Prognostic role of preoperative albumin-bilirubin score in posthepatectomy liver failure and mortality: a systematic review and meta-analysis

Pang, Qing; Zhou, Shuai; Liu, Shuangchi; Liu, Huichun

doi:10.1007/s13304-021-01080-w

Cited by 19 publications

(16 citation statements)

References 46 publications

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“…A preoperative CT scan is also needed in cases of high-grade injuries to evaluate the future liver remnant after resection to personalize the treatment strategy [93,94]. It is also important to underline that in general surgical procedures in an emergency context, an inefficient diagnosis may require an urgent laparoscopy [95][96][97].…”

Section: Discussionmentioning

confidence: 99%

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Brunese,

Avella,

Cappuccio

et al. 2024

JPM

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Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.

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Section: Discussionmentioning

confidence: 99%

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Brunese,

Avella,

Cappuccio

et al. 2024

JPM

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“…In recent years, there have been efforts to estimate the degree of HF using existing imaging and blood tests, such as aspartate aminotransferase-platelet ratio index (APRI) [29,30], and fibrosis index based on the four factors (FIB-4 index) [31]. Model for end-stage liver disease score (MELD) [32,33], albumin-bilirubin (ALBI) score [34] and Child-Pugh score have been associated with postoperative complications [35]. Type IV collagen and serum hyaluronic acid predict post-hepatectomy liver failure and correlate with HF stage [36][37][38][39].…”

Section: Discussionmentioning

confidence: 99%

Comparison between Intravoxel Incoherent Motion and Splenic Volumetry to Predict Hepatic Fibrosis Staging in Preoperative Patients

Arakane,

Okada,

Nakazawa

et al. 2023

Diagnostics

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Intravoxel incoherent motion (IVIM) and splenic volumetry (SV) for hepatic fibrosis (HF) prediction have been reported to be effective. Our purpose is to compare the HF prediction of IVIM and SV in 67 patients with pathologically staged HF. SV was divided by body surface area (BSA). IVIM indices, such as slow diffusion-coefficient related to molecular diffusion (D), fast diffusion-coefficient related to perfusion in microvessels (D*), apparent diffusion-coefficient (ADC), and perfusion related diffusion-fraction (f), were calculated by two observers (R1/R2). D (p = 0.718 for R1, p = 0.087 for R2) and D* (p = 0.513, p = 0.708, respectively) showed a poor correlation with HF. ADC (p = 0.034, p = 0.528, respectively) and f (p < 0.001, p = 0.007, respectively) decreased as HF progressed, whereas SV/BSA increased (p = 0.015 for R1). The AUCs of SV/BSA (0.649–0.698 for R1) were higher than those of f (0.575–0.683 for R1 + R2) for severe HF (≥F3–4 and ≥F4), although AUCs of f (0.705–0.790 for R1 + R2) were higher than those of SV/BSA (0.628 for R1) for mild or no HF (≤F0–1). No significant differences to identify HF were observed between IVIM and SV/BSA. SV/BSA allows a higher estimation for evaluating severe HF than IVIM. IVIM is more suitable than SV/BSA for the assessment of mild or no HF.

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“…Indeed, comorbidities and frailty should be managed in elderly patients to predict inter-individual variability [103,104]. It is a crucial point: an early detection of CRLM could improve long-term outcomes in this subset of patients, considering the mortality rate reported for major hepatectomies [71,105].. Future perspectives are linked to AI applications to predict liver failure or future liver remnants in the preoperative evaluation of patients affected by huge liver tumors [106,107].…”

Section: Chemotherapy Regimenmentioning

confidence: 99%

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

Avella,

Cappuccio,

Cappuccio

et al. 2023

Life

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Background: Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM. Methods: A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened. Results: We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT). Conclusions: Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.

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Prognostic role of preoperative albumin-bilirubin score in posthepatectomy liver failure and mortality: a systematic review and meta-analysis

Cited by 19 publications

References 46 publications

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Comparison between Intravoxel Incoherent Motion and Splenic Volumetry to Predict Hepatic Fibrosis Staging in Preoperative Patients

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

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