Article in Cancers, 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.
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
13 authors.
Antonino GuerrisiRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.
Maria Teresa MaccalliniDepartement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy.
Italia FalconeSAFU, Department of Research, Advanced Diagnostics and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.ORCID 0000-0003-2796-0792
Alessandro ValentiRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.ORCID 0009-0001-1327-9296
Ludovica MiseoRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.
Sara UnganiaMedical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy.ORCID 0000-0001-7606-958X
Vincenzo DolcettiDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.
Fabio ValentiUOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.ORCID 0000-0002-1098-7705
Marianna CerroDepartement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy.
Flora DesiderioRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.ORCID 0000-0002-8801-0259
Fabio CalabròMedical Oncology 1, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.ORCID 0000-0002-7863-3540
Virginia FerraresiSarcomas and Rare Tumors Departmental Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.
Michelangelo RussilloSarcomas and Rare Tumors Departmental Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.
Funding
Italian Ministry of Health GR-2019-12369697Ricerca Corrente ISG RC ISG 2025
6 · The paper itself
Abstract
PubMed holds no abstract for this paper.
Indexed as
artificial intelligencemetastatic melanomapatients stratificationprognosisradiomics features
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.
Metastatic Melanoma Prognosis Prediction Using a TC Radiomic-Based Machine Learning Model: A Preliminary Study. · full record | OpenQuestion