Background

Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.

Methods

Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs).

Results

A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%).

Conclusion

This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

中文翻译：

---

一种在乳房MRI上区分恶性和良性灶的机器学习方法。

背景

通过放射学特征区分乳腺磁共振成像（MRI）上的恶性病灶与良性病灶。

方法

这项回顾性研究纳入了45例患者中的45个增强灶，以针刺活检或影像学随访作为参考标准。有12例恶性病变和33例良性病变。通过5年以上的阴性随访确认的8个良性病变和15个经组织病理学确认的恶性病变被添加到数据集中，以为机器学习分析提供参考案例。所有MRI检查均使用1.5-T扫描仪进行。静脉内注射0.1 mmol / kg的gadobenate dimeglumine后，获得了一个三维T1加权的未增强序列，随后是四个动态序列。由专业的放射乳师对增强的病灶进行了分割，提取了200多个放射学特征，并应用了进化机器学习方法（“输入选择和测试的训练”）。

结果

基于35个选定特征的k近邻分类器被确定为性能最佳的机器学习方法。考虑到45个增强病灶和23个其他病例，该分类器的灵敏度为27/27（100％，95％CI为87–100％），特异性为37/41（90％，95％CI为77-97）。 ％），准确度为64/68（94％，95％CI 86–98％）。

结论

这项初步研究表明了放射学方法表征乳腺MRI增强病灶的可行性。