Incorporating axillary-lateral thoracic vessel juncture dosimetric variables improves model for predicting lymphedema in patients with breast cancer: A validation analysis

Abstract

Background: A relationship between the axillary-lateral thoracic vessel juncture (ALTJ) dose and lymphedema rate has been reported in patients with breast cancer. The purpose of this study was to validate this relationship and explore whether incorporation of the ALTJ dose-distribution parameters improves the prediction model's accuracy. Methods: A total of 1,449 women with breast cancer who were treated with multimodal therapies from two institutions were analyzed. We categorized regional nodal irradiation (RNI) as limited RNI, which excluded level I/II, vs extensive RNI, which included level I/II. The ALTJ was delineated retrospectively, and dosimetric and clinical parameters were analyzed to determine the accuracy of predicting the development of lymphedema. Decision tree and random forest algorithms were used to construct the prediction models of the obtained dataset. We used Harrell's C-index to assess discrimination. Results: The median follow-up time was 77.3 months, and the 5-year lymphedema rate was 6.8 %. According to the decision tree analysis, the lowest lymphedema rate (5-year, 1.2 %) was observed in patients with ≤ six removed lymph nodes and ≤ 66 % ALTJ V35Gy. The highest lymphedema rate was observed in patients with > 15 removed lymph nodes and an ALTJ maximum dose (Dmax) of > 53 Gy (5-year, 71.4 %). Patients with > 15 removed lymph nodes and an ALTJ Dmax ≤ 53 Gy had the second highest rate (5-year, 21.5 %). All other patients had relatively minor differences, with a rate of 9.5 % at 5 years. Random forest analysis revealed that the model's C-index increased from 0.84 to 0.90 if dosimetric parameters were included instead of RNI (P <.001). Conclusion: The prognostic value of ALTJ for lymphedema was externally validated. The estimation of lymphedema risk based on individual dose-distribution parameters of the ALTJ seemed more reliable than that based on the conventional RNI field design. © 2023 The Author(s)