Evidence map›Paper›PMID 42890946›Full record

ArticleJournal of medical physics

Automated Radiotherapy Planning for Head-and-neck Cancer: Multi-institutional Development, Validation, and Clinical Implementation of Monaco Scripting.

Pichandi Anchineyan, Yuvarajan Sankaran, H Mahedran, Sam Skennet Das

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In one paragraph

Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Pichandi AnchineyanHCG Hospital, Bengaluru, Karnataka, India.
Yuvarajan SankaranHCG Hospital, Bengaluru, Karnataka, India.
H MahedranHCG Hospital, Vijayawada, Andhra Pradesh, India.
Sam Skennet DasHCG Hospital, Bengaluru, Karnataka, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 ( 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.

Indexed as

Automated treatment planningc#multi-criteria optimizationProKnowradiotherapy planningscript

Identifiers

PMID42890946
PMCPMC13675034

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.