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BackgroundLung nodules are being detected at an increasing rate year by year with high-resolution computed tomography (HRCT) being widely used. Ground-glass opacity nodule is one of the special types of pulmonary nodules that is confirmed to be closely associated with early stage of lung cancer. Very little is known about solitary ground-glass opacity nodules (SGGNs). In this study, we analyzed the clinical, pathological, and radiological characteristics of SGGNs on HRCT.MethodsA total of 95 resected SGGNs were evaluated with HRCT scan. The clinical, pathological, and radiological characteristics of these cases were analyzed.ResultsEighty-one adenocarcinoma and 14 benign nodules were observed. The nodules included 12 (15%) adenocarcinoma in situ (AIS), 14 (17%) minimally invasive adenocarcinoma (MIA), and 55 (68%) invasive adenocarcinoma (IA). No patients with recurrence till date have been identified. The positive expression rates of anaplastic lymphoma kinase and ROS-1 (proto-oncogene tyrosine-protein kinase ROS) were only 2.5% and 8.6%, respectively. The specificity and accuracy of HRCT of invasive lung adenocarcinoma were 85.2% and 87.4%. The standard uptake values of only two patients determined by 18F-FDG positron emission tomography/computed tomography (PET/CT) were above 2.5. The size, density, shape, and pleural tag of nodules were significant factors that differentiated IA from AIS and MIA. Moreover, the size, shape, margin, pleural tag, vascular cluster, bubble-like sign, and air bronchogram of nodules were significant determinants for mixed ground-glass opacity nodules (all P<0.05).ConclusionWe analyzed the clinical, pathological, and radiological characteristics of SGGNs on HRCT and found that the size, density, shape, and pleural tag of SGGNs on HRCT are found to be the determinant factors of IA. In conclusion, detection of anaplastic lymphoma kinase expression and performance of PET/CT scan are not routinely recommended.

Background: Growth rate is an independent risk factor for lung cancer in screened pulmonary nodules.This study aimed to clarify growth characteristics of pulmonary nodules in routine clinical practice and examine whether volume doubling time (VDT) can predict the malignancy of these nodules. Methods: We retrospectively enrolled patients with 5-30-mm-sized pulmonary nodules that had been surgically resected after a follow-up of at least 3 months. Two follow-up computed tomography (CT) images with similar thickness and long interval were obtained. Then, three-dimensional (3D) manual segmentation for all nodules was performed on two follow-up CT scans. Subsequently, VDT was calculated for nodules with a change in volume of at least 25%. Results: Overall, 305 pulmonary nodules in 305 patients (men, 36.7%; median age, 57) were included. The mean increased diameter, mass, and volume of benign (n=86) and malignant (n=219) nodules were 0.09 vs.2.37 mm, 0.10 vs. 0.66 g, and 32.74 vs. 1,871.28 mm3, respectively (P<0.05). In total, 24 of 86 benign nodules (28%, 18 grew and 6 shrank) and 121 of 219 malignant nodules (55%, 114 grew and 7 shrank) changed over time. The median VDTs of growing benign and malignant nodules were 389 and 526 days, respectively, (P=0.18), and the area under the receiver operating characteristic (ROC) curve was 0.67 (0.55-0.78), with a sensitivity and specificity of 69% and 58%, respectively. The median VDT for growing nodules was 339 days for inflammatory pseudotumors, 226 days for granulomas, 640 days for benign tumors, 1,541 days for enlarged lymph nodes, 762 days for adenocarcinoma in situ, 954 days for microinvasive adenocarcinoma, 534 days for invasive adenocarcinoma, and 118 days for squamous cell carcinoma. Conclusions: In routine clinical practice, many malignant nodules could grow slowly or even remain stable over time. Regarding growing nodules, the diagnostic value of VDT was limited.

Length of hospital stay (LOS) of asthma can be a reflection of the disease burden faced by patients, and it is also sensitive to air pollution. This study aims at estimating and validating the effects of air pollution and readmission on the LOS for those who have asthma, considering their readmission history, minimum temperature, and threshold effects of air pollutants. In addition, sex, age, and season were also constructed for stratification to achieve more precise and specific results. The results show that no significant effects of PM and NO on LOS were observed in any of the patients, but there were significant effects of PM and NO on LOS when a stratifying subgroup analysis was performed. The effect of PM on LOS was found to be lower than that of PM and higher than that of NO . SO did not have a significant effect on LOS for patients with asthma in our study. Our study confirmed that the adverse effects of air pollutants (such as PM ) on LOS for patients with asthma existed; in addition, these effects vary for different stratifications. We measured the effects of air pollutants on the LOS for patients with asthma, and this study offers policy makers quantitative evidence that can support relevant policies for health care resource management and ambient air pollutants control.

In this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field.Background: Despite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients.Methods: We searched literature from 2000 through July 31 th , 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as "artificial intelligence", "AI", "deep learning", "lung cancer", "NSCLC", "SCLC" were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions. Conclusions: AI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients' information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.