Evidence map›Paper›PMID 37064257›Full record

ReviewCentral European journal of urology2023

Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.

Pietro Tramanzoli, Daniele Castellani, Virgilio De Stefano, Carlo Brocca, Carlotta Nedbal, Giuseppe Chiacchio, Andrea Benedetto Galosi, Rodrigo Donalisio Da Silva, Jeremy Yuen-Chun Teoh, Ho Yee Tiong and 3 more

Open access · greenAbstract readReview
In one paragraph

Review in Central European journal of urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
2.1field-weighted citation impact, top 14% of its field
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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
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

13 authors at 6 institutions in 5 countries.

Pietro TramanzoliUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Daniele CastellaniUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Virgilio De StefanoUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Carlo BroccaUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Carlotta NedbalUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Giuseppe ChiacchioUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Andrea Benedetto GalosiUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Rodrigo Donalisio Da SilvaDivision of Urology, Denver Health Medical Center, University of Colorado, Denver, USA.
Jeremy Yuen-Chun TeohDepartment of Surgery, S.H.Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China.
Ho Yee TiongDepartment of Urology, National University Hospital, Singapore, Singapore.
Nithesh NaikDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Bhaskar K SomaniDepartment of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom.
Vineet GauharDepartment of Urology, Ng Teng Fong General Hospital, Singapore, Singapore.
Marche Polytechnic University · ITChinese University of Hong Kong · CNManipal Academy of Higher Education · INNational University Hospital · SGNg Teng Fong General Hospital · SGUniversity of Colorado Denver · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and grading of renal and bladder cancer. Material and methods: A literature search was performed in June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. Results: Twenty-two papers were included, 4 were pertinent to bladder cancer, and 18 to renal cancer. Radiomics outperforms the visual assessment by radiologists in contrast-enhanced computed tomography (CECT) to predict muscle invasion but are equivalent to CT reporting by radiologists in predicting lymph node metastasis. Magnetic resonance imaging (MRI) radiomics outperforms radiological reporting for lymph node metastasis. Radiomics perform better than radiologists reporting the probability of renal cell carcinoma, improving interreader concordance and performance. Radiomics also helps to determine differences in types of renal pathology and between malignant lesions from their benign counterparts. Radiomics can be helpful to establish a model for differentiating low-grade from high-grade clear cell renal cancer with high accuracy just from contrast-enhanced CT scans. Conclusions: Our review shows that radiomic models outperform individual reports by radiologists by their ability to incorporate many more complex radiological features.

Indexed as

computer-assisteddiagnosisneoplasm stagingradiomicsrenal neoplasmsurinary bladder neoplasms

Identifiers

PMID37064257
PMCPMC10091893
OpenAlexW4323345701

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.