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Purpose The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. Methods In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. Conclusion Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p

Background Computed Tomography Coronary Angiography (CTCA) is an effective non-invasive imaging modality for anatomo-functional assessment of coronary artery disease (CAD). Radiomics features have been used for diagnosis or outcome prediction, however, their potential value for characterizing flow limiting coronary lesions has not been explored. Purpose To assess whether application of novel radiomics and machine learning (ML) techniques on CTCA derived datasets improves characterization of functionally significant coronary lesions. Methods Consecutive patients with stable chest pain and intermediate pre-test likelihood for CAD, who underwent CTCA and PET-or SPECT-Myocardial Perfusion Imaging (MPI) respectively, were prospectively evaluated and included in the analysis. PET-MPI was considered abnormal when >1 contiguous segments showed both stress Myocardial Blood Flow ≤2.3mL/g/min and Myocardial Flow Reserve (MFR) ≤2.5 for 15O-water or Results A total of 292 coronary vessels (140 with corresponding PET-MPI data and 152 with SPECT MPI data) were analysed. Plaque burden and stenosis severity were the only independent predictors of impaired myocardial perfusion on PET-MPI, with an AUC = 0.749, (95% CI: 0.658–0.826). Stenosis severity, kurtosis, contrast, interquartile range and entropy were predictors of an abnormal PET-MPI result and their combination resulted in an AUC = 0.854, (95% CI: 0.775–0.914). The difference between the 2 models was statistically significant (p-diff: 0.02, 95% CI: 0.0165–0.194). Stenosis severity was the only predictor of a DR on SPECT-MPI, AUC = 0.624 (95% CI: 0.542–0.702). Small Dependence High Gray Level Emphasis, Cluster Prominence, Region Length, wavelet Median and square Median were predictors of a positive SPECT result, with AUC = 0.816, (95% CI: 0.745–0.875). The difference between the two models was statistically significant (p-diff: 0.006, 95% CI: 0.152–0.329) Conclusion Radiomic futures can be combined with anatomical and morphological characteristics of coronary lesions in CTCA imaging and provide valuable complementary information for characterizing functionally significant coronary lesions. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work was supported from European Regional Development Fund, Operational Programme “Competitiveness, Entrepreneurship and Innovation 2014-2022 (EPAnEK)”, titled: The Greek Research Infrastructure for Personalized Medicine (pMED-GR)

The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches.In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives.Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

International audience; We report the studies and optimization of scintillation light measurements in an updated version of the XEMIS1 prototype for the development of the XEMIS2 camera. A novel monolithic liquid xenon Compton camera, named XEMIS2 (XEnon Medical Imaging System), attempts to achieve low-activity small-animal imaging using the 3-gamma imaging technique. This emerging detector relies on the time projection chamber technique: it will be able to perform a simultaneous detection of the three γ-rays emitted by a specific radionuclide, such as scandium-44, and to produce a good quality image with a remarkable diminution of radiopharmaceutical activity at the same time. Vacuum Ultraviolet (VUV) scintillation light and ionization charge carriers generated from the recoiling particles within the detector are detected and used to reconstruct the interaction position and deposited energy. A cost-effective self-triggering scintillation signal read-out and data acquisition (DAQ) system has been developed to achieve a continuous data read-out with negligible electronics dead time. The DAQ prototype has been installed and qualified in an updated version of the XEMIS1 detector. It reaches the performance specifications in scintillation light measurements. Moreover, scintillation signals can also be used for the virtual segmentation of the monolithic detection volume through the matching algorithm of the scintillation and ionization signals based on the Light Collection Map (LCM). This spatial pre-localization of the physical events, called the virtual fiducialization of the active volume, is used to lower the detector occupancy rate when the administered activity is increased to lessen the examination time. The XEMIS1 experimental LCMs indicate that each PMT owns an individual field of view so as to segment the active volume virtually. The preparation work for the XEMIS2 camera operation has been completed in the updated XEMIS1 detector while the XEMIS2 scintillation light measurement system is under commissioning in Nantes Centre Hospitalier Universitaire. •The XEMIS2 camera oriented to the whole-body small animal 3-gamma medical imaging is presented.•The XEMIS2 system is a monolithic liquid xenon Compton camera with a 24 cm axial field of view.•A cost-effective 16-channel self-triggering scintillation signal front-end read-out electronics named XSRETOT is reported.•The XEMIS1 experimental light collection maps can be used for the virtual segmentation of the monolithic detection volume.

