ObjectiveTo evaluate the application value of three-dimensional (3D) reconstruction in preoperative surgical diagnosis of new classification criteria for lung adenocarcinoma, which is helpful to develop a deep learning model of artificial intelligence in the auxiliary diagnosis and treatment of lung cancer.MethodsThe clinical data of 173 patients with ground-glass lung nodules with a diameter of ≤2 cm, who were admitted from October 2018 to June 2020 in our hospital were retrospectively analyzed. Among them, 55 were males and 118 were females with a median age of 61 (28-82) years. Pulmonary nodules in different parts of the same patient were treated as independent events, and a total of 181 subjects were included. According to the new classification criteria of pathological types, they were divided into pre-invasive lesions (atypical adenomatous hyperplasia and and adenocarcinoma in situ), minimally invasive adenocarcinoma and invasive adenocarcinoma. The relationship between 3D reconstruction parameters and different pathological subtypes of lung adenocarcinoma, and their diagnostic values were analyzed by multiplanar reconstruction and volume reconstruction techniques.ResultsIn different pathological types of lung adenocarcinoma, the diameter of lung nodules (P<0.001), average CT value (P<0.001), consolidation/tumor ratio (CTR, P<0.001), type of nodules (P<0.001), nodular morphology (P<0.001), pleural indenlation sign (P<0.001), air bronchogram sign (P=0.010), vascular access inside the nodule (P=0.005), TNM staging (P<0.001) were significantly different, while nodule growth sites were not (P=0.054). At the same time, it was also found that with the increased invasiveness of different pathological subtypes of lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. Meanwhile, nodule diameter and the average CT value or CTR were independent risk factors for malignant degree of lung adenocarcinoma.ConclusionImaging signs of lung adenocarcinoma in 3D reconstruction, including nodule diameter, the average CT value, CTR, shape, type, vascular access conditions, air bronchogram sign, pleural indenlation sign, play an important role in the diagnosis of lung adenocarcinoma subtype and can provide guidance for personalized therapy to patients in clinics.
ObjectiveTo explore the predictive value of CT signs of mixed ground-glass nodules in the pathological subtype and differentiation of lung adenocarcinoma. MethodsThe clinical data of 66 patients with mixed ground-glass nodules pathologically diagnosed as invasive adenocarcinoma (IAC) in the Second Department of Thoracic Surgery, the First Affiliated Hospital of Xiamen University from May to December 2021 were retrospectively analyzed, including 20 males and 46 females, aged 35-75 years. The CT findings were analyzed before operation, and the lesion profile was cut after operation to distinguish the ground-glass and solid components, and the pathological results of different positions were obtained. According to the postoperative pathological results, the patients were divided into a low-risk group (containing adherent type and no components of micropapillary subtype and solid subtype, n=16), a medium-risk group (containing niple or acinar type and no components of micropapillary subtype and solid subtype, n=38), and a high-risk group (containing micropapillary or solid subtype, n=12). The relationships between CT features and the pathological subtype and degree of differentiation were analyzed and compared. ResultsIn 66 patients with IAC, the infiltration degree of solid components was greater than that of ground-glass components. When the solid component ratio (CTR) was≥25% (sensitivity 90.2%, specificity 64.0%, P=0.005), and the average CT value was＞−283.95 HU (sensitivity 82.9%, specificity 64.0%, P=0.000), the histological grade was more inclined to medium and low differentiation. The CTR, Ki-67, average CT value and histological grade of IAC in the medium- and high-risk groups were higher than those of nodules in the low-risk group. ConclusionThe infiltration degree of solid components is higher than that of ground-glass components in IAC mixed ground-glass nodules. The pathological subtype, Ki-67 expression and histological grade of lung adenocarcinoma can be predicted according to its CT characteristics, which has important clinical significance for determining the timing of surgery.
Whether anatomical segmentectomy can replace lobectomy in the treatment of early-stage lung cancer remains controversial. A large number of studies have been conducted for decades to explore whether pulmonary segmentectomy can treat early-stage lung cancer, which is actually to explore the indications of intentional segmentectomy. With the development of scientific researches, it is found that many characteristics affect the malignancy of lung cancer, and the different grades of each characteristic affect the prognosis of patients. It is worth exploring whether different surgical approaches can be used for early-stage lung cancer with different characteristics and different grades. This article reviews the literature and studies to discuss the advances in indications of segmentectomy for early-stage lung in terms of tumor size, consolidation-to-tumor ratio, pathological classification and tumor location, respectively. The objective of this review is to help thoracic surgeons to objectively and scientifically select the surgical method according to the clinical characteristics of early-stage lung cancer.
Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.
Objective To explore the correlation between lymph node metastasis and clinicopathological features of lung adenocarcinoma with diameter≤3 cm. Methods The clinicopathologic data of the patients with lung adenocarcinoma≤3 cm in diameter were retrospectively analyzed. The relationship between lymph node metastasis and age, gender, smoking history, pathological subtype, tumor location, tumor diameter, pleural invasion, vascular invasion and other factors was analyzed. The risk factors of lymph node metastasis were analyzed by univariate and multivariate logistic regression. Results Finally 1 718 patients were collected, including 697 males and 1 021 females with an average age of 58.89±9.85 years. The total lymph node metastasis rate was 12.9%, among whom 452 patients of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) did not have lymph node metastasis, and the lymph node metastasis rate of invasive lung adenocarcinoma was 17.5%. Multivariate analysis showed that tumor diameter, micropapillary subtype, solid subtype, micropapillary component, solid component, vascular invasion and pleural invasion were independent risk factors for lymph node metastasis of invasive lung adenocarcinoma with diameter≤3 cm (P<0.05). While age, lepidic subtype and lepidic component were independent protective factors for lymph node metastasis (P<0.05). Conclusion Clinicopathological features can help predict lymph node metastasis of lung adenocarcinoma with diameter≤3 cm.
ObjectiveTo evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. MethodsA retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. ResultsIn the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, burr sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), burr sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians' prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. ConclusionThis AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.