CyTran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation

Ristea, Nicolae-Cătălin; Miron, Andreea-Iuliana; Savencu, Olivian; Verga, Nicolae; Khan, Fahad Shahbaz; Ionescu, Radu Tudor

doi:10.1016/j.neucom.2023.03.072

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…Artificial intelligence (AI) techniques are often used to group (cluster) items [ 13 ], such that items within each group are more similar to each other than to items from other groups [ 14 , 15 , 16 ]. To discover such groups of research articles on multiple organ dysfunction syndrome (MODS), we conducted an AI-based analysis of the surveyed articles.…”

Section: Methodsmentioning

confidence: 99%

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

Ionescu (Miron),

Atasiei,

Ionescu

et al. 2024

Cancers

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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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Section: Methodsmentioning

confidence: 99%

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

Ionescu (Miron),

Atasiei,

Ionescu

et al. 2024

Cancers

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“…These influence the quality of life of oncology patients, especially concerning cognitive impairment, fatigue, or long-term pain, as reported by Lacourt and Heijnen in their study [59]. For breast cancer patients treated with adjuvant chemotherapy, neuroimaging studies have been conducted, revealing a range of structural and functional alterations in the brain, including changes in grey matter volume and density [60][61][62]. This could explain the cognitive deficits faced by breast cancer patients.…”

Section: Neurologic Dysfunctionmentioning

confidence: 96%

Assessing the Impact of Organ Failure and Metastases on Quality of Life in Breast Cancer Patients: A Prospective Study Based on Utilizing EORTC QLQ-C30 and EORTC QLQ-BR45 Questionnaires in Romania

Ionescu (Miron),

Anghel,

Antone-Iordache

et al. 2024

JPM

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Breast cancer (BC) significantly impacts the quality of life (QoL) of affected individuals. This study, conducted at Colțea Clinical Hospital, Bucharest, aimed to assess the impact of organ failures and metastases on QoL in breast cancer patients using EORTC QLQ-C30 and EORTC QLQ-BR45 questionnaires and the survival rate to understand the clinical journey and the quality of life status in breast cancer patients. From January 2019 to October 2022, a prospective, observational study surveyed 874 patients, revealing 201 fatalities, 66 refusals, and 607 eligible participants. Results indicated statistically significant differences in various QoL aspects for patients experiencing heart failure, including physical functioning, pain, insomnia, global health status, and overall summary score. Kidney failure exhibited significance in physical functioning for QLQ-C30 and body image, sexual functioning, and endocrine sexual symptoms for QLQ-BR45. Respiratory failure demonstrated significant differences across multiple QoL domains. Patients with bone metastases reported lower physical functioning (p = 0.006) and increased pain (p = 0.002). This study has revealed an overall 5-year life expectancy of 68.8%, with survival rates of 93.8% for Stage I, 86.3% for Stage II, and 77.2% for Stage III breast cancer. Metastatic cancer patients have shown a 35.6% survival rate over 45 months, with a median survival duration of 36 months. A significant limitation of our study was the administration of the questionnaire only once, preventing us from quantifying the impact of specific treatment types on quality of life. This study emphasizes the necessity of using standardized QoL assessments in clinical practice from the initial presentation to ongoing follow-up.

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“…Furthermore, AI-based methods for virtual contrast enhancement 11,24–30 could raise the diagnostic quality of noncontrast data in situations where contrast agent administration is contraindicated, such as in patients with severe allergies or, in case of CT imaging, patients with severe hyperthyroidism 31…”

mentioning

confidence: 99%

AI as a New Frontier in Contrast Media Research

Haubold,

Hosch,

Jost

et al. 2023

Invest Radiol

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Artificial intelligence (AI) techniques are currently harnessed to revolutionize the domain of medical imaging. This review investigates 3 major AI-driven approaches for contrast agent management: new frontiers in contrast agent dose reduction, the contrast-free question, and new applications. By examining recent studies that use AI as a new frontier in contrast media research, we synthesize the current state of the field and provide a comprehensive understanding of the potential and limitations of AI in this context. In doing so, we show the dose limits of reducing the amount of contrast agents and demonstrate why it might not be possible to completely eliminate contrast agents in the future. In addition, we highlight potential new applications to further increase the radiologist's sensitivity at normal doses. At the same time, this review shows which network architectures provide promising approaches and reveals possible artifacts of a paired image-to-image conversion. Furthermore, current US Food and Drug Administration regulatory guidelines regarding AI/machine learning–enabled medical devices are highlighted.

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CyTran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation

Cited by 14 publications

References 31 publications

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

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

Assessing the Impact of Organ Failure and Metastases on Quality of Life in Breast Cancer Patients: A Prospective Study Based on Utilizing EORTC QLQ-C30 and EORTC QLQ-BR45 Questionnaires in Romania

AI as a New Frontier in Contrast Media Research

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