Evidence map›Paper›PMID 42327577›Full record

ReviewJournal of Cancer2026

Development of nasopharyngeal carcinoma target delineation: from two-dimensional radiotherapy to adaptive precision radiotherapy.

Xiaojing Yang, Lijun Liu, Huicai Zhang, Hanru Ren, Jie Fu

Abstract readReview
In one paragraph

Review in Journal of Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

5 authors.

Xiaojing YangDepartment of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lijun LiuDepartment of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Huicai ZhangDepartment of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hanru RenDepartment of Orthopedics, Shanghai Pudong Hospital, Pudong Medical Center, Fudan University, Shanghai, China.
Jie FuDepartment of Radiation Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nasopharyngeal carcinoma (NPC) is mainly treated with radiotherapy. With the advancements of imaging and radiotherapy techniques, the delineation of NPC target areas has evolved from empirical field setting in the two-dimensional era to three-dimensional conformal radiotherapy (3D-CRT), intensity modulated radiotherapy (IMRT), and image-guided adaptive radiotherapy (ART), significantly improving treatment accuracy and safety. This review systematically explores the development process of target area delineation in NPC, focusing on the evolution and standardization of target areas in the nasopharynx and neck lymph nodes, and combines the role of multimodal imaging such as MRI and PET-CT in target area delineation. The article further examines the clinical response strategies and research trends of target area changes after induction chemotherapy (IC), and looks forward to the potential of ART and artificial intelligence in future target area delineation. The review aims to provide clinical practitioners with a scientific, rational, and personalized approach to target delineation, optimizing treatment plans and improving clinical outcomes.

Indexed as

Nasopharyngeal carcinomaRadiotherapyTarget delineation

Identifiers

PMID42327577
PMCPMC13280625

What OpenQuestion holds

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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.