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

This chapter outlines the use of “modern” methods of risk management with the use of bunker derivatives for hedging the fluctuations in bunker prices. It presents an overview of the bunker market. The chapter considers key economic variables affecting bunker prices. It examines the Over-The-Counter (OTC) forward bunker agreements as a tool for hedging bunker price risk. The chapter focuses on the market of bunker fuel oil futures, including early efforts, the practice of cross-hedging and the current bunker futures market. It discusses bunker swaps and bunker options as important tools to hedge bunker price risk, respectively. Bunker fuel is the residue of the distillation process of crude oil, i.e. after all the higher components have been evaporated or boiled off. The demand for bunkers comes from shipowners and vessel operators, including charterers, who have a vessel on a time-charter agreement.

This chapter discusses in-depth the available freight rate derivatives and introduce their trading characteristics. It provides a comprehensive discussion of the past and present use and the development of freight derivatives. The chapter presents early efforts of trading shipping freight derivatives. It introduces the first ever freight futures contract, the Baltic International Freight Futures Exchange (BIFFEX) contract and the first freight option contracts traded. The chapter provides an overview of the history of BIFFEX, from its creation back in May 1985, until its termination in April 2002. It discusses the creation of a derivatives market to serve the needs of the maritime industry; namely, the International Maritime Exchange (IMAREX) from its creation in 2001 until its termination in 2013. The chapter focuses on the current state of the market for freight derivatives. It discusses Over-The-Counter (OTC) Forward Freight Agreements (FFAs) and presents their characteristics and specifications.

This chapter focuses on practical examples of how options can be used in freight rate markets. It introduces the main characteristics of freight options. The chapter discusses strategies using freight options for hedging purposes. It shows how a shipowner and a charterer may hedge their positions in the physical freight market by entering a freight option contract. The chapter focuses on the ways freight options may be used in favor of the shipowners seeking finance through a bank loan agreement, by creating a secure floor for their freight rate revenue, while being open to realise benefits if the market increases. It discusses trading on freight options as an asset class that provides an alternative opportunity for an investor. The chapter explains the economics behind freight options markets and reviews the most recent empirical evidence in the topic. It provides a comprehensive coverage of the different methods and techniques used in measuring and managing market risk in freight markets.

PURPOSE/OBJECTIVE(S): (Chemo)-radiotherapy is the standard treatment for patients with locally advanced lung cancer (LALC) not accessible to surgery. Despite strict application of dose constraints, acute toxicities such as acute pulmonary toxicity (APT) remain frequent, and may impact treatment's compliance and patients' quality of life. Previously, on a population treated with intensity-modulated photon therapy or passive scattering proton therapy, spatial dose patterns associated with APT were identified in the lower lungs, especially in the lower right lung. In the present study, we aim to define these spatial dose patterns on a retrospective cohort treated by volumetric-arc therapy (VMAT) and to validate our findings prospectively. MATERIALS/METHODS For the training cohort, we retrospectively included all patients treated in our institution by VMAT for a LALC between 2015 and 2018. APT was scored according to the CTCAE v4.0 scale. All dose maps were registered to a thorax phantom using a segmentation-based elastic registration. Voxel-based analysis of local dose differences was performed with a non-parametric permutation test accounting for n = 10.000 permutations, producing a 3-dimensional significance maps on which clusters of voxels that exhibited significant dose differences (P < 0.05) between the two toxicity groups (APT ≥ grade 2 vs APT < grade 2) were identified. A dose cut-off was defined by maximizing the Youden-Index derived from the Receiver Operative Curve. For the validation cohort, we applied the predefined spatial dose pattern and the dose cut-off to an observational prospective cohort. The model was evaluated using the Area under the curve (AUC) and the balanced accuracy (Bacc: mean of the sensitivity and specificity). RESULTS 167 and 42 patients were included in the training and validation cohorts, with respective APT rates of 22.2% and 21.4%. In the training cohort, a cluster of voxels was identified in the lower right lung with a cut off of 30.3 Gy for the Dmean(mean-dose). This spatial-based model resulted in an AUC of 0.69 and a Bacc of 0.71. Using the same voxel cluster and the Dmeancut-off on the validation cohort, the model resulted in an AUC of 0.74 and a Bacc of 0.69. CONCLUSION Our model corroborates previous findings regarding the possible role of the lower right lung regarding the development of APT and successfully evaluates its robustness in a prospective cohort treated by VMAT. Regional radiosensitivity should be considered in usual lung dose constraints.

