Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, China.
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
J Transl Med. 2021 May 4;19(1):191. doi: 10.1186/s12967-021-02849-8.
The timeliness of diagnostic testing after positive screening remains suboptimal because of limited evidence and methodology, leading to delayed diagnosis of lung cancer and over-examination. We propose a radiomics approach to assist with planning of the diagnostic testing interval in lung cancer screening.
From an institute-based lung cancer screening cohort, we retrospectively selected 92 patients with pulmonary nodules with diameters ≥ 3 mm at baseline (61 confirmed as lung cancer by histopathology; 31 confirmed cancer-free). Four groups of region-of-interest-based radiomic features (n = 310) were extracted for quantitative characterization of the nodules, and eight features were proven to be predictive of cancer diagnosis, noise-robust, phenotype-related, and non-redundant. A radiomics biomarker was then built with the random survival forest method. The patients with nodules were divided into low-, middle- and high-risk subgroups by two biomarker cutoffs that optimized time-dependent sensitivity and specificity for decisions about diagnostic workup within 3 months and about repeat screening after 12 months, respectively. A radiomics-based follow-up schedule was then proposed. Its performance was visually assessed with a time-to-diagnosis plot and benchmarked against lung RADS and four other guideline protocols.
The radiomics biomarker had a high time-dependent area under the curve value (95% CI) for predicting lung cancer diagnosis within 12 months; training: 0.928 (0.844, 0.972), test: 0.888 (0.766, 0.975); the performance was robust in extensive cross-validations. The time-to-diagnosis distributions differed significantly between the three patient subgroups, p < 0.001: 96.2% of high-risk patients (n = 26) were diagnosed within 10 months after baseline screen, whereas 95.8% of low-risk patients (n = 24) remained cancer-free by the end of the study. Compared with the five existing protocols, the proposed follow-up schedule performed best at securing timely lung cancer diagnosis (delayed diagnosis rate: < 5%) and at sparing patients with cancer-free nodules from unnecessary repeat screenings and examinations (false recommendation rate: 0%).
Timely management of screening-detected pulmonary nodules can be substantially improved with a radiomics approach. This proof-of-concept study's results should be further validated in large programs.
由于证据和方法有限，阳性筛查后的诊断检测及时性仍然不理想，导致肺癌诊断延迟和过度检查。我们提出一种基于放射组学的方法来辅助肺癌筛查中诊断检测间隔的规划。
我们从一个基于机构的肺癌筛查队列中回顾性选择了 92 名基线时直径≥3mm 的肺结节患者（61 例经组织病理学证实为肺癌；31 例证实无癌）。对结节进行了四组基于感兴趣区域的放射组学特征（n=310）提取，证明了 8 个特征可预测癌症诊断、抗噪、表型相关且不冗余。然后，使用随机生存森林方法构建放射组学生物标志物。通过两个生物标志物截断值将有结节的患者分为低、中、高危亚组，分别优化了 3 个月内进行诊断性检查和 12 个月后重复筛查的决策的时间依赖性敏感性和特异性。然后提出了一种基于放射组学的随访计划。通过诊断时间图进行直观评估，并与 Lung RADS 和其他四种指南方案进行基准测试。
放射组学生物标志物在预测 12 个月内肺癌诊断方面具有较高的时间依赖性曲线下面积（95%置信区间）；训练：0.928（0.844，0.972），测试：0.888（0.766，0.975）；在广泛的交叉验证中表现稳健。三个患者亚组之间的诊断时间分布有显著差异，p<0.001：高危患者（n=26）中有 96.2%在基线筛查后 10 个月内得到诊断，而低危患者（n=24）中有 95.8%在研究结束时仍无癌。与其他五个现有方案相比，建议的随访计划在确保及时进行肺癌诊断方面表现最佳（延迟诊断率：<5%），并可使无癌结节患者避免不必要的重复筛查和检查（错误推荐率：0%）。
基于放射组学的方法可显著改善对筛查发现的肺结节的管理。本概念验证研究的结果应在大型项目中进一步验证。
