Radiobiological Study for HDR Cervical Brachytherapy: Dosimetric Correlations and Clinical Validation

Purpose: High-dose-rate (HDR) brachytherapy remains a cornerstone in the definitive management of locally advanced cervical cancer. While conventional metrics derived from dose-volume histograms (DVHs) guide treatment planning, radiobiological modeling—using tumor control probability (TCP) and normal tissue complication probability (NTCP)—offers enhanced predictive capacity for clinical outcomes. This study evaluates the clinical applicability of TCP and NTCP models to optimize HDR brachytherapy. Materials and Methods: A retrospective dosimetric analysis was conducted on 30 cervical cancer patients (FIGO stage IIB–IIIB), treated with HDR intracavitary brachytherapy using Fletcher-Suit-Delclos applicators between 2022 and 2024. TCP was calculated applying a Poisson-based linear-quadratic model, whereas NTCP estimations for rectum, bladder, and sigmoid colon were based on the Lyman-Kutcher-Burman model. Correlations between radiobiological indices and conventional dosimetric parameters were assessed via Pearson correlations. Results: TCP values demonstrated exceptional tumor control probabilities, ranging from 99.37% to 99.85% (median: 99.80%). NTCP values exhibited notable variabilities: rectum (0.0003%–0.3885%, median 0.0178%), bladder (0.0032%–0.6938%, median 0.0731%), and sigmoid colon (0.0000%–0.0405%, median 0.0001%). Significant positive correlations were observed between TCP and HR-CTV D90 (r=0.62, p<0.01) and between NTCP and D2cc values for rectum (r=0.58, p<0.05) and bladder (r=0.52, p<0.05). Patients exhibiting NTCP>0.5% demonstrated an increased risk of grade ≥2 late toxicities (odds ratio 3.2; 95% CI 1.4–7.3). Conclusions: Radiobiological modeling integrating TCP and NTCP substantially complements dosimetric parameters, enabling improved prediction of therapeutic outcomes and toxicity risks in HDR brachytherapy for cervical cancer. The strong correlations observed endorse the integration of these tools into routine clinical workflows to facilitate personalized treatment optimization.