Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China.
Eur Radiol. 2023 May;33(5):3072-3082. doi: 10.1007/s00330-023-09453-y. Epub 2023 Feb 15.
To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists.
A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy.
The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05).
The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert.
• Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.
构建低剂量 CT（LDCT）的放射组学模型，以预测浸润性非黏液型肺腺癌（IPA）的分化程度，并与定量语义模型和放射科医生的诊断性能进行比较。
根据扫描仪，将 682 个肺结节分为主要队列（181 级 1；254 级 2；64 级 3）和验证队列（69 级 1；99 级 2；15 级 3）。使用有序逻辑回归构建放射组学和定量语义模型。通过受试者工作特征曲线下面积（AUC）和准确性评估模型和放射科医生的诊断性能。
放射组学模型在验证队列中表现出出色的诊断性能（AUC，1 级与 2/3 级比较为 0.900（95%CI：0.847-0.939）；AUC，1 级/2 级与 3 级比较为 0.929（95%CI：0.882-0.962）；准确性，0.803（95%CI：0.737-0.857））。放射组学模型与放射科专家的诊断性能无显著差异（AUC，1 级与 2/3 级比较为 0.840（95%CI：0.779-0.890），p=0.130；AUC，1 级/2 级与 3 级比较为 0.852（95%CI：0.793-0.900），p=0.170；准确性，0.743（95%CI：0.673-0.804），p=0.079），但优于定量语义模型和经验不足的放射科医生（均 p<0.05）。
LDCT 的放射组学模型可用于预测肺癌筛查中 IPA 的分化程度，其诊断性能可与放射科专家相媲美。
• 早期识别浸润性非黏液型肺腺癌的新型分化程度，可为进一步监测、手术策略或更多辅助治疗提供指导。
• 放射组学模型的诊断性能与放射科专家相当，优于定量语义模型和经验不足的放射科医生。
• LDCT 的放射组学模型可用于预测肺癌筛查中浸润性非黏液型肺腺癌的分化程度。
