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.
Cancer Med. 2024 Nov;13(21):e70372. doi: 10.1002/cam4.70372.
Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules.
One thousand and ninety-eight patients with 6-30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I-T), and two external test cohorts (E-T1, E-T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account.
The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p < 0.05, IDI = 0.137, p < 0.05). In the I-T, E-T1, and E-T2, the AUCs of DLRCN were 0.946 (95% CI: 0.936, 0.955), 0.948 (95% CI: 0.933, 0.963) and 0.962 (95% CI: 0.945, 0.979), The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (p > 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules.
The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN.
正确区分肺部良、恶性结节可以避免不必要的有创检查。本研究旨在构建深度学习放射组学临床列线图（DLRCN）以预测肺部结节的恶性程度。
纳入三家中心经组织病理学诊断的 6-30mm 肺部结节患者 1098 例，分为原始队列（PC）、内部测试队列（I-T）和两个外部测试队列（E-T1、E-T2）。该列线图通过整合脂肪组织放射组学特征、结节内和结节周围深度学习特征以及临床特征来诊断肺部结节的恶性程度。采用最小绝对收缩和选择算子（LASSO）进行特征选择。通过校准曲线、曲线下面积（AUC）和决策曲线分析（DCA）评估 DLRCN 的性能。此外，我们将其与三位放射科医生进行了比较。还考虑了净重新分类改善（NRI）、综合判别改善（IDI）和亚组分析。
纳入脂肪组织放射组学特征可显著提高 NRI 和 IDI（NRI=1.028，p<0.05，IDI=0.137，p<0.05）。在 I-T、E-T1 和 E-T2 中，DLRCN 的 AUC 分别为 0.946（95%CI：0.936，0.955）、0.948（95%CI：0.933，0.963）和 0.962（95%CI：0.945，0.979）。校准曲线显示实际概率与预测概率之间具有良好的预测准确性（p>0.05）。DCA 表明 DLRCN 具有临床应用价值。在相同特异性下，DLRCN 的敏感性比放射科医生评估提高了 8.6%。进一步对脂肪组织放射组学特征进行亚组分析，结果表明其对确定肺部结节的恶性程度具有补充价值。
DLRCN 在预测肺部结节恶性程度方面表现良好，与放射科医生评估相当。脂肪组织放射组学特征显著提高了 DLRCN 的性能。
