Evidence map›Paper›PMID 40269312›Full record

ReviewBritish journal of cancer2025

Optimal timing of cancer treatments: a call for emerging evidence from clinical trials and real-world studies.

Jianrong Zhang, Rebecca Venchiarutti, Xiaofei Wang, Qihua He

Abstract readReview
In one paragraph

Review in British journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

4 authors.

Jianrong ZhangMelbourne Medical School & Centre for Cancer Research, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia. jianrong.zhang@unimelb.edu.au.ORCID http://orcid.org/0000-0003-4688-5222
Rebecca VenchiaruttiDepartment of Head and Neck Surgery, Chris O'Brien Lifehouse, Camperdown, NSW, Australia.
Xiaofei WangDepartment of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Qihua HeDepartment of Thoracic Surgery and Oncology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer treatment has entered the era of personalised or precision medicine. Biomarker-driven therapies provide improved treatment efficacy and manageable toxicity profiles compared to systemic standard-of-care therapies. They also drive the development of combining non-surgical treatments, extending indications to early-stage tumours and further refining treatment lines with more precise options. The current treatment landscape, however, has introduced a complexity of approaches to cancer treatment, including the optimal timing of when to initiate and discontinue these treatments. Of note, treatment timing usually lacks evaluation in clinical trials and can be variable in real-world settings due to the impacts of medical, healthcare, and social factors. Given that more patients can benefit from multi-modality strategies, a better understanding of the prognostic impact of treatment-to-treatment intervals (TTIs) - the intervals between combined treatments and between treatment lines - is needed. Studies for this purpose can rely on existing trial and real-world data and be context-specific for treatment options, therapeutic settings, cancer types and biomarkers, healthcare settings or systems. This perspective article calls for emerging evidence of the optimal timing of cancer treatments. We anticipate that new studies on the optimal timing will bring new insights into how to better use cancer treatments, further improving treatment efficacy.

Indexed as

NeoplasmsTime-to-TreatmentClinical Trials as TopicHumansPrecision MedicineTime Factors

Identifiers

PMID40269312
PMCPMC12152163

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.