The image‑based preoperative fistula risk score (preFRS) predicts postoperative pancreatic fistula in patients undergoing pancreatic head resection

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a common severe surgical complication after pancreatic surgery. Current risk stratification systems mostly rely on intraoperatively assessed factors like manually determined gland texture or blood loss. We developed a preoperatively available image-based risk score predicting CR-POPF as a complication of pancreatic head resection. Frequency of CR-POPF and occurrence of salvage completion pancreatectomy during the hospital stay were associated with an intraoperative surgical (sFRS) and image-based preoperative CT-based (rFRS) fistula risk score, both considering pancreatic gland texture, pancreatic duct diameter and pathology, in 195 patients undergoing pancreatic head resection. Based on its association with fistula-related outcome, radiologically estimated pancreatic remnant volume was included in a preoperative (preFRS) score for POPF risk stratification. Intraoperatively assessed pancreatic duct diameter (p < 0.001), gland texture (p < 0.001) and high-risk pathology (p < 0.001) as well as radiographically determined pancreatic duct diameter (p < 0.001), gland texture (p < 0.001), high-risk pathology (p = 0.001), and estimated pancreatic remnant volume (p < 0.001) correlated with the risk of CR-POPF development. PreFRS predicted the risk of CR-POPF development (AUC = 0.83) and correlated with the risk of rescue completion pancreatectomy. In summary, preFRS facilitates preoperative POPF risk stratification in patients undergoing pancreatic head resection, enabling individualized therapeutic approaches and optimized perioperative management

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.

BACKGROUND Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). METHODS Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. RESULTS On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. CONCLUSIONS Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology

Edema-like symptoms are common in ultra-distance cyclists and driven by overdrinking, use of analgesics and female sex - a study of 919 athletes.

BACKGROUND: Ultra-endurance cyclists regularly report various extents of bodily decline during long-distance bicycle rides, including potential kidney function-related symptoms such as swelling of body parts and urine changes. This study aimed to assess the prevalence of these symptoms in a representative cohort of ultra-endurance cyclists and shed light on potential predictors related to the ride, the rider and the rider's behavior. METHODS: Between November 26 and December 14, 2020, 1350 people participated in an online survey investigating potential kidney-related symptoms of ultra-distance cycling. Frequency and severity of edema-like ("swelling") symptoms and perceived changes in urine output, concentration and quality were associated with ride-related factors, demographic parameters and rider behavior-related variables. RESULTS: A total of 919 participants met the predefined inclusion criteria. The majority (N = 603, 65.6%) stated that they suffered from at least one potential kidney function-related symptom, out of which 498 (54.2%) stated one or more edema-like ("swelling") symptoms. In correlational and multiple regression analyses, female sex, intake of analgesics and drinking strategies correlated with swelling symptoms. Further analyses indicated that drinking due to thirst and/or drinking adapted to ambient sweating and temperature negatively correlated with swelling symptoms, whereas "drinking as much as possible" enhanced these. Intake of analgesics was moderately positively correlated with swelling symptoms. CONCLUSIONS: According to our survey, edema-like symptoms occur in the majority of ultra-distance cyclists and female sex, drinking strategy and intake of analgesic drugs are major predictors thereof. Studies are needed to investigate the underlying pathophysiological processes of such symptoms

Data publication: Portable Droplet-Based Real-Time Monitoring of Pancreatic α-Amylase in Postoperative Patients

research data on amylase concentration detection (Pancreatic α-Amylase in Postoperative Patients) with millifluidic device and plate reader and their statistical analysi

More is More? Total Pancreatectomy for Periampullary Cancer as an Alternative in Patients with High-Risk Pancreatic Anastomosis: A Propensity Score-Matched Analysis

