Stereotactic radiotherapy (SRT) is recommended for treatment of brain oligometastasis (BoM) in patients with controlled primary disease. Where contrast enhancement enlargement occurs during follow-up, distinguishing between radionecrosis and progression presents a critical challenge. Without pathological confirmation, decision-making may be inappropriate and delayed. Quantitative imaging features extracted from routinely performed examinations are of interest in potentially addressing this problem. We explored the added value of the radiomics method for the differential diagnosis of these two entities. Twenty patients who received SRT for BoM, from any primary location, were included (8 radionecrosis, 12 progressions, pathologically confirmed). We assessed the clinical relevance of 1,766 radiomics features, extracted using IBEX software, from the first T1-weighted postcontrast magnetic resonance imaging (MRI) after SRT showing a lesion modification. We evaluated seven feature-selection methods and 12 classification methods in terms of respective predictive performance. The classification accuracy was measured using Cohen's kappa after leave-one-out cross-validation. In this work, the best predictive power reached was a Cohen's kappa of 0.68 (overall accuracy of 85%), expressing a strong agreement between the algorithm prediction and the histological gold standard. Prediction accuracy was 75% for radionecrosis, and 91% for progression. The area under a curve reached 0.83 using a bagging algorithm trained with the chi-square score features set. These findings indicated that the radiomics method is able to discriminate radionecrosis from progression in an accurate, early and noninvasive way. This promising study is a proof of concept, preceding a larger prospective study for defining a robust model to support decision-making in BoM. In summary, distinguishing between radionecrosis and progression is challenging without pathology. We built a classification model based on imaging data and machine learning. Using this model, we were able predict progression and radionecrosis in, respectively, 91% and 75% of cases.

中文翻译：

---

放射线学方法用于脑部寡发性局部立体定向放射治疗后的放射性坏死与进展的鉴别诊断。

建议将立体定向放射疗法（SRT）用于控制原发性疾病患者的脑部少发转移（BoM）。在随访过程中出现造影剂增强扩大的地方，区分放射性坏死和进展是一个严峻的挑战。未经病理证实，决策可能不适当且会延迟。从常规检查中提取的定量成像特征是潜在解决此问题的兴趣所在。我们探索了放射学方法对这两个实体的鉴别诊断的附加价值。包括从任何主要地点接受BoM的SRT的20例患者（8例坏死，12例进展，经病理证实）。我们评估了使用IBEX软件提取的1766项放射学特征的临床相关性，从SRT之后的第一个T1加权对比后磁共振成像（MRI）可以看出病变。根据各自的预测性能，我们评估了7种特征选择方法和12种分类方法。留一法交叉验证后，使用Cohenκ测量分类准确度。在这项工作中，达到的最佳预测能力是Cohen的kappa值为0.68（总准确度为85％），表示算法预测与组织学金标准之间有很强的一致性。放射性坏死的预测准确性为75％，进展的预测准确性为91％。使用通过卡方得分特征集训练的装袋算法，曲线下的面积达到0.83。这些发现表明，放射线学方法能够以准确，早期的无创方式。这项前瞻性研究是一项概念验证，之前是一项用于定义可靠模型以支持BoM决策的大型前瞻性研究。总而言之，没有病理学就很难区分放射性坏死和进展。我们基于影像数据和机器学习建立了分类模型。使用该模型，我们能够分别预测91％和75％的病例的进展和放射性坏死。