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自动狭窄测量软件在冠状动脉重度狭窄测量分析中的价值

Automated Estimation of Stenosis Severity in Coronary Computed Tomography Angiography

    摘要
  • 摘要:
      目的  探究自动狭窄测量软件在冠状动脉计算机断层血管造影(computed tomography angiography，CTA)的冠脉狭窄程度测量中的价值。
      方法  回顾性纳入同时行冠脉CTA及冠脉数字减影血管造影(digital subtraction angiography, DSA)的患者。将狭窄程度≥70%列为重度狭窄，以DSA为金标准，计算AW4.6软件自动狭窄程度分析(直径法与面积法)和人工诊断法的敏感度、特异度、阳性预测值及阴性预测值。
      结果  研究纳入57例患者，共178处病灶。在与DSA诊断的一致性分析中，软件法均具有中到高度一致性(Kappa值：0.716~0.804，P < 0.001)；人工诊断一致性为低到中度(Kappa值：0.385~0.533，P < 0.001)。在基于患者的分析中，软件面积法敏感度(100%)和阴性预测值(100%)最高，软件直径法特异度(90.48%)和阳性预测值(94.12%)最高，与人工诊断比较差异均有统计学意义(P < 0.016 7)。在基于血管的分析中，软件面积法敏感度(96.92%)和阴性预测值(97.89%)最高，软件直径法特异度(94.69%)和阳性预测值(90.16%)最高，与人工诊断比较差异均有统计学意义(P < 0.016 7)。
      结论  与人工诊断相比，软件自动测量在鉴别重度冠脉狭窄(狭窄程度≥70%)中具有较高的准确度，能为相应的临床治疗提供指导。

     

    Abstract:
      Objective  To determine the value of automated detection in computed tomography angiography (CTA) for cases with greater than 70% coronary stenosis.
      Methods  Fifty-seven patients who had both coronary CTA and digital subtraction angiography (DSA) were retrospectively recruited in this study. The patients were categorized into two groups using a cutoff value of 70% stenosis in DSA. The AW4.6 software was used to estimate the diameter and square values from the data obtained from CTA. The sensitivity (SE), specificity (SPE), positive predictive value (PPV) and negative predictive value (NPV) of the automated CTA estimations were calculated.
      Results  A total of 178 vessels from the 57 patients were analyzed. The automated CTA estimations had moderate to high levels of agreements (Kappa value: 0.716-0.804, P < 0.001) with the DSA diagnoses, compared with low to moderate levels of agreements (Kappa value: 0.385-0.533, P < 0.001) in manual interpretations. The square estimations generated high SE (100%) and NPV (100%) for patient diagnoses (P < 0.016 7 vs. manual interpretations). The diameter estimations generated high SPE (90.48%) and PPV (94.12%) for patient diagnoses (P < 0.016 7, vs. manual interpretations). Similarly, high SE (96.92%) and NPV (97.89%) were found for square estimations in vessel diagnoses, while high SPE (94.69%) and PPV (90.16%) were found for diameter estimations in vessel diagnoses.
      Conclusion  Both automated diameter and square algorithms have high accuracy for diagnosing patients with greater than 70% coronary artery stenosis. The AW4.6 can improve the detection of severe stenosis that needs stent interventions.

     

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