International audience; Purpose: (Chemo)-radiotherapy (RT) is the gold standard treatment for patients with locally advanced lung cancer non accessible for surgery. However, current toxicity prediction models rely on clinical and dose volume histograms (DVHs) and remain unsufficient. The goal of this work is to investigate the added predictive value of the radiomics approach applied to dose maps regarding acute and late toxicities in both the lungs and esophagus.Methods: Acute and late toxicities scored using the CTCAE v4.0 were retrospectively collected on patients treated with RT in our institution. Radiomic features were extracted from 3D dose maps considering Gy values as grey-levels in images. DVH and usual clinical factors were also considered. Three toxicity prediction models (clinical only, clinical + DVH and combined, i.e., including clinical + DVH + radiomics) were incrementally trained using a neural network on 70% of the patients for prediction of grade ≥2 acute and late pulmonary toxicities (APT/LPT) and grade ≥2 acute esophageal toxicities (AET). After bootstrapping (n = 1000), optimal cut-off values were determined based on the Youden Index. The trained models were then evaluated in the remaining 30% of patients using balanced accuracy (BAcc).Results: 167 patients were treated from 2015 to 2018: 78% non small-cell lung cancers, 14% small-cell lung cancers and 8% other histology with a median age at treatment of 66 years. Respectively, 22.2%, 16.8% and 30.0% experienced APT, LPT and AET. In the training set (n = 117), the corresponding BAcc for clinical only/clinical + DVH/combined were 0.68/0.79/0.92, 0.66/0.77/0.87 and 0.68/0.73/0.84. In the testing evaluation (n = 50), these trained models obtained a corresponding BAcc of 0.69/0.69/0.92, 0.76/0.80/0.89 and 0.58/0.73/0.72.Conclusion: In patients with a lung cancer treated with RT, radiomic features extracted from 3D dose maps seem to surpass usual models based on clinical factors and DVHs for the prediction of APT and LPT.

Carrying out large multicenter studies is one of the key goals to be achieved towards a faster transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics data analysis, hence the need for integrating radiomic features extracted from images acquired in different centers. This is challenging as radiomic features exhibit variable sensitivity to differences in scanner model, acquisition protocols and reconstruction settings, which is similar to the so-called ‘batch-effects’ in genomics studies. In this review we discuss existing methods to perform data integration with the aid of reducing the unwanted variation associated with batch effects. We also discuss the future potential role of deep learning methods in providing solutions for addressing radiomic multicentre studies.

International audience; Multicenter studies are needed to demonstrate the clinical potential value of radiomics as a prognostic tool. However, variability in scanner models, acquisition protocols and reconstruction settings are unavoidable and radiomic features are notoriously sensitive to these factors, which hinders pooling them in a statistical analysis. A statistical harmonization method called ComBat was developed to deal with the "batch effect" in gene expression microarray data and was used in radiomics studies to deal with the "center-effect". Our goal was to evaluate modifications in ComBat allowing for more flexibility in choosing a reference and improving robustness of the estimation. Two modified ComBat versions were evaluated M-ComBat allows to transform all features distributions to a chosen reference, instead of the overall mean, providing more flexibility. B-ComBat adds bootstrap and Monte Carlo for improved robustness in the estimation. BM-ComBat combines both modifications. The four versions were compared regarding their ability to harmonize features in a multicenter context in two different clinical datasets. The first contains 119 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging and positron emission tomography imaging. In that case ComBat was applied with 3 labels corresponding to each center. The second one contains 98 locally advanced laryngeal cancer patients from 5 centers with contrast-enhanced computed tomography. In that specific case, because imaging settings were highly heterogeneous even within each of the five centers, unsupervised clustering was used to determine two labels for applying ComBat. The impact of each harmonization was evaluated through three different machine learning pipelines for the modelling step in predicting the clinical outcomes, across two performance metrics (balanced accuracy and Matthews correlation coefficient). Before harmonization, almost all radiomic features had significantly different distributions between labels. These differences were successfully removed with all ComBat versions. The predictive ability of the radiomic models was always improved with harmonization and the improved ComBat provided the best results. This was observed consistently in both datasets, through all machine learning pipelines and performance metrics. The proposed modifications allow for more flexibility and robustness in the estimation. They also slightly but consistently improve the predictive power of resulting radiomic models.

