Evidence map›Paper›PMID 40336872›Full record

ReviewPrecision radiation oncology2023

Dual-Energy CT in Breast Cancer: Current Applications and Future Outlooks.

Shaolan Guo, Tianye Liu, Guobin Qu, Jian Xu, Qingzeng Liu, Qian Zhao, Zhao Bi, Wanhu Li, Jian Zhu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Precision radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Shaolan GuoShandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Tianye LiuShandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Guobin QuShandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Jian XuShandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.ORCID https://orcid.org/0000-0003-2257-4593
Qingzeng LiuShandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Qian ZhaoShandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Zhao BiShandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Wanhu LiShandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.
Jian ZhuShandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the most prevalent cancerous tumor in women, characterized by different subtypes and varying responses to treatment. The continued evolution of breast cancer diagnosis and management has resulted in a transition from a one-size-fits-all approach to a new era of personalized treatment plans. Therefore, it is essential to accurately identify the biological characteristics of breast tissue in order to minimize unnecessary biopsies of benign lesions and improve the overall clinical process, leading to reduced expenses and complications associated with invasive biopsy procedures. Challenges for future research include finding ways to predict the response of breast cancer patients to adjuvant systemic treatment. Dual-energy CT (DECT) is a new imaging technology integrating functional imaging and molecular imaging. Over the past decade, DECT has gained relevancy, especially in oncological radiology. This article proposed a literature review of the application and research status of DECT in breast cancer treatment strategy determination and prognosis prediction.

Indexed as

Breast cancerClinical diagnosisDual‐energy CTPrognosis predictionQuantitative parameters

Identifiers

PMID40336872
PMCPMC11935073

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.