Objective

Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable.

Methods

To test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume.

Results

Our findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature.

Conclusions

In summary, our results support accurate multiclass ML classification regarding brain metastases distribution.

中文翻译：

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使用机器学习和深度学习模型对几种原发性癌症脑转移的空间分布进行比较分析

客观的

脑转移（BM）与预后不良和死亡率增加相关，这使其成为重大的临床挑战。研究 BM 有助于改善早期检测和监测。然而，对不同原发性癌症的 BM 解剖分布进行系统比较仍然基本上无法实现。

方法

为了检验解剖学 BM 分布因原发癌症类型而异的假设，我们分析了五种不同原发癌症类型沿主成分 (PC) 轴的 BM 空间坐标。该数据集包括 3949 个颅内转移瘤，按原发癌症类型标记并具有六个特征。我们使用 PC 坐标来突出各种癌症类型之间的区别。我们利用不同的机器学习 (ML) 算法（RF、SVM、TabNet DL）模型来建立原发性癌症诊断、BM 空间坐标、年龄和目标体积之间的关系。

结果

我们的研究结果表明，PC1 与 Y 轴的对齐程度最高，其次是 Z 轴，与 X 轴的相关性最小。根据 PC1 与 PC2 图，我们发现乳腺癌和肺癌、乳腺癌和肾癌之间的解剖学扩散模式存在显着差异。相比之下，肾癌和肺癌以及肺癌和黑色素瘤则表现出相似的模式。我们的 ML 和 DL 结果表明，区分不同原发癌症的 BM 分布具有很高的准确性，其中使用多项式核的 SVM 算法实现了 97% 的准确率，而 TabNet 则达到了 96%。 RF 算法将 PC1 列为最重要的区分特征。

结论

总之，我们的结果支持关于脑转移分布的准确多类 ML 分类。