Artificial intelligence-advanced imaging for solid-type lung adenocarcinoma: Toward greater clinical relevance.
Authors
Affiliations (7)
Affiliations (7)
- Department of Surgery, Teikyo University School of Medicine, Tokyo, Japan.
- Department of General Thoracic Surgery, Shonan Kamakura General Hospital, Kamakura, Japan.
- Department of Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan.
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan.
- Department of General Thoracic Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
- Osaka University Institute for Radiation Science, Osaka University Graduate School of Medicine, Osaka, Japan.
Abstract
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 tumors, specifically in regards to lymph node metastasis and prognosis. 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 tumors measuring ≤20 mm (n = 117) to determine their potential use in candidate selection for limited resection. 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 tumors 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%. An AI-based volumetric analysis using a cV/tV threshold of ≥ 0.72 showed potential for predicting lymph node metastasis in this single-center retrospective study of lung adenocarcinoma ≤30 mm. External validation in diverse, multicentre cohorts is essential before clinical implementation.