Background!#!Postpancreatectomy morbidity remains significant even in high-volume centers and frequently results in delay or suspension of indicated adjuvant oncological treatment. This study investigated the short-term and long-term outcome after primary total pancreatectomy (PTP) and pylorus-preserving pancreaticoduodenectomy (PPPD) or Whipple procedure, with a special focus on administration of adjuvant therapy and oncological survival.!##!Methods!#!Patients who underwent PTP or PPPD/Whipple for periampullary cancer between January 2008 and December 2017 were retrospectively analyzed. Propensity score-matched analysis was performed to compare perioperative and oncological outcomes. Correspondingly, cases of rescue completion pancreatectomy (RCP) were analyzed.!##!Results!#!In total, 41 PTP and 343 PPPD/Whipple procedures were performed for periampullary cancer. After propensity score matching, morbidity (Clavien-Dindo classification (CDC) ≥ IIIa, 31.7% vs. 24.4%; p = 0.62) and mortality rates (7.3% vs. 2.4%, p = 0.36) were similar in PTP and PPPD/Whipple. Frequency of adjuvant treatment administration (76.5% vs. 78.4%; p = 0.87), overall survival (513 vs. 652 days; p = 0.47), and progression-free survival (456 vs. 454 days; p = 0.95) did not significantly differ. In turn, after RCP, morbidity (CDC ≥ IIIa, 85%) and mortality (40%) were high, and overall survival was poor (median 104 days). Indicated adjuvant therapy was not administered in 77%.!##!Conclusions!#!In periampullary cancers, PTP may provide surgical and oncological treatment outcomes comparable with pancreatic head resections and might save patients from RCP. Especially in selected cases with high-risk pancreatic anastomosis or preoperatively impaired glucose tolerance, PTP may provide a safe treatment alternative to pancreatic head resection

Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data

Abstract Clinically relevant postoperative pancreatic fistula (CR-POPF) can significantly affect the treatment course and outcome in pancreatic cancer patients. Preoperative prediction of CR-POPF can aid the surgical decision-making process and lead to better perioperative management of patients. In this retrospective study of 108 pancreatic head resection patients, we present risk models for the prediction of CR-POPF that use combinations of preoperative computed tomography (CT)-based radiomic features, mesh-based volumes of annotated intra- and peripancreatic structures and preoperative clinical data. The risk signatures were evaluated and analysed in detail by visualising feature expression maps and by comparing significant features to the established CR-POPF risk measures. Out of the risk models that were developed in this study, the combined radiomic and clinical signature performed best with an average area under receiver operating characteristic curve (AUC) of 0.86 and a balanced accuracy score of 0.76 on validation data. The following pre-operative features showed significant correlation with outcome in this signature ( $$p < 0.05$$ p < 0.05 ) - texture and morphology of the healthy pancreatic segment, intensity volume histogram-based feature of the pancreatic duct segment, morphology of the combined segment, and BMI. The predictions of this pre-operative signature showed strong correlation (Spearman correlation co-efficient, $$\rho = 0.7$$ ρ = 0.7 ) with the intraoperative updated alternative fistula risk score (ua-FRS), which is the clinical gold standard for intraoperative CR-POPF risk stratification. These results indicate that the proposed combined radiomic and clinical signature developed solely based on preoperatively available clinical and routine imaging data can perform on par with the current state-of-the-art intraoperative models for CR-POPF risk stratification

The importance of machine learning in autonomous actions for surgical decision making

Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path

Dual role of HDAC10 in lysosomal exocytosis and DNA repair promotes neuroblastoma chemoresistance

Drug resistance is a leading cause for treatment failure in many cancers, including neuroblastoma, the most common solid extracranial childhood malignancy. Previous studies from our lab indicate that histone deacetylase 10 (HDAC10) is important for the homeostasis of lysosomes, i.e. acidic vesicular organelles involved in the degradation of various biomolecules. Here, we show that depleting or inhibiting HDAC10 results in accumulation of lysosomes in chemotherapy-resistant neuroblastoma cell lines, as well as in the intracellular accumulation of the weakly basic chemotherapeutic doxorubicin within lysosomes. Interference with HDAC10 does not block doxorubicin efflux from cells via P-glycoprotein inhibition, but rather via inhibition of lysosomal exocytosis. In particular, intracellular doxorubicin does not remain trapped in lysosomes but also accumulates in the nucleus, where it promotes neuroblastoma cell death. Our data suggest that lysosomal exocytosis under doxorubicin treatment is important for cell survival and that inhibition of HDAC10 further induces DNA double-strand breaks (DSBs), providing additional mechanisms that sensitize neuroblastoma cells to doxorubicin. Taken together, we demonstrate that HDAC10 inhibition in combination with doxorubicin kills neuroblastoma, but not non-malignant cells, both by impeding drug efflux and enhancing DNA damage, providing a novel opportunity to target chemotherapy resistance

Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives

Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment