Evidence map›Paper›PMID 38610225›Full record

ReviewHealthcare (Basel, Switzerland)2024

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment.

Massimiliano Bassi, Rita Vaz Sousa, Beatrice Zacchini, Anastasia Centofanti, Francesco Ferrante, Camilla Poggi, Carolina Carillo, Ylenia Pecoraro, Davide Amore, Daniele Diso and 4 more

Open access · goldAbstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. 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

14 authors at 1 institution in 1 country.

Massimiliano BassiDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0002-2722-4974
Rita Vaz SousaDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Beatrice ZacchiniDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Anastasia CentofantiDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Francesco FerranteDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0002-1580-6379
Camilla PoggiDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Carolina CarilloDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Ylenia PecoraroDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Davide AmoreDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Daniele DisoDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Marco AnileDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Tiziano De GiacomoDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0003-1061-5773
Federico VenutaDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Jacopo VannucciDivision of Thoracic Surgery, Department of General Surgery and Surgical Specialties "Paride Stefanini", Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy.
Policlinico Umberto I · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.

Indexed as

artificial intelligenceelderlylung cancermachine learningoctogenarianspreoperativeradiomics

Identifiers

PMID38610225
PMCPMC11011722
OpenAlexW4394581092

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.