Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.

BACKGROUND: The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY: This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. RESULTS: AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. CONCLUSIONS: AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.

Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement.

RATIONALE AND OBJECTIVES: Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules. MATERIALS AND METHODS: A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic. RESULTS: The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05). CONCLUSION: The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.

The correlation study of ground glass opacity and lepidic growth pattern component in stage I lung invasive adenocarcinoma / 中华肿瘤杂志

Objective@#To investigate the correlation between Ground Glass Opacity (GGO) component proportion and quantitative classification of lepidic growth pattern in pathological stage Ⅰpulmonary adenocarcinoma.@*Methods@#Pathological and HRCT data of 183 stage Ⅰ invasive adenocarcinoma patients from January 2005 to December 2012 were retrospectively reviewed. The proportion of GGO was calculated from diameter and volume.The correlation between GGO component proportion and lepidic growth pattern in pathological were analyzed by Spearman correlation.@*Results@#Among 183 patients, the proportion of GGO component calculated by maximum diameter method and three-dimensional computerized quantification was 0.43±0.35 and 0.20±0.18, respectively. The percentage of lepidic growth pattern component using semi-quantitative analysis of pathological sections was 0.29±0.25.The proportion of GGO by diameter and three-dimensional computerized quantification was significantly correlated with the percentage of lepidic growth pattern component (r=0.599, P< 0.001; r=0.620, P<0.001).@*Conclusions@#There was a positive correlation between the content of lepidic growth pattern and the content of GGO in the small adenocarcinoma. Three-dimensional computerized quantification was a better method in preoperational evaluation.