Evidence map›Paper›PMID 41847691›Full record

ArticleFrontiers in oncology2026

Knowledge-based planning for intensity-modulated proton therapy of the breast and chest wall with regional lymph nodes: development, validation, and comparative evaluation of two models.

Parker Anderson, Yihang Xu, Robert Kaderka, Jonathan Cyriac, Hayden Guerrero, Elizabeth Bossart, Cristiane Takita, Nesrin Dogan

Abstract read
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Article in Frontiers in oncology, 2026. 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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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Parker AndersonDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Yihang XuDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Robert KaderkaDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Jonathan CyriacDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Hayden GuerreroDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Elizabeth BossartDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Cristiane TakitaDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.
Nesrin DoganDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Knowledge-based planning (KBP) can improve efficiency and consistency in radiotherapy. In intensity-modulated proton therapy (IMPT) for breast cancer, particularly with regional lymph nodes, treatment planning remains complex. This study developed, validated, and compared two KBP models for IMPT of the breast and chest wall (CW) to enhance plan quality, reduce inter-planner variability, and streamline workflows. Materials and methods: Fifty patients (25 left-sided, 25 right-sided) previously treated with IMPT to the breast or CW including regional lymph nodes were used for model development. A combined model was trained on all patients, while side-specific models were developed separately for left- and right-sided treatments. Planning objectives were refined via iterative replanning of 20 training cases. For validation, 20 additional patients (10 left, 10 right) were evaluated using the respective models. Dosimetric metrics for target volumes and organs at risk (OARs) were compared among model-generated and clinical plans using paired t-tests ( Results: All KBP-generated plans met clinical constraints. Both models achieved target coverage comparable to clinical plans. The combined model produced cooler hot spots in axillary, supraclavicular, and internal mammary nodes, with modest improvements in OAR sparing (heart, esophagus, trachea, thyroid). The side-specific model showed similar benefits, particularly for esophagus and thyroid sparing. Between models, side-specific plans had cooler hot spots, while the combined model offered slightly better OAR sparing. Physician review deemed all KBP plans clinically acceptable, preferring the KBP plan in 11 of 20 cases. Conclusions: KBP models can generate clinically acceptable IMPT plans for breast and CW with regional lymph nodes, achieving comparable target coverage to clinical plans with modest improvements in OAR sparing and hot spot reduction, supporting their potential to streamline treatment planning.

Indexed as

breast cancerdosimetryintensity modulated proton therapyknowledge based planningmachine learning

Identifiers

PMID41847691
PMCPMC12989385

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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.