Expression of Concern: De Bari et al. Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer? Cancers 2022, 14, 4043

In this notice, the <i>Cancers</i> Editorial Office alerts readers to concerns related to this article [...].

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Publication Details

Journal
Cancers
Published
2025-04-23
DOI
https://doi.org/10.3390/cancers17091400
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Expression of Concern: De Bari et al. Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer? Cancers 2022, 14, 4043

Cancers Editorial Office
Cancers
Radiomics and Machine Learning in Medical Imaging
article

Expression of Concern: De Bari et al. Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer? Cancers 2022, 14, 4043

Cancers Editorial Office
article en

Abstract

In this notice, the <i>Cancers</i> Editorial Office alerts readers to concerns related to this article [...].

CancersVol. 17(9)
Multidisciplinary Digital Publishing Institute (Switzerland) (CH)
Openalex Percentile: Top 87%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Expression of Concern: De Bari et al. Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer? Cancers 2022, 14, 4043 — Cancers Editorial Office · Cancers (2025) | TGRS Research Map | TGRS