Abstract
BACKGROUND: Rectal cancer patients are potential beneficiaries of adaptive radiotherapy (ART) which demands considerable resources. Currently, there is no definite guidance on what kind of patients and when will benefit from ART. This study aimed to develop and validate a methodology for estimating ART requirements in rectal cancer before treatment course. METHODS AND MATERIALS: This study involved 66 rectal cancer patients from center 1 and 27 patients from center 2. The ART requirements were evaluated by comparing 8 dose volume histogram (DVH) metrics of targets and organs at risk (OARs) between planning and treatment fractions. Tolerance ranges of deviation of DVH metrics were derived from 10 patients and applied to assess fractional variability. Eighteen features, encompassing diagnostic, dosimetric, and time-related information, were utilized to formulate a stepwise logistic regression model for fraction-level ART requirement estimation. The super parameters were determined through 5-fold cross-validation with 250 training fractions and the methodology was validated with 109 internal testing fractions and 134 external testing fractions. RESULTS: The area under the curve (AUC) of training dataset was 0.74 (95% CI: 0.61 to 0.85), while in the internal and external testing, the AUC achieved 0.76 (95% CI: 0.60-0.90) and 0.68 (95% CI: 0.56-0.81). Using a best (or clinical applicable) cut-off value of 33.4% (11%), the predictive model achieved a sensitivity of 46.2% (69.2%) and specificity of 97.9% (68.7%). During the modeling, 5 features were retained: Homogeneity index (OR = 6.06, 95% CI: 2.93-14.8), planning target volume (OR = 1.77, 95% CI: 1.17-2.69), fraction dose (OR = 45.37, 95% CI: 5.74-469), accumulated dose (OR = 2.29, 95% CI: 1.35-4.14), and whether neoadjuvant chemoradiotherapy (OR > 1000). CONCLUSION: ART requirements are associated with target volume, target dose homogeneity, fraction dose, dose accumulation and whether neoadjuvant radiotherapy. The predictive model exhibited the capability to predict fraction-level ART requirements.