Evidence map›Paper›PMID 34697169›Full record

ArticleIn vivo (Athens, Greece)

Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy.

Francesca De Felice, Valentino Valentini, Marco De Vincentiis, Cira Rosaria Tiziana Di Gioia, Daniela Musio, Aida Angela Tummolo, Ludovica Isabella Ricci, Valeria Converti, Silvia Mezi, Daniela Messineo and 12 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in In vivo (Athens, Greece). 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
1.3field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  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

22 authors at 2 institutions in 1 country.

Francesca De FeliceDepartment of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy; fradefelice@hotmail.it.
Valentino ValentiniDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Marco De VincentiisDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Cira Rosaria Tiziana Di GioiaDepartment of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Daniela MusioDepartment of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Aida Angela TummoloDepartment of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Ludovica Isabella RicciDepartment of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Valeria ConvertiDepartment of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Silvia MeziDepartment of Medical Oncology B, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Daniela MessineoDepartment of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Gianluca TenoreDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Marco Della MonacaDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Massimo RalliDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Francesco VulloDepartment of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Andrea BotticelliDepartment of Clinical and molecular oncology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Edoardo BraunerDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Paolo PrioreDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Romeo UmbertoDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Paolo MarchettiDepartment of Clinical and molecular oncology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Carlo Della RoccaDepartment of Medical Oncology B, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Antonella PolimeniDepartment of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Vincenzo TomboliniDepartment of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy.
Sapienza University of Rome · ITPoliclinico Umberto I · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimTo investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT). PATIENTS AND

methodsConsecutive SGMT patients were identified, and a data set included nine predictor variables and a dependent variable [disease-free survival (DFS) event] was standardized. The open-source R software was used. Survival outcomes were estimated by the Kaplan-Meier method. The random forest approach was used to select the important explanatory variables. A classification tree that optimally partitioned SGMT patients with different DFS rates was built.

resultsIn total, 54 SGMT patients were included in the final analysis. Five-year DFS was 62.1%. The top two important variables identified were pathologic node (pN) and pathologic tumor (pT). Based on these explanatory variables, patients were partitioned in three groups, including pN0, pT1-2 pN+ and pT3-4 pN+ with 26%, 38% and 75% probability of recurrence, respectively. Accordingly, 5-year DFS rates were 73.7%, 57.1% and 34.3%, respectively.

conclusionThe proposed decision tree algorithm is an appropriate tool to partition SGMT patients. It can guide decision-making and future research in the SGMT field.

Indexed as

Neoplasm Recurrence, LocalSalivary Gland NeoplasmsChemoradiotherapyChemoradiotherapy, AdjuvantDisease-Free SurvivalHumansMachine LearningRetrospective Studiesartificial intelligenceclassification treedecision treeDFSdisease-free survivalMachine learningrecurrencesalivary gland cancertumor

Identifiers

PMID34697169
PMCPMC8627718
OpenAlexW3210743573

What OpenQuestion holds

Textmetadata
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.