Evidence map›Paper›PMID 42738300›Full record

ArticleCancers2026

Personalized Oncology: Organizing Cancer Treatment Around Patient Biology.

Julianna Lisziewicz, Oliver Wueseke, Franco Lori

Abstract read
In one paragraph

Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Julianna LisziewiczVERDI Solutions GmbH, 1010 Vienna, Austria.ORCID 0000-0002-3349-4495
Oliver WuesekeImmuLogics GmbH, 48155 Muenster, Germany.
Franco LoriVERDI Solutions GmbH, 1010 Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical guidelines remain the foundation of evidence-based oncology, translating population-derived evidence into treatment recommendations for defined patient groups. However, patients with rare cancers, complex tumor biology, or treatment-refractory disease may reach a point at which applicable evidence is limited, or guideline-recommended treatment options have been exhausted. We propose Personalized Oncology as a complementary framework for treatment selection in these situations. Rather than asking only which treatments benefited similar patients, Personalized Oncology asks which available treatment is expected to provide the most favorable benefit-risk profile for the individual patient. It does so by integrating population-derived medical evidence with patient-specific biological evidence generated from the individual cancer. Importantly, exhaustion of guideline-recommended treatments does not necessarily mean exhaustion of biologically supported treatment opportunities. The supporting evidence, rationale, alternatives, limitations, and uncertainty are documented to enable transparent clinical decision-making. Neither population-derived evidence nor patient-specific biological evidence determines in advance whether an individual patient will benefit. Treatment response must therefore be systematically monitored and incorporated into subsequent decisions. Personalized Oncology standardizes this process while preserving individualized clinical judgment. Systematic capture of each patient's biology, treatment, and outcome can create a continuous learning framework in which experience from individual patients informs future cancer care.

Indexed as

artificial intelligencecancer vaccinesimmunotherapypatient biologypersonalized oncologypersonalized treatmentprecision oncologyrare cancerstumor biology

Identifiers

PMID42738300
PMCPMC13565357

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.