ArXiv Preprint
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.
Lennart Brocki, Neo Christopher Chung
2023-03-20
