ABSTRACT
BACKGROUND: Lung cancer has the highest morbidity and mortality in the world, and immunotherapies have been developed for this disease in recent years. However, activation of the immune system can cause immune-related adverse events (irAEs), and checkpoint inhibitor-related pneumonitis (CIP), can be the most severe and fatal. But few reports have systematically examined the spectrum of imaging findings of this condition. Therefore, the objective of this paper is to investigate the high-resolution computed tomography (HRCT) characteristics of CIP in patients with lung cancer. OBJECTIVE: To investigate the HRCT characteristics of CIP in patients with lung cancer. METHODS: HRCT patterns in 41 lung cancer patients who developed CIP after treatment with immune checkpoint inhibitors were retrospectively characterized by interstitial lung disease classification, and their severity was graded. Specific HRCT characteristics related to CIP were identified. RESULTS: There are 4 types of immunotherapy-induce pneumonitis patterns (organizing pneumonia OP 19 cases, nonspecific interstitial pneumonia NSIP 8 cases, acute interstitial pneumonia AIP 7 cases, 7 cases of undetermined type) and image grade (13 cases of grade 1, 17 cases of grade 2, 11 cases of grade 3, 0 cases of grade 4) were identified. Spatial distribution characteristics of these lesions were noted (17 cases predominantly distributed in tumor-containing lobes, 6 cases predominantly distributed in non-tumor-containing lobes, and no specific predilection in 18 cases). Specific CT imaging features found in CIP included, in the order of prevalence, the following: ground glass opacities (38 cases), subpleural/vertical line (37 cases), interstitial thickening around the bronchovascular bundles (36 cases), reticulation (34 cases), fine reticular shadow (31 cases), consolidation (31 cases), small cystic shadow (24 cases, may not having honeycombing), small nodules (17 cases), bronchiectasis (15 cases), honeycombing (11 cases), mosaic sign (11 cases), and pleural effusion (18 cases). CONCLUSION: HRCT of CIP predominantly manifests as ground glass opacities, reticulation, subpleural/vertical line, interstitial thickening around the bronchovascular bundle, and consolidation.
ABSTRACT
BACKGROUND: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis. METHODS: 420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists' diagnoses. RESULTS: The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS. CONCLUSIONS: A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.