Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans.

BACKGROUND: Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment. PURPOSE: To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques. MATERIALS AND METHODS: We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM). RESULTS: The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task. CONCLUSION: The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information.

Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT.

PURPOSE: Evaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma. METHODS: This retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented. RESULTS: Six hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer. CONCLUSIONS: Nodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.

Accuracy and Efficiency of Patient Setup Using Surface Imaging versus Skin Tattoos for Accelerated Partial Breast Irradiation.

Purpose: Skin tattoos represent the standard approach for surface alignment and setup of breast cancer radiation therapy, yet permanent skin markings contribute to adverse cosmesis and patient dissatisfaction. With the advent of contemporary surface-imaging technology, we evaluated setup accuracy and timing between "tattoo-less" and traditional tattoo-based setup techniques. Methods and Materials: Patients receiving accelerated partial breast irradiation (APBI) underwent traditional tattoo-based setup (TTB), alternating daily with a tattoo-less setup via surface imaging using AlignRT (ART). Following initial setup, position was verified via daily kV imaging, with matching on surgical clips representing ground truth. Translational shifts (TS) and rotational shifts (RS) were ascertained, as were setup time and total in-room time. Statistical analyses used the Wilcoxon signed rank test and Pitman-Morgan variance test. Results: A total of 43 patients receiving APBI and 356 treatment fractions were analyzed (174 TTB fractions and 182 using ART). For tattoo-less setup via ART, the median absolute TS were 0.31 cm in the vertical (range, 0.08-0.82), 0.23 cm in the lateral (0.05-0.86), and 0.26 cm in the longitudinal (0.02-0.72) axes. For TTB setup, the corresponding median TS were 0.34 cm (0.05-1.98), 0.31 cm (0.09-1.84), and 0.34 cm (0.08-1.25), respectively. The median magnitude shifts were 0.59 (0.30-1.31) for ART and 0.80 (0.27-2.13) for TTB. ART was not statistically distinguishable from TTB in terms of TS, except in the longitudinal direction (P = .154, .059, and .021, respectively), and was superior to TTB for magnitude shift (P < .001). The variance of each TS variable was significantly narrower for ART compared with TTB (P ≤ .001 vertical, P = .001 lateral, P = .005 longitudinal). The median absolute RS for ART was 0.64° rotation (range, 0.00-1.90), 0.65° roll (0.05-2.90), and 0.30° pitch (0.00-1.50). The corresponding median RS for TTB were 0.80° (0.00-2.50), 0.64° (0.00-3.00), and 0.46° (0.00-2.90), respectively. ART setup was not statistically different from TTB in terms of RS (P = .868, .236, and .079, respectively). ART showed lower variance than TTB in terms of pitch (P = .009). The median total in-room time was shorter for ART than TTB (15.42 vs 17.25 minutes; P = .008), as was the median setup time (11.12 vs 13.00 minutes; P = .001). Moreover, ART had a narrower distribution of setup time with fewer lengthy outliers versus TTB. Conclusions: These findings suggest that a tattoo-less setup approach with AlignRT may be sufficiently accurate and expeditious to supplant surface tattoos for patients receiving APBI. Further analyses with larger cohorts will determine whether tattoo-based approaches can be replaced by noninvasive surface imaging.