Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules / 中国肺癌杂志

BACKGROUND@#Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules.@*METHODS@#Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed.@*RESULTS@#In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively.@*CONCLUSIONS@#Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.

Evaluation of Three Different Methods to Establish Animal Models of Acanthamoeba Keratitis

PURPOSE: To produce animal models of Acanthamoeba keratitis and to evaluate the advantages and adaptation range of each of the three methods employed. MATERIALS AND METHODS: Mice and Wistar rats in three groups of 15 rats and 15 mice each were used to establish the models. Right corneas in group A were scratched and challenged with Acanthamoeba. Those in group B were scratched and covered with contact lenses incubated with Acanthamoeba. Those in group C received an intrastromal injection of Acanthamoeba. Five rats and 5 mice in each group were used for histopathological investigations and the other 10 in each group were used for clinical evaluation. The models were evaluated by slit lamp examination, microscopic examination and culture of corneal scrapings, HE staining of corneal sections, and pathological scoring of the infections. RESULTS: Four rats and 6 mice in group A, 7 rats and 8 mice in group B, and 10 rats and 10 mice in group C developed typical Acanthamoeba keratitis. CONCLUSION: Corneal scratching alone has the lowest infection rate, while scratching and then covering with contaminated contact lenses has a moderate rate of infection and most closely mimics what happens in most human infections. Intrastromal injection of Acanthamoeba gives a much higher infection rate and more severe Acanthamoeba keratitis.