One model to use them all: training a segmentation model with complementary datasets.

PURPOSE: Understanding surgical scenes is crucial for computer-assisted surgery systems to provide intelligent assistance functionality. One way of achieving this is via scene segmentation using machine learning (ML). However, such ML models require large amounts of annotated training data, containing examples of all relevant object classes, which are rarely available. In this work, we propose a method to combine multiple partially annotated datasets, providing complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets. METHODS: Our method aims to combine available data with complementary labels by leveraging mutual exclusive properties to maximize information. Specifically, we propose to use positive annotations of other classes as negative samples and to exclude background pixels of these binary annotations, as we cannot tell if a positive prediction by the model is correct. RESULTS: We evaluate our method by training a DeepLabV3 model on the publicly available Dresden Surgical Anatomy Dataset, which provides multiple subsets of binary segmented anatomical structures. Our approach successfully combines 6 classes into one model, significantly increasing the overall Dice Score by 4.4% compared to an ensemble of models trained on the classes individually. By including information on multiple classes, we were able to reduce the confusion between classes, e.g. a 24% drop for stomach and colon. CONCLUSION: By leveraging multiple datasets and applying mutual exclusion constraints, we developed a method that improves surgical scene segmentation performance without the need for fully annotated datasets. Our results demonstrate the feasibility of training a model on multiple complementary datasets. This paves the way for future work further alleviating the need for one specialized large, fully segmented dataset but instead the use of already existing datasets.

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 hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency 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 predictions' correspondence to regions of known clinical relevance 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.

Portable droplet-based real-time monitoring of pancreatic α-amylase in postoperative patients.

Postoperative complications after pancreatic surgery are frequent and can be life-threatening. Current clinical diagnostic strategies involve time-consuming quantification of α-amylase activity in abdominal drain fluid, which is performed on the first and third postoperative day. The lack of real-time monitoring may delay adjustment of medical treatment upon complications and worsen prognosis for patients. We report a bedside portable droplet-based millifluidic device enabling real-time sensing of drain α-amylase activity for postoperative monitoring of patients undergoing pancreatic surgery. Here, a tiny amount of drain liquid of patient samples is continuously collected and co-encapsulated with a starch reagent in nanoliter-sized droplets to track the fluorescence intensity released upon reaction with α-amylase. Comparing the α-amylase levels of 32 patients, 97 % of the results of the droplet-based millifluidic system matched the clinical data. Our method reduces the α-amylase assay duration to approximately 3 min with the limit of detection 7 nmol/s·L, enabling amylase activity monitoring at the bedside in clinical real-time. The presented droplet-based platform can be extended for analysis of different body fluids, diseases, and towards a broader range of biomarkers, including lipase, bilirubin, lactate, inflammation, or liquid biopsy markers, paving the way towards new standards in postoperative patient monitoring.

Case-Based Serious Gaming for Complication Management in Colorectal and Pancreatic Surgery: Prospective Observational Study.

