Purpose Preoperative nutrition is beneficial for malnourished cancer patients. Yet, there is little evidence whether or not it should be given to nonmalnourished patients. The aim of this study was to assess the need to introduce preoperative nutritional support in patients without malnutrition at qualification for surgery. Methods This was a prospective, two-arm, randomized, controlled, open-label study. Patients in interventional group received nutritional supplementation for 14 days before surgery, while control group kept on to their everyday diet. Each patient’s nutritional status was assessed twice—at qualification (weight loss in 6 months, laboratory parameters: albumin, total protein, transferrin, and total lymphocyte count) and 1 day before surgery (change in body weight and laboratory parameters). After surgery, all patients were followed up for 30 days for postoperative complications. Results Fifty-four patients in interventional and 48 in control group were analyzed. In postoperative period, patients in control group suffered from significantly higher (p < 0.001) number of serious complications compared with patients receiving nutritional supplementation. Moreover, levels of all laboratory parameters declined significantly (p < 0.001) in these patients, while in interventional arm were stable (albumin and total protein) or raised (transferrin and total lymphocyte count). Conclusions Preoperative nutritional support should be introduced for nonmalnourished patients as it helps to maintain proper nutritional status and reduce number and severity of postoperative complications compared with patients without such support.

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.

The role of adjuvant chemotherapy in stage T2-T3N0 colon cancer (CC) is controversial and there are currently no reliable factors allowing for individual selection of patients with high risk of relapse for such therapy. We searched for microRNA-based signature with prognostic significance in this group. We assessed by qRT-PCR expression of 754 microRNAs (miRNAs) in tumour samples from 85 stage pT2-3N0 CC patients treated with surgery alone. MiRNA expression was compared between two groups of patients: 40 and 45 patients who did and did not develop distant metastases after resection, respectively. Additionally, miRNA expression was compared between CC and normal colon mucosa samples and between the mismatch repair (MMR) competent and deficient tumours. Low expression of miR-1300 and miR-939 was significantly correlated with shorter distant metastasis-free survival (DMFS) in Cox univariate analysis (p.adjusted = 0.049). The expression signature of five miRNAs (miR-1296, miR-135b, miR-539, miR-572 and miR-185) was found to be prognostic [p = 1.28E−07, HR 8.4 (95 % CI: 3.81–18.52)] for DMFS and cross-validated in a leave-one-out analysis, with the sensitivity and specificity of 74 and 78 %, respectively. The expression of miR-592 was significantly associated with the MMR status (p.adjusted <0.01). The expression of several novel miRNAs were found to be tumour specific, e.g. miR-888, miR-523, miR-18b, miR-302a, miR-423-5p, miR-582-3p (p < 0.05). We developed a miRNA expression signature that may be predictive for the risk of distant relapse in early stage CC and confirmed previously reported association between miR-592 expression and MMR status.

Trustworthiness has become a key concept for the ethical development and application of artificial intelligence (AI) in medicine. Various guidelines have formulated key principles, such as fairness, robustness, and explainability, as essential components to achieve trustworthy AI. However, conceptualizations of trustworthy AI often emphasize technical requirements and computational solutions, frequently overlooking broader aspects of fairness and potential biases. These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. This viewpoint article presents an interdisciplinary approach to analyzing trust in AI and trustworthy AI within the medical context, focusing on (1) social sciences and humanities conceptualizations and legal perspectives on trust and (2) their implications for trustworthy AI in health care. It focuses on real-world challenges in medicine that are often underrepresented in theoretical discussions to propose a more practice-oriented understanding. Insights were gathered from an interdisciplinary workshop with experts from various disciplines involved in the development and application of medical AI, particularly in oncological imaging and genomics, complemented by theoretical approaches related to trust in AI. Results emphasize that, beyond common issues of bias and fairness, knowledge and human involvement are essential for trustworthy AI. Stakeholder engagement throughout the AI life cycle emerged as crucial, supporting a human- and multicentered framework for trustworthy AI implementation. Findings emphasize that trust in medical AI depends on providing meaningful, user-oriented information and balancing knowledge with acceptable uncertainty. Experts highlighted the importance of confidence in the tool's functionality, specifically that it performs as expected. Trustworthiness was shown to be not a feature but rather a relational process, involving humans, their expertise, and the broader social or institutional contexts in which AI tools operate. Trust is dynamic, shaped by interactions among individuals, technologies, and institutions, and ultimately centers on people rather than tools alone. Tools are evaluated based on reliability and credibility, yet trust fundamentally relies on human connections. The article underscores the development of AI tools that are not only technically sound but also ethically robust and broadly accepted by end users, contributing to more effective and equitable AI-mediated health care. Findings highlight that building AI trustworthiness in health care requires a human-centered, multistakeholder approach with diverse and inclusive engagement. To promote equity, we recommend that AI development teams involve all relevant stakeholders at every stage of the AI lifecycle—from conception, technical development, clinical validation, and real-world deployment.

