Abstract #4838

Evaluating the pairwise AdaBoost model in predicting the efficacy of chemo-radiotherapy for advanced rectal cancer under small sample size

Qinglei Shi¹, Xiaoming Xi*², Yilong Yin³, Jie Kuang⁴, Gaofeng Shi⁴, Xu Yan⁵, Yi Qu⁶, and Dongsheng Zhou⁷

¹School of Software, Shan Dong University, Jinan, China, ²School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China, ³School of Software, Software Park Campus, Shandong University, Jinan, China, ⁴CT Room, Radiology Department, Hebei Medical University Affilited 4th Hospital, Shi Jiazhuang, China, ⁵MR Scientific Marketing, Siemens Healthcare, Shanghai, China, Shanghai, China, ⁶Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China, ⁷Department of Breast and Thyroid Surgery, Shandong Provinvial Qianfoshan Hospital, The First Hospital Affiliated with Shandong First Medical University, Jinan, China

This paper proposed a pairwise AdaBoost model in predicting the therapeutic effect of non-metastatic LARC treated with neoadjuvant chemotherapy-radiation therapy based on radiomics signatures coming from ADC maps. Compared with traditional models, the pairwise AdaBoost model has ability to enlarge the number of training samples, which is useful to improve the generalization ability of the model. The experimental results demonstrated that the pairwise AdaBoost model seems can improve the accuracy and robustness of the model in predicting the treatment effect for locally LARC treated with neoadjuvant chemotherapy-radiation therapy.

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