BACKGROUND: The potential risk and subsequent impact of serious complications after pancreatic and colorectal surgery can be significantly reduced through early recognition, correct assessment, and timely initiation of appropriate therapy. Serious gaming (SG) is an innovative teaching method that combines play with knowledge acquisition, increased concentration, and quick decision-making and could therefore be used for clinically oriented education. OBJECTIVE: This study aims to develop a case-based SG platform for complication management in pancreatic and colorectal surgery, validate the application by comparing game courses of various professional groups in the health care sector, and test the acceptance of the developed platform in the context of clinical education by measuring levels of usability and applicability within the framework of a validity and usefulness analysis. METHODS: In this observational trial, a novel SG for management of postoperative complications was developed and prospectively validated in a cohort of 131 human caregivers with varying experience in abdominal surgery. A total of 6 realistic patient cases were implemented, representing common complications after pancreatic and colorectal surgery. Cases were developed and illustrated using anonymized images, data, and histories of postoperative patients. In the prospective section of this study, following a brief case presentation, participants were asked to triage the virtual patient, make an initial suspected diagnosis, and design a 3-step management plan, throughout which the results of selected diagnostic and therapeutic actions were presented. Participants' proposed case management was compared to ideal case management according to clinical guidelines. Usability, applicability, validity, and acceptance of the application were assessed using the Trier Teaching Evaluation Inventory as part of a noncomparative analysis. In addition, a comparative analysis of conventional teaching and learning formats was carried out. RESULTS: A total of 131 cases were answered. Physicians selected more appropriate therapeutic measures than nonphysicians. In the Trier Teaching Evaluation Inventory, design, structure, relevance, timeliness, and interest promotion were predominantly rated positively. Most participants perceived the application to be superior to conventional lecture-based formats (training courses, lectures, and seminars) in terms of problem-solving skills (102/131, 77.9%), self-reflection (102/131, 77.9%), and usability and applicability (104/131, 79.4%). CONCLUSIONS: Case-based SG has educational potential for complication management in surgery and could thereby contribute to improvements in postoperative patient care.

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.

BACKGROUND: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features. METHODS: To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen. They comprise the amount of blood and smoke in the surgical field, six instruments, and two anatomic structures. Annotation of selected frames from robot-assisted minimally invasive esophagectomies was performed by at least three independent medical experts. To test whether AL reduces annotation effort, we performed a prospective annotation study comparing AL with equidistant sampling (EQS) for frame selection. Multiple Bayesian ResNet18 architectures were trained on a multicentric dataset, consisting of 22 videos from two centers. RESULTS: In total, 14,004 frames were tag annotated. A mean F1-score of 0.75 ± 0.16 was achieved for all features. The highest F1-score was achieved for the instruments (mean 0.80 ± 0.17). This result is also reflected in the inter-rater-agreement (1-rater-kappa > 0.82). Compared to EQS, AL showed better recognition results for the instruments with a significant difference in the McNemar test comparing correctness of predictions. Moreover, in contrast to EQS, AL selected more frames of the four less common instruments (1512 vs. 607 frames) and achieved higher F1-scores for common instruments while requiring less training frames. CONCLUSION: We presented ten surgomic features relevant for bleeding events in esophageal surgery automatically extracted from surgical video using ML. AL showed the potential to reduce annotation effort while keeping ML performance high for selected features. The source code and the trained models are published open source.

Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise - an experimental study.

BACKGROUND: Lack of anatomy recognition represents a clinically relevant risk in abdominal surgery. Machine learning (ML) methods can help identify visible patterns and risk structures; however, their practical value remains largely unclear. MATERIALS AND METHODS: Based on a novel dataset of 13 195 laparoscopic images with pixel-wise segmentations of 11 anatomical structures, we developed specialized segmentation models for each structure and combined models for all anatomical structures using two state-of-the-art model architectures (DeepLabv3 and SegFormer) and compared segmentation performance of algorithms to a cohort of 28 physicians, medical students, and medical laypersons using the example of pancreas segmentation. RESULTS: Mean Intersection-over-Union for semantic segmentation of intra-abdominal structures ranged from 0.28 to 0.83 and from 0.23 to 0.77 for the DeepLabv3-based structure-specific and combined models, and from 0.31 to 0.85 and from 0.26 to 0.67 for the SegFormer-based structure-specific and combined models, respectively. Both the structure-specific and the combined DeepLabv3-based models are capable of near-real-time operation, while the SegFormer-based models are not. All four models outperformed at least 26 out of 28 human participants in pancreas segmentation. CONCLUSIONS: These results demonstrate that ML methods have the potential to provide relevant assistance in anatomy recognition in minimally invasive surgery in near-real-time. Future research should investigate the educational value and subsequent clinical impact of the respective assistance systems.

Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study.

