Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Department of Radiology, West Virginia University, Morgantown, WV, USA.
Sci Rep. 2023 Apr 15;13(1):6157. doi: 10.1038/s41598-023-33098-y.
A deep learning model (LCP CNN) for the stratification of indeterminate pulmonary nodules (IPNs) demonstrated better discrimination than commonly used clinical prediction models. However, the LCP CNN score is based on a single timepoint that ignores longitudinal information when prior imaging studies are available. Clinically, IPNs are often followed over time and temporal trends in nodule size or morphology inform management. In this study we investigated whether the change in LCP CNN scores over time was different between benign and malignant nodules. This study used a prospective-specimen collection, retrospective-blinded-evaluation (PRoBE) design. Subjects with incidentally or screening detected IPNs 6-30 mm in diameter with at least 3 consecutive CT scans prior to diagnosis (slice thickness ≤ 1.5 mm) with the same nodule present were included. Disease outcome was adjudicated by biopsy-proven malignancy, biopsy-proven benign disease and absence of growth on at least 2-year imaging follow-up. Lung nodules were analyzed using the Optellum LCP CNN model. Investigators performing image analysis were blinded to all clinical data. The LCP CNN score was determined for 48 benign and 32 malignant nodules. There was no significant difference in the initial LCP CNN score between benign and malignant nodules. Overall, the LCP CNN scores of benign nodules remained relatively stable over time while that of malignant nodules continued to increase over time. The difference in these two trends was statistically significant. We also developed a joint model that incorporates longitudinal LCP CNN scores to predict future probability of cancer. Malignant and benign nodules appear to have distinctive trends in LCP CNN score over time. This suggests that longitudinal modeling may improve radiomic prediction of lung cancer over current models. Additional studies are needed to validate these early findings.
一种用于肺内不定性结节（IPN）分层的深度学习模型（LCP CNN），其鉴别能力优于常用的临床预测模型。然而，LCP CNN 评分基于单个时间点，当有先前的影像学研究时，会忽略纵向信息。在临床上，IPN 通常会随时间进行随访，结节大小或形态的时间趋势会为管理提供信息。本研究旨在探讨 LCP CNN 评分随时间的变化在良性和恶性结节之间是否存在差异。本研究采用前瞻性标本采集、回顾性盲法评估（PRoBE）设计。纳入偶然或筛查发现的直径 6-30mm 的 IPN 患者，且在诊断前至少有 3 次连续 CT 扫描（切片厚度≤1.5mm），并且同一结节存在。疾病结局通过活检证实的恶性肿瘤、活检证实的良性疾病以及至少 2 年影像学随访无生长来判定。使用 Optellum LCP CNN 模型对肺结节进行分析。进行图像分析的研究人员对所有临床数据均不知情。对 48 个良性结节和 32 个恶性结节进行了 LCP CNN 评分分析。良性和恶性结节的初始 LCP CNN 评分无显著差异。总体而言，良性结节的 LCP CNN 评分随时间相对稳定，而恶性结节的评分随时间持续增加，这两种趋势的差异具有统计学意义。我们还开发了一种联合模型，该模型将纵向 LCP CNN 评分纳入其中，以预测未来癌症的发生概率。良性和恶性结节的 LCP CNN 评分随时间呈现出不同的趋势。这表明，与当前模型相比，纵向建模可能会提高对肺癌的放射组学预测。需要进一步的研究来验证这些早期发现。
