Liver Segmentation and its Application to Hepatic Interventions

The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning

Post hepatectomy liver failure (PHLF) – Recent advances in prevention and clinical management

Under embargo until: 2021-09-10Background Posthepatectomy liver failure (PHLF) is a relatively rare but feared complication following liver surgery, and associated with high morbidity, mortality and cost implications. Significant advances have been made in detailed preoperative assessment, particularly of the liver function in an attempt to predict and mitigate this complication. Methods A detailed search of PubMed and Medline was performed using keywords “liver failure”, “liver insufficiency”, “liver resection”, “postoperative”, and “post-hepatectomy”. Only full texts published in English were considered. Particular emphasis was placed on literature published after 2015. A formal systematic review was not found feasible hence a pragmatic review was performed. Results The reported incidence of PHLF varies widely in reported literature due to a historical absence of a universal definition. Incorporation of the now accepted definition and grading of PHLF would suggest the incidence to be between 8 and 12%. Major risk factors include background liver disease, extent of resection and intraoperative course. The vast majority of mortality associated with PHLF is related to sepsis, organ failure and cerebral events. Despite multiple attempts, there has been little progress in the definitive and specific management of liver failure. This review article discusses recent advances made in detailed preoperative evaluation of liver function and evidence-based targeted approach to managing PHLF. Conclusion PHLF remains a major cause of mortality following liver resection. In absence of a specific remedy, the best approach is mitigating the risk of it happening by detailed assessment of liver function, patient selection and general care of a critically ill patient.acceptedVersio

Artificial Intelligence and Liver Transplant:Predicting Survival of Individual Grafts

The demand for liver transplantation far outstrips the supply of deceased donor organs, and so, listing and allocation decisions aim to maximize utility. Most existing methods for predicting transplant outcomes use basic methods, such as regression modeling, but newer artificial intelligence (AI) techniques have the potential to improve predictive accuracy. The aim was to perform a systematic review of studies predicting graft outcomes following deceased donor liver transplantation using AI techniques and to compare these findings to linear regression and standard predictive modeling: donor risk index (DRI), Model for End‐Stage Liver Disease (MELD), and Survival Outcome Following Liver Transplantation (SOFT). After reviewing available article databases, a total of 52 articles were reviewed for inclusion. Of these articles, 9 met the inclusion criteria, which reported outcomes from 18,771 liver transplants. Artificial neural networks (ANNs) were the most commonly used methodology, being reported in 7 studies. Only 2 studies directly compared machine learning (ML) techniques to liver scoring modalities (i.e., DRI, SOFT, and balance of risk [BAR]). Both studies showed better prediction of individual organ survival with the optimal ANN model, reporting an area under the receiver operating characteristic curve (AUROC) 0.82 compared with BAR (0.62) and SOFT (0.57), and the other ANN model gave an AUC ROC of 0.84 compared with a DRI (0.68) and SOFT (0.64). AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. When compared with the standard techniques, AI methods are dynamic and are able to be trained and validated within every population. However, the high accuracy of AI may come at a cost of losing explainability (to patients and clinicians) on how the technology works

Hepatocellular carcinoma

Liver cancer is the second leading cause of cancer-related deaths globally and has an incidence of approximately 850,000 new cases per year. Hepatocellular carcinoma (HCC) represents approximately 90% of all cases of primary liver cancer. The main risk factors for developing HCC are well known and include hepatitis B and C virus infection, alcohol intake and ingestion of the fungal metabolite aflatoxin B1. Additional risk factors such as non-alcoholic steatohepatitis are also emerging. Advances in the understanding of the molecular pathogenesis of HCC have led to identification of critical driver mutations; however, the most prevalent of these are not yet druggable targets. The molecular classification of HCC is not established, and the Barcelona Clinic Liver Cancer staging classification is the main clinical algorithm for the stratification of patients according to prognosis and treatment allocation. Surveillance programmes enable the detection of early-stage tumours that are amenable to curative therapies - resection, liver transplantation or local ablation. At more developed stages, only chemoembolization (for intermediate HCC) and sorafenib (for advanced HCC) have shown survival benefits. There are major unmet needs in HCC management that might be addressed through the discovery of new therapies and their combinations for use in the adjuvant setting and for intermediate- and advanced-stage disease. Moreover, biomarkers for therapy stratification, patient-tailored strategies targeting driver mutations and/or activating signalling cascades, and validated measurements of quality of life are needed. Recent failures in the testing of systemic drugs for intermediate and advanced stages have indicated a need to refine trial designs and to define novel approaches

2018 Korean Liver Cancer Association-National Cancer Center Korea Practice Guidelines for the Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common cancer globally and the fourth most common cancer in men in Korea, where the prevalence of chronic hepatitis B infection is high in middle-aged and elderly patients. These practice guidelines will provide useful and constructive advice for the clinical management of patients with HCC. A total of 44 experts in hepatology, oncology, surgery, radiology, and radiation oncology in the Korean Liver Cancer Association-National Cancer Center Korea Practice Guideline Revision Committee revised the 2014 Korean guidelines and developed new recommendations that integrate the most up-to-date research findings and expert opinions.ope

