Artificial Intelligence-Advanced Imaging for Solid-Type Lung Adenocarcinoma: Towards Greater Clinical Relevance

Objectives Non-small-cell lung cancer (NSCLC) lesions appearing solid on imaging are highly likely to be malignant. However, “solid” has a subjective definition. This study aimed to determine whether artificial intelligence (AI)-based imaging analysis can offer a more objective and clinically meaningful definition of solid tumours, specifically in regards to lymph node metastasis and prognosis. Methods This study included 216 patients with cN0 lung adenocarcinoma (pathological invasive diameter ≤ 30 mm) who underwent lobectomy with lymph node dissection. AI software was used to calculate the consolidation-to-tumour diameter (cD/tD) and volume (cV/tV) ratios, which were then compared with radiologist-defined cD/tD. To determine the optimal cutoff values, correlations between the pathological invasive diameter/tumour diameter ratio (PathoiD/tD) and lymph node metastasis were evaluated. Subsequently, these values were applied to a subset of tumours measuring ≤20 mm (n = 117) to determine their potential use in candidate selection for limited resection. Results A cV/tV cutoff value of ≥0.72 accurately predicted lymph node metastasis and improved concordance between AI and radiologist solid tumour assessment while maintaining a similar PathoiD/tD to the conventional (cD/tD = 1.0) threshold. Among tumours measuring ≤20 mm in the low solid-component group (cV/tV <0.72), lymph node metastases were not observed; the 5-year recurrence-free survival rate was 100%. Conclusions An AI-based volumetric analysis using a cV/tV threshold of ≥0.72 showed potential for predicting lymph node metastasis in this single-centre retrospective study of lung adenocarcinoma ≤30 mm. External validation in diverse, multicentre cohorts is essential before clinical implementation.