Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.

Flow cytometric analysis of hepatopancreatic cells from Armadillidium vulgare highlights terrestrial isopods as efficient environmental bioindicators in ex vivo settings.

Several studies have reported the high bioindication capacity of Isopoda (Crustacea, Oniscidea), which is related to their important ability to accumulate contaminants, usefulness in soil ecotoxicology and bioindication activities. Any change in the isopod population, diversity and life cycle can indicate relevant pollution levels. The analysis of target tissues, such as the hepatopancreas, is another emerging approach (from a cytologic/histological level) to detect contaminant accumulation from different sources. In this study, tissue disaggregation procedures were optimised in the hepatopancreas, and flow cytometry (FC) was applied to detect cell viability and several cell functions. After disaggregation, two hepatopancreatic cell types, small (S) and big (B), were still recognisable: they differed in morphology and behaviour. The analyses were conducted for the first time on isopods from sites under different conditions of ecological disturbance through cytometric re-interpretation of ecological-environmental parameters. Significant differences in cell functional parameters were found, highlighting that isopod hepatopancreatic cells can be efficiently analysed by FC and represent standardisable, early biological indicators, tracing environmental-induced stress through cytologic/histologic analyses.

A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer.

BACKGROUND: Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. AIM: We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. CONCLUSIONS: Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.

Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection.

BACKGROUND: The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. AIM: To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. METHODS: Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models' performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. RESULT: Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. CONCLUSIONS: The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.

matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model.

Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive-inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation.

Razanandrongobe sakalavae, a gigantic mesoeucrocodylian from the Middle Jurassic of Madagascar, is the oldest known notosuchian.

Razanandrongobe sakalavae Maganuco, Dal Sasso & Pasini, 2006 is a large predatory archosaur from the Middle Jurassic (