能（AUC = 0.80，95%CI：0.63 - 0.97）（单独临床模型的AUC = 0.66，95%CI：0.47 - 0.85）。虽然列线图与单独的放射组学特征相比没有统计学上的显著改善（>0.05），但它明显优于临床模型（训练中<0.001；验证中 = 0.0337）。
整合临床和放射组学数据的放射组学列线图对ALK突变具有强大的预测能力，突出了基于CT的无创放射组学在指导个性化治疗方面的潜力。然而，列线图与单独的放射组学特征之间缺乏显著差异表明该队列中临床变量的增量价值有限。局限性包括回顾性设计、单中心数据和类别不平衡（ALK阳性病例较少），这可能影响可推广性。需要外部验证以确认临床实用性。
CT衍生的放射组学特征和列线图在肺腺癌术前ALK突变预测方面显示出前景。这些工具可以通过识别靶向治疗的候选者来增强临床决策，尽管需要进一步验证以优化它们在不同人群中的应用。
