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Background and Objectives: The current study aimed to explore the effect of nutritional prehabilitation on the clinical prognosis of elderly patients undergoing abdominal cancer surgery. Methods and Study Design: A retrospective study was conducted, where participants were divided into two groups based on whether they received oral nutritional supplementation at the first outpatient visit. The nutritional prehabilitation group (n=41) adopted a nutritional prehabilitation mode (a standard energy intake of 25-30 kcal/kg· d was recommended). While the control group (n=55) received routine care. All patients underwent laparoscopic surgery according to the National Comprehensive Cancer Network (NCCN) guidelines. Changes in nutritional status, complications, psychological status, symptoms, hospitalization days, and expenditures were compared between the two groups. Results: Both groups of patients experienced weight loss. However, the decline in body weight in the prehabilitation group was less than that in the control group (-1.88 vs. -2.56 kg, p < 0.001). In the comparison of nutritional prehabilitation group and control group, significant improvements were observed in the Hospital Anxiety Scale scores (5 vs. 5, p = 0.01) and MD Anderson Symptom Inventory scores (3 vs. 0, p < 0.001) respectively. The infection rate in the nutritional prehabilitation group was lower than that in the control group (17.1% vs. 36.4%, p = 0.04). Additionally, patients in the nutritional prehabilitation group had significantly fewer hospitalization days at discharge (14.3 vs. 17.1 days, p = 0.03). Conclusions: In elderly patients undergoing abdominal cancer surgery, a nutritional prehabilitation model may help maintain better physical and mental status, reduce infection rates, and shorten hospitalization days. [ABSTRACT FROM AUTHOR]

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Oncology. It discusses the use of magnetic resonance imaging (MRI) and artificial intelligence (AI) in adaptive radiotherapy for abdominal and pelvic cancer treatment. The editorial highlights the potential benefits of MRI-guided radiotherapy (MRIgRT) in reducing radiation-induced toxicity and the challenges of online delineation, treatment planning, and quality assurance. It also summarizes eight manuscripts published in the journal that explore the use of MRI and AI for online guidance in adaptive radiotherapy for abdominal and pelvic cancer treatment. The editorial aims to guide multidisciplinary teams in developing and enhancing the use of MRI and AI in online adaptive radiotherapy. [Extracted from the article]

The significance of nutritional care in the management of cancer, particularly in the surgical treatment of abdominal cancer, is increasingly acknowledged. Body composition analysis, such as the Bioelectric impedance assay (BIA), and functional tests, e.g., handgrip strength, are used when assessing nutritional status alongside general and nutritional history, clinical examination, and laboratory tests. The primary approach in nutritional care is individually adjusted nutritional counselling and the use of medical nutrition, especially oral nutritional supplements. The aim of the study was to investigate the effects of perioperative nutritional care on body composition and functional status in patients with carcinoma of the gastrointestinal tract, hepatobiliary system, and pancreas.

Background: The impact of regional anesthesia and analgesia (RAA) on long-term survival following cancer surgery is a topic of debate. The aim of this study was to investigate the effects of perioperative RAA on long-term oncological outcomes in patients undergoing major abdominal cancer surgery. Methods: The authors searched computerized databases and reference lists from inception to December 20, 2022. All studies that investigated the effects of perioperative RAA on long-term oncological outcomes following major abdominal cancer surgery were included. Using the inverse variance method with a random-effects model, hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Results: The systematic review included 51 retrospective studies, one prospective study, and three randomized controlled trials (RCTs), with a total of 95,046 patients. The results showed that perioperative RAA may improve long-term overall survival (HR: 0.85, 95% CI: 0.80 to 0.91, P = 0.00, I2 = 60.2%). However, there was no significant association between perioperative RAA and reduced cancer recurrence (HR: 0.98, 95% CI: 0.90 to 1.03, P = 0.31, I2 = 52.3%). When performing a pooled analysis of the data from the three RCTs, no statistically significant effect of RAA was found in either case. Conclusion: The systematic review suggests perioperative RAA may improve long-term overall survival but does not appear to reduce cancer recurrence in patients undergoing major abdominal cancer surgery. The limited number of RCTs included in this study did not confirm this finding, highlighting the need for further RCTs to corroborate these results.

Simple Summary: Worldwide gastrointestinal (GI) malignancies account for about 25% of the global cancer incidence. For some malignancies, screening programs, such as routine colon cancer screenings, have largely aided in the early diagnosis of those at risk. However, even after diagnosis, many GI malignancies lack robust biomarkers to serve as definitive staging and prognostic tools to aid in clinical decision-making. Radiomics uses high-throughput data to extract various features from medical images with the potential to aid personalized precision medicine. Machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. We reviewed the fundamental concepts of radiomics such as imaging data acquisition, lesion segmentation, feature design, and interpretation specific to GI cancer studies and assessed the clinical applications of radiomics and machine learning in diagnosis, staging, evaluation of tumor prognosis, and treatment response. Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities. [ABSTRACT FROM AUTHOR]

