Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging

Abstract

Background: The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR).

Methods: A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient. These were first analyzed using a CCR image, then with CCR plus deep BSI, then with CCR plus DES radiography, and finally with DTS images. Receiver-operating characteristic curves were used to evaluate the performance of the 6 observers in the detection of pulmonary nodules.

Results: For the 6 observers, the average area under the curve improved significantly from 0.717 with CCR to 0.848 with CCR plus deep BSI (P<0.01), 0.834 with CCR plus DES radiography (P<0.01), and 0.939 with DTS (P<0.01). Comparisons between CCR and CCR plus deep BSI found that the sensitivities of the assessments by the 3 residents increased from 53.2% to 69.5% (P=0.014) for nodules located in the upper lung field, from 30.6% to 44.6% (P=0.015) for nodules that were partially/completely obscured by the bone, and from 33.2% to 45.8% (P=0.006) for nodules <10 mm.

Conclusions: The deep BSI technique can significantly increase the sensitivity of radiology residents for solitary pulmonary nodules compared with CCR. Increased detection was seen mainly for smaller nodules, nodules with partial/complete obscuration, and nodules located in the upper lung field.

Keywords: Bone suppression; digital tomosynthesis; dual-energy subtraction; solitary pulmonary nodules.

Figure 1

Flowchart of inclusion and exclusion criteria. CCR, conventional chest radiography; CT, computed tomography.

Figure 2

Technical route of deep bone suppression imaging (deep BSI) software. Basic prediction pipeline of bone images using deep convolutional neural network (ConvNet). Gradients of the downscaled input conventional chest radiography (Is) and the upscaled bone image (Bs_-1) were predicted by a unit for a coarse scale as the input feature maps of the ConvNet. Predicted gradients of the bone image at a finer scale as the outputs of the ConvNet, which were integrated to reconstruct the bone image Bs. conv. = convolutional neural network. ReLU = rectified linear unit. I is an input CXR. G is a gradient image. Bs is the predicted bone image at scale 1/2^S-s (s = 1, 2,…, S). S is the level number of the cascade. x and y is the horizontal and vertical gradients.

Figure 3

Receiver-operating characteristic (ROC) curves of all observers' performance for conventional chest radiography (CCR), conventional chest radiography plus deep bone suppression imaging (deep BSI), conventional chest radiography plus dual-energy subtraction (DES), and digital tomosynthesis (DTS). (A) Graphs show ROC curves for the detection of solitary pulmonary nodules by 3 radiology residents. Differences in areas under the curve (AUCs) were statistically significant between CCR and CCR+DES, between CCR and CCR+deep BSI, and between CCR and DTS (P<0.05). (B) Graphs show ROC curves for the detection of solitary pulmonary nodules by 3 radiologists. Differences in AUCs between CCR and CCR+DES, between CCR and CCR+deep BSI, and between CCR and DTS were statistically significant (P<0.05). (C) ROC curves for the detection of solitary pulmonary nodules by all observers. Differences in AUCs between CCR and CCR+DES, between CCR and CCR+deep BSI, and between CCR and DTS were statistically significant (P<0.05).

Figure 4

Images of a 53-year-old man with lung cancer in the right upper lung field. (A,C) Conventional chest radiography images show cancer partly obscured by ribs. (B,D) Deep bone suppression imaging of soft tissues clearly show the cancer location, with different lengths of burrs. (E,G) Dual-energy subtraction soft-tissue images also clearly show cancer. (F,H) Lesion features are apparent on digital tomosynthesis images, and even include pleural traction signs. (I,J) Computed tomography images show the nodule more details, with shallow lobulation, different lengths burrs, and pleural stretch. (K,L) Cellular immunohistochemistry results display creatine kinase (CK)(+) and CK5/6(+), and liquid-based cytology revealed atypical-shaped cells. Hematoxylin-eosin staining shows large and hyperchromatic nuclei with many mitotic images, and the pathological results indicated squamous cell carcinoma. The image indicated by the arrow represents pulmonary nodules. Magnification: G, 20, H, 400.