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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.