Evidence map›Paper›PMID 38841160›Full record

ReviewFrontiers in oncology2024

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma.

Kangning Meng, Guanzhong Gong, Rui Liu, Shanshan Du, Yong Yin

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 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

5 authors.

Kangning MengDepartment of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China.
Guanzhong GongDepartment of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, China.
Rui LiuDepartment of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China.
Shanshan DuDepartment of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, China.
Yong YinDepartment of Radiation Physics, Shandong First Medical University Affiliated Cancer Hospital, Shandong Cancer Hospital and Institute (Shandong Cancer Hospital), Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular Carcinoma (HCC) is one of the most common malignant neoplasms. With the advancement of technology, the precision of radiotherapy (RT) for HCC has considerably increased, and it is an indispensable modality in the comprehensive management of HCC. Some RT techniques increase the radiation dose to HCC, which decreases the radiation dose delivered to the surrounding normal liver tissue. This approach significantly improves the efficacy of HCC treatment and reduces the incidence of Radiation-induced Liver Disease (RILD). Clear imaging and precise determination of the Gross Target Volume (GTV) are prerequisites of precise RT of HCC. The main hindrances in determining the HCC GTV include indistinct tumor boundaries on imaging and the impact on respiratory motion. The integration of multimodal imaging, four-dimensional imaging, and artificial intelligence (AI) techniques can help overcome challenges for HCC GTV. In this article, the advancements in medical imaging and precise determination for HCC GTV have been reviewed, providing a framework for the precise RT of HCC.

Indexed as

GTV determinationhepatocellular carcinomaimage segmentation - deep learningmultimodal imagingradiotherapy

Identifiers

PMID38841160
PMCPMC11150548

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.