Evidence map›Paper›PMID 34439136›Full record

ArticleCancers2021

Modelling Radiation-Induced Salivary Dysfunction during IMRT and Chemotherapy for Nasopharyngeal Cancer Patients.

Anna Cavallo, Nicola Alessandro Iacovelli, Nadia Facchinetti, Tiziana Rancati, Salvatore Alfieri, Tommaso Giandini, Alessandro Cicchetti, Carlo Fallai, Rossana Ingargiola, Lisa Licitra and 6 more

Abstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

16 authors.

Anna CavalloDepartment of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Nicola Alessandro IacovelliDepartment of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0003-2556-9079
Nadia FacchinettiDepartment of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Tiziana RancatiProstate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0002-7849-603X
Salvatore AlfieriDepartment of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Tommaso GiandiniDepartment of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Alessandro CicchettiProstate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0003-1062-2930
Carlo FallaiDepartment of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Rossana IngargiolaDepartment of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Lisa LicitraDepartment of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Laura LocatiDepartment of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Stefano CavalieriDepartment of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0003-1294-6859
Emanuele PignoliDepartment of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Domenico Attilio RomanelloDepartment of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Riccardo ValdagniProstate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Ester OrlandiDepartment of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.

Funding

Italian Ministry of Health. 5x1000-2016
6 · The paper itself

Abstract

backgroundRadiation-induced xerostomia is one of the most prevalent adverse effects of head and neck cancer treatment, and it could seriously affect patients' qualities of life. It results primarily from damage to the salivary glands, but its onset and severity may also be influenced by other patient-, tumour-, and treatment-related factors. We aimed to build and validate a predictive model for acute salivary dysfunction (aSD) for locally advanced nasopharyngeal carcinoma (NPC) patients by combining clinical and dosimetric factors.

methodsA cohort of consecutive NPC patients treated curatively with IMRT and chemotherapy at 70 Gy (2-2.12 Gy/fraction) were utilised. Parotid glands (cPG, considered as a single organ) and the oral cavity (OC) were selected as organs-at-risk. The aSD was assessed at baseline and weekly during RT, grade ≥ 2 aSD chosen as the endpoint. Dose-volume histograms were reduced to the Equivalent Uniform Dose (EUD). Dosimetric and clinical/treatment features selected via LASSO were inserted into a multivariable logistic model. Model validation was performed on two cohorts of patients with prospective aSD, and scored using the same schedule/scale: a cohort (NPC_V) of NPC patients (as in model training), and a cohort of mixed non-NPC head and neck cancer patients (HNC_V).

resultsThe model training cohort included 132 patients. Grade ≥ 2 aSD was reported in 90 patients (68.2%). Analyses resulted in a 4-variables model, including doses of up to 98% of cPG (cPG_D98%, OR = 1.04), EUD to OC with

conclusioncPG D98% and the high doses received by small OC volumes were found to have the most impact on grade ≥ 2 acute xerostomia, with age and smoking history acting as a dose-modifying factor. Findings on the development population were confirmed in two prospectively collected validation populations.

Indexed as

acute toxicitynasopharyngeal cancerNTCP modellingradiotherapyvalidation

Identifiers

PMID34439136
PMCPMC8392585

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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.