AI-basierte volumetrische Analyse der Lebermetastasenlast bei Patienten mit neuroendokrinen Neoplasmen (NEN)

Background: Quantification of liver tumor load in patients with liver metastases from neuroendocrine neoplasms is essential for therapeutic management. However, accurate measurement of three-dimensional (3D) volumes is time-consuming and difficult to achieve. Even though the common criteria for assessing treatment response have simplified the measurement of liver metastases, the workload of following up patients with neuroendocrine liver metastases (NELMs) remains heavy for radiologists due to their increased morbidity and prolonged survival. Among the many imaging methods, gadoxetic acid (Gd-EOB)-enhanced magnetic resonance imaging (MRI) has shown the highest accuracy. Methods: 3D-volumetric segmentation of NELM and livers were manually performed in 278 Gd-EOB MRI scans from 118 patients. Eighty percent (222 scans) of them were randomly divided into training datasets and the other 20% (56 scans) were internal validation datasets. An additional 33 patients from a different time period, who underwent Gd-EOB MRI at both baseline and 12-month follow-up examinations, were collected for external and clinical validation (n = 66). Model measurement results (NELM volume; hepatic tumor load (HTL)) and the respective absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) for baseline and follow-up-imaging were used and correlated with multidisciplinary cancer conferences (MCC) decisions (treatment success/failure). Three readers manually segmented MRI images of each slice, blinded to clinical data and independently. All images were reviewed by another senior radiologist. Results: The model’s performance showed high accuracy between NELM and liver in both internal and external validation (Matthew’s correlation coefficient (ϕ): 0.76/0.95, 0.80/0.96, respectively). And in internal validation dataset, the group with higher NELM volume (> 16.17 cm3) showed higher ϕ than the group with lower NELM volume (ϕ = 0.80 vs. 0.71; p = 0.0025). In the external validation dataset, all response variables (∆absNELM; ∆absHTL; ∆relNELM; ∆relHTL) reflected significant differences across MCC decision groups (all p < 0.001). The AI model correctly detected the response trend based on ∆relNELM and ∆relHTL in all the 33 MCC patients and showed the optimal discrimination between treatment success and failure at +56.88% and +57.73%, respectively (AUC: 1.000; P < 0.001). Conclusions: The created AI-based segmentation model performed well in the three-dimensional quantification of NELMs and HTL in Gd-EOB-MRI. Moreover, the model showed good agreement with the evaluation of treatment response of the MCC’s decision.Hintergrund: Die Quantifizierung der Lebertumorlast bei Patienten mit Lebermetastasen von neuroendokrinen Neoplasien ist für die Behandlung unerlässlich. Eine genaue Messung des dreidimensionalen (3D) Volumens ist jedoch zeitaufwändig und schwer zu erreichen. Obwohl standardisierte Kriterien für die Beurteilung des Ansprechens auf die Behandlung die Messung von Lebermetastasen vereinfacht haben, bleibt die Arbeitsbelastung für Radiologen bei der Nachbeobachtung von Patienten mit neuroendokrinen Lebermetastasen (NELMs) aufgrund der höheren Fallzahlen durch erhöhte Morbidität und verlängerter Überlebenszeit hoch. Unter den zahlreichen bildgebenden Verfahren hat die Gadoxetsäure (Gd-EOB)-verstärkte Magnetresonanztomographie (MRT) die höchste Genauigkeit gezeigt. Methoden: Manuelle 3D-Segmentierungen von NELM und Lebern wurden in 278 Gd-EOB-MRT-Scans von 118 Patienten durchgeführt. 80% (222 Scans) davon wurden nach dem Zufallsprinzip in den Trainingsdatensatz eingeteilt, die übrigen 20% (56 Scans) waren interne Validierungsdatensätze. Zur externen und klinischen Validierung (n = 66) wurden weitere 33 Patienten aus einer späteren Zeitspanne des Multidisziplinäre Krebskonferenzen (MCC) erfasst, welche sich sowohl bei der Erstuntersuchung als auch bei der Nachuntersuchung nach 12 Monaten einer Gd-EOB-MRT unterzogen hatten. Die Messergebnisse des Modells (NELM-Volumen; hepatische Tumorlast (HTL)) mit den entsprechenden absoluten (ΔabsNELM; ΔabsHTL) und relativen Veränderungen (ΔrelNELM; ΔrelHTL) bei der Erstuntersuchung und der Nachuntersuchung wurden zum Vergleich mit MCC-Entscheidungen (Behandlungserfolg/-versagen) herangezogen. Drei Leser segmentierten die MRT-Bilder jeder Schicht manuell, geblindet und unabhängig. Alle Bilder wurden von einem weiteren Radiologen überprüft. Ergebnisse: Die Leistung des Modells zeigte sowohl bei der internen als auch bei der externen Validierung eine hohe Genauigkeit zwischen NELM und Leber (Matthew's Korrelationskoeffizient (ϕ): 0,76/0,95 bzw. 0,80/0,96). Und im internen Validierungsdatensatz zeigte die Gruppe mit höherem NELM-Volumen (> 16,17 cm3) einen höheren ϕ als die Gruppe mit geringerem NELM-Volumen (ϕ = 0,80 vs. 0,71; p = 0,0025). Im externen Validierungsdatensatz wiesen alle Antwortvariablen (∆absNELM; ∆absHTL; ∆relNELM; ∆relHTL) signifikante Unterschiede zwischen den MCC-Entscheidungsgruppen auf (alle p < 0,001). Das KI-Modell erkannte das Therapieansprechen auf der Grundlage von ∆relNELM und ∆relHTL bei allen 33 MCC-Patienten korrekt und zeigte bei +56,88% bzw. +57,73% eine optimale Unterscheidung zwischen Behandlungserfolg und -versagen (AUC: 1,000; P < 0,001). Schlussfolgerungen: Das Modell zeigte eine hohe Genauigkeit bei der dreidimensionalen Quantifizierung des NELMs-Volumens und der HTL in der Gd-EOB-MRT. Darüber hinaus zeigte das Modell eine gute Übereinstimmung bei der Bewertung des Ansprechens auf die Behandlung mit der Entscheidung des Tumorboards

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor

Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review

Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets.Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication).Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning.Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver tissues segmentation based on automated ML-based technique

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in the literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later. 2022 The Author(s)This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National LibraryScopu