Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation

Background and Objective: Computer-aided diagnosis (CAD) systems promote accurate diagnosis and re-duce the burden of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate de-tection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule ma-lignancy evaluation depends on heuristic inference from low-dose computed tomography (LDCT) volume to malignant probability, and lacks clinical cognition. Methods: In this paper, we propose a joint radiology analysis and malignancy evaluation network called R2MNet to evaluate pulmonary nodule malignancy via the analysis of radiological characteristics. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping (CDAM) to visualize features and shed light on the decision process of deep neural networks (DNNs). Results: Experimental results on the lung image database consortium image collection (LIDC-IDRI) dataset demonstrate that the proposed method achieved an area under curve (AUC) of 96 . 27% and 97 . 52% on nodule radiology analysis and nodule malignancy evaluation, respectively. In addition, explanations of CDAM features proved that the shape and density of nodule regions are two critical factors that influence a nodule to be inferred as malignant. This process conforms to the diagnosis cognition of experienced radiologists. Conclusion: The network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results by incorporating radiology analysis with nodule malignancy evaluation. Besides, model interpretation with CDAM features shed light on the focus regions of DNNs during the estimation of nodule malignancy probabilities. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduced a joint radiology analysis and malignancy evaluation network for assessing pulmonary nodule malignancy with satisfactory performance. Interpretations of CDAM features highlighted the critical role of shape and density of nodule regions in influencing malignancy evaluation, aligning with the diagnostic cognition of experienced radiologists.