Sphaeroptica: A tool for pseudo-3D visualization and 3D measurements on arthropods.

Natural history collections are invaluable reference collections. Digitizing these collections is a transformative process that improves the accessibility, preservation, and exploitation of specimens and associated data in the long term. Arthropods make up the majority of zoological collections. However, arthropods are small, have detailed color textures and share small, complex and shiny structures, which poses a challenge to conventional digitization methods. Sphaeroptica is a multi-images viewer that uses a sphere of oriented images. It allows the visualization of insects including their tiniest features, the positioning of landmarks, and the extraction of 3D coordinates for measuring linear distances or for use in geometric morphometrics analysis. The quantitative comparisons show that the measures obtained with Sphaeroptica are similar to the measurements derived from 3D µCT models with an average difference inferior to 1%, while featuring the high resolution of color stacked pictures with all details like setae, chaetae, scales, and other small and/or complex structures. Shaeroptica was developed for the digitization of small arthropods but it can be used with any sphere of aligned images resulting from the digitization of objects or specimens with complex surface and shining, black, or translucent texture which cannot easily be digitized using structured light scanner or Structure-from-Motion (SfM) photogrammetry.

Integrated and Interoperable Platform for Detecting Masses on Mammograms.

The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

[The use of medical imaging as a therapeutic patient education tool in radiotherapy : a feasibility study]. / L'utilisation de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient en radiothérapie : étude de faisabilité.

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 Orthanc¼. 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.

De par sa fréquence et son taux de mortalité élevé, le cancer représente un problème de santé publique majeur. Parmi les traitements possibles, la radiothérapie tient une place importante et fait appel massivement à l'imagerie médicale. Jusqu'ici réservé aux professionnels, ce type d'outil s'ouvre à un usage plus large, y compris par le patient lui-même dans une perspective éducative. Mais cette utilisation est restée peu explorée jusqu'à présent. Une étude expérimentale de faisabilité a ainsi été menée au sein du service de Radiothérapie du CHU de Liège sur des patients adultes avec cancer ou métastases pulmonaires, répartis en deux groupes randomisés. En plus des informations habituellement données par le radiothérapeute, le groupe expérimental a bénéficié d'une intervention consistant en la visualisation en 3D de ses propres images médicales via le logiciel libre et open-source «Stone of Orthanc¼. Les résultats de l'étude indiquent un taux de refus faible (8,2 %) pour les 15 patients recrutés. Bien que non significatif, le groupe expérimental a montré, par rapport au groupe contrôle, un gain médian dans la perception globale de connaissances ainsi qu'une diminution des scores liés à l'anxiété et à la détresse émotionnelle. Une réduction significative (p = 0,043) est observée pour le score de dépression. Les résultats positifs de l'étude de faisabilité encouragent la poursuite des travaux et renforcent le positionnement de l'usage de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient.

[Contribution of free software to medical imaging]. / Apport du logiciel libre en imagerie médicale.

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.

La recherche médicale recourt à des données de plus en plus massives, complexes et interdépendantes dont l'analyse nécessite l'usage d'algorithmes spécialisés. Afin de reproduire et valider les résultats d'une étude scientifique de manière indépendante, il n'est, dès lors, plus suffisant de partager le texte de l'article en «open-access¼ complété avec les données brutes en «open-data¼. Il convient désormais d'également partager les algorithmes qui ont servi à l'analyse des données avec d'autres équipes de chercheurs. Le logiciel libre et «open-source¼ répond précisément à ce besoin de diffuser les connaissances techniques à grande échelle. Dans cet article, nous présentons plusieurs exemples de logiciels libres utilisés en médecine, avec une attention particulière portée à l'imagerie médicale.

Client-Side Application of Deep Learning Models Through Teleradiology.

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.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach.

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.

The Orthanc Ecosystem for Medical Imaging.

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.

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.

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.

(18)F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. ADVANCES IN KNOWLEDGE: 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.

FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy.

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.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge.

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.

FDG PET/CT for rectal carcinoma radiotherapy treatment planning: comparison of functional volume delineation algorithms and clinical challenges.

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± 16.34 ml); the gradient-based method (18.97± 16.83ml); OASIS 45% (15.89 ± 12.68 ml); Philips 45% (14.52 ± 10.91 ml); OASIS 2.5 (41.6 2 ± 33.26 ml); Philips 2.5 (40 ± 31.27 ml). 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.