A. Barateau

Purpose Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone‐beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo‐CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy. Methods Forty‐four patients received VMAT for H&N cancer (70‐63‐56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU‐D curve method), (b) a water–air‐bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT ref ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT ref and pCT of each method. Results In the entire body, the MAEs and MEs of the DLM, HU‐D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and −36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 ± 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D mean and 5 ± 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D mean ). For the parotid gland D mean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate ± standard deviation was 98.1 ± 1.2%, 91.0 ± 5.3%, 97.9 ± 1.6%, and 98.8 ± 0.7% for the DLM, HU‐D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU‐D curve method, DAM, and DIR method differed significantly from those of the DLM. Conclusions For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCT‐based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.

Purpose In the context of adaptive radiation therapy (ART) for locally advanced cervical carcinoma (LACC), this study proposed an original cone‐beam computed tomography (CBCT)‐guided “Evolutive library” and evaluated it against four other known radiotherapy (RT) strategies. Material and methods For 20 patients who underwent intensity‐modulated radiation therapy (IMRT) for LACC, three planning CTs [with empty (EB), intermediate (IB), and full (FB) bladder volumes], a CT scan at 20 Gy and bi‐weekly CBCTs for 5 weeks were performed. Five RT strategies were simulated for each patient: “Standard RT” was based on one IB planning CT; “internal target volume (ITV)‐based RT” was an ITV built from the three planning CTs; “RT with one mid‐treatment replanning (MidTtReplan)” corresponded to the standard RT with a replanning at 20 Gy; “Pretreatment library ART” using a planning library based on the three planning CTs; and the “Evolutive library ART”, which was the “Pretreatment library ART” strategy enriched by including some CBCT anatomies into the library when the daily clinical target volume (CTV) shape differed from the ones in the library. Two planning target volume (PTV) margins of 7 and 10 mm were evaluated. All the strategies were geometrically compared in terms of the percentage of coverage by the PTV, for the CTV and the organs at risk (OAR) delineated on the CBCT. Inadequate coverage of the CTV and OARs by the PTV was also assessed using deformable image registration. The cumulated dose distributions of each strategy were likewise estimated and compared for one patient. Results The “Evolutive library ART” strategy involved a number of added CBCTs: 0 for 55%; 1 for 30%; 2 for 5%; and 3 for 10% of patients. Compared with the other four, this strategy provided the highest CTV geometric coverage by the PTV, with a mean (min–max) coverage of 98.5% (96.4–100) for 10 mm margins and 96.2% (93.0–99.7) for 7 mm margins ( P < 0.05). Moreover, this strategy significantly decreased the geometric coverage of the bowel. CTV undercoverage by PTV occurred in the anterior and superior uterine regions for all strategies. The dosimetric analysis at 7 mm similarly demonstrated that the “Evolutive library ART” increased the V 42.75Gy of the CTV by 27%, 20%, 13%, and 28% compared with “Standard RT”, “ITV‐based RT”, “MidTtReplan”, and “Pretreatment library ART”, respectively. The dose to the bowel was also decreased by the “Evolutive library ART” compared with that by the other strategies. Conclusion The “Evolutive library ART” is a personalized ART strategy that comprises a pretreatment planning library of three CT scans, enriched for half of the patients by one to three per‐treatment CBCTs. This original strategy increased both the CTV coverage and bowel sparing compared with all the other tested strategies and enables us to consider a PTV margin reduction.