The international shipping industry is susceptible to heightened market volatility manifested in significant freight rate fluctuations and thus diversifying and hedging the associated risks have become central to shipping business practice. Building on the extant literature on shipping freight derivatives, this study develops a portfolio-based methodological framework aiming to improve freight rate risk management. The study also offers, for the first time, evidence of the hedging performance of the recently developed container freight futures market. Our approach utilises portfolios of container, dry bulk and tanker freight futures along with corresponding portfolios of physical freight rates in order to improve the efficacy of risk diversification for shipping market practitioners. The empirical findings uncovered in this study have important implications for overall business, commercial, and hedging strategies in the shipping industry, while they can ultimately lead to a more liquid and efficient freight futures market.

Purpose/objective(s) In locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (CRT), surgery could be omitted in patients with complete pathological response (pCR) without compromising progression-free and overall survivals. Our objective is to develop a radiomic model based on Magnetic Resonance Imaging (MRI) and contrast-enhanced computed tomography (CT) scans to predict the pathologic complete response (pCR) to neoadjuvant CRT in LARC. Materials/methods All patients treated for a LARC with neoadjuvant CRT and subsequent surgery in 2 centers in the West of France (Insitut of Cancerologie Brittany Occidental (ICBO) and Nantes) were considered. Both pre-CRT pelvic MRIs and contrast-enhanced CT-scans were mandatory for inclusion. The tumor was manually segmented on the T2-weighted and diffusion axial sequences and on the contrast-enhanced CT-scan. Eighty-eight radiomic parameters (15 shape and geometry features, 11 first-order and 62 second-order) were extracted from each sequence using the in-house MirasO software, with a total of 1056 features by patient. The overall cohort was randomly split into two independent cohorts (training: 70% and testing: 30%). A strict feature set selection workflow based on the Spearman's correlation coefficient and the Area Under the Curve (AUC) was developed to reduce the number of features. Based on these selected features, three pCR prediction models (clinical, radiomics and combined: clinical + radiomics) were developed on the training set only with a random forest approach and a Bootstrap internal validation with n = 1000 replications. An optimal cut-off maximizing the model's performance was defined on the training set. Each model was then evaluated on the testing set, based on the AUC and the C-statistic calculated with the pre-defined cut-off. Finally, a posteriori harmonization using the ComBat approach was applied to account for imaging modalities heterogeneity. Results Of the 124 included patients, 14 had a complete response (11,3%). In the training set, the clinical model based on 2 parameters (initial T-stage and degree of tumor differentiation) obtained an AUC of 0.67 and a C-statistic of 0.65. The radiomic model based on 1 parameter (entropy histogram derived from T2 sequence) obtained an AUC of 1 and a C-statistic of 1. The combined clinical and radiomic model was strictly identical to the radiomics model and thus had the same performance. On the testing set, models resulted in a C-statistic of 0.70/0.73/0.73 for the clinical, radiomics and combined models, respectively. Finally, after harmonization, the radiomic model achieved a C-statistic of 0.77 on the testing set while the combined resulted in a C-statistic of 0 .75. Conclusion Radiomic model based on T2-weighted pre-therapeutic MRIs sequences could help to predict pCR after neoadjuvant CRT in LARC.

