# Nomogram based on computed tomography fractal dimension for predicting spread through air spaces in lung adenocarcinoma

## TL;DR

This study creates a CT-based model using fractal dimension to predict air space tumor spread in early-stage lung cancer.

## Contribution

A novel CT-based nomogram integrating fractal dimension for noninvasive prediction of STAS in lung adenocarcinoma.

## Key findings

- FD 3D and imaging features like CTR and morphological irregularity are independent predictors of STAS.
- The nomogram achieved high AUC (0.894) with strong sensitivity and specificity in predicting STAS.
- High-risk group had significantly higher STAS prevalence (85.71% vs. 17.65%).

## Abstract

This study aimed to develop and validate a CT-based nomogram incorporating three-dimensional fractal dimension (FD 3D) to noninvasively predict tumor spread through air spaces (STAS) in stage IA lung adenocarcinoma.

A retrospective analysis was performed on 110 patients with stage IA lung adenocarcinoma who underwent surgical resection. CT morphological features and fractal-dimension metrics were collected. Patients were categorized into STAS-positive (n = 48) and STAS-negative (n = 62) groups based on pathology. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of STAS. Receiver operating characteristic (ROC) curve analysis evaluated predictive performance, and a nomogram model was constructed and internally validated. Based on the nomogram score, patients were further stratified into low- and high-risk STAS groups using the optimal cutoff value determined by the maximum Youden index.

Univariate analysis showed significant differences in consolidation-to-tumor ratio (CTR) (p < 0.001), morphological irregularity (p = 0.006), lobulation (p = 0.039), pleural indentation (p = 0.004), vascular convergence (p = 0.010), and FD 3D (p < 0.001) between groups. Multivariate analysis identified CTR, morphological irregularity, lobulation, and FD 3D as independent predictors of STAS in stage IA lung adenocarcinoma. The nomogram model achieved an area under the curve (AUC) of 0.894 (95%CI: 0.821–0.944; p < 0.001), with a sensitivity of 75.00% and a specificity of 90.32%. At the optimal cutoff value of 0.56, the model demonstrated a positive predictive value (PPV) of 85.71% in the high-risk group (n = 42, 38.18%) and a negative predictive value (NPV) of 82.35% in the low-risk group (n = 68, 61.82%), with significant differences in STAS prevalence between groups (85.71% vs. 17.65%, χ2 = 46.18, p < 0.001).

The CT-based nomogram integrating FD 3D and key imaging features can noninvasively predict STAS status in stage IA lung adenocarcinoma. This model shows promise for assisting surgical decision-making, though prospective studies are needed to validate its clinical utility.