Evidence map›Paper›PMID 40549137›Full record

ArticlePhysical and engineering sciences in medicine2025

A novel inverse treatment planning training method for inexperienced treatment planners.

Shadab Momin, Kirk Luca, Yang Lei, Chih-Wei Chang, Huiqiao Xie, Tiffany Kei, Jacob Adams, Tanisha Davis, Xiaofeng Yang, Ashesh Jani and 2 more

Abstract read
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Article in Physical and engineering sciences in medicine, 2025. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Shadab MominDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA. shadab.momin@emory.edu.
Kirk LucaDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Yang LeiDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Chih-Wei ChangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Huiqiao XieDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Tiffany KeiDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Jacob AdamsDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Tanisha DavisDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Xiaofeng YangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Ashesh JaniDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Justin RoperDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Jiahan ZhangDepartment of Radiation Oncology, The Mount Sinai Hospital, New York, NY, 10029, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowledge-based treatment planning (KBP) has been traditionally employed to improve consistency and efficiency in radiation treatment planning. This work introduces a unique use of the KBP model to build site-specific treatment planning modules to provide inexperienced treatment planners with planning intuitions and enhanced learning experience. Our experimental design divided seven planners into two main categories: (a) three planners with more than 6 months of experience and (b) four planners with less than a month of experience. Prior to going through the training module, each planner optimized a treatment plan until they were satisfied with the plan quality on two validation cases. These two plans effectively recorded their baseline planning performance. This was followed by the initiation of treatment planning training on three different training cases. The training process required each planner to first reach their best plan quality, which was then evaluated by the training module. The training module generates a spatial representation of areas of improvement in the form of structures with instructions regarding the current dose value and its ideal dose value. Each planner then focused on incorporating the suggestions made by the training module (i.e., iteration 1, 2…). After training, each planner transferred their knowledge from training to re-optimize the two validation cases. The performance of each planner was evaluated based on the improvements in plan quality before and after the training via clinically relevant dose-volume metrics. The results of this heuristic approach show plan quality improvements after the training for less experienced planners. For instance, the mean dose to bladder, rectum, and penile bulb was reduced by 3.0 Gy, 4.0 Gy, and 2.5 Gy, respectively. An average reduction of 4.6% and 8.2% was achieved for bladder V

Indexed as

Radiotherapy Planning, Computer-AssistedHumansMaleRadiotherapy DosageKnowledge based treatment planningProstate cancerRadiation treatment planningTreatment planning training

Identifiers

PMID40549137

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Registered trials

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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.