Predicting visceral pleural invasion in lung adenocarcinoma presenting as part‐solid density utilizing a nomogram model combined with radiomics and clinical features

Author:

Wang Fen1,Pan Xianglong2,Zhang Teng3,Zhong Yan3,Wang Chenglong4,Li Hai5,Wang Jun2ORCID,Guo Lili1ORCID,Yuan Mei3ORCID

Affiliation:

1. Department of Medical Imaging The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University Huai'an China

2. Department of Thoracic Surgery The First Affiliated Hospital of Nanjing Medical University Nanjing China

3. Department of Radiology The First Affiliated Hospital of Nanjing Medical University Nanjing China

4. Shanghai Key Laboratory of Magnetic Resonance East China Normal University Shanghai China

5. Department of Pathology The First Affiliated Hospital of Nanjing Medical University Nanjing China

Abstract

AbstractBackgroundTo develop and validate a preoperative nomogram model combining the radiomics signature and clinical features for preoperative prediction of visceral pleural invasion (VPI) in lung nodules presenting as part‐solid density.MethodsWe retrospectively reviewed 156 patients with pathologically confirmed invasive lung adenocarcinomas after surgery from January 2016 to August 2019. The patients were split into training and validation sets by a ratio of 7:3. The radiomic features were extracted with the aid of FeAture Explorer Pro (FAE). A CT‐based radiomics model was constructed to predict the presence of VPI and internally validated. Multivariable regression analysis was conducted to construct a nomogram model, and the performance of the models were evaluated with the area under the receiver operating characteristic curve (AUC) and compared with each other.ResultsThe enrolled patients were split into training (n = 109) and validation sets (n = 47). A total of 806 features were extracted and the selected 10 optimal features were used in the construction of the radiomics model among the 707 stable features. The AUC of the nomogram model was 0.888 (95% CI: 0.762–0.961), which was superior to the clinical model (0.787, 95% CI: 0.643–0.893; p = 0.049) and comparable to the radiomics model (0.879, 95% CI: 0.751–0.965; p > 0.05). The nomogram model achieved a sensitivity of 90.5% and a specificity of 76.9% in the validation dataset.ConclusionsThe nomogram model could be considered as a noninvasive method to predict VPI with either highly sensitive or highly specific diagnoses depending on clinical needs.

Publisher

Wiley

Subject

Pulmonary and Respiratory Medicine,Oncology,General Medicine

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