Breast Cancer Radiotherapy Plan Metrics: A Principal Component Analysis Using the Mean Absolute Dose Deviation.

Introduction Radiotherapy for breast cancer must deliver a sufficient dose to the targets while limiting the dose to the heart, lungs, and contralateral breast. Treatment plans are evaluated on many dose-volume metrics at once, and the balance between target coverage and organ sparing depends on the patient's setup and on the radiotherapy technique. We investigate the relationships among doses to targets, organs, and patient and treatment factors, and examine whether principal component analysis (PCA) can display these relationships in a small factorial planning dataset. Methods PCA was applied to the data from a quasi-experimental two-level factorial treatment planning study of breast cancer adjuvant radiotherapy. The quasi-experiment assessed all combinations of five variables in eight patients: tumor laterality (left vs. right), surgery (mastectomy vs. breast-conserving), target volume (with vs. without lymph nodes), patient's setup position (supine vs. prone) (each patient underwent simulation in the two positions), and radiation delivery (conventional radiotherapy vs. tomotherapy) (both plans were computed in each setup position), which resulted in 32 distinct combinations of the variables. Results PCA showed that tumor left laterality and prone setup were associated with higher heart and contralateral breast doses; nodal irradiation and conventional radiotherapy were associated with higher lung doses, whereas post-mastectomy radiation contributed to high doses to both heart and lung. Tomotherapy was associated with reduced lung doses, better target coverage, slightly reduced heart doses, and increased contralateral breast doses. Conclusion PCA on a factorial design identified correlations between treatment factors and dosimetric outcomes with a minimal number of patients, which might be important in exploratory research, reducing the initial burden of accruing patients.