Resume Objectifs La surexpression de la proteine p16 et le statut HPV (Human papilloma virus) sont reconnus comme facteurs de bon pronostic independants dans les cancers des voies aerodigestives superieures (VADS). Il a ete suggere que les parametres de la TEP/TDM au 18FDG initiale seraient egalement des facteurs pronostiques independants. Notre objectif etait d’etudier le lien entre les donnees de la virologie et les donnees de la TEP/TDM pretherapeutique dans les cancers des VADS localement avances, traites par radiochimiotherapie. Methodes Quarante patients presentant un cancer des VADS avec des volumes tumoraux superieurs a 3 cm3 ont ete inclus prospectivement. Ils avaient tous beneficie d’une TEP/TDM initiale dont ont ete extraits le volume metabolique, l’intensite de fixation (SUV), l’activite globale et des parametres d’heterogeneite locale, regionale et globale et de forme. Ces parametres ont ete confrontes a l’analyse virologique des biopsies pretraitement : expression de p16 en immunohistochimie (IHC), recherche de l’ADN HPV 16 et statut HPV (p16 + ADN HPV 16). Resultats Les patients p16+ presentaient des tumeurs plus hypermetaboliques avec des SUVmax (p = 0,028) et SUVmean (p = 0,02) eleves. Elles etaient plus heterogenes localement avec une correlation plus faible (p = 0,004). Les formes des lesions initiales etaient moins complexes chez les patients p16+ et HPV+, avec un D2Bmax moins eleve (p = 0,03). Conclusion Les cancers des VADS localement avances ont des caracteristiques differentes en TEP lorsque les tumeurs sont p16+. Le lien entre ces caracteristiques biologiques et le devenir des patients reste a etablir.

This paper examines dynamic hedges in the natural gas futures markets for different horizons and explores the gains from devising risk management strategies. Despite the substantial progress made in developing hedging models, forecast combinations have not been tested. We fill this gap by proposing a framework for combining hedge-ratio predictions. Composite hedge-ratios lead to significant reduction in portfolio risk, whether spot prices are partially predictable or not. We offer insights on hedging effectiveness across seasons, backwardationcontango conditions and the asymmetric profiles of long-short hedgers. We conclude that forecast combinations better reconcile realized performance with the hedging process, mitigating model instability.

Resume Objectifs L’analyse de texture des images TEP connait un essor particulierement important ces dernieres annees. Nous avons souhaite identifier les indices de texture les plus robustes et pertinents chez des patients atteints de lymphome du manteau (LCM), hemopathie pour laquelle l’utilisation de la SUV reste controversee. Materiel et methodes Les patients inclus dans cette etude sont issus d’une etude ancillaire a l’essai multicentrique de phase III LyMa. Pour chaque patient, la lesion metaboliquement la plus active a ete segmentee selon 3 differentes methodes : Fuzzy Locally Adaptive Bayesian, 40 % de la SUVmax et un seuil de SUV a 2,5. Quinze parametres de texture differents lies a l’heterogeneite tumorale ont ete calcules pour chaque lesion, soit 45 mesures au total. Resultats Les donnees TEP-FDG de 92 patients provenant de 28 centres differents ont ete incluses. Cinq parametres etaient tres significativement dependants de la methode de segmentation. Les 15 differents indices de texture etaient correles entre eux. Les differents indices de texture les plus correles entre eux ont ete regroupes en fonction de leur index de correlation, permettant ainsi d’identifier 4 groupes. Les correlations entre SUV et index de texture etaient faibles. Huit parametres etaient tres correles au volume et trois de ces index devenaient independants au-dela d’un certain volume-seuil. Cinq parametres etaient independants des volumes d’interet dans lesquels ils avaient ete calcules. Conclusion Seuls quelques parametres de texture semblent avoir une valeur ajoutee en apportant une information quantitative non redondante et complementaire des indices deja existants.

