Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu, China.
The Department of CT, Tianshui Combine traditional Chinese and Western Medicine Hospital, Tianshui, Gansu, China.
Clin Respir J. 2023 May;17(5):394-404. doi: 10.1111/crj.13604. Epub 2023 Mar 21.
This study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients.
Data were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response.
Among 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration.
This new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.
本研究旨在探索 CT 放射组学和临床特征对 COVID-19 患者治疗反应的预测价值。
从 COVID-19 患者的临床/辅助检查和随访中收集数据。在基线胸部 CT 上进行全肺放射组学特征提取。评估放射组学、临床和联合特征（列线图）预测治疗反应。
在 36 例 COVID-19 患者中，分别有 1 例、21 例、13 例和 1 例为轻症、普通型、重型和危重型。25 例（1 例轻症、18 例普通型和 6 例重型）对治疗反应良好，11 例反应差/一般。入院时的临床分类（p=0.025）和血清肌酐（p=0.010）以及放射组学分析的小面积强调（p=0.036）在两组之间有显著差异。基于放射组学、临床特征和列线图构建了预测模型，其曲线下面积分别为 0.651、0.836 和 0.869。列线图具有良好的校准度。
这种新的、非侵入性和低成本的预测模型结合了放射组学和临床特征，有助于识别可能对治疗反应不佳的 COVID-19 患者。
