Evidence map›Paper›PMID 41281625›Full record

ArticleClinical and translational radiation oncology2026

Deep learning [

Lorenzo Lo Faro, Hubert S Gabryś, Simon Burgermeister, Daniel Abler, Maksym Fritsak, Maiwand Ahmadsei, Ciro Franzese, Adrien Depeursinge, Michel A Cuendet, Stephanie Tanadini-Lang and 4 more

Abstract read
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In one paragraph

Article in Clinical and translational radiation oncology, 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

14 authors.

Lorenzo Lo FaroDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Hubert S GabryśDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Simon BurgermeisterDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Daniel AblerDept. of Oncology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
Maksym FritsakDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Maiwand AhmadseiDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Ciro FranzeseDept. of Radiotherapy & Radiosurgery, IRCCS Humanitas Hospital, Via Manzoni, Rozzano, Milan, Italy.
Adrien DepeursingeInstitute of Information Systems, University of Applied Sciences Western Switzerland, Sierre, Switzerland.
Michel A CuendetDept. of Oncology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
Stephanie Tanadini-LangDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Panagiotis BalermpasDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Marta ScorsettiDept. of Radiotherapy & Radiosurgery, IRCCS Humanitas Hospital, Via Manzoni, Rozzano, Milan, Italy.
Matthias GuckenbergerDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.
Sebastian M ChristDept. of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: Artificial intelligence is increasingly used in radiation oncology, yet its application for tumor burden (TB) estimation remains limited. This study evaluated the performance of a [ Materials and methods: This retrospective study included 165 stage IV melanoma patients who underwent [ Results: PARS demonstrated an overall recall (sensitivity) of 68.9 %, though with modest precision (46.8 %). Performance was location-dependent, with highest precision observed for lung lesions (74.0 %) and lowest for bone lesions (32.9 %). For lesions detected by both methods, PARS tended to underestimate lesion volumes by an average (median) of 0.9 cc (median relative percentage difference (MRPD) =  -34.3 %), with a good agreement (intraclass correlations coefficient (ICC) = 0.77). The global TB in the whole cohort was overestimated by 28.3 %, but patient-level TB was on average (median) underestimated by 1.1 cc (MRPD =   -18.4 %) with high variability with a median absolute relative percentage difference (MARPD) = 68.6 %) and poor agreement (intraclass correlation coefficient (ICC) = 0.28). Conclusions: PARS shows potential for treatment decision support with moderate accuracy in lesion detection and lesion volume estimation, but demonstrates significant variability in TB estimation, highlighting the need for further model refinements before clinical adoption.

Indexed as

AutosegmentationDeep-learningMetastatic melanoma

Identifiers

PMID41281625
PMCPMC12639252

What OpenQuestion holds

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Registered trials

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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.