Resume Objectif Evaluer la valeur pronostique de differents facteurs pretherapeutiques definis avec la tomographie par emission de positons au ( 18 F)-fluorodesoxyglucose (TEP-FDG) pour le cancer de l’œsophage localise. Patients et methodes Nous avons realise une etude retrospective monocentrique a partir des dossiers de 83 patients atteints de cancers de l’œsophage traites a visee curative. La chimioradiotherapie a ete soit exclusive, soit suivie de chirurgie. Les criteres constitutifs, carcinologiques et derives de la TEP-FDG ( maximum standardized uptake value [SUV max ], volume tumoral metabolique de la lesion primitive [ metabolically active tumour volume ] defini soit avec le logiciel « fuzzy locally adaptive bayesian » [MATV FLAB ], soit avec un seuillage [MATV seuil ], et le total lesion glycolysis [TLG FLAB et TLG seuil ]) ont ete correles avec la survie. Resultats La duree mediane de suivi etait de 21,8 mois (0,16–104). La duree mediane de survie etait de 22 mois (intervalle de confiance a 95 % [IC95 %] : 15,2–28,9). Il y a eu 67 deces, 49 lies au cancer et 18 a une autre cause. Aucun des facteurs testes n’avait un impact significatif sur la survie globale. Pour la survie specifique, en analyse unifactorielle, le MATV FLAB ( p = 0,025), le TLG FLAB ( p = 0,04) et le TLG seuil ( p = 0,04) avaient un impact significatif. En analyse multifactorielle, seul le MATV FLAB avait un impact significatif ( p = 0,049) : MATV FLAB inferieur a 18 cm 3 : 31,2 mois (IC95 % : 21,7–non atteint) et MATV FLAB superieur a 18 cm 3 : 20 mois (IC95 % : 11,1–228,9). Conclusion Sur la TEP-FDG initiale, le volume metabolique defini avec l’algorithme de segmentation automatique FLAB etait un facteur pronostique interessant qui merite d’etre evalue sur une cohorte plus importante de patients.

To the best of our knowledge, this is the first paper, to utilise a novel dynamic factor model to investigate the economic relationships between 65 commodity (including oil, energy, metal, ores, and agricultural products), maritime transportation (including tanker and dry bulk freight rates) and financial (including derivatives products) markets, under three different frequencies (daily, weekly and monthly) in a single methodological framework. The paper overcomes a limitation of the previous literature that uses pairwise models, and as such, informational cross-market relationships which may have been ignored or not captured are uncovered for the first time. Results indicate strong economic relationships from commodity to freight markets, with crude oil prices serving as the leading (price discovery) indicator among all the investigated markets, but also between different sub-markets. The results are important to international investors and traders, but also provide essential insights to policymakers and regulators, in terms of commercial strategies, asset positioning, network supply chain modelling, asset investment allocation, budgeting and risk management.

High capital intensity and reliance on debt financing are among the most prominent characteristics of the shipping industry. The corporate finance literature has documented that beyond a certain threshold, leverage can hamper a firm’s ability to raise capital, and as a result, have a bearing on its corporate investment policy. The new, more restrictive, financing landscape in the shipping sector has put the management of capital structure on the spotlight as a key driver of investment policy, financial health, and thus, firm success. In this paper, we examine for the first time the link between the financing policy of shipping companies and their corporate investment decisions. We focus on the impact of deviations from target capital structure on mergers and acquisitions (MA an increasingly important corporate growth vehicle for shipping companies, with directly measurable outcomes. Deviations from optimal leverage display a strong association with the likelihood to consummate acquisitions, deal size, the financing method as well as the MA a pattern with direct policy implications for shipping companies, their management teams, and shareholders.

Financing shipping related investment projects has always been a focal area of debate and research within the international maritime industry since access to funding can determine the competitiveness of a capital-intensive business as well as its success or failure under adverse market conditions. This paper provides, for the first time, a comprehensive and structured survey of all published research in the area of shipping finance and investment. The review spans approximately four decades (1979-2018) of empirical evidence, including 162 studies published in 48 scholarly journals, complemented with select books and book chapters. The study provides a bibliometric analysis and comprehensive synthesis of existing research offering an invaluable source of information for both the academic community and business practice, shaping the future research agenda in shipping finance and investment.

The shipping industry facilitates substantially the operation of a global supply network and plays a pivotal role in the global transport of products. Shipping positively affects the lives of billions of people, with 90% of the world’s international trade travelling by sea. The challenges of the sector are multiple due to the cyclical nature of the industry, the global political and economic instability, the bulk volume of environmental laws and the volatility of bunker fuel and freight rates. The international nature of the industry possesses significant challenges because regulations are usually taken in a global level and enforced on the local level in which there are various environmental and national peculiarities.