INTRODUCTION: Complex oncological procedures pose various surgical challenges including dissection in distinct tissue planes and preservation of vulnerable anatomical structures throughout different surgical phases. In rectal surgery, violation of dissection planes increases the risk of local recurrence and autonomous nerve damage resulting in incontinence and sexual dysfunction. This work explores the feasibility of phase recognition and target structure segmentation in robot-assisted rectal resection (RARR) using machine learning. MATERIALS AND METHODS: A total of 57 RARR were recorded and subsets of these were annotated with respect to surgical phases and exact locations of target structures (anatomical structures, tissue types, static structures, and dissection areas). For surgical phase recognition, three machine learning models were trained: LSTM, MSTCN, and Trans-SVNet. Based on pixel-wise annotations of target structures in 9037 images, individual segmentation models based on DeepLabv3 were trained. Model performance was evaluated using F1 score, Intersection-over-Union (IoU), accuracy, precision, recall, and specificity. RESULTS: The best results for phase recognition were achieved with the MSTCN model (F1 score: 0.82 ± 0.01, accuracy: 0.84 ± 0.03). Mean IoUs for target structure segmentation ranged from 0.14 ± 0.22 to 0.80 ± 0.14 for organs and tissue types and from 0.11 ± 0.11 to 0.44 ± 0.30 for dissection areas. Image quality, distorting factors (i.e. blood, smoke), and technical challenges (i.e. lack of depth perception) considerably impacted segmentation performance. CONCLUSION: Machine learning-based phase recognition and segmentation of selected target structures are feasible in RARR. In the future, such functionalities could be integrated into a context-aware surgical guidance system for rectal surgery.

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

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 ([Formula: see text]) - 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, [Formula: see text]) 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.

Significance of Distinct Liquid Biopsy Compartments in Evaluating Somatic Mutations for Targeted Therapy Selection in Cancer of Unknown Primary.

PURPOSE: Cancer of unknown primary (CUP) accounts for 2-5% of all cancer diagnoses, wherein standard investigations fail to reveal the original tumor site. Basket trials allocate targeted therapeutics based on actionable somatic mutations, independent of tumor entity. These trials, however, mostly rely on variants identified in tissue biopsies. Since liquid biopsies (LB) represent the overall tumor genomic landscape, they may provide an ideal diagnostic source in CUP patients. To identify the most informative liquid biopsy compartment, we compared the utility of genomic variant analysis for therapy stratification in two LB compartments (circulating cell-free (cf) and extracellular vesicle (ev) DNA). METHODS: CfDNA and evDNA from 23 CUP patients were analyzed using a targeted gene panel covering 151 genes. Identified genetic variants were interpreted regarding diagnostic and therapeutic relevance using the MetaKB knowledgebase. RESULTS: LB revealed a total of 22 somatic mutations in evDNA and/or cfDNA in 11/23 patients. Out of the 22 identified somatic variants, 14 are classified as Tier I druggable somatic variants. Comparison of variants identified in evDNA and cfDNA revealed an overlap of 58% of somatic variants in both LB compartments, whereas over 40% of variants were only found in one or the other compartment. CONCLUSION: We observed substantial overlap between somatic variants identified in evDNA and cfDNA of CUP patients. Nonetheless, interrogation of both LB compartments can potentially increase the rate of druggable alterations, stressing the significance of liquid biopsies for possible primary-independent basket and umbrella trial inclusion.

The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science.

Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations of surgical instruments and low-volume weak annotations of specific abdominal organs. The Dresden Surgical Anatomy Dataset provides semantic segmentations of eight abdominal organs (colon, liver, pancreas, small intestine, spleen, stomach, ureter, vesicular glands), the abdominal wall and two vessel structures (inferior mesenteric artery, intestinal veins) in laparoscopic view. In total, this dataset comprises 13195 laparoscopic images. For each anatomical structure, we provide over a thousand images with pixel-wise segmentations. Annotations comprise semantic segmentations of single organs and one multi-organ-segmentation dataset including segments for all eleven anatomical structures. Moreover, we provide weak annotations of organ presence for every single image. This dataset markedly expands the horizon for surgical data science applications of computer vision in laparoscopic surgery and could thereby contribute to a reduction of risks and faster translation of Artificial Intelligence into surgical practice.

