Automated Radiotherapy Planning for Head-and-neck Cancer: Multi-institutional Development, Validation, and Clinical Implementation of Monaco Scripting
Abstract
Purpose: The purpose of this study was to develop, validate, and clinically implement a standardized automated treatment planning framework using Monaco scripting and to evaluate its dosimetric performance, consistency, and organ-at-risk (OAR) sparing compared with conventional manual planning for head-and-neck cancer (HNC) across a multi-institutional network. Materials and Methods: A Pareto-based multi-criteria optimization template was iteratively developed, validated on >300 retrospective cases, and implemented across 14 centers. Forty-seven HNC patients were replanned using clinically approved plan (CA), AutoPlan (AP), and AutoPlan iteration (API) approaches and categorized into single-target (n = 11), two-target (n = 35), and three-target (n = 1) cohorts. Plan quality was analyzed in ProKnow using scorecard evaluation, boxplots, histograms, and scatter-plot correlations focusing on target coverage and doses to critical organs including brainstem, spinal cord, and bilateral parotids. Results: Automated workflows demonstrated superior consistency and OAR sparing compared with CA plans. AP achieved the highest overall scorecard performance, while API consistently produced the most stable dose distributions with lower variability and weaker correlations between PTV_High coverage and OAR doses. CA plans exhibited the widest dose variability and stronger OAR dose dependence. Clinically, the automated scripts achieved >70% automation and have been used in over 850 patients with minimal manual intervention. Conclusion: Automated Monaco scripting significantly improves plan quality, consistency, and OAR sparing in HNC radiotherapy, supporting routine clinical adoption – particularly of iterative API workflows – for standardized multi-center practice.
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BibTeX
@article{anchineyan2026automated,
title = {{Automated Radiotherapy Planning for Head-and-neck Cancer: Multi-institutional Development, Validation, and Clinical Implementation of Monaco Scripting}},
author = {Pichandi Anchineyan and Yuvarajan Sankaran and H. Mahedran and Sam Skennet Das},
journal = {Journal of Medical Physics},
year = {2026},
month = jul,
doi = {10.4103/jmp.jmp_47_26},
url = {https://doi.org/10.4103/jmp.jmp_47_26},
note = {CC BY-NC-SA},
}
RIS
TY - JOUR
TI - Automated Radiotherapy Planning for Head-and-neck Cancer: Multi-institutional Development, Validation, and Clinical Implementation of Monaco Scripting
AU - Pichandi Anchineyan
AU - Yuvarajan Sankaran
AU - H. Mahedran
AU - Sam Skennet Das
PY - 2026
DA - 2026/07/01
JO - Journal of Medical Physics
DO - 10.4103/jmp.jmp_47_26
UR - https://doi.org/10.4103/jmp.jmp_47_26
AB - Purpose: The purpose of this study was to develop, validate, and clinically implement a standardized automated treatment planning framework using Monaco scripting and to evaluate its dosimetric performance, consistency, and organ-at-risk (OAR) sparing compared with conventional manual planning for head-and-neck cancer (HNC) across a multi-institutional network. Materials and Methods: A Pareto-based multi-criteria optimization template was iteratively developed, validated on >300 retrospective cases, and implemented across 14 centers. Forty-seven HNC patients were replanned using clinically approved plan (CA), AutoPlan (AP), and AutoPlan iteration (API) approaches and categorized into single-target (n = 11), two-target (n = 35), and three-target (n = 1) cohorts. Plan quality was analyzed in ProKnow using scorecard evaluation, boxplots, histograms, and scatter-plot correlations focusing on target coverage and doses to critical organs including brainstem, spinal cord, and bilateral parotids. Results: Automated workflows demonstrated superior consistency and OAR sparing compared with CA plans. AP achieved the highest overall scorecard performance, while API consistently produced the most stable dose distributions with lower variability and weaker correlations between PTV_High coverage and OAR doses. CA plans exhibited the widest dose variability and stronger OAR dose dependence. Clinically, the automated scripts achieved >70% automation and have been used in over 850 patients with minimal manual intervention. Conclusion: Automated Monaco scripting significantly improves plan quality, consistency, and OAR sparing in HNC radiotherapy, supporting routine clinical adoption – particularly of iterative API workflows – for standardized multi-center practice.
SN - 0971-6203
SN - 1998-3913
ER -