Aspects of chemotherapy and photon and proton radiotherapy in patients with gastric cancer

Gastric cancer remains a major health problem worldwide. The addition of chemotherapy alone or in combination with radiotherapy to surgery in local gastric cancer improves outcome. In more advanced stages, the optimal palliative chemotherapy remains unknown, as well as the effect of different regimens on the patients’ quality of life. The aim of this thesis was to explore a new concept in chemotherapy, i.e. the sequential approach, and a new modality in radiotherapy, i.e. proton therapy, in the treatment of patients with gastric cancer. Quality of life (QoL) in patients treated with chemotherapy, and target delineation in radiotherapy of gastric cancer, were also studied. In Paper I, we evaluated the efficacy of sequential chemotherapy in patients with locally advanced and/or metastatic gastric cancer, with alternating irinotecan and docetaxel in combination with infusion 5-Fu. Eighty-one patients were randomized. No differences favoring either arm were found with respect to response rate, overall survival (OS), or toxicity. The median OS of 11 months indicated that the sequential approach was effective and similar to triple combinations, with potentially less toxicity. In Paper II, we evaluated the effect of sequential chemotherapy on the QoL in the same cohort. It was measured before, during, and after treatment. There were no statistically significant differences in QoL scores between the two treatment arms and no changes in mean scores during treatment. During the last 8 weeks of treatment, a significantly larger portion of patients with radiological response reported sustained or better QoL scores than those with no radiological response. In Paper III, we investigated the effect of inter physician variation on the delineation of target volumes in gastric cancer patients treated with perioperative chemoradiotherapy (CRT). Despite the use of a delineation atlas, we found a large variation in CTV and PTV volumes. There was only a small variation in target coverage and doses to organs at risk (OARs) in the corresponding plans. In Paper IV, we compared proton therapy to modern photon radiotherapy with respect to doses to OARs in gastric cancer patients treated with perioperative CRT. Protons offered significantly lower doses to the left kidney, liver, and spinal cord, and statistically lower risks for all types and malignant secondary neoplasms compared to photons. In Paper V, we evaluated the importance of daily anatomical variations, i.e. intestinal gas filling, on the dose distribution of proton beam therapy. The effect of intestinal gas variations on the PTV/CTV coverage was large. The sparing effect of protons was, however, sustained or the dose to the OARs did not significantly exceed the dose delivered with photons. In conclusion, sequential chemotherapy and proton radiotherapy are attractive alternatives in the treatment of gastric cancer. Standardization of target definitions in CRT, e.g. by reducing the inter physician variation, is important and should also be further investigated

Nuclear Imaging and Therapy:Towards a Personalized Approach in HCC and NET

This thesis explores new applications of nuclear imaging and therapy in patients with hepatocellular carcinoma (HCC) and neuroendocrine tumors (NET). These diseases are often detected late, making curative therapy not always possible. Developments in positron emission tomography (PET) and radionuclide therapy have led to new nuclear agents. The aim of this thesis is to provide insight into several new applications of current and new tracers in the diagnosis and treatment of HCC and NET.One of the investigated tracers is 18F-DOPA, which is currently used for NET tumors that are negative on 68Ga-labeled somatostatin analog (SSA) PET scans. Our study confirms the equivalent detection of 18F-DOPA in tumor detection compared to 68Ga-SSAs. Selective internal radiation therapy (SIRT) uses yttrium-90 radioactive resin spheres that are intravascularly injected into the liver. Higher than usual dosages (>120 Gy) appear to lead to better results in tumor reduction and the effects not only seem to be greater but also longer lasting.Furthermore, we demonstrated that 11C-Choline and 18F-FDG together find more tumors that are relevant for clinical decision-making in patients suspected of HCC recurrence. The thesis also offers two prospective study protocols, namely a comparison of 68Ga-DOTA-TOC with the new somatostatin tracer 18F-SiTATE in NET and a comparison of ablation with SIRT as a bridge strategy in liver transplantation. These results suggest that broader use of 18F-DOPA in PET diagnosis of NET is possible and that higher tumor-targeted dosages in SIRT can lead to better treatment

Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery

Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after

Hepatocellular Carcinoma

This open access book offers a comprehensive review of hepatocellular carcinoma (HCC) with a particular focus on the pathobiology and clinical aspects of the disease, including diagnosis and treatment. HCC is becoming one of the most common causes of cancer-related death worldwide. It is the fifth most common malignancy in men and the ninth in women, with an estimated 500,000 to 1 million new cases annually around the world. Independent of its cause, cirrhosis is considered a major clinical and histopathological risk factor for HCC development. Five percent of all cirrhotic patients develop HCC every year. Diagnostic tools for HCC include blood tests, high-quality imaging studies and liver biopsy. The treatment of HCC depends on the size and location of the HCC and includes surgical resection, liver transplantation, endovascular approaches, percutaneous ablation, and medical treatments. The book is organized into four parts – overview, diagnosis, management strategies, and recommendations – and aims to provide surgeons and clinicians with a valuable resource for complete and up-to-date research on the clinical aspects and management of HCC