Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their practical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical challenges for integrating diagnostic DNNs into radiological and oncological applications is their lack of interpretability, preventing clinicians from understanding the model predictions. Therefore, we study and propose the integration of expert-derived radiomics and DNN-predicted biomarkers in interpretable classifiers which we call ConRad, for computerized tomography (CT) scans of lung cancer. Importantly, the tumor biomarkers are predicted from a concept bottleneck model (CBM) such that once trained, our ConRad models do not require labor-intensive and time-consuming biomarkers. In our evaluation and practical application, the only input to ConRad is a segmented CT scan. The proposed model is compared to convolutional neural networks (CNNs) which act as a black box classifier. We further investigated and evaluated all combinations of radiomics, predicted biomarkers and CNN features in five different classifiers. We found the ConRad models using non-linear SVM and the logistic regression with the Lasso outperform others in five-fold cross-validation, although we highlight that interpretability of ConRad is its primary advantage. The Lasso is used for feature selection, which substantially reduces the number of non-zero weights while increasing the accuracy. Overall, the proposed ConRad model combines CBM-derived biomarkers and radiomics features in an interpretable ML model which perform excellently for the lung nodule malignancy classification.

翻译：尽管深度神经网络（DNNs）在计算机视觉领域展现出前所未有的性能，但其在癌症诊断与预后中应用医学影像的实践仍受限。将诊断性DNNs整合到放射学与肿瘤学应用的关键挑战之一在于其缺乏可解释性，致使临床医生无法理解模型预测结果。为此，我们研究并提出将专家衍生的放射组学特征与DNN预测的肿瘤生物标志物整合到可解释分类器中，该模型命名为ConRad，应用于肺癌计算机断层扫描（CT）分析。值得注意的是，肿瘤生物标志物通过概念瓶颈模型（CBM）预测获得，因此一旦训练完成，我们的ConRad模型无需依赖耗时费力的人工标注生物标志物。在评估和实际应用中，ConRad的唯一输入是分割后的CT影像。我们将所提模型与作为黑箱分类器的卷积神经网络（CNN）进行对比。进一步地，我们系统研究了放射组学特征、预测生物标志物及CNN特征在五种不同分类器中的所有组合。实验发现，采用非线性支持向量机（SVM）和带有Lasso正则化的逻辑回归的ConRad模型在五折交叉验证中表现最优，但我们强调ConRad的核心优势在于其可解释性。Lasso用于特征选择，在显著减少非零权重数量的同时提升准确率。总体而言，所提出的ConRad模型将CBM衍生的生物标志物与放射组学特征融合于可解释机器学习模型中，在肺结节恶性分类任务中表现出色。