ACOSA - Auto Crop and Optimization Setup Algorithm - Preliminary comparative evaluation in radiotherapy

Sep 23, 2026

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ACOSA (Auto Crop and Optimization Setup Algorithm) is an ESAPI-script-based method designed to overcome limits of commercial auto-planning modules that mainly handle single, conventional disease types. The algorithm simulates a physicist by automatically cropping target regions and setting optimization parameters for multiple diseases using input prescriptions. Retrospective analysis of 20 glioma and head-and-neck cases compares ACOSA with Eclipse AutoCrop without OAR dose-limit constraints. ACOSA shows better CI, GI, D2, and low-dose fall-off metrics (Ratio20/30/40), while AutoCrop slightly leads in HI and D98, supporting clinical applicability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Background on auto-planning and scripting

Need for multi-disease versatility

Methods

ESAPI scripting approach and simulated physicist workflow

Case selection and comparison design

Results

Target homogeneity, conformity, and dose metrics

Conclusions

Clinical reliability and comparison to Eclipse AutoCrop

ACOSA: A Script-Based Algorithm for Multi-Disease Target Crop and Optimization in Radiotherapy Technology in Cancer Research & Treatment Volume 25: 1-10 © The Author(s) 2026 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/15330338251411617](DOI: 10.1177/15330338251411617)[ ](DOI: 10.1177/15330338251411617)[journals.sagepub.com/home/tct](journals.sagepub.com/home/tct) Han Guo, MSc 1,* , Zhiqing Xiao, MSc 1,*, Huandi Zhou, MD 1,2 , Yanqiang Wang, BSc 1, Miao Wang, MSc 1, Xiaotong Lin, MSc 1 , Junling Liu, BSc 1, Xiuwu Li, MSc 1, and Xiaoying Xue, PhD 1,2 Abstract Introduction: Commercial automatic planning modules are currently limited to single, conventional disease types, which severely restricts their utility when dealing with unconventional plans. Given that such unconventional plans are actually the norm in most hospitals, there is an urgent need for an automatic planning algorithm that can be applied to a wide range of clinical situations. To address this issue, we developed an algorithm capable of automatically cropping target areas and setting optimization conditions for multiple diseases, known as the Auto Crop and Optimization Setup Algorithm (ACOSA). This paper presents the principles of ACOSA and conducts a preliminary comparative evaluation of its performance against existing solutions. Methods: The development of ACOSA utilized the Eclipse Script Application Programming Interface (ESAPI) scripting language provided by Eclipse. Based on the input prescriptions, the algorithm simulates the operations of a physicist, automatically crops the target areas, and sets appropriate optimization parameters. Retrospectively, 20 cases of glioma and head and neck cancers were selected. Without considering organ-at-risk dose limits, dose calculations were performed using both ACOSA and Eclipse’s built-in AutoCrop, and a dosimetric comparison was conducted. Results: In terms of target volume homogeneity index (HI) and D98, the AutoCrop group demonstrated slight superiority over the ACOSA group. However, the ACOSA group exhibited superior performance in conformity index (CI), gradient index (GI), D2, and particularly in parameters reﬂecting the rate of low-dose fall-off outside the target volume, including Ratio20, Ratio30, and Ratio40, when compared to the AutoCrop group. Conclusions: ACOSA can be reliably applied in clinical settings and demonstrates superiority over the AutoCrop module of the Eclipse planning system. Keywords ESAPI, multi-disease, assisted treatment planning, auto plan, user interface Received: 5 September 2025; revised: 12 November 2025; accepted: 9 December 2025 Introduction With the continuous advancement of radiotherapy technology, the proportion of three- dimensional conformal intensity-modulated plans has gradually increased. To improve the efﬁciency and quality of intensity-modulated plan design, extensive research on automatic plan design has emerged, achieving signiﬁcant progress. Early studies on dose prediction focused on predicting the dose distribution to Organs At Risk (OAR) based on the spatial relationship between OARs and target areas, thereby guiding plan design. 1,2 With the advent of Eclipse RapidPlan technology, it became possible to automatically add optimization parameters 1 Department of Radiation Oncology, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China 2Hebei Key Laboratory of Etiology Tracing and Individualized Diagnosis and Treatment for Digestive System Carcinoma, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China *Contributed equally to this work. Corresponding Author: Xiaoying Xue, PhD, Department of Radiotherapy, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Xinhua District, Shijiazhuang City, Hebei Province, China. Email: xxy0636@hebmu.edu.cn Creative Commons Non

FAQs

What problem does ACOSA address in radiotherapy auto-planning?

Commercial automatic planning modules are limited to single, conventional disease types, reducing usefulness for unconventional plans common across hospitals.

How does ACOSA work technically?

ACOSA uses the Eclipse Script Application Programming Interface (ESAPI) to simulate physicist operations: it automatically crops target areas and sets optimization parameters based on input prescriptions.

How did ACOSA perform compared with Eclipse AutoCrop?

ACOSA showed superiority in conformity index (CI), gradient index (GI), D2, and low-dose fall-off parameters (Ratio20, Ratio30, Ratio40), while AutoCrop was slightly better in HI and D98.

Sep 23, 2026

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