Evidence map›Paper›PMID 35280799›Full record

ArticleFrontiers in oncology2022

A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.

Giuditta Chiloiro, Luca Boldrini, Francesco Preziosi, Davide Cusumano, Poonam Yadav, Angela Romano, Lorenzo Placidi, Jacopo Lenkowicz, Nicola Dinapoli, Michael F Bassetti and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
1.2field-weighted citation impact, top 21% 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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 11 citations in OpenAlex.

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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

12 authors at 4 institutions in 2 countries.

Giuditta ChiloiroDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Luca BoldriniDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Francesco PreziosiDipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy.
Davide CusumanoDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Poonam YadavDepartment of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States.
Angela RomanoDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Lorenzo PlacidiDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Jacopo LenkowiczDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Nicola DinapoliDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Michael F BassettiDepartment of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States.
Maria Antonietta GambacortaDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Vincenzo ValentiniDipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.
Istituti di Ricovero e Cura a Carattere Scientifico · ITAgostino Gemelli University Polyclinic · ITUniversity of Wisconsin–Madison · USUniversità Cattolica del Sacro Cuore · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the "delta radiomics" approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS). Methods: Patients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon-Mann-Whitney test and area under receiver operating characteristic (ROC) curve (AUC). Results: 48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92). Conclusions: The results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results.

Indexed as

delta radiomicsMRgRTneoadjuvant chemoradiotherapypredictive modelrectal cancer

Identifiers

PMID35280799
PMCPMC8907443
OpenAlexW4213449016

What OpenQuestion holds

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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.