Journal of Personalized Medicine (Jan 2023)

Gender Medicine in Clinical Radiology Practice

  • Giuliana Giacobbe,
  • Vincenza Granata,
  • Piero Trovato,
  • Roberta Fusco,
  • Igino Simonetti,
  • Federica De Muzio,
  • Carmen Cutolo,
  • Pierpaolo Palumbo,
  • Alessandra Borgheresi,
  • Federica Flammia,
  • Diletta Cozzi,
  • Michela Gabelloni,
  • Francesca Grassi,
  • Vittorio Miele,
  • Antonio Barile,
  • Andrea Giovagnoni,
  • Nicoletta Gandolfo

DOI
https://doi.org/10.3390/jpm13020223
Journal volume & issue
Vol. 13, no. 2
p. 223

Abstract

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Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

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