Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

Although diffuse cysts in the lung can be found in many diseases, they are uncommon in metastatic lung adenocarcinoma. They are even more unusual after the administration of immune checkpoint inhibitors. A case of lung adenocarcinoma that developed diffuse cysts in the lungs during treatment with nivolumab is reported. The patient was a 60-year-old woman with postoperative recurrent lung adenocarcinoma in mediastinal lymph nodes and pleural dissemination. After first-line treatment with cisplatin, pemetrexed, and bevacizumab, computed tomography (CT) showed disease progression. Treatment was then switched to nivolumab. After 5 courses of nivolumab, CT showed multiple ground-glass nodules in her lungs. After 4 more courses of nivolumab, the ground-glass nodules increased in size, and cystic air spaces appeared in their centers. The patient did not have any symptoms. Laboratory tests showed no evidence of infection or nivolumab-induced pneumonitis. Sialyl Lewis X-i antigen increased, and positron emission tomography showed abnormal uptake of 18F-fluorodeoxyglucose in these lesions. Considering this evidence, the cystic lesions were diagnosed as multiple lung metastases. Various differential diagnoses should be considered when diffuse cystic lesions are found in the lungs after the administration of immune checkpoint inhibitors.

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MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis

Thorax ◽

10.1136/thoraxjnl-2018-212430 ◽

2019 ◽

Vol 74 (12) ◽

pp. 1131-1139 ◽

Cited By ~ 10

Author(s):

Susan K Mathai ◽

Stephen Humphries ◽

Jonathan A Kropski ◽

Timothy S Blackwell ◽

Julia Powers ◽

...

Keyword(s):

Deep Learning ◽

Pulmonary Fibrosis ◽

Learning Algorithm ◽

Quantitative Ct ◽

First Degree Relatives ◽

Deep Learning Algorithm ◽

Increased Risk ◽

Ct Evaluation ◽

Ct Analysis ◽

Quantitative Ct Analysis

BackgroundRelatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT.MethodsFirst-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed.FindingsIn 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex.InterpretationPrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities.

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