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Abstract Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.

Abstract Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. Results Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10–7, 3 × 10–4, 4 × 10–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. Conclusion Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. Relevance statement Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. Key points • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines. Graphical Abstract

Abstract High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Abstract The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives.

Abstract Photoacoustic imaging is an increasingly popular method of exploring the tumour microenvironment, which can provide insight into tumour oxygenation status and potentially treatment response assessment. Currently, the measurements most commonly performed on such images are the mean and median of the pixel values of the tumour volumes of interest. We investigated expanding the set of measurements that can be extracted from these images by adding radiomic features. In particular, we found that Skewness was sensitive to differences between basal and luminal patient derived xenograft cancer models with an $$\eta ^2$$ η 2 of 0.86, and that it was robust to variations in confounding factors such as reconstruction type and wavelength. We also built discriminant models with radiomic features that were correlated with the underlying tumour model and were independent from each other. We then ranked features by their importance in the model. Skewness was again found to be an important feature, as were 10th Percentile, Root Mean Squared, and several other texture-based features. In summary, this paper proposes a methodology to select radiomic features extracted from photoacoustic images that are robust to changes in acquisition and reconstruction parameters, and discusses features found to have discriminating power between the underlying tumour models in a pre-clinical dataset.

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a “smart CT” paintbrush tool; the integration of NVIDIA’s Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.

BackgroundHigh-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.MethodsIn this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.ResultsFive patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.ConclusionsWe developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.

Abstract Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.

Abstract Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75–90%) was found to be highly robust (

BackgroundPathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.MethodsOmental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).ResultsThe performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.ConclusionsCT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

Inéditos

Microrrelatos inéditos de Lorena Escudero

Patrimonio y turismo tienen objetivos socioeconómicos, culturales y territoriales comunes. La conservación de los recursos patrimoniales y su proceso de transformación en productos turísticos son un incentivo para la revitalización de la identidad cultural a nivel regional, nacional e internacional. La relación entre cultura, patrimonio y turismo brinda al visitante experiencias únicas, que fortalecen a Cuenca como destino prioritario a nivel mundial. Palabras clave: Cuenca, Ecuador, Patrimonio Cultural de la Humanidad, turismo.

The occurrence of microorganisms from the Vibrio genus in saline lakes from northern Chile had been evidenced using Numerical Taxonomy decades before and, more recently, by phylogenetic analyses of environmental samples and isolates. Most of the knowledge about this genus came from marine isolates and showed temperature and salinity to be integral agents in shaping the niche of the Vibrio populations. The stress tolerance phenotypes of Vibrio sp. Teb5a1 isolated from Salar de Atacama was investigated. It was able to grow without NaCl and tolerated up to 100 g/L of the salt. Furthermore, it grew between 17° and 49°C (optimum 30°C) in the absence of NaCl, and the range was expanded into cold temperature (4-49°C) in the presence of the salt. Other additional adaptive strategies were observed in response to the osmotic stress: pigment production, identified as the known antibacterial prodigiosin, swimming and swarming motility and synthesis of a polar flagellum. It is possible to infer that environmental congruence might explain the cellular phenotypes observed in Vibrio sp. considering that coupling between temperature and salinity tolerance, the production of antibacterial agents at higher temperatures, flagellation and motility increase the chance of Vibrio sp. to survive in salty environments with high daily temperature swings and UV radiation.

