# Artificial intelligence as an independent reader of risk-dominant lung nodules: influence of CT reconstruction parameters

## TL;DR

This study shows that CT reconstruction settings affect how well AI classifies lung nodules, but not their detection, highlighting the need for consistent imaging parameters in AI-based screening.

## Contribution

First study to assess how CT reconstruction parameters influence AI performance in detecting and classifying risk-dominant lung nodules.

## Key findings

- Reconstruction kernels significantly impact AI's nodule type classification but not detection rates.
- Using radiologist-preferred kernels improves AI agreement with human readers in nodule classification.
- Changing slice thickness/interval had no significant effect on AI performance.

## Abstract

To assess the impact of reconstruction parameters on AI’s performance in detecting and classifying risk-dominant nodules in a baseline low-dose CT (LDCT) screening among a Chinese general population.

Baseline LDCT scans from 300 consecutive participants in the Netherlands and China Big-3 (NELCIN-B3) trial were included. AI analyzed each scan reconstructed with four settings: 1 mm/0.7 mm thickness/interval with medium-soft and hard kernels (D45f/1 mm, B80f/1 mm) and 2 mm/1 mm with soft and medium-soft kernels (B30f/2 mm, D45f/2 mm). Reading results from consensus read by two radiologists served as reference standard. At scan level, inter-reader agreement between AI and reference standard, sensitivity, and specificity in determining the presence of a risk-dominant nodule were evaluated. For reference-standard risk-dominant nodules, nodule detection rate, and agreement in nodule type classification between AI and reference standard were assessed.

AI-D45f/1 mm demonstrated a significantly higher sensitivity than AI-B80f/1 mm in determining the presence of a risk-dominant nodule per scan (77.5% vs. 31.5%, p < 0.0001). For reference-standard risk-dominant nodules (111/300, 37.0%), kernel variations (AI-D45f/1 mm vs. AI-B80f/1 mm) did not significantly affect AI’s nodule detection rate (87.4% vs. 82.0%, p = 0.26) but substantially influenced the agreement in nodule type classification between AI and reference standard (87.7% [50/57] vs. 17.7% [11/62], p < 0.0001). Change in thickness/interval (AI-D45f/1 mm vs. AI-D45f/2 mm) had no substantial influence on any of AI’s performance (p > 0.05).

Variations in reconstruction kernels significantly affected AI’s performance in risk-dominant nodule type classification, but not nodule detection. Ensuring consistency with radiologist-preferred kernels significantly improved agreement in nodule type classification and may help integrate AI more smoothly into clinical workflows.

Question
Patient management in lung cancer screening depends on the risk-dominant nodule, yet no prior studies have assessed the impact of reconstruction parameters on AI performance for these nodules.

Findings
The difference between reconstruction kernels (AI-D45f/1 mm vs. AI-B80f/1 mm, or AI-B30f/2 mm vs. AI-D45f/2 mm) significantly affected AI’s performance in risk-dominant nodule type classification, but not nodule detection.

Clinical relevance
The use of kernel for AI consistent with radiologist’s choice is likely to improve the overall performance of AI-based CAD systems as an independent reader and support greater clinical acceptance and integration of AI tools into routine practice.