Evidence map›Paper›PMID 38982134›Full record

ArticleScientific reports2024

Cylindrical TGR as early radiological predictor of RLT progression in GEPNETs: a proof of concept.

Federica Scalorbi, Enrico Matteo Garanzini, Giuseppina Calareso, Chiara Marzi, Gabriella Di Rocco, Giovanni Argiroffi, Michela Baccini, Sara Pusceddu, Alfonso Marchianò, Marco Maccauro

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Triple-tracer PET (Scientific reports · 2026
    Article
  2. Article
  3. 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

10 authors.

Federica Scalorbi *Nuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.
Enrico Matteo Garanzini *Department of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.
Giuseppina CalaresoDepartment of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.
Chiara MarziDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Viale Morgagni 59, 50134, Florence, Italy. chiara.marzi@unifi.it.
Gabriella Di RoccoDepartment of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.
Giovanni ArgiroffiNuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.
Michela BacciniDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Viale Morgagni 59, 50134, Florence, Italy.
Sara PuscedduDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Alfonso MarchianòDepartment of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.
Marco MaccauroNuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to assess the predictive capability of cylindrical Tumor Growth Rate (cTGR) in the prediction of early progression of well-differentiated gastro-entero-pancreatic tumours after Radio Ligand Therapy (RLT), compared to the conventional TGR. Fifty-eight patients were included and three CT scans per patient were collected at baseline, during RLT, and follow-up. RLT response, evaluated at follow-up according to RECIST 1.1, was calculated as a percentage variation of lesion diameters over time (continuous values) and as four different RECIST classes. TGR between baseline and interim CT was computed using both conventional (approximating lesion volume to a sphere) and cylindrical (called cTGR, approximating lesion volume to an elliptical cylinder) formulations. Receiver Operating Characteristic (ROC) curves were employed for Progressive Disease class prediction, revealing that cTGR outperformed conventional TGR (area under the ROC equal to 1.00 and 0.92, respectively). Multivariate analysis confirmed the superiority of cTGR in predicting continuous RLT response, with a higher coefficient for cTGR (1.56) compared to the conventional one (1.45). This study serves as a proof of concept, paving the way for future clinical trials to incorporate cTGR as a valuable tool for assessing RLT response.

Indexed as

Disease ProgressionPancreatic NeoplasmsStomach NeoplasmsTomography, X-Ray ComputedAdultAgedFemaleHumansIntestinal NeoplasmsMaleMiddle AgedNeuroendocrine TumorsProof of Concept StudyROC CurveTumor BurdenDisease progressionGEP-NETsRECISTv1.1RLTTGR

Identifiers

PMID38982134
PMCPMC11233714

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.