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Ionescu Miron AI, Atasiei DI, Ionescu RT, Ultimescu F, Barnonschi AA, Anghel AV, Anghel CA, Antone-Iordache IL, Mitre R, Bobolocu AM, Zamfir A, Lișcu HD, Coniac S, Șandru F. Prediction of Subclinical and Clinical Multiple Organ Failure Dysfunction in Breast Cancer Patients-A Review Using AI Tools. Cancers (Basel) 2024; 16:381. [PMID: 38254870 DOI: 10.3390/cancers16020381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/07/2024] [Accepted: 01/13/2024] [Indexed: 01/24/2024] Open
Abstract
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.
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Affiliation(s)
- Andreea-Iuliana Ionescu Miron
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Department of Medical Oncology, Colțea Clinical Hospital, 030167 Bucharest, Romania
| | - Dimitrie-Ionut Atasiei
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Radu-Tudor Ionescu
- Department of Computer Science, University of Bucharest, 010041 Bucharest, Romania
| | - Flavia Ultimescu
- Department of Pathology, Institute of Oncology "Prof. Dr. Alexandru Trestioreanu", 022328 Bucharest, Romania
- Department of Pathological Anatomy, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Andrei-Alexandru Barnonschi
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Alexandra-Valentina Anghel
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Cătălin-Alexandru Anghel
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Ionuț-Lucian Antone-Iordache
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Ruxandra Mitre
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Department of Medical Oncology, Colțea Clinical Hospital, 030167 Bucharest, Romania
| | - Alexandra Maria Bobolocu
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Andreea Zamfir
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Horia-Dan Lișcu
- Department of Oncological Radiotherapy and Medical Imaging, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Department of Radiotherapy, Colțea Clinical Hospital, 030167 Bucharest, Romania
| | - Simona Coniac
- Department of Medical Oncology, Colțea Clinical Hospital, 030167 Bucharest, Romania
- Department of Endocrinology, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Florica Șandru
- Department of Dermatovenerology, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Department of Dermatology, Elias University Emergency Hospital, 011461 Bucharest, Romania
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Huang R, Wu W, Guo Y, Ou L, Gong X, Yang C, Lei R. Development of a model for predicting mortality of breast cancer admitted to Intensive Care Unit. Afr Health Sci 2022; 22:155-165. [PMID: 36910387 PMCID: PMC9993303 DOI: 10.4314/ahs.v22i3.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background There is still not a mortality prediction model built for breast cancer admitted to intensive care unit (ICU). Objectives We aimed to build a prognostic model with comprehensive data achieved from eICU database. Methods Outcome was defined as all-cause in-hospital mortality. Least absolute shrinkage and selection operator (LASSO) was conducted to select important variables which were then taken into logistic regression to build the model. Bootstrap method was then conducted for internal validation. Results 448 patients were included in this study and 79 (17.6%) died in hospital. Only 5 items were included in the model and the area under the curve (AUC) was 0.844 (95% confidence interval [CI]: 0.804-0.884). Calibration curve and Brier score (0.111, 95% CI: 0.090-0.127) showed good calibration of the model. After internal validation, corrected AUC and Brier score were 0.834 and 0.116. Decision curve analysis (DCA) also showed effective clinical use of the model. The model can be easily assessed on website of https://breastcancer123.shinyapps.io/BreastCancerICU/. Conclusions The model derived in this study can provide an accurate prognosis for breast cancer admitted to ICU easily, which can help better clinical management.
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Affiliation(s)
- Renfeng Huang
- Department of head, neck and breast surgery, Yue Bei People's Hospital, Shaoguan, China
| | - Wanming Wu
- Department of head, neck and breast surgery, Yue Bei People's Hospital, Shaoguan, China
| | - Yan Guo
- Department of ophthalmology, Yue Bei People's Hospital, Shaoguan, China
| | - Linyang Ou
- Department of head, neck and breast surgery, Yue Bei People's Hospital, Shaoguan, China
| | - Xumeng Gong
- Department of head, neck and breast surgery, Yue Bei People's Hospital, Shaoguan, China
| | - Chuansheng Yang
- Department of head, neck and breast surgery, Yue Bei People's Hospital, Shaoguan, China
| | - Ruiwen Lei
- Department of head, neck and breast surgery, Yue Bei People's Hospital, Shaoguan, China
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