Evidence map›Paper›PMID 42621907›Full record

ReviewCancer innovation2026

Clinical Advances in Breast Cancer: A Comprehensive Overview of Full-Course Disease Management.

Xuenan Peng, Yujing Tan, Fei Ma

Abstract readReview
In one paragraph

Review in Cancer innovation, 2026. 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

3 authors.

Xuenan PengDepartment of Medical Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China.
Yujing TanDepartment of Medical Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China.
Fei MaDepartment of Medical Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China.ORCID https://orcid.org/0000-0001-9432-1902

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The landscape of breast cancer (BC) treatment has reached a pivotal turning point in recent years, marked by a succession of breakthrough findings from landmark international clinical trials. These advances have fundamentally reshaped treatment paradigms across distinct molecular subtypes. In the field of hormone receptor-positive (HR-positive)/human epidermal growth factor receptor 2-negative (HER2-negative) BC, the introduction of novel targeted agents has continuously optimized therapeutic strategies. Meanwhile, the management of endocrine therapy resistance has evolved into an innovative framework characterized by multitarget therapeutic combination approaches. Concurrently, the extensive impact of the DESTINY-Breast series has constructed a stratified, precision medicine system covering the entire disease course of HER2-positive BC. In triple-negative breast cancer, the ASCENT-03 and ASCENT-04 trials have successfully positioned antibody-drug conjugates as a cornerstone of first-line treatment for advanced disease. This article focuses on the latest advances in BC, systematically reviewing recent clinical evidence and practical experience in medical oncology to provide a robust reference for clinical diagnosis and treatment.

Indexed as

breast cancerendocrine therapyimmunotherapytargeted therapytreatment advancement

Identifiers

PMID42621907
PMCPMC13487701

What OpenQuestion holds

Textmetadata
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.