Abstract #0864

An MRI-based nomogram predicts brain metastasis response to targeted therapy in lung cancer patients: A multi-center study

Junwei Chen¹, Jiaji Mao¹, Junhao Li¹, Baoxun Li¹, Haojiang Li², Daiying Lin³, Xuewen Fang⁴, Fang Xiao⁵, Zehe Huang⁶, Wensheng Wang⁷, Shaoxian Chen³, Zonghuan Cai³, Manqiu Liang⁴, Shengzhang Pan⁶, Dabiao Deng⁷, Zhiyuan Wu⁸, and Jun Shen¹

¹Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China, ²Sun Yat-sen University Cancer Center, Guangzhou, China, ³Shantou Central Hospital, Shantou, China, ⁴The Tenth Affiliated Hospital of Southern Medical University, Dongguan People's Hospital, Dongguan, China, ⁵The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China, ⁶Qinzhou First People's Hospital, Qinzhou, China, ⁷Guangdong 999 Brain Hospital, Guangzhou, China, ⁸Capital Medical University, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, lung cancer patients with brain metastasis

Motivation: To determine an effective individualized treatment decision for lung cancer patients with brain metastasis (BrM) to receive targeted therapy.

Goal(s): we developed an MRI-based nomogram that can predict the response of lung cancer BrM to targeted therapy using multi-center data.

Approach: Clinical predictors, radiomics and deep learning features extracted from BrM baseline MR images were incorporated to establish the nomogram using the LASSO logistics coefficients.

Results: The nomogram can accurately predict the 6-month and 12-month responses of BrM to targeted therapy across the training cohort, internal validation cohort, and external test set, outperforming all other models.

Impact: The MRI-based nomogram can be used as a pretreatment and personalized tool to predict response to targeted therapy in lung cancer patients with BrMs and thus assist in optimizing treatment for lung cancer patients who suffer from BrMs.

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