Evidence map›Paper›PMID 33178948›Full record

ReviewBJR open2019

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling.

Yi Luo, Huan-Hsin Tseng, Sunan Cui, Lise Wei, Randall K Ten Haken, Issam El Naqa

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BJR open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 pooled it
–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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  7. Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026
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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

6 authors.

Yi LuoDepartment of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA.ORCID 0000-0003-2519-5900
Huan-Hsin TsengDepartment of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA.
Sunan CuiDepartment of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA.
Lise WeiDepartment of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA.
Randall K Ten HakenDepartment of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA.
Issam El NaqaDepartment of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA.

Funding

TREATMENT VERIFICATION AND TREATMENT PLAN REFINEMENTP01CA059827 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BALTER, JAMES M, LAWRENCE, THEODORE S · 1993 to 2018
$37.9M
Combined radiation acoustics and ultrasound imaging for real-time guidance in radiotherapyR37CA222215 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2018 to 2024
$3.5M
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 P01 CA059827NCI NIH HHS R01 CA233487NCI NIH HHS R37 CA222215
6 · The paper itself

Abstract

Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes' prediction, but also needs to be made based on an informed understanding of the relationship among patients' characteristics, radiation response and treatment plans. As more patients' biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) ("black box") technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the "black box" stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.

Identifiers

PMID33178948
PMCPMC7592485

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.