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.

The use and future perspective of Artificial Intelligence-A survey among German surgeons.

Purpose: Clinical abundance of artificial intelligence has increased significantly in the last decade. This survey aims to provide an overview of the current state of knowledge and acceptance of AI applications among surgeons in Germany. Methods: A total of 357 surgeons from German university hospitals, academic teaching hospitals and private practices were contacted by e-mail and asked to participate in the anonymous survey. Results: A total of 147 physicians completed the survey. The majority of respondents (n = 85, 52.8%) stated that they were familiar with AI applications in medicine. Personal knowledge was self-rated as average (n = 67, 41.6%) or rudimentary (n = 60, 37.3%) by the majority of participants. On the basis of various application scenarios, it became apparent that the respondents have different demands on AI applications in the area of "diagnosis confirmation" as compared to the area of "therapy decision." For the latter category, the requirements in terms of the error level are significantly higher and more respondents view their application in medical practice rather critically. Accordingly, most of the participants hope that AI systems will primarily improve diagnosis confirmation, while they see their ethical and legal problems with regard to liability as the main obstacle to extensive clinical application. Conclusion: German surgeons are in principle positively disposed toward AI applications. However, many surgeons see a deficit in their own knowledge and in the implementation of AI applications in their own professional environment. Accordingly, medical education programs targeting both medical students and healthcare professionals should convey basic knowledge about the development and clinical implementation process of AI applications in different medical fields, including surgery.

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.

BACKGROUND: Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics. METHODS: We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features' clinical relevance and technical feasibility. RESULTS: In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was "surgical skill and quality of performance" for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was "Instrument" (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were "intraoperative adverse events", "action performed with instruments", "vital sign monitoring", and "difficulty of surgery". CONCLUSION: Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.

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.

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.

The Impact of Pancreatic Head Resection on Blood Glucose Homeostasis in Patients with Chronic Pancreatitis.

BACKGROUND: Chronic pancreatitis (CP) often leads to recurrent pain as well as exocrine and/or endocrine pancreatic insufficiency. This study aimed to investigate the effect of pancreatic head resections on glucose metabolism in patients with CP. METHODS: Patients who underwent pylorus-preserving pancreaticoduodenectomy (PPPD), Whipple procedure (cPD), or duodenum-preserving pancreatic head resection (DPPHR) for CP between January 2011 and December 2020 were retrospectively analyzed with regard to markers of pancreatic endocrine function including steady-state beta cell function (%B), insulin resistance (IR), and insulin sensitivity (%S) according to the updated Homeostasis Model Assessment (HOMA2). RESULTS: Out of 141 pancreatic resections for CP, 43 cases including 31 PPPD, 2 cPD and 10 DPPHR, met the inclusion criteria. Preoperatively, six patients (14%) were normoglycemic (NG), 10 patients (23.2%) had impaired glucose tolerance (IGT) and 27 patients (62.8%) had diabetes mellitus (DM). In each subgroup, no significant changes were observed for HOMA2-%B (NG: p = 0.57; IGT: p = 0.38; DM: p = 0.1), HOMA2-IR (NG: p = 0.41; IGT: p = 0.61; DM: p = 0.18) or HOMA2-%S (NG: p = 0.44; IGT: p = 0.52; DM: p = 0.51) 3 and 12 months after surgery, respectively. CONCLUSION: Pancreatic head resections for CP, including DPPHR and pancreatoduodenectomies, do not significantly affect glucose metabolism within a follow-up period of 12 months.

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.

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.

The HDAC6/8/10 inhibitor TH34 induces DNA damage-mediated cell death in human high-grade neuroblastoma cell lines.