Se estudiaron las comunidades estratificadas de microorganismos fotosintéticos que se encuentran en el Salar de Llamará ubicado en el desierto de Atacama, norte de Chile, mediante métodos microscópicos y espectrofotométricos. El espesor de la zona fótica de los tapetes descritos varió entre 8 y 30 mm lo cual podría atribuirse a la granulometría y la composición mineralógica de los sedimentos. Se diferencian tres tipos de tapetes. El primero con una única capa pigmentada de color verde; el segundo con capas de colores verde y naranja y un tercero en el que se observa, además de las capas verde y naranja, una de color púrpura. En uno de los sitios muestreados no se encontraron capas pigmentadas. Debajo de la zona pigmentada el sedimento es de color blanco, excepto en uno de los sectores donde se observó una coloración negra atribuible a sulfuro de hierro. Los microorganismos predominantes de la capa naranja fueron diatomeas y cianobacterias unicelulares principalmente de los grupos Cyanothece y Synechococcus. Las cianobacterias filamentosas Microccoleus sp. y Oscillatoria sp. fueron las más abundantes en la capa verde. No se observaron diatomeas en los sitios estudiados donde la salinidad del agua intersticial osciló entre 12 y 33 %. En la capa verde de estos sitios predominaron las cianobacterias cocoides, de los grupos Synechococcus, Cyanothece y Gloeocapsa y del género Gloeobacter, sobre las cianobacterias filamentosas. La capa púrpura estuvo compuesta principalmente por bacterias fototróficas anoxigénicas similares a células de los géneros Chromatium y Thiocapsa. Los espectros de absorción revelaron que la clorofila a es el pigmento más abundante en la mayoría de las muestras analizadas. Los valores integrados de clorofila a y bacterioclorofila a alcanzaron 230 y 144 mg m-2 en el espesor de la zona pigmentada, respectivamente. También se detectaron abundantes microorganismos no fotosintéticos en los tapetes incluyendo cocos y bacilos no identificados. En todos los tapetes muestreados en el Salar se encontraron bacterias reductoras de sulfato.Stratified photosynthetic microbial mats are described from the Salar de Llamará, a salt flat basin located in the Atacama desert of northern Chile. Microscopic and spectrophotometric techniques were used. The thickness of the photic zone of these communities spans 8 to 30 mm. This is probably due to the grain size and mineralogical composition of associated sediments. Three different types of mats were recognized. A first one was characterized by a green pigmented layer; a second with orange and green coloured layers, and the third with orange and green layers and an additional purple layer. At one sampling site, no pigmented layers were present. Sediments underlying the mats were white, but in one site, black coloured sediments were observed; this dark colour is probably the result of iron sulphide precipitation. Predominant microorganisms in the orange pigmented layers were diatoms and unicellular cyanobacteria, mainly from the Cyanothece and Synechococcus groups. Filamentous cyanobacteria Microleus sp. and Oscillatoria sp. were the most abundant in the green layer. When interstitial brines reached salinities between 12 and 33 %, no diatoms were observed, and the coccoidal cyanobacteria from the Synechococcus, Cyanothece and Gloeocapsa groups and genus Gloeobacter predominated over filamentous Cyanobacteria in the green layer. The purple layer was built primarily of anoxygenic phototrophic bacteria similar to cells of the genera Chromatium and Thiocapsa. Absorption spectra revealed that chlorophyll a is the most abundant pigment in most of analyzed samples. Integrated values of chlorophyll a and bacteriochlorophyll a reached values of up to 230 and 144 mg m-2 along all of the pigmented zone, respectively. Abundant non-photosynthetic microorganisms were found in the mats, including unidentified cocci and bacilli. Sulphate reducing bacteria were present in all the sampled mats.

Powered by machine learning, computer-aided diagnostics support clinicians by streamlining their work. In cancer screening, for instance, this technique often involves an automated detection of potential cancerous lesions from X-ray Computed Tomography (CT) images reconstructed a priori and unaware of the detection task. Since different reconstruction algorithms enhance different image attributes, a natural question is whether adapting the algorithms to the downstream task is relevant to improving the performance of computer-aided diagnosis tools. This paper provides empirical evidence about the performance of the taskadapted framework for joint reconstruction-segmentation of lung nodules from CT scans. We describe the procedures that allow the joint training of reconstruction and segmentation operators. We also report that relevant segmentation metrics improve by up to twenty percentage points in the four angular-sparsity and X-ray dose settings studied. Finally, we give practical recommendations for the procedure's hyperparameter tuning. Our results suggest that the imaging pipeline could benefit from computing different images adapted to the clinician's eyes and the automatic segmentation tools, outlining future improvements in patient care and clinical tasks. [ABSTRACT FROM AUTHOR]

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future. [ABSTRACT FROM AUTHOR]