Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.

PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction. METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation. RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction. CONCLUSION: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.

Omitting Lymph Node Dissection for Small Ground-Glass Opacity-Dominant Tumors.

BACKGROUND: The purpose of this study was to determine the optimal extent of lymph node dissection required in patients with small (≤3 cm) radiologically ground-glass opacity-dominant, peripheral, non-small cell lung cancer tumors. METHODS: The study analyzed the clinicopathologic findings and surgical outcomes of 988 patients with radiologic, ground-glass opacity-dominant non-small cell lung cancer without lymph node involvement who underwent complete resection of the primary tumor between 2010 and 2020. Patients were followed up for 54.5 months (median). Kaplan-Meier curves and the log-rank test were used in statistical analyses of the prognosis. RESULTS: Median age, whole tumor size, solid tumor size, and maximum standardized uptake values were 68 years, 1.7 cm, 0.4 cm, and 0.9, respectively. Sixty percent of the cohort was female (n = 590). Wedge resection, segmentectomy, and lobectomy were performed in 206, 372, and 410 patients, respectively. A total of 982 of 988 (99%) tumors were adenocarcinomas. One patient had hilar lymph node involvement; however, no mediastinal lymph node metastasis or hilar or mediastinal lymph node recurrence was detected. The 5-year overall survival rate was 96.5% (95% CI, 94.8%-97.7%). Excellent survival outcomes were achieved regardless of procedure (wedge resection, 94.7% [95% CI, 89.1%-97.5%]; segmentectomy, 96.9% [95% CI, 93.7%-98.5%]; and lobectomy, 97.1% [95% CI, 94.4%-98.5%]). CONCLUSIONS: Omitting lymph node dissection may be acceptable with curative intent for small tumors with radiologic ground-glass opacity dominance. Appropriate surgical procedures such as wedge resection, segmentectomy, or lobectomy can provide satisfactory outcomes in patients with indolent tumors if surgical margins are secured.

Association between pathological infiltrative tumor growth pattern and prognosis in patients with resected lung squamous cell carcinoma.

INTRODUCTION: Lung squamous cell carcinoma (LUSC) usually shows expansive growth with large tumor nests; few reports on invasive growth patterns (INF) in LUSC have been associated with poor prognosis in gastrointestinal and urothelial cancers. In this study, we examine the association between INF and the prognosis of LUSC. MATERIALS AND METHODS: We analyzed INF as a potential prognostic factor in 254 consecutive patients with LUSC who underwent complete surgical resection at our hospital between 2008 and 2017. INF was classified into 3 categories based on the structure of the tumor other than the large round solid nest of tumor cells. RESULTS: INF was categorized as INFa in 59 patients (23 %) with only well-demarcated large solid tumor cell nests, INFb in 89 patients (35 %) with medium to small, alongside large solid nests, and INFc in 98 patients (39 %) with cord-like/small nests or isolated cells plus large or medium solid nests. No significant lymph node metastasis differences were observed between INFc and INFa/b tumors. However, in patients with p-stage I, INFc had a poorer prognosis with regard to recurrence-free survival (RFS), with a 5-year RFS rate of 53.3 %, compared to 74.9 % for INFa/b (p = 0.010). CONCLUSION: Our study highlights a novel pathological concept of INF in LUSC, and contributed to the proposal that it is a factor indicating an unfavorable prognosis in patients with early-stage LUSC. A prospective multicenter study is warranted for INFc patients, as careful follow-up and adjuvant chemotherapy might lead to the early detection and prevention of recurrence.

Feasibility and comparative prognosis of segmentectomy versus lobectomy in centrally located small and solid dominant cN0 non-small cell lung cancer.

OBJECTIVES: To determine the feasibility of segmentectomy in patients with central, whole tumor size ≤2 cm and radiologically solid-dominant cN0 non-small cell lung cancer (NSCLC). METHODS: We retrospectively reviewed 1240 patients who underwent lobectomy or segmentectomy for small and radiologically solid-dominant cN0 NSCLC between January 2010 and December 2022. The inclusion criteria encompassed centrally located tumors, defined as tumors located in the inner two-thirds of the pulmonary parenchyma. Propensity score matching was applied to balance the baseline characteristics in the 2 study groups. RESULTS: Among the 299 eligible patients, no significant differences in recurrence-free survival (RFS) and overall survival (OS) were observed between the segmentectomy (n = 121) and lobectomy (n = 178) groups (P = .794 and .577, respectively). After propensity score matching, no significant differences in hilar and mediastinal lymph node upstaging were found among the 93 matched patients (P = 1.00), and locoregional recurrence was comparable in the segmentectomy (n = 4) and lobectomy (n = 4) groups. RFS and OS did not differ significantly between the 2 groups (P = .700 and .870, respectively). Propensity score-adjusted multivariable Cox analysis for RFS and OS indicated that segmentectomy was not an independent prognostic factor (RFS: hazard ratio [HR], 0.89; 95% confidence interval [CI], 0.43-1.85; P = .755; OS: HR, 1.09; 95% CI, 0.38-3.14; P = .860). CONCLUSIONS: Segmentectomy may be a viable treatment option, with local control and prognosis comparable to that of lobectomy in appropriately selected patients with central, small (≤2 cm), and radiologically solid-dominant NSCLC.

AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma.

OBJECTIVES: Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules. MATERIALS AND METHODS: Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments. RESULTS: Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies. CONCLUSION: In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.