Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, China.
Radiol Med. 2023 Sep;128(9):1103-1115. doi: 10.1007/s11547-023-01683-w. Epub 2023 Jul 18.
To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth.
Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (≥ 50%, ≥ 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results.
296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis ≥ 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis ≥ 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis ≥ 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis ≥ 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion.
CerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance.
利用独立的双中心数据集（以数字减影血管造影（DSA）为金标准）对头部和颈部 CT 血管造影（CTA）中自动狭窄检测的性能进行外部验证，并研究影响因素。
回顾性纳入 2019 年 1 月至 2021 年 12 月期间行头颈部 CTA 和 DSA 的患者。使用基于 CTA 的 CerebralDoc 自动评估狭窄程度。评估 CerebralDoc 在不同水平（每位患者、每个区域、每条血管和每个节段）和阈值（≥50%、≥70%和≥100%）的性能。采用逻辑回归识别与假阴性结果相关的独立因素。
共分析了 296 例患者。各水平和各阈值的特异性均较高，超过 92%。曲线下面积范围从较差（区域分析狭窄程度≥70%时为 0.615，95%CI：0.544，0.686）到优秀（患者分析狭窄程度≥50%时为 0.945，95%CI：0.905，0.985）。敏感性范围从狭窄程度≥70%的节段分析时的 0.714（95%CI：0.675，0.750）到狭窄程度≥50%的患者分析时的 0.895（95%CI：0.849，0.919）。多因素逻辑回归分析显示，假阴性结果主要更可能发生在特定的狭窄部位（特别是颈内动脉的 M2 段和颅底段）和闭塞处。
CerebralDoc 有潜力自动检测头颈部 CTA 的狭窄程度，但需要进一步努力来优化其性能。