Laparoscopic appendectomy (LA) for treatment of acute appendicitis has gained acceptance with its considerable benefits over open appendectomy. LA, however, can involve some adverse outcomes: morbidity, prolonged length of hospital stay (LOS) and hospital readmission. Identification of predictive factors may help to identify and tailor treatment for patients with higher risk of these adverse events. Our aim was to identify risk factors for serious morbidity, prolonged LOS and hospital readmission after LA. A database compiled information of patients admitted for acute appendicitis from eighteen Polish and German surgical centers. It included factors related to the patient characteristics, peri- and postoperative period. Univariate and multivariate logistic regression models were used to identify risk factors for serious perioperative complications, prolonged LOS, and hospital readmissions in acute appendicitis cases. 4618 laparoscopic appendectomy patients were included. First, although several risk factors for serious perioperative complications (C-D III-V) were found in the univariate analysis, in the multivariate model only the presence of intraoperative adverse events (OR 4.09, 95% CI 1.32–12.65, p = 0.014) and complicated appendicitis (OR 3.63, 95% CI 1.74–7.61, p = 0.001) was statistically significant. Second, prolonged LOS was associated with the presence of complicated appendicitis (OR 2.8, 95% CI: 1.53–5.12, p = 0.001), postoperative morbidity (OR 5.01, 95% CI: 2.33–10.75, p < 0.001), conversions (OR 6.48, 95% CI: 3.48–12.08, p < 0.001) and reinterventions after primary procedure (OR 8.79, 95% CI: 3.2–24.14, p < 0.001) in the multivariate model. Third, although several risk factors for hospital readmissions were found in univariate analysis, in the multivariate model only the presence of postoperative complications (OR 10.33, 95% CI: 4.27–25.00), reintervention after primary procedure (OR 5.62, 95% CI: 2.17–14.54), and LA performed by resident (OR 1.96, 95% CI: 1.03–3.70) remained significant. Laparoscopic appendectomy is a safe procedure associated with low rates of complications, prolonged LOS, and readmissions. Risk factors for these adverse events include complicated appendicitis, postoperative morbidity, conversion, and re-intervention after the primary procedure. Any occurrence of these factors during treatment should alert the healthcare team to identify the patients that require more customized treatment to minimize the risk for adverse outcomes.

Background The aim of this study was to assess outcomes of laparoscopic sleeve gastrectomy (LSG) as a stand-alone bariatric operation according to the Bariatric Analysis and Reporting Outcome System (BAROS). Methods Out of 112 patients included and operated on initially, 84 patients (F/M, 63:21) were followed up for 14–56 months (mean 22 ± 6.75). Patients lost to follow-up did not attend scheduled follow-up visits or they have withdrawn their consent. Mean age was 39 years (range 17–67; SD ± 12.09) with mean initial BMI 44.62 kg/m² (range 29.39–82.8; SD ± 8.17). Statistical significance was established at the p < 0.05 level. Results Mean operative time was 61 min (30–140 min) with mean hospital stay of 1.37 days (0–4; SD ± 0.77). Excellent global BAROS outcome was achieved in 13% of patients, very good in 30%, good in 34.5%, fair 9.5% and failure in 13% patients 12 months after surgery. Females achieved significantly better outcomes than males with the mean 46.5% of excess weight loss (EWL) versus 35.3% of EWL at 12 months (p = 0.02). The mean percentage of excess weight loss (%EWL) was 43.6% at 12 months and 46.6% at 24 months. Major surgical complication rate was 7.1%; minor surgical complication rate 8.3%. There was one conversion (1.2%) due to the massive bleeding. Comorbidities improved or resolved in numerous patients: arterial hypertension in 62%, diabetes mellitus in 68.3%, respectively. Conclusions Presented LSG series shows that the LSG as a stand-alone procedure provides acceptable %EWL and good global BAROS outcomes. It significantly improves comorbidities as well.