Evidence map›Paper›PMID 35675722›Full record

ArticleComputer methods and programs in biomedicine2022

Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making.

Wenbo Sun, Dipesh Niraula, Issam El Naqa, Randall K Ten Haken, Ivo D Dinov, Kyle Cuneo, Judy Jionghua Jin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
    Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
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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

7 authors.

Wenbo SunDepartment of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA. Electronic address: sunwbgt@umich.edu.
Dipesh NiraulaDepartment of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA. Electronic address: Dipesh.Niraula@moffitt.org.
Issam El NaqaDepartment of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA. Electronic address: Issam.ElNaqa@moffitt.org.
Randall K Ten HakenDepartment of Radiation Oncology, University of Michigan, Ann Arbor, USA. Electronic address: rth@med.umich.edu.
Ivo D DinovDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.
Kyle CuneoDepartment of Radiation Oncology, University of Michigan, Ann Arbor, USA. Electronic address: kcuneo@umich.edu.
Judy Jionghua JinDepartment of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA. Electronic address: jhjin@umich.edu.

Funding

Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
NCI NIH HHS R01 CA233487
6 · The paper itself

Abstract

In the precision medicine era, there is a growing need for precision radiotherapy where the planned radiation dose needs to be optimally determined by considering a myriad of patient-specific information in order to ensure treatment efficacy. Existing artificial-intelligence (AI) methods can recommend radiation dose prescriptions within the scope of this available information. However, treating physicians may not fully entrust the AI's recommended prescriptions due to known limitations or at instances when the AI recommendation may go beyond physicians' current knowledge. This paper lays out a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. Towards this goal, Gaussian process (GP) models are integrated with deep neural networks (DNNs) to quantify the uncertainty of the treatment outcomes given by physicians and AI recommendations, respectively, which are further used as a guideline to educate clinical physicians and improve AI models performance. The proposed method is demonstrated in a comprehensive dataset where patient-specific information and treatment outcomes are prospectively collected during radiotherapy of 67 non-small cell lung cancer (NSCLC) patients and are retrospectively analyzed.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsArtificial IntelligenceClinical Decision-MakingHumansRetrospective StudiesArtificial intelligenceComputer model calibrationDecision makingGaussian process modelingPrecision medicine

Identifiers

PMID35675722
PMCPMC11058561

What OpenQuestion holds

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Registered trials

None linked

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.