Background and Objective

Non-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learning model to predict peritoneal metastasis in gastric cancer patients using a small and imbalanced computed tomography (CT) image dataset.

Methods

A retrospective dataset involving CT images acquired from 159 patients is assembled, including 121 and 38 cases with and without peritoneal metastasis, respectively. A computer-aided detection scheme is first applied to segment primary gastric tumor volumes and initially compute 315 image features. Then, five gradients boosting machine (GBM) models embedded with five feature selection methods (including random projection algorithm, principal component analysis, least absolute shrinkage, and selection operator, maximum relevance and minimum redundancy, and recursive feature elimination) along with a synthetic minority oversampling technique, are built to predict the risk of peritoneal metastasis. All GBM models are trained and tested using a leave-one-case-out cross-validation method.

Results

Results show that the GBM model embedded with a random projection algorithm yields a significantly higher prediction accuracy (71.2%) than the other four GBM models (p<0.05). The precision, sensitivity, and specificity of this optimal GBM model are 65.78%, 43.10%, and 87.12%, respectively.

Conclusions

This study demonstrates that CT images of the primary gastric tumors contain discriminatory information to predict the risk of peritoneal metastasis, and a random projection algorithm is a promising method to generate optimal feature vector, improving the performance of machine learning based prediction models.

中文翻译：

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应用随机投影算法优化机器学习模型，利用 CT 图像预测胃癌患者腹膜转移

背景和目的

手术前非侵入性预测癌症转移风险对于确定哪些患者可以从新辅助化疗中受益可以发挥重要作用。本研究旨在调查和测试应用随机投影算法来开发和优化基于放射组学的机器学习模型的优势，以使用小型且不平衡的计算机断层扫描（CT）图像数据集来预测胃癌患者的腹膜转移。

方法

收集了 159 名患者的 CT 图像的回顾性数据集，其中分别包括 121 例和 38 例有腹膜转移的患者和 38 例没有腹膜转移的患者。首先应用计算机辅助检测方案来分割原发性胃肿瘤体积并最初计算 315 个图像特征。然后，五个梯度增强机（GBM）模型嵌入了五种特征选择方法（包括随机投影算法、主成分分析、最小绝对收缩和选择算子、最大相关性和最小冗余以及递归特征消除）以及合成少数方法过采样技术是为了预测腹膜转移的风险而建立的。所有 GBM 模型均使用留一情况交叉验证方法进行训练和测试。

结果

结果表明，嵌入随机投影算法的 GBM 模型的预测精度 (71.2%) 显着高于其他四种 GBM 模型 (p<0.05)。该最佳 GBM 模型的精确度、敏感性和特异性分别为 65.78%、43.10% 和 87.12%。

结论

这项研究表明，原发性胃肿瘤的 CT 图像包含预测腹膜转移风险的判别信息，而随机投影算法是生成最佳特征向量、提高基于机器学习的预测模型性能的有前途的方法。