Evidence map›Paper›PMID 33197530›Full record

ArticleInternational journal of radiation oncology, biology, physics2021

Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance.

K Hasse, J Scholey, B P Ziemer, Y Natsuaki, O Morin, T D Solberg, E Hirata, G Valdes, A Witztum

Abstract read
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

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.

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

7 citing papers in PubMed.

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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

9 authors.

K HasseDepartment of Radiation Oncology, University of California, San Francisco, California. Electronic address: Katelyn.hasse@ucsf.edu.
J ScholeyDepartment of Radiation Oncology, University of California, San Francisco, California.
B P ZiemerDepartment of Radiation Oncology, University of California, San Francisco, California.
Y NatsuakiDepartment of Radiation Oncology, University of California, San Francisco, California.
O MorinDepartment of Radiation Oncology, University of California, San Francisco, California.
T D SolbergFDA, Washington, District of Columbia.
E HirataDepartment of Radiation Oncology, University of California, San Francisco, California.
G ValdesDepartment of Radiation Oncology, University of California, San Francisco, California.
A WitztumDepartment of Radiation Oncology, University of California, San Francisco, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeOur purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019. METHODS AND MATERIALS: In this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements.

resultsIn the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement.

conclusionsIt is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.

Indexed as

Machine LearningRadiotherapy DosageROC CurveHumansQuality Assurance, Health CareRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-Assisted

Identifiers

PMID33197530
PMCPMC9758537

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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.