Introduction
With
18
F-FDG PET/CT, tumor uptake intensity and heterogeneity have been associated with outcome in several cancers. This study aimed at investigating whether
18
F-FDG uptake intensity, ...volume or heterogeneity could predict the outcome in patients with non-small cell lung cancers (NSCLC) treated by stereotactic body radiation therapy (SBRT).
Methods
Sixty-three patients with NSCLC treated by SBRT underwent a
18
F-FDG PET/CT before treatment. Maximum and mean standard uptake value (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), as well as 13 global, local and regional textural features were analysed. The predictive value of these parameters, along with clinical features, was assessed using univariate and multivariate analysis for overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS). Cutoff values were obtained using logistic regression analysis, and survivals were compared using Kaplan-Meier analysis.
Results
The median follow-up period was 27.1 months for the entire cohort and 32.1 months for the surviving patients. At the end of the study, 25 patients had local and/or distant recurrence including 12 who died because of the cancer progression. None of the clinical variables was predictive of the outcome, except age, which was associated with DFS (HR 1.1,
P
= 0.002). None of the
18
F-FDG PET/CT or clinical parameters, except gender, were associated with OS. The univariate analysis showed that only dissimilarity (D) was associated with DSS (HR = 0.822,
P
= 0.037), and that several metabolic measurements were associated with DFS. In multivariate analysis, only dissimilarity was significantly associated with DSS (HR = 0.822,
P
= 0.037) and with DFS (HR = 0.834,
P
< 0.01).
Conclusion
The textural feature dissimilarity measured on the baseline
18
F-FDG PET/CT appears to be a strong independent predictor of the outcome in patients with NSCLC treated by SBRT. This may help selecting patients who may benefit from closer monitoring and therapeutic optimization.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
This paper reviews the components of Orthanc, a free and open-source, highly versatile ecosystem for medical imaging. At the core of the Orthanc ecosystem, the Orthanc server is a lightweight vendor ...neutral archive that provides PACS managers with a powerful environment to automate and optimize the imaging flows that are very specific to each hospital. The Orthanc server can be extended with plugins that provide solutions for teleradiology, digital pathology, or enterprise-ready databases. It is shown how software developers and research engineers can easily develop external software or Web portals dealing with medical images, with minimal knowledge of the DICOM standard, thanks to the advanced programming interface of the Orthanc server. The paper concludes by introducing the Stone of Orthanc, an innovative toolkit for the cross-platform rendering of medical images.
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NUK, OBVAL, SBMB, SBNM, UL, UM, UPUK, VSZLJ
Medical research uses increasingly massive, complex and interdependent data, the analysis of which requires the use of specialized algorithms. In order to independently reproduce and validate the ...results of a scientific study, it is no longer sufficient to share the text of the article as an open-access document, together with the raw research data according to the open-data approach. It is now also needed to share the algorithms used to analyze the data with other research teams. Free and open-source software precisely responds to this need to disseminate technical knowledge at a large scale. In this paper, we present several examples of free software used in medicine, with a particular focus on medical imaging.
Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to ...the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Deep learning models for radiology are typically deployed either through cloud-based platforms, through on-premises infrastructures, or though heavyweight viewers. This tends to restrict the audience ...of deep learning models to radiologists working in state-of-the-art hospitals, which raises concerns about the democratization of deep learning for medical imaging, most notably in the context of research and education. We show that complex deep learning models can be applied directly inside Web browsers, without resorting to any external computation infrastructure, and we release our code as free and open-source software. This opens the path to the use of teleradiology solutions as an effective way to distribute, teach, and evaluate deep learning architectures.
The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based ...approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Treating metastatic colorectal cancer with anti-EGFR monoclonal antibodies is recommended only for patients whose tumour does not harbour mutations of KRAS or NRAS. The aim of this study was to ...investigate the biology of rectal cancers and specifically to evaluate the relationship between fluorine-18 fludeoxyglucose ((18)F-FDG) positron emission tomography (PET) intensity and heterogeneity parameters and their mutational status.
