Evidence map›Paper›PMID 40563630›Full record

ReviewCancers2025

New Approaches in Radiotherapy.

Matthew Webster, Alexander Podgorsak, Fiona Li, Yuwei Zhou, Hyunuk Jung, Jihyung Yoon, Olga Dona Lemus, Dandan Zheng

Abstract readReview
In one paragraph

Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. c-Medical sciences (Basel, Switzerland) · 2026
    Review
  10. Gender disparities in historical context, workforce structure, and decision-making.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
    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

8 authors.

Matthew WebsterDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.ORCID 0009-0008-4306-5441
Alexander PodgorsakDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.ORCID 0000-0001-6351-703X
Fiona LiDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.ORCID 0000-0002-2423-6953
Yuwei ZhouDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.
Hyunuk JungDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.ORCID 0000-0002-2004-6925
Jihyung YoonDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.ORCID 0000-0001-8588-3380
Olga Dona LemusDepartment of Radiation Oncology, University of Miami, Coral Gables, FL 33146, USA.ORCID 0000-0002-8424-1885
Dandan ZhengDepartment of Radiation Oncology, University of Rochester, Rochester, NY 14627, USA.ORCID 0000-0003-2259-1633

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiotherapy (RT) has undergone transformative advancements since its inception over a century ago. This review highlights the most promising and impactful innovations shaping the current and future landscape of RT. Key technological advances include adaptive radiotherapy (ART), which tailors treatment to daily anatomical changes using integrated imaging and artificial intelligence (AI), and advanced image guidance systems, such as MR-LINACs, PET-LINACs, and surface-guided radiotherapy (SGRT), which enhance targeting precision and minimize collateral damage. AI and data science further support RT through automation, improved segmentation, dose prediction, and treatment planning. Emerging biological and targeted therapies, including boron neutron capture therapy (BNCT), radioimmunotherapy, and theranostics, represent the convergence of molecular targeting and radiotherapy, offering personalized treatment strategies. Particle therapies, notably proton and heavy ion RT, exploit the Bragg peak for precise tumor targeting while reducing normal tissue exposure. FLASH RT, delivering ultra-high dose rates, demonstrates promise in sparing normal tissue while maintaining tumor control, though clinical validation is ongoing. Spatially fractionated RT (SFRT), stereotactic techniques and brachytherapy are evolving to treat challenging tumor types with enhanced conformality and efficacy. Innovations such as 3D printing, Auger therapy, and hyperthermia are also contributing to individualized and site-specific solutions. Across these modalities, the integration of imaging, AI, and novel physics and biology-driven approaches is redefining the possibilities of cancer treatment. This review underscores the multidisciplinary and translational nature of modern RT, where physics, engineering, biology, and informatics intersect to improve patient outcomes. While many approaches are in various stages of clinical adoption and investigation, their collective impact promises to redefine the therapeutic boundaries of radiation oncology in the coming decade.

Indexed as

adaptive radiotherapyadvanced image guidanceartificial intelligence and data scienceboron neutron capturebrachytherapyflash radiotherapyheavy ion radiotherapyproton radiotherapyradioimmunotherapyspatially fractionated radiotherapystereotactic radiotherapytheranostics

Identifiers

PMID40563630
PMCPMC12190917

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.