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.

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment / Bassi, Massimiliano; Vaz Sousa, Rita; Zacchini, Beatrice; Centofanti, Anastasia; Ferrante, Francesco; Poggi, Camilla; Carillo, Carolina; Pecoraro, Ylenia; Amore, Davide; Diso, Daniele; Anile, Marco; De Giacomo, Tiziano; Venuta, Federico; Vannucci, Jacopo. - In: HEALTHCARE. - ISSN 2227-9032. - 12:7(2024). [10.3390/healthcare12070803]

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

Bassi, Massimiliano;Vaz Sousa, Rita;Zacchini, Beatrice;Centofanti, Anastasia;Ferrante, Francesco;Poggi, Camilla;Carillo, Carolina;Pecoraro, Ylenia;Amore, Davide;Diso, Daniele;Anile, Marco;De Giacomo, Tiziano;Venuta, Federico;Vannucci, Jacopo
2024

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.
2024
artificial intelligence; elderly; lung cancer; machine learning; octogenarians; preoperative; radiomics
01 Pubblicazione su rivista::01a Articolo in rivista
Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment / Bassi, Massimiliano; Vaz Sousa, Rita; Zacchini, Beatrice; Centofanti, Anastasia; Ferrante, Francesco; Poggi, Camilla; Carillo, Carolina; Pecoraro, Ylenia; Amore, Davide; Diso, Daniele; Anile, Marco; De Giacomo, Tiziano; Venuta, Federico; Vannucci, Jacopo. - In: HEALTHCARE. - ISSN 2227-9032. - 12:7(2024). [10.3390/healthcare12070803]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1714103
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