Evidence map›Paper›PMID 42298779›Full record

ArticleJournal of applied clinical medical physics2026

Cross-anatomical evaluation of a deep-learning auto-contouring system: qualitative, geometric, and dosimetric validation.

Sara Endo, Masaki Nakamura, Takeshi Fujisawa, Hidehiro Hojo, Hidenobu Tachibana

Abstract readValidation Study
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sara EndoRadiation Safety and Quality Assurance Division, National Cancer Center Hospital East, Chiba, Japan.
Masaki NakamuraDepartment of Radiation Oncology, National Cancer Center Hospital East, Chiba, Japan.
Takeshi FujisawaDepartment of Radiation Oncology, National Cancer Center Hospital East, Chiba, Japan.
Hidehiro HojoDepartment of Radiation Oncology, National Cancer Center Hospital East, Chiba, Japan.
Hidenobu TachibanaRadiation Safety and Quality Assurance Division, National Cancer Center Hospital East, Chiba, Japan.

Funding

JSPS KAKENHI 23K28464
6 · The paper itself

Abstract

backgroundAccurate and consistent delineation of target and normal structures is essential for safe and effective radiotherapy. Compared with manual or atlas-based methods, deep learning-based auto-contouring has demonstrated improved efficiency and reduced interobserver variability. However, its performance can vary across anatomical regions and clinical contexts, and comprehensive multi-site validation remains limited. Existing studies have tended to assess only geometric similarity or a single region, providing insufficient insight into real-world usability and dosimetric influence. A systematic evaluation that integrates physician-based quality assessment, quantitative geometric metrics, and plan-level dosimetry across diverse anatomical sites is needed to support broader clinical implementation of deep learning-based auto-contouring for differentiation between target and normal structures. PURPOSE: To perform the first practice-oriented, cross-anatomical evaluation of the SYNAPSE Radiotherapy system (v1.5) and to assess its qualitative usability, geometric agreement (Dice similarity coefficient [DSC], mean distance to agreement [MDA], and maximum Hausdorff distance [HD]), and plan-level dosimetric impact across six anatomical sites.

methodsWe retrospectively analyzed data for 120 patients across six sites, namely, the head, head and neck, thorax, abdomen, male pelvis, and female pelvis. Thirty organs at risk were auto-segmented using the SYNAPSE Radiotherapy platform, and three board-certified radiation oncologists graded the clinical usability of the platform on a 4-point scale. Geometric agreement was quantified using the DSC, MDA, and maximum HD. Plan-level dosimetric comparisons were performed for representative targets. Institutional review board approval was obtained (number 2017-440).

resultsOverall, 76.1% of auto-contours were qualitatively judged to be clinically usable (score ≥3). Geometric thresholds were achieved for most structures (DSC > 0.8 in 54.8%, MDA < 2.5 mm in 71.5%, and maximum HD < 12.5 mm in 50%), with only 8/29 being major outliers. Auto-contours tended to be larger than manual contours in 72% of cases. Dosimetrically, 88.1% of organs showed dose-volume differences within ±5%, and dose constraint achievement rates were comparable between auto-contours and manual contours (93.9% vs. 95.%). Median doses to planning target volumes (e.g., prostate and iliac nodes) were also consistent, indicating minimal clinical impact.

conclusionsThe SYNAPSE Radiotherapy system showed high overall clinical usability with organ-dependent variability. Our findings expand the platform-specific evidence for the SYNAPSE Radiotherapy system by linking geometry to dosimetric impact and supporting clinical deployment with an organ-specific quality assurance policy, while highlighting contexts in which targeted expert review remains necessary for safe clinical translation.

Indexed as

Deep LearningImage Processing, Computer-AssistedNeoplasmsOrgans at RiskRadiotherapy Planning, Computer-AssistedFemaleHumansMaleRadiometryRadiotherapy DosageRadiotherapy, Intensity-ModulatedRetrospective Studiesdeep learning‐based auto‐contouringdosimetric impactgeometric accuracyorgans at risk (OARs)radiotherapy planning

Identifiers

PMID42298779
PMCPMC13269653

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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.