Miao Shidi, Dong Qi, Liu Le, Xuan Qifan, An Yunfei, Qi Hongzhuo, Wang Qiujun, Liu Zengyao, Wang Ruitao
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China.
Phys Med. 2025 Jan;129:104877. doi: 10.1016/j.ejmp.2024.104877. Epub 2024 Dec 16.
Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate benign and malignant pulmonary nodules.
This study proposes a deep learning (DL) approach to explore the potential predictive value of dual imaging markers, including intrathoracic fat (ITF), in patients with pulmonary nodules.
We enrolled 1321 patients with pulmonary nodules from three centers. Image feature extraction was performed on computed tomography (CT) images of pulmonary nodules and ITF by DL, multimodal information was used to discriminate benign and malignant in patients with pulmonary nodules.
Here, the areas under the receiver operating characteristic curve (AUC) of the model for ITF combined with pulmonary nodules were 0.910(95 % confidence interval [CI]: 0.870-0.950, P = 0.016), 0.922(95 % CI: 0.883-0.960, P = 0.037) and 0.899(95 % CI: 0.849-0.949, P = 0.033) in the internal test cohort, external test cohort1 and external test cohort2, respectively, which were significantly better than the model for pulmonary nodules. Intrathoracic fat index (ITFI) emerged as an independent influencing factor for benign and malignant in patients with pulmonary nodules, correlating with a 9.4 % decrease in the risk of malignancy for each additional unit.
This study demonstrates the potential auxiliary predictive value of ITF as a noninvasive imaging biomarker in assessing pulmonary nodules.
肺癌领域的近期研究强调了身体成分，尤其是脂肪组织作为预后因素的重要作用。然而，在结合脂肪组织以鉴别良性和恶性肺结节方面仍缺乏实践。
本研究提出一种深度学习（DL）方法，以探索双成像标志物，包括胸内脂肪（ITF），在肺结节患者中的潜在预测价值。
我们纳入了来自三个中心的1321例肺结节患者。通过DL对肺结节和ITF的计算机断层扫描（CT）图像进行图像特征提取，使用多模态信息鉴别肺结节患者的良恶性。
在此，ITF联合肺结节模型在内部测试队列、外部测试队列1和外部测试队列2中的受试者操作特征曲线（AUC）下面积分别为0.910（95%置信区间[CI]：0.870 - 0.950，P = 0.016）、0.922（95% CI：0.883 - 0.960，P = 0.037）和0.899（95% CI：0.849 - 0.949，P = 0.033），显著优于肺结节模型。胸内脂肪指数（ITFI）成为肺结节患者良恶性的独立影响因素，每增加一个单位，恶性风险降低9.4%。
本研究证明了ITF作为一种无创成像生物标志物在评估肺结节方面的潜在辅助预测价值。
