NDLI: Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study

Content Provider	Springer Nature : BioMed Central
Author	Song, Wei Shang, Wen Li, Chunying Bian, Xinyu Lu, Hong Ma, Jun Yu, Dahai
Abstract	Background The purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique. Methods A total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models. Results The models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models. Conclusions Machine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans. Trial registration: This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-024-02496-5.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-024-02496-5
Journal	Radiation Oncology
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2024-07-31
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology Deep learning Prediction Classification Quality assurance Machine parameters Class imbalance
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

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