Abstract：
OBJECTIVE: To develop and validate a nomogram combining radiomics and pathology features to distinguish between aldosterone-producing adenomas (APAs) and nonfunctional adrenal adenomas (NF-AAs).
METHODS: Consecutive patients diagnosed with adrenal adenomas via computed tomography (CT) or pathologic analysis between January 2011 and November 2022 were eligible for inclusion in this retrospective study. CT images and hematoxylin & eosin-stained slides were used for annotation and feature extraction. The selected radiomics and pathology features were used to develop a risk model using various machine learning models, and the area under the receiver operating characteristic curve (AUC) was determined to evaluate diagnostic performance. The predicted results from radiomics and pathology features were combined and visualized using a nomogram.
RESULTS: A total of 211 patients (APAs, n = 59; NF-AAs, n = 152) were included in this study, with patients randomly divided into either the training set or the testing set at a ratio of 8:2. The ExtraTrees model yielded a sensitivity of 0.818, a specificity of 0.733, and an accuracy of 0.756 (AUC = 0.817; 95% confidence interval [CI]: 0.675-0.958) in the radiomics testing set and a sensitivity of 0.999, a specificity of 0.842, and an accuracy of 0.867 (AUC = 0.905, 95% CI: 0.792-1.000) in the pathology testing set. A nomogram combining radiomics and pathology features demonstrated a strong performance (AUC = 0.912; 95% CI: 0.807-1.000).
CONCLUSIONS: A nomogram combining radiomics and pathology features demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians to distinguish between APAs and NF-AAs.
METHODS: Consecutive patients diagnosed with adrenal adenomas via computed tomography (CT) or pathologic analysis between January 2011 and November 2022 were eligible for inclusion in this retrospective study. CT images and hematoxylin & eosin-stained slides were used for annotation and feature extraction. The selected radiomics and pathology features were used to develop a risk model using various machine learning models, and the area under the receiver operating characteristic curve (AUC) was determined to evaluate diagnostic performance. The predicted results from radiomics and pathology features were combined and visualized using a nomogram.
RESULTS: A total of 211 patients (APAs, n = 59; NF-AAs, n = 152) were included in this study, with patients randomly divided into either the training set or the testing set at a ratio of 8:2. The ExtraTrees model yielded a sensitivity of 0.818, a specificity of 0.733, and an accuracy of 0.756 (AUC = 0.817; 95% confidence interval [CI]: 0.675-0.958) in the radiomics testing set and a sensitivity of 0.999, a specificity of 0.842, and an accuracy of 0.867 (AUC = 0.905, 95% CI: 0.792-1.000) in the pathology testing set. A nomogram combining radiomics and pathology features demonstrated a strong performance (AUC = 0.912; 95% CI: 0.807-1.000).
CONCLUSIONS: A nomogram combining radiomics and pathology features demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians to distinguish between APAs and NF-AAs.
摘要：
目的：开发并验证结合影像组学和病理学特征的列线图，以区分醛固酮产生腺瘤（APAs）和非功能性肾上腺腺瘤（NF-AAs）。
方法：2011年1月至2022年11月间通过计算机断层扫描（CT）或病理分析诊断为肾上腺腺瘤的连续患者纳入本回顾性研究。CT图像和苏木精和伊红染色的载玻片用于注释和特征提取。选择的影像组学和病理学特征用于使用各种机器学习模型开发风险模型，并测定受试者工作特征曲线下面积(AUC)以评价诊断性能。将来自影像组学和病理学特征的预测结果组合并使用列线图可视化。
结果：总共211例患者（APA，n=59；NF-AA，n=152)被纳入本研究，患者以8:2的比例随机分为训练集或测试集。ExtraTrees模型在影像组学测试集中的灵敏度为0.818，特异性为0.733，准确性为0.756（AUC=0.817；95％置信区间[CI]：0.675-0.958），在病理学测试集中的灵敏度为0.999，特异性为0.842，准确性为0.867（AUC=0.905，95％CI：0.792-1.000）。结合影像组学和病理学特征的列线图显示出很强的性能(AUC=0.912;95%CI:0.807-1.000)。
结论：结合影像组学和病理学特征的列线图显示出较强的预测准确性和辨别能力。该模型可以帮助临床医生区分APA和NF-AA。
方法：2011年1月至2022年11月间通过计算机断层扫描（CT）或病理分析诊断为肾上腺腺瘤的连续患者纳入本回顾性研究。CT图像和苏木精和伊红染色的载玻片用于注释和特征提取。选择的影像组学和病理学特征用于使用各种机器学习模型开发风险模型，并测定受试者工作特征曲线下面积(AUC)以评价诊断性能。将来自影像组学和病理学特征的预测结果组合并使用列线图可视化。
结果：总共211例患者（APA，n=59；NF-AA，n=152)被纳入本研究，患者以8:2的比例随机分为训练集或测试集。ExtraTrees模型在影像组学测试集中的灵敏度为0.818，特异性为0.733，准确性为0.756（AUC=0.817；95％置信区间[CI]：0.675-0.958），在病理学测试集中的灵敏度为0.999，特异性为0.842，准确性为0.867（AUC=0.905，95％CI：0.792-1.000）。结合影像组学和病理学特征的列线图显示出很强的性能(AUC=0.912;95%CI:0.807-1.000)。
结论：结合影像组学和病理学特征的列线图显示出较强的预测准确性和辨别能力。该模型可以帮助临床医生区分APA和NF-AA。
