Accuracy of solid portion size measured on multiplanar volume rendering images for assessing invasiveness in lung adenocarcinoma manifesting as subsolid nodules

Wang-Jia Li; Zhi-Gang Chu; Dan Li; Wei-Wei Jing; Qiu-Ling Shi; Fa-Jin Lv

doi:10.21037/qims-23-942

Accuracy of solid portion size measured on multiplanar volume rendering images for assessing invasiveness in lung adenocarcinoma manifesting as subsolid nodules

Quant Imaging Med Surg. 2024 Feb 1;14(2):1971-1984. doi: 10.21037/qims-23-942. Epub 2024 Jan 18.

Authors

Wang-Jia Li^#¹, Zhi-Gang Chu^#¹, Dan Li², Wei-Wei Jing¹, Qiu-Ling Shi³, Fa-Jin Lv^{1

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5

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Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing, China.
³ State Key Laboratory of Ultrasound in Medicine and Engineering, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
⁴ State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China.
⁵ Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China.
⁶ Institute of Medical Data, Chongqing Medical University, Chongqing, China.

^# Contributed equally.

Abstract

Background: The solid component of subsolid nodules (SSNs) is closely associated with the invasiveness of lung adenocarcinoma, and its accurate assessment is crucial for selecting treatment method. Therefore, this study aimed to evaluate the accuracy of solid component size within SSNs measured on multiplanar volume rendering (MPVR) and compare it with the dimensions of invasive components on pathology.

Methods: A pilot study was conducted using a chest phantom to determine the optimal MPVR threshold for the solid component within SSN, and then clinical validation was carried out by retrospective inclusion of patients with pathologically confirmed solitary SSN from October 2020 to October 2021. The radiological tumor size on MPVR and solid component size on MPVR (RSS_m) and on lung window (RSS_l) were measured. The size of the tumor and invasion were measured on the pathological section, and the invasion, fibrosis, and inflammation within SSNs were also recorded. The measurement difference between computed tomography (CT) and pathology, inter-observer and inter-measurement agreement were analyzed. Receiver operating characteristic (ROC) analysis and Bland-Altman plot were performed to evaluate the diagnostic efficiency of MPVR.

Results: A total of 142 patients (mean age, 54±11 years, 39 men) were retrospectively enrolled in the clinical study, with 26 adenocarcinomas in situ, 92 minimally invasive adenocarcinomas (MIAs), and 24 invasive adenocarcinomas (IAs). The RSS_l was significantly smaller than pathological invasion size with fair inter-measurement agreement [intraclass correlation coefficient (ICC) =0.562, P<0.001] and moderate interobserver agreement (ICC =0.761, P<0.001). The RSS_m was significantly larger than pathological invasion size with the excellent inter-measurement agreement (ICC =0.829, P<0.001) and excellent (ICC =0.952, P<0.001) interobserver agreement. ROC analysis showed that the cutoff value of RSS_m for differentiating adenocarcinoma in situ from MIA and MIA from IA was 1.85 and 6.45 mm (sensitivity: 93.8% and 95.5%, specificity: 85.7% and 88.2%, 95% confidence internal: 0.914-0.993 and 0.900-0.983), respectively. The positive predictive value-and negative predictive value of MPVR in predicting invasiveness were 92.8% and 100%, respectively.

Conclusions: Using MPVR to predict the invasive degree of SSN had high accuracy and good inter-observer agreement, which is superior to lung window measurements and helpful for clinical decision-making.

Keywords: Computed tomography (CT); ground-glass nodule (GGN); multiplanar volume rendering (MPVR); pulmonary adenocarcinoma.