Background/Objectives: This study evaluated the daily delivered dose in prostate cancer patients using the automated artificial intelligence (AI)-based software Adaptbox (v2.3.2, Therapanacea). The aim was to assess target coverage and organ-at-risk (OAR) exposure. Methods: Twenty patients were included. All received 80 Gy in 40 fractions to the prostate and 56 Gy simultaneously to the seminal vesicles using two-arc VMAT on a TrueBeam STx, with daily CBCT for setup. For each fraction, CBCT images were imported into Adaptbox. A synthetic CT (sCT) was generated using a deep learning algorithm. OARs were automatically segmented, while targets were propagated from the planning CT (pCT) using rigid registration. Dose calculation was performed using Adaptbox’s collapse-cone algorithm. Dose parameters were extracted for each session and compared with planned values. Results: All 800 fractions were analyzed. The planning target volume (PTV) remained consistent with planning, with a maximum deviation of 0.1% for both PTVs. For the rectum, 78.38%, 77.75%, and 78.13% of fractions exceeded planned doses for V70Gy, V76Gy and V80Gy, respectively. One patient had five consecutive fractions with >5% deviation across all rectal metrics. For the bladder, 52.34% of fractions exceeded the planned V80Gy, and two patients had ≥5 consecutive fractions with >5% deviation; however, this was attributed to contouring inaccuracies. Conclusions: This AI-based workflow enables reliable daily dose reconstruction and can identify clinically relevant OAR dose deviations that may support adaptive interventions, although accurate contouring remains essential.
Prunaretty et al. (Tue,) studied this question.