BACKGROUND Proliferative activity prediction is important for determining individual treatment strategies for patients with acromegaly, and tumor proliferative activity is usually measured by the expression of Ki-67. OBJECTIVE This study aimed to assess the value of a magnetic resonance imaging (MRI)-based radiomics approach in predicting the Ki-67 index of acromegaly patients. METHODS A total of 138 patients with acromegaly were retrospectively reviewed and randomly assigned to primary and validation cohorts. Radiomics features were extracted from MR images, and then the elastic net and recursive feature elimination algorithms were applied to determine critical radiomics features for constructing a radiomics signature. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomics nomogram incorporating a radiomics signature and selected clinical features was constructed for individual predictions. Twenty-five acromegaly patients were enrolled for multicenter model validation. RESULTS Seventeen radiomics features were selected to construct a radiomics signature that achieved an area under the curve (AUC) value of 0.96 and 0.89 in the primary cohort and the validation cohort, respectively. A radiomics nomogram that incorporated the radiomics signature and eight selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.94 in the primary cohort and 0.91 in the validation cohort. The radiomics signature in the multicenter validation achieved an accuracy of 88.2%. The analysis of the decision curve showed that the radiomics signature and radiomics nomogram were clinically useful for patients with acromegaly. CONCLUSIONS The radiomics signature developed in this study could aid neurosurgeons in predicting the Ki-67 index of patients with acromegaly and could contribute to non-invasive measurement of proliferative activity, affecting individual treatment strategies.

中文翻译：

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基于定量放射学方法的肢端肥大症患者的非侵入性实时增殖活性估算：一项多中心研究。

背景技术增殖活性预测对于确定肢端肥大症患者的个体治疗策略是重要的，并且通常通过Ki-67的表达来测量肿瘤增殖活性。目的本研究旨在评估基于磁共振成像（MRI）的放射学方法在预测肢端肥大症患者的Ki-67指数中的价值。方法回顾性分析了138例肢端肥大症患者，并将其随机分为主要人群和验证人群。从MR图像中提取放射线特征，然后应用弹性网和递归特征消除算法来确定关键的放射线特征，以构建放射线签名。随后，使用多变量logistic回归分析来选择信息最丰富的临床特征，并结合了放射学特征和所选临床特征的放射学诺模图用于个体预测。25名肢端肥大症患者参加了多中心模型验证。结果选择了17个放射学特征来构建放射学特征，该特征在主要队列和验证队列中分别达到0.96和0.89的曲线下面积（AUC）值。构造了包含放射线学特征和八个选定临床特征的放射线照相列线图，并显示出良好的区分度和校准度，主要队列的AUC为0.94，验证队列的AUC为0.91。多中心验证中的放射学特征达到了88.2％的准确度。对决策曲线的分析表明，放射线学签名和放射线照相诺模图在临床上对肢端肥大症患者有用。结论本研究开发的放射学特征可以帮助神经外科医生预测肢端肥大症患者的Ki-67指数，并有助于无创测量增殖活性，影响个体治疗策略。