Evidence map›Paper›PMID 38428426›Full record

ArticleCell reports. Medicine2024

Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.

Ruishan Liu, Lisa Wang, Shemra Rizzo, Marius Rene Garmhausen, Navdeep Pal, Sarah Waliany, Sarah McGough, Yvonne G Lin, Zhi Huang, Joel Neal and 2 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07343024 (Methods of Identifying Effective Off-Guideline Treatments for Advanced Cancer Patients), which is not on this map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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.

NCT07343024 recruitingnot on this mapstarted 2026, after this paper: background citation

Methods of Identifying Effective Off-Guideline Treatments for Advanced Cancer Patients (CC N-of-1)

TypeobservationalSponsorCancer CommonsRan2026 to 2028Enrolled300ConditionsAdvanced Cancer
3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
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  6. Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025
    Review
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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

12 authors.

Ruishan LiuDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Computer Science, University of Southern California, Los Angeles, CA, USA.
Lisa WangGenentech, South San Francisco, CA, USA.
Shemra RizzoGenentech, South San Francisco, CA, USA.
Marius Rene GarmhausenGenentech, South San Francisco, CA, USA.
Navdeep PalGenentech, South San Francisco, CA, USA.
Sarah WalianySchool of Medicine, Stanford University, Stanford, CA, USA.
Sarah McGoughGenentech, South San Francisco, CA, USA.
Yvonne G LinGenentech, South San Francisco, CA, USA.
Zhi HuangDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Joel NealSchool of Medicine, Stanford University, Stanford, CA, USA.
Ryan CoppingGenentech, South San Francisco, CA, USA. Electronic address: copping.ryan@gene.com.
James ZouDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. Electronic address: jamesz@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients' response to treatments.

Indexed as

Antineoplastic AgentsNeoplasmsHumansOff-Label UseAntineoplastic Agents

Identifiers

PMID38428426
PMCPMC10983036

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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.