Fig. 1 3D Surface Torso Images Investigation of Missing Data Strategies for Clinical Data of Breast Reconstruction Patients Krista M. Nicklaus, Jun Liu, Greg P. Reece, Fatima A. Merchant, Michelle C. Fingeret, Mia K. Markey   The University of Texas at Austin 107 W. Dean Keeton, Austin, TX, 78712, USA E-mail: k.nicklaus@utexas.edu Abstract Breast reconstruction can be an important step to psychological recovery for women after mastectomy. Our team has developed a database of 505 women undergoing breast reconstruction at The University of Texas MD Anderson Cancer Center, including medical records, psychosocial wellbeing measures, and 3D surface torso images at numerous time points during the reconstruction process. We have incomplete data due to patient drop out, nonresponses, recording errors, and poor image quality. In this study, we investigate patterns of missingness and missing data methods to recapture breast volume, smoking history, and number of previous pregnancies. 1. Introduction Breast reconstruction is the process of reforming the breast mound(s) to achieve an acceptable aesthetic result to the patient after mastectomy. The decision to undergo reconstruction is complicated, and our long term goal is to use patient data in a case-based reasoning decision aid. Missing data is a major barrier to constructing our decision aid. This study aims to address three missing patient datatypes. 2. Materials and Methods Our dataset consists of data from 505 women who underwent breast reconstruction at The University of Texas MD Anderson Cancer Center from baseline to 18+ months after reconstruction. The variables analyzed are breast volume computed from 3D surface torso images, smoking history at time of recruitment, and number of previous pregnancies. Volume data: Calculate two symmetry measures between the left and right breast: lowest visible point to level of the sternal notch and lateral point to midline for patients who have no missing volume data. Correlate the symmetry ratios with volume ratios and apply to patients who have one missing breast volume. Pregnancies: Use mean imputation by replacing missing values with the average number of pregnancies. Smoking: Calculate the ratio of yes to no answers for the question: “Have you smoked at least 100 cigarettes in your lifetime?”. Randomly assign the missing cases to yes or no, maintaining the overall ratio. The results from a case-based reasoning retrieval algorithm using data imputed with listwise deletion and the data imputed with the above methods is compared. 3. Results We have shown proof-of-concept that clinical data can be imputed and provide meaningful information for analysis. A B C D Fig. 1 3D Surface Torso Images A) Original image, B) Volume measurement C) Inframammary Fold to Mid-Clavicle symmetry measure (VS), D) Lateral Point to Midline symmetry measure (HS) Table. 1 Imputation vs Listwise Deletion: Mean Kendall’s tau CBR prediction accuracy by reconstruction type Fig. 3 Equations to calculate vertical symmetry (VS) and horizontal symmetry (HS) Proceedings of the 2018 ASEE Gulf-Southwest Section Annual Conference The University of Texas at Austin April 4-6, 2018