开发和内部验证基于CT放射组学的HIV相关性支氏肺孢子虫肺炎严重程度分类模型

摘要(中文译文)

人类免疫缺陷病毒（HIV）相关肺炎（PJP）的严重程度评估在临床上具有重要意义，因为动脉血气指标虽为标准方法，但可能无法充分反映胸部计算机断层扫描（CT）上所见的肺部受累异质性。本研究旨在开发并内部验证一种CT影像组学模型，用于对确诊HIV相关PJP的成人患者进行严重程度分类。这项回顾性单中心研究纳入了96例确诊HIV相关PJP并在就诊时接受胸部CT检查的成年患者。根据室内空气动脉血气标准将疾病严重程度分为轻度和中重度，中重度疾病定义为动脉血氧分压（PaO）<70 mmHg或肺泡-动脉氧梯度（A-aDO）≥35 mmHg。临床变量从病历中回顾性收集。患者按7：3的比例随机分为训练队列和测试队列。从双侧肺实质中提取影像组学特征。经最小绝对收缩和选择算子回归后，将系数非零的特征纳入最终影像组学模型。同时，在训练队列中开发了纳入血清乳酸脱氢酶、β-D-葡聚糖和CD4计数的临床逻辑回归模型，并在测试队列中进行测试。采用受试者工作特征曲线下面积（AUC）及95%置信区间（CI）、校准分析、决策曲线分析和成对DeLong检验比较来评估模型性能。96例患者中，38例归类为轻度，58例归类为中重度。中重度疾病患者呼吸困难发生频率更高、炎症标志物水平更高、CD4计数更低。最终影像组学模型纳入10个特征。在训练队列中，影像组学模型的AUC为0.92（95% CI：0.85-0.97），而临床逻辑回归模型为0.65（95% CI：0.51-0.78）。在测试队列中，影像组学模型的AUC数值上更高，为0.89（95% CI：0.72-1.00），其次为临床逻辑回归模型的0.84（95% CI：0.68-0.97）。使用各自的分类阈值，影像组学模型在测试队列中的敏感度为0.778（95% CI：0.548-0.910），特异度为0.818（95% CI：0.523-0.949），而临床逻辑回归模型的敏感度为0.722（95% CI：0.491-0.875），特异度为0.909（95% CI：0.623-0.984）。测试队列中的成对DeLong检验显示，影像组学模型与临床逻辑回归模型之间差异无统计学意义。在这项小型单中心研究中，CT影像组学模型对HIV相关PJP的严重程度分类显示出良好的区分能力，但这些发现尚属初步，在临床使用前需要外部多中心验证。

Abstract

Severity assessment of human immunodeficiency virus (HIV)-associated pneumonia (PJP) is clinically important because arterial blood gas indices are standard but may not fully capture the heterogeneity of lung involvement on chest computed tomography (CT). We aimed to develop and internally validate a CT radiomics model for classifying severity in adults with confirmed HIV-associated PJP. This retrospective single-center study included 96 adult patients with confirmed HIV-associated PJP who underwent chest CT at presentation. Disease severity was classified as mild or moderate-to-severe according to room-air arterial blood gas criteria, with moderate-to-severe disease defined as arterial partial pressure of oxygen (PaO) <70 mmHg or an alveolar-arterial oxygen gradient (A-aDO) ≥35 mmHg. Clinical variables were retrospectively collected from medical records. Patients were randomly divided into training and test cohorts at a ratio of 7:3. Radiomics features were extracted from the bilateral lung parenchyma. After least absolute shrinkage and selection operator regression, features with non-zero coefficients were included in the final radiomics model. In parallel, a clinical logistic model incorporating serum lactate dehydrogenase, β-D-glucan, and CD4 count was developed in the training cohort and tested in the test cohort. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs), calibration analysis, decision curve analysis, and pairwise DeLong test comparisons. Of the 96 patients, 38 were classified as mild and 58 as moderate-to-severe. Patients with moderate-to-severe disease had a higher frequency of dyspnea, higher levels of inflammatory markers, and lower CD4 count. The final radiomics model included 10 features. In the training cohort, the radiomics model achieved an AUC of 0.92 (95% CI: 0.85-0.97), compared with 0.65 (95% CI: 0.51-0.78) for the clinical logistic model. In the test cohort, the radiomics model showed a numerically higher AUC of 0.89 (95% CI: 0.72-1.00), followed by the clinical logistic model at 0.84 (95% CI: 0.68-0.97). Using their respective classification thresholds, the radiomics model yielded a sensitivity of 0.778 (95% CI: 0.548-0.910) and a specificity of 0.818 (95% CI: 0.523-0.949) in the test cohort, while the clinical logistic model yielded a sensitivity of 0.722 (95% CI: 0.491-0.875) and a specificity of 0.909 (95% CI: 0.623-0.984). Pairwise DeLong tests in the test cohort showed no statistically significant difference between the radiomics model and the clinical logistic model. In this small single-center study, the CT radiomics model showed promising discrimination for severity classification in HIV-associated PJP, but these findings are preliminary and require external multicenter validation before clinical use.

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