[18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study

Minglei Yang; Xiaoxiao Li; Chuang Cai; Chunli Liu; Minjie Ma; Wendong Qu; Sheng Zhong; Enkuo Zheng; Huangkai Zhu; Feng Jin; Huazheng Shi

doi:10.1007/s00330-023-10503-8

[¹⁸F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study

Eur Radiol. 2023 Dec 21. doi: 10.1007/s00330-023-10503-8. Online ahead of print.

Authors

Minglei Yang^#¹, Xiaoxiao Li^#², Chuang Cai^#³, Chunli Liu², Minjie Ma⁴, Wendong Qu⁵, Sheng Zhong⁶, Enkuo Zheng¹, Huangkai Zhu¹, Feng Jin⁷, Huazheng Shi⁸

Affiliations

¹ Department of Thoracic Surgery, Ningbo No. 2 Hospital, Ningbo, China.
² Shanghai Universal Cloud Medical Imaging Diagnostic Center, Shanghai, China.
³ School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China.
⁴ Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
⁵ Department of Thoracic Surgery, The Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China. quwendonga@126.com.
⁶ Tailai Inc, Shanghai, China.
⁷ Shandong Key Laboratory of Infectious Respiratory Diseases, Shandong Public Health Clinical Center, Shandong University, Shandong, China. jf13791123070@163.com.
⁸ Shanghai Universal Cloud Medical Imaging Diagnostic Center, Shanghai, China. 1090830052@qq.com.

^# Contributed equally.

PMID: 38127071
DOI: 10.1007/s00330-023-10503-8

Abstract

Objectives: This study aims to develop and validate a radiomics model based on ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography ([¹⁸F]FDG PET-CT) images to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC).

Materials and methods: One hundred eighty-five patients receiving neoadjuvant chemoimmunotherapy for NSCLC at 5 centers from January 2019 to December 2022 were included and divided into a training cohort and a validation cohort. Radiomics models were constructed via the least absolute shrinkage and selection operator (LASSO) method. The performances of models were evaluated by the area under the receiver operating characteristic curve (AUC). In addition, genetic analyses were conducted to reveal the underlying biological basis of the radiomics score.

Results: After the LASSO process, 9 PET-CT radiomics features were selected for pCR prediction. In the validation cohort, the ability of PET-CT radiomics model to predict pCR was shown to have an AUC of 0.818 (95% confidence interval [CI], 0.711, 0.925), which was better than the PET radiomics model (0.728 [95% CI, 0.610, 0.846]), CT radiomics model (0.732 [95% CI, 0.607, 0.857]), and maximum standard uptake value (0.603 [95% CI, 0.473, 0.733]) (p < 0.05). Moreover, a high radiomics score was related to the upregulation of pathways suppressing tumor proliferation and the infiltration of antitumor immune cell.

Conclusion: The proposed PET-CT radiomics model was capable of predicting pCR to neoadjuvant chemoimmunotherapy in NSCLC patients.

Clinical relevance statement: This study indicated that the generated ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography radiomics model could predict pathological complete response to neoadjuvant chemoimmunotherapy, implying the potential of our radiomics model to personalize the neoadjuvant chemoimmunotherapy in lung cancer patients.

Key points: • Recognizing patients potentially benefiting neoadjuvant chemoimmunotherapy is critical for individualized therapy of lung cancer. • [¹⁸F]FDG PET-CT radiomics could predict pathological complete response to neoadjuvant immunotherapy in non-small cell lung cancer. • [¹⁸F]FDG PET-CT radiomics model could personalize neoadjuvant chemoimmunotherapy in lung cancer patients.

Keywords: Chemoimmunotherapy; Non-small cell lung cancer; PET-CT; Pathological response; Radiomics.