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Prediction of Subclinical and Clinical Multiple Organ Failure Dysfunction in Breast Cancer Patients-A Review Using AI Tools.

Andreea Iuliana IonescuDimitrie-Ionut AtasieiRadu Tudor IonescuFlavia UltimescuAndrei-Alexandru BarnonschiAlexandra-Valentina AnghelCătălin-Alexandru AnghelIonuț-Lucian Antone-IordacheRuxandra MitreAlexandra Maria BobolocuAndreea ZamfirHoria Dan LiscuSimona ConiacFlorica Șandru
Published in: Cancers (2024)
This review explores the interconnection between precursor lesions of breast cancer (typical ductal hyperplasia, atypical ductal/lobular hyperplasia) and the subclinical of multiple organ failure syndrome, both representing early stages marked by alterations preceding clinical symptoms, undetectable through conventional diagnostic methods. Addressing the question "Why patients with breast cancer exhibit a tendency to deteriorate", this study investigates the biological progression from a subclinical multiple organ failure syndrome, characterized by insidious but indisputable lesions, to an acute (clinical) state resembling a cascade akin to a waterfall or domino effect, often culminating in the patient's demise. A comprehensive literature search was conducted using PubMed, Google Scholar, and Scopus databases in October 2023, employing keywords such as "MODS", "SIRS", "sepsis", "pathophysiology of MODS", "MODS in cancer patients", "multiple organ failure", "risk factors", "cancer", "ICU", "quality of life", and "breast cancer". Supplementary references were extracted from the retrieved articles. This study emphasizes the importance of early identification and prevention of the multiple organ failure cascade at the inception of the malignant state, aiming to enhance the quality of life and extend survival. This pursuit contributes to a deeper understanding of risk factors and viable therapeutic options. Despite the existence of the subclinical multiple organ failure syndrome, current diagnostic methodologies remain inadequate, prompting consideration of AI as an increasingly crucial tool for early identification in the diagnostic process.
Keyphrases
  • risk factors
  • intensive care unit
  • systematic review
  • oxidative stress
  • squamous cell carcinoma
  • machine learning
  • depressive symptoms
  • physical activity
  • mechanical ventilation
  • free survival
  • squamous cell