Evidence map›Paper›PMID 38165207›Full record

ArticleActa otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale2024

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling.

Claudia Montenegro, Alberto Paderno, Marco Ravanelli, Carlotta Pessina, Fatima-Ezzahra Nassih, Davide Lancini, Francesca Del Bon, Davide Mattavelli, Davide Farina, Cesare Piazza

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

Article in Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.9field-weighted citation impact, top 11% 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, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Predictive Modelling Using Thyroid Cartilage Segmentation and Radiomic Features: A Feasibility Study.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2025
    Article
  5. Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025
    Article
  6. Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2024
    Article
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 at 4 institutions in 2 countries.

Claudia MontenegroUnit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy.
Alberto PadernoUnit of Otorhinolaryngology, IRCCS Humanitas Research Hospital, Milan, Italy.
Marco RavanelliDepartment of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, School of Medicine, Brescia, Italy.
Carlotta PessinaDepartment of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, School of Medicine, Brescia, Italy.
Fatima-Ezzahra NassihUnit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy.
Davide LanciniUnit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy.
Francesca Del BonUnit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy.
Davide MattavelliUnit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy.
Davide FarinaDepartment of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, School of Medicine, Brescia, Italy.
Cesare PiazzaUnit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy.
Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia · ITUniversity of Brescia · ITIRCCS Humanitas Research Hospital · ITSurgical Specialties (Canada) · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival. Materials and methods: We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software. Results: The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (>670 mm Conclusions: Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor.

Indexed as

Laryngeal NeoplasmsNeoplasm InvasivenessThyroid CartilageAdultAgedAged, 80 and overCarcinoma, Squamous CellFemaleHumansMaleMiddle AgedNeoplasm StagingPrognosisRetrospective Studiescartilage infiltrationlaryngeal cancermachine learningpredictive modelprognostic factorsthyroid cartilage

Identifiers

PMID38165207
PMCPMC11166214
OpenAlexW4390508004

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.