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Background: Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current status and progress of ultrasound-based AI models within hepatopancreatobiliary surgery have not been evaluated in literature. Therefore, this review aimed to provide an overview of ultrasound-based AI models used for hepatopancreatobiliary surgery, evaluating current advancements, validation, and predictive accuracies. Method: Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using AI models on ultrasound for patients undergoing hepatopancreatobiliary surgery. To be eligible for inclusion, studies needed to apply AI methods on ultrasound imaging for patients undergoing hepatopancreatobiliary surgery. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. Results: AI models have been primarily used within hepatopancreatobiliary surgery, to predict tumor recurrence, differentiate between tumoral tissues, and identify lesions during ultrasound imaging. Most studies have combined radiomics with convolutional neural networks, with AUCs up to 0.98. Conclusion: Ultrasound-based AI models have demonstrated promising accuracies in predicting early tumoral recurrence and even differentiating between tumoral tissue types during and after hepatopancreatobiliary surgery. However, prospective studies are required to evaluate if these results will remain consistent and externally valid. [ABSTRACT FROM AUTHOR]

Purpose: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. Methods: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). Results: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. Conclusion: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches. [ABSTRACT FROM AUTHOR]

Background the efficacy and outcomes of geriatric rehabilitation (GR) have previously been investigated. However, a systematic synthesis of the aspects that are important to patients regarding the quality of GR does not exist. Objective the aim of this scoping review was to systematically synthesise the patients' perspective on the quality of GR. Methods we followed the Scoping Review framework and gathered literature including a qualitative study design from multiple databases. The inclusion criteria were: a qualitative study design; a geriatric population; that patients had participated in a geriatric rehabilitation programme and that geriatric rehabilitation was assessed by the patient. The results sections of the included studies were analysed using a thematic analysis approach. Results twenty articles were included in this review. The main themes identified were: (i) the need for information about the rehabilitation process, (ii) the need for telling one's story, (iii) the need for support (physical, psychological, social and how to cope with limitations), (iv) the need for shared decision-making and autonomy, (v) the need for a stimulating rehabilitation environment and (vi) the need for rehabilitation at home. Conclusion in this study, we identified the aspects that determine the quality of rehabilitation from the patient's perspective, which may lead to a more holistic perspective on the quality of GR. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with HER2-positive metastatic breast cancer develop brain metastases in up to 30% of cases. The aim of this systematic review and meta-analysis was to determine the effect of different systemic therapies in patients with HER2-positive metastatic breast cancer and brain metastases, acknowledging the heterogeneity and sometimes low quality of 51 included studies. Tucatinib (combined with trastuzumab and capecitabine) and trastuzumab-deruxtecan appear to constitute the most effective systemic therapy, while pyrotinib might be an option in Asian patients. Preferably, future research will comprise of randomized controlled trials, including patients with active and/or inactive brain metastases. Aim: Patients with HER2-positive (HER2+) metastatic breast cancer (mBC) develop brain metastases (BM) in up to 30% of cases. Treatment of patients with BM can consist of local treatment (surgery and/or radiotherapy) and/or systemic treatment. We undertook a systematic review and meta-analysis to determine the effect of different systemic therapies in patients with HER2+ mBC and BM. Methods: A systematic search was performed in the databases PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection and the Wiley/Cochrane Library. Eligible articles included prospective or retrospective studies reporting on the effect of systemic therapy on objective response rate (ORR) and/or median progression free survival (mPFS) in patients with HER2+ mBC and BM. The timeframe within the databases was from inception to 19 January 2022. Fixed-effects meta-analyses were used. Quality appraisal was performed using the ROBINS-I tool. Results: Fifty-one studies were included, involving 3118 patients. Most studies, which contained the largest patient numbers, but also often carried a moderate-serious risk of bias, investigated lapatinib and capecitabine (LC), trastuzumab-emtansine (T-DM1) or pyrotinib. The best quality data and/or highest ORR were described with tucatinib (combined with trastuzumab and capecitabine, TTC) and trastuzumab-deruxtecan (T-DXd). TTC demonstrated an ORR of 47.3% in patients with asymptomatic and/or active BM. T-DXd achieved a pooled ORR of 64% (95% CI 43–85%, I2 0%) in a heavily pretreated population with asymptomatic BM (3 studies, n = 96). Conclusions: Though our meta-analysis should be interpreted with caution due to the heterogeneity of included studies and a related serious risk of bias, this review provides a comprehensive overview of all currently available systemic treatment options. T-Dxd and TTC that appear to constitute the most effective systemic therapy in patients with HER2+ mBC and BM, while pyrotinib might be an option in Asian patients. [ABSTRACT FROM AUTHOR]

The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia. A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database. Sixteen studies—involving 837 CRC patients and 1618 controls—were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73–91%) and 70% (95% CI 63–77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied. Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes. • Using urinary volatile organic compounds (VOCs), colorectal cancer (CRC) can be detected with 84% sensitivity and 70% specificity. • The most distinctive solitary urinary VOC was butanal, with an area under the curve of 0.98. • Combining FIT with urinary VOCs would detect 33% more CRCs. • VOCs elucidate underlying pathophysiologic processes. [ABSTRACT FROM AUTHOR]