3D Lung Nodule Classification in Computed Tomography Images

Lung cancer is the leading cause of cancer death worldwide. Usually, the diagnosis is made by physicians reading computed tomographies, CTs a task prone to errors. There is a lot of subjectivity in the nodules’ diagnose assessment. That, among other causes, can lead to an incorrect initial diagnosis, delaying the correct treatment, which can have a high impact on the patient’s life [1]. The use of Computer-Aided Diagnosis, CADx, can be a great help for this problem by assisting doctors assessing lung nodule malignancy, giving a second opinion. However, physicians end up avoiding the use of CADx [2], because can not understand the “how and why” of its diagnostic results. To increase the physicians’ confidence in the CADx system, Shen et al. [3] proposed a deep hierarchical semantic convolutional neural network, HSCNN, that besides having as result the malignancy’ diagnosis, also predicts the features related to it, giving this way visual evidence of the diagnosis. As input for HSCNN Shen et al. used LIDC-IDRI, Lung Image Database Consortium and Image Database Resource Initiative, a widely used public dataset, that has 2,668 abnormalities annotated by radiologists regarding the semantic features that they present [4]. This dataset has a high variance between the assessments given by physicians, for the same nodule, in the same semantic feature, especially in lobulation and spiculation, the two LIDC-IDRI features most relevant to malignancy. For this reason, Shen et al. did not predict in their work these two characteristics. With that motivation, a question suggested by Shen et al. was raised: will the decrease of variability in LIDC-IDRI semantic features evaluations improve the HSCNN malignancy prediction? To answer this question was presented as a possible solution to obtain a more coherently annotated LIDC-IDRI dataset. For this purpose, a dataset analysis and literature review were initially realised, which allowed the creation of more accurate semantic characteristics evaluation criteria than the existing ones. After these were created and validated by radiologists at the São João Hospital Center, the LIDC-IDRI database was revised, following a proposed methodology based on the devised criteria. Finally, the reclassified dataset was used as input to the HSCNN to evaluate its impact on malignancy prediction. 1,386 nodules were used and all annotations were binarised. The network training was performed using 4-fold cross-validation. The results of this proposed solution improved only, compared to the work of Shen et al., in the sensitivity, with a better prediction of when the nodules are truly malignant. The malignancy prediction obtained an accuracy of 0.78, an area under the receiver operating characteristic curve, AUC of ROC, of 0.74, a sensitivity of 0.83, and a specificity of 0.89. In the Shen et al. work were obtained as accuracy 0.84, as AUC of ROC 0.86, as sensitivity 0.71 and as specificity 0.89. The analysis of the network incorrect predictions revealed that most of them were small nodules, with a lot of matter around, with solid calcification, or juxta-pleural. The correctly predicted nodules were solitary, or with pleural-tail. However, the comparison with Shen et al. work is not linear since the quantity of data used was different (1,386 nodules instead of the 4,252 used in Shen et al. work) and the conversion to binary was also performed differently, which may have influenced