151 patients with newly diagnosed rectal cancer were included in this retrospective study. All patients underwent a baseline (18)F-FDG PET/CT within a median time interval of 27 days of tumour tissue sampling, which was performed before any treatment. Standardized uptake values (SUVs), volume-based parameters and texture analysis were studied. We retrospectively performed KRAS genotyping on codons 12, 13, 61, 117 and 146, NRAS genotyping on codons 12, 13 and 61 and BRAF on codon 600. Associations between PET/CT parameters and the mutational status were assessed using univariate and multivariate analysis.
83 (55%) patients had an RAS mutation: 74 KRAS and 9 NRAS, while 68 patients had no mutation (wild-type tumours). No patient had BRAF mutation. First-order features based on intensity histogram analysis were significantly associated with RAS mutations: maximum SUV (SUVmax) (p-value = 0.002), mean SUV (p-value = 0.006), skewness (p-value = 0.049), SUV standard deviation (p-value = 0.001) and SUV coefficient of variation (SUVcov) (p-value = 0.001). Both SUVcov and SUVmax showed an area under the curve of 0.65 with sensitivity of 56% and 69%, respectively, and specificity of 64% and 52%, respectively. None of the volume-based (metabolic tumour volume and total lesion glycolysis), nor local or regional textural features were associated with the presence of RAS mutations.
Although rectal cancers with KRAS or NRAS mutations display a significantly higher glucose metabolism than wild-type cancers, the accuracy of the currently proposed quantitative metrics extracted from (18)F-FDG PET/CT is not sufficiently high for playing a meaningful clinical role.
RAS-mutated rectal cancers have a significantly higher glucose metabolism. However, the accuracy of (18)F-FDG PET/CT quantitative metrics is not as such as the technique could play a clinical role.
PET/CT imaging could improve delineation of rectal carcinoma gross tumor volume (GTV) and reduce interobserver variability. The objective of this work was to compare various functional volume ...delineation algorithms. We enrolled 31 consecutive patients with locally advanced rectal carcinoma. The FDG PET/CT and the high dose CT (CTRT) were performed in the radiation treatment position. For each patient, the anatomical GTVRT was delineated based on the CTRT and compared to six different functional/metabolic GTVPET derived from two automatic segmentation approaches (FLAB and a gradient‐based method); a relative threshold (45% of the SUVmax) and an absolute threshold (SUV>2.5), using two different commercially available software (Philips EBW4 and Segami OASIS). The spatial sizes and shapes of all volumes were compared using the conformity index (CI). All the delineated metabolic tumor volumes (MTVs) were significantly different. The MTVs were as follows (mean±SD):GTVRT(40.6±31.28ml); FLAB(21.36±16.34ml); the gradient‐based method (18.97±16.83ml); OASIS45%(15.89±12.68ml); Philips45%(14.52±10.91ml); OASIS2.5(41.62±33.26ml); Philips2.5(40±31.27ml). CI between these various volumes ranged from 0.40 to 0.90. The mean CI between the different MTVs and the GTVCT was <0.4. Finally, the DICOM transfer of MTVs led to additional volume variations. In conclusion, we observed large and statistically significant variations in tumor volume delineation according to the segmentation algorithms and the software products. The manipulation of PET/CT images and MTVs, such as the DICOM transfer to the Radiation Oncology Department, induced additional volume variations.
PACS number: 87.55.D‐
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of ...investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Because of its prevalence and high mortality rate, cancer is a major public health challenge. Radiotherapy is an important treatment option, and makes extensive use of medical imaging. Until now, ...this type of tool has been reserved to professionals, but it is now opening up to wider use, including by patients themselves for educational purposes. However, this type of usage has been little explored so far. An experimental feasibility study was carried out in the radiotherapy department of the University Hospital of Liège on adult patients with cancer or pulmonary metastases, assigned to two randomized groups. In addition to the usual information given by the radiotherapist, the patients of the experimental group benefited from an intervention consisting in the 3D visualization of their own medical images via the free and open-source computer software «Stone of Orthanc». The study results show a low refuse rate (8.2 %) for the 15 patients recruited. Although non-significant, the experimental group showed a median gain in global perception of knowledge, a decrease in anxiety scores and emotional distress. A significant reduction (p = 0.043) was observed for the depression score. The positive results of the feasibility study encourage further work and reinforce the positioning of medical imaging as a tool for therapeutic patient education.
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