Abstract Background Medical treatment quality has been shown to be better in high volume than in low volume hospitals. However, this relationship has not yet been confirmed in abdominal cancer in Switzerland and is relevant for referral of patients and healthcare planning. Thus, the present study investigates the association between hospital volumes for surgical resections of colon, gastric, rectal, and pancreatic carcinomas and outcomes. Methods This retrospective analysis is based on anonymized claims data of patients with mandatory health insurance at Helsana Group, a leading health insurance in Switzerland. Outcome parameters were length of hospital stay, mortality and cost during the inpatient stay as well as at 1-year follow-up. Hospital volume information was derived from the Quality Indicators dataset provided by the Swiss Federal Office of Public Health. The impact of hospital volume on the different treatment outcomes was statistically tested using generalized estimating equations (GEE) models, taking into account the non-independence of observations from the same hospital. Results The studies included 2′859 resections in patients aged 18 years and older who were hospitalized for abdominal cancer surgery between 2014 and 2018. Colon resections were the most common procedures (n = 1′690), followed by rectal resections (n = 709). For rectal, colon and pancreatic resections, an increase in the mean number of interventions per hospital and a reduction of low volume hospitals could be observed. For the relationship between hospital volume and outcomes, we did not observe a clear dose-response relationship, as no significantly better outcomes were observed in the higher-volume category than in the lower-volume category. Even though a positive “routine effect” cannot be excluded, our results suggest that even hospitals with low volumes are able to achieve comparable treatment outcomes to larger hospitals. Conclusion In summary, this study increases transparency on the relationship between hospital volume and treatment success. It shows that simple measures such as defining a minimum number of procedures only might not lead to the intended effects if other factors such as infrastructure, the operating team or aggregation level of the available data are not taken into account.

Objective:Postoperative pulmonary complications (PPC) are one of the most important causes of postoperative morbidity and mortality. The ARISCAT Risk Index is a seven-variable regression model that classifies patients as low, intermediate, and high risk and is used to estimate the overall incidence of postoperative pulmonary complications. In our study, we aimed to evaluate the effectiveness of ASA and ARISCAT index in predicting pulmonary complications in after laparoscopic abdominal cancer surgery. Materials and Method:After the approval of Hospital Ethics Committee, 100 patients scheduled to have major laparoscopic abdominal cancer surgery were included in the study. Preoperative ASA and ARISCAT score, comorbidities, intraoperative fluid amount, ventilation parameters, pneumoperitoneum pressure and duration of surgery were recorded. Patients were followed up for pulmonary complications until discharge from hospital. The length of hospital stay was recorded. Results:Pulmonary complications developed in 11 patients postoperatively. PPC incidence was found similiar within ASA and ARISCAT risk groups (p> 0.05). There was no significant correlation between ASA and ARISCAT risk index and PPC (p= 0.23 and p= 0.89, respectively). The number of fluids administered intraoperatively was significantly higher in patients who developed PPC (p= 0.018). There was a significant correlation between sugical site and PPC development (p= 0.012). The duration of surgery and length of hospital stay was longer in patients with PPC (pË,0.05). Conclusion:We found that both ASA score and ARISCAT risk index are not sufficient to predict postoperative pulmonary complications in laparoscopic major abdominal cancer surgery, and the surgical site, long operation time and the volume of fluid used intraoperatively are important independent risk factors for pulmonary complications. [ABSTRACT FROM AUTHOR]

Abstract Background In radiotherapy, respiratory-induced tumor motion is typically measured using a single four-dimensional computed tomography acquisition (4DCT). Irregular breathing leads to inaccurate motion estimates, potentially resulting in undertreatment of the tumor and unnecessary dose to healthy tissue. The aim of the research was to determine if a daily pre-treatment 4DMRI-strategy led to a significantly improved motion estimate compared to single planning 4DMRI (with or without outlier rejection). Methods 4DMRI data sets from 10 healthy volunteers were acquired. The first acquisition simulated a planning MRI, the respiratory motion estimate (constructed from the respiratory signal, i.e. the 1D navigator) was compared to the respiratory signal in the subsequent scans (simulating 5–29 treatment fractions). The same procedure was performed using the first acquisition of each day as an estimate for the subsequent acquisitions that day (2 per day, 4–20 per volunteer), simulating a daily MRI strategy. This was done for three outlier strategies: no outlier rejection (NoOR); excluding 5% of the respiratory signal whilst minimizing the range (Min95) and excluding the datapoints outside the mean end-inhalation and end-exhalation positions (MeanIE). Results The planning MRI median motion estimates were 27 mm for NoOR, 18 mm for Min95, and 13 mm for MeanIE. The daily MRI median motion estimates were 29 mm for NoOR, 19 mm for Min95 and 15 mm for MeanIE. The percentage of time outside the motion estimate were for the planning MRI: 2%, 10% and 32% for NoOR, Min95 and MeanIE respectively. These values were reduced with the daily MRI strategy: 0%, 6% and 17%. Applying Min95 accounted for a 30% decrease in motion estimate compared to NoOR. Conclusion A daily MRI improved the estimation of respiratory motion as compared to a single 4D (planning) MRI significantly. Combining the Min95 technique with a daily 4DMRI resulted in a decrease of inclusion time of 6% with a 30% decrease of motion. Outlier rejection alone on a planning MRI often led to underestimation of the movement and could potentially lead to an underdosage. Trial registration: protocol W15_373#16.007

In 1848, Rudolf Ludwig Karl Virchow described an association of left supraclavicular lymphadenopathy with abdominal malignancy. The left supraclavicular lymph node later became commonly referred to as Virchow's node. Charles-Emile Troisier went on to describe the physical exam finding of an enlarged left supraclavicular lymph node, later termed Troisier's sign. Subsequent studies confirmed a predilection of abdominal and pelvic malignancies to preferentially metastasize to the left supraclavicular node. Identification of a pathologically enlarged left supraclavicular node raises the suspicion for abdominopelvic malignancy, particularly in the absence of right supraclavicular lymphadenopathy, and provides a safe and easy target for biopsy. Supraclavicular lymph nodes also represent a great target for diagnosis of metastatic thoracic malignancies, although thoracic malignancies can involve either right or left supraclavicular nodes and do not show a predilection for either. This article presents a review of the history, anatomy, pathophysiology, clinical significance, radiological appearance, and biopsy of Virchow's node. Key points are illustrated with relevant cases. [ABSTRACT FROM AUTHOR]