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Purpose: Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children's Research Hospital. Methods: Sixty-three CSI patient CT scans were involved in the study. Preplanning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total. Results: The algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan. Conclusions: We successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: Target delineation for radiation therapy is a time‐consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post‐operative target delineation, particularly for CT‐based studies. To this end, we trained a CT‐based autocontouring model to contour the post‐operative GTV of pediatric patients with medulloblastoma. Methods: One hundred four retrospective pediatric CT scans were used to train a GTV auto‐contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = < 25%, 2 = 25–50%, 3 = > 50–75%, and 4 = > 75–100%). Contrast and the contrast‐to‐noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre‐selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). Results: Eighty patients were pre‐selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 ± 0.29 and 0.67 ± 0.22 on the models without pre‐selected training data and 0.55 ± 13.01 and 0.83 ± 0.17 on the models with pre‐selected data, respectively. The DSC on the CTV expansions were 0.90 ± 0.13. Conclusion: We successfully automatically contoured continuous GTVs within the posterior fossa on scans that had contrast > ± 10 HU. CT‐Based auto‐contouring algorithms have potential to positively impact centers with limited MRI access. [ABSTRACT FROM AUTHOR]

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools. [ABSTRACT FROM AUTHOR]

Purpose: We developed and tested an automatic field‐in‐field (FIF) solution for whole‐brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. Methods: A configurable auto‐planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user‐configured constraints of dose‐volume histogram coverage and least‐squared cost functions. The algorithm was retrospectively tested on 17 whole‐brain patients. First, an in‐house landmark‐based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician‐defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto‐optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five‐point scale. Results: The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm3] from 959 ± 498 cm3 to 145 ± 224 cm3. On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto‐plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits. Conclusions: This algorithm successfully produced high‐quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow. [ABSTRACT FROM AUTHOR]

A pediatric normal tissue effects in the clinic (PENTEC) comprehensive review of patients with childhood cancer who received radiation therapy (RT) to the liver was performed to develop models that may inform RT dose constraints for the liver and improve risk forecasting of toxicities. A systematic literature search was performed to identify published data on hepatic toxicities in children. Treatment and outcome data were extracted and used to generate normal tissue complication probability (NTCP) models. Complications from both whole and partial liver irradiation were considered. For whole liver irradiation, total body irradiation and non–total body irradiation treatments were considered, but it was assumed that the entire liver received the prescribed dose. For partial liver irradiation, only Wilms tumor flank field RT could be analyzed. However, a prescribed dose assumption could not be applied, and there was a paucity of analyzable liver dosimetry data. To associate the dose-volume exposures with the partial volume complication data from flank irradiation, liver dose-volume metrics were reconstructed for Wilms tumor flank RT using age-specific computational phantoms as a function of field laterality and superior extent of the field. The literature search identified 2103 investigations pertaining to hepatic sinusoidal obstructive syndrome (SOS) and liver failure in pediatric patients. All abstracts were screened, and 241 articles were reviewed in full by the study team. A model was developed to calculate the risk of developing SOS after whole liver RT. RT dose (P =.006) and receipt of nonalkylating chemotherapy (P =.01) were significant. Age <20 years at time of RT was borderline significant (P =.058). The model predicted a 2% risk of SOS with zero RT dose, 6.1% following 10 Gy, and 14.5% following 20 Gy to the whole liver (modeled as the linear-quadratic equivalent dose in 2-Gy fractions [α/β = 3 Gy]). Patients with Wilms tumor treated with right flank RT had a higher observed rate of SOS than patients receiving left flank RT, but data were insufficient to generate an NTCP model for partial liver irradiation. From the phantom-based dose reconstructions, mean liver dose was estimated to be 2.16 ± 1.15 Gy and 6.54 ± 2.50 Gy for left and right flank RT, respectively, using T10-T11 as the superior field border and a prescription dose of 10.8 Gy (based on dose reconstruction). Data were sparse regarding rates of late liver injury after RT, which suggests low rates of severe toxicity after treatment for common pediatric malignancies. This pediatric normal tissue effects in the clinic (PENTEC) review provides an NTCP model to estimate the risk of hepatic SOS as a function of RT dose following whole liver RT and quantifies the range of mean liver doses from typical Wilms tumor flank irradiation fields. Patients treated with right flank RT had higher rates of SOS than patients treated with left flank RT, but data were insufficient to develop a model for partial liver irradiation. Risk of SOS was estimated to be approximately ≤6% in pediatric patients receiving whole liver doses of <10 Gy. [ABSTRACT FROM AUTHOR]