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Development and validation of 18F-FDG PET/CT radiomics-based nomogram to predict visceral pleural invasion in solid lung adenocarcinoma

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Abstract

Objectives

This study aimed to establish a radiomics model based on 18F-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively.

Methods

We retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with 18F-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts.

Results

165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739).

Conclusions

A PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Author notes

  1. Nan Cui and Jiatong Li have contributed equally to this work.

Authors and Affiliations

  1. PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China

    Nan Cui, Jiatong Li, Wei Liu, Hongyang Yao, Mingshan Li & Kezheng Wang

  2. Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China

    Zhiyun Jiang

  3. Department of Epidemiology, School of Public Health, Harbin Medical University, 157 Baojian Road, Harbin, 150081, Heilongjiang, China

    Zhiping Long

  4. Interventional Vascular Surgery Department, The 4th Affiliated Hospital of Harbin Medical University, Harbin Medical University, 37 Yiyuan Road, Harbin, 150001, Heilongjiang, China

    Wei Li

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  1. Nan Cui
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Cui, N., Li, J., Jiang, Z. et al. Development and validation of 18F-FDG PET/CT radiomics-based nomogram to predict visceral pleural invasion in solid lung adenocarcinoma. Ann Nucl Med 37, 605–617 (2023). https://doi.org/10.1007/s12149-023-01861-w

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  • DOI: https://doi.org/10.1007/s12149-023-01861-w

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