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Predicting Radiation Pneumonitis Integrating Clinical Information, Medical Text, and 2.5D Deep Learning Features in
Wenting Wang1, Mingcheng Ren2, Jing Ren3
1Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China.
Purpose:
To construct a prediction model for radiation pneumonitis (RP) in lung cancer patients based on clinical information, medical text, and 2.5D deep learning (DL) features.
Methods And Materials:
A total of 356 patients with lung cancer from the Heping Campus of the First Hospital of China Medical University were randomly divided at a 7:3 ratio into training and validation cohorts, and 238 patients from 3 other centers were included in the testing cohort for assessing model generalizability. We used the term frequency-inverse document frequency method to generate numerical vectors from computed tomography (CT) report texts. The CT and radiation therapy dose slices demonstrating the largest lung region of interest across the coronal and transverse planes were considered as the central slice; moreover, 3 slices above and below the central slice were selected to create comprehensive 2.5D data. We extracted DL features via DenseNet121, DenseNet201, and Twins-SVT and integrated them via multi-instance learning (MIL) fusion. The performances of the 2D and 3D DL models were also compared with the performance of the 2.5D MIL model. Finally, RP prediction models based on clinical information, medical text, and 2.5D DL features were constructed, validated, and tested.
Results:
The 2.5D MIL model based on CT was significantly better than the 2D and 3D DL models in the training, validation, and test cohorts. The 2.5D MIL model based on radiation therapy dose was considered to be the optimal model in the test1 cohort, whereas the 2D model was considered to be the optimal model in the training, validation, and test3 cohorts, with the 3D model being the optimal model in the test2 cohort. A combined model achieved Area Under Curve values of 0.964, 0.877, 0.868, 0.884, and 0.849 in the training, validation, test1, test2, and test3 cohorts, respectively.
Conclusion:
We propose an RP prediction model that integrates clinical information, medical text, and 2.5D MIL features, which provides new ideas for predicting the side effects of radiation therapy.
