Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma

Xin Tang; Jiangtao Liang; Bolin Xiang; Changfeng Yuan; Luoyu Wang; Bin Zhu; Xiuhong Ge; Min Fang; Zhongxiang Ding

doi:10.3389/fonc.2022.803824

Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma

Front Oncol. 2022 Feb 3:12:803824. doi: 10.3389/fonc.2022.803824. eCollection 2022.

Authors

Xin Tang^{1

2}, Jiangtao Liang³, Bolin Xiang⁴, Changfeng Yuan², Luoyu Wang⁵, Bin Zhu⁴, Xiuhong Ge⁶, Min Fang⁴, Zhongxiang Ding⁶

Affiliations

¹ The Fourth Clinical College, Zhejiang Chinese Medical University, Hangzhou, China.
² Department of Radiology, Hangzhou Wuyunshan Hospital (Hangzhou Health Promotion Research Institute), Hangzhou, China.
³ Department of Radiology, Hangzhou Universal Medical Imaging Diagnostic Center, Hangzhou, China.
⁴ Department of Radiology, Zhejiang Quhua Hospital, Quzhou, China.
⁵ Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China.
⁶ Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China.

Abstract

Objective: To investigate the diagnostic value of positron emission tomography (PET)/magnetic resonance imaging (MRI) radiomics in predicting the histological classification of lung adenocarcinoma and lung squamous cell carcinoma.

Methods: PET/MRI radiomics and clinical data were retrospectively collected from 61 patients with lung cancer. According to the pathological results of surgery or fiberscope, patients were divided into two groups, lung adenocarcinoma and squamous cell carcinoma group, which were set as positive for adenocarcinoma (40 cases) and negative for squamous cell carcinoma (21 cases). The radiomics characteristics most related to lung cancer classification were calculated and selected using radiomics software, and the two lung cancer groups were randomly assigned into a training set (70%) and a test set (30%). Maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods in the uAI Research Portal software (United Imaging Intelligence, China) were used to select the desired characteristics from 2600 features extracted from MRI and PET. Eight optimal features were finally retained through 5-fold cross-validation, and a PET/MRI fusion model was constructed. The predictive ability of this model was evaluated by the difference in area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve.

Results: AUC of PET/MRI model for the training group and test group were 0.886 (0.787-0.985) and 0.847 (0.648-1.000), respectively. PET/MRI radiomics features revealed different degrees of correlation with the classification of lung adenocarcinoma and squamous cell carcinoma, with significant differences.

Conclusion: The prediction model constructed based on PET/MRI radiomics features can predict the preoperative histological classification of lung adenocarcinoma and squamous cell carcinoma without seminality and repeatability. It can also provide an objective basis for accurate clinical diagnosis and individualized treatment, thus having important guiding significance for clinical treatment.

Keywords: PET/MRI; adenocarcinoma; cancer; lung; radiomic; squamous.