High histone deacetylase (HDAC) 8 and HDAC10 expression levels have been identified as predictors of exceptionally poor outcomes in neuroblastoma, the most common extracranial solid tumor in childhood. HDAC8 inhibition synergizes with retinoic acid treatment to induce neuroblast maturation in vitro and to inhibit neuroblastoma xenograft growth in vivo. HDAC10 inhibition increases intracellular accumulation of chemotherapeutics through interference with lysosomal homeostasis, ultimately leading to cell death in cultured neuroblastoma cells. So far, no HDAC inhibitor covering HDAC8 and HDAC10 at micromolar concentrations without inhibiting HDACs 1, 2 and 3 has been described. Here, we introduce TH34 (3-(N-benzylamino)-4-methylbenzhydroxamic acid), a novel HDAC6/8/10 inhibitor for neuroblastoma therapy. TH34 is well-tolerated by non-transformed human skin fibroblasts at concentrations up to 25 µM and modestly impairs colony growth in medulloblastoma cell lines, but specifically induces caspase-dependent programmed cell death in a concentration-dependent manner in several human neuroblastoma cell lines. In addition to the induction of DNA double-strand breaks, HDAC6/8/10 inhibition also leads to mitotic aberrations and cell-cycle arrest. Neuroblastoma cells display elevated levels of neuronal differentiation markers, mirrored by formation of neurite-like outgrowths under maintained TH34 treatment. Eventually, after long-term treatment, all neuroblastoma cells undergo cell death. The combination of TH34 with plasma-achievable concentrations of retinoic acid, a drug applied in neuroblastoma therapy, synergistically inhibits colony growth (combination index (CI) < 0.1 for 10 µM of each). In summary, our study supports using selective HDAC inhibitors as targeted antineoplastic agents and underlines the therapeutic potential of selective HDAC6/8/10 inhibition in high-grade neuroblastoma.

A kinome-wide RNAi screen identifies ALK as a target to sensitize neuroblastoma cells for HDAC8-inhibitor treatment.

The prognosis of advanced stage neuroblastoma patients remains poor and, despite intensive therapy, the 5-year survival rate remains less than 50%. We previously identified histone deacetylase (HDAC) 8 as an indicator of poor clinical outcome and a selective drug target for differentiation therapy in vitro and in vivo. Here, we performed kinome-wide RNAi screening to identify genes that are synthetically lethal with HDAC8 inhibitors. These experiments identified the neuroblastoma predisposition gene ALK as a candidate gene. Accordingly, the combination of the ALK/MET inhibitor crizotinib and selective HDAC8 inhibitors (3-6 µM PCI-34051 or 10 µM 20a) efficiently killed neuroblastoma cell lines carrying wildtype ALK (SK-N-BE(2)-C, IMR5/75), amplified ALK (NB-1), and those carrying the activating ALK F1174L mutation (Kelly), and, in cells carrying the activating R1275Q mutation (LAN-5), combination treatment decreased viable cell count. The effective dose of crizotinib in neuroblastoma cell lines ranged from 0.05 µM (ALK-amplified) to 0.8 µM (wildtype ALK). The combinatorial inhibition of ALK and HDAC8 also decreased tumor growth in an in vivo zebrafish xenograft model. Bioinformatic analyses revealed that the mRNA expression level of HDAC8 was significantly correlated with that of ALK in two independent patient cohorts, the Academic Medical Center cohort (n = 88) and the German Neuroblastoma Trial cohort (n = 649), and co-expression of both target genes identified patients with very poor outcome. Mechanistically, HDAC8 and ALK converge at the level of receptor tyrosine kinase (RTK) signaling and their downstream survival pathways, such as ERK signaling. Combination treatment of HDAC8 inhibitor with crizotinib efficiently blocked the activation of growth receptor survival signaling and shifted the cell cycle arrest and differentiation phenotype toward effective cell death of neuroblastoma cell lines, including sensitization of resistant models, but not of normal cells. These findings reveal combined targeting of ALK and HDAC8 as a novel strategy for the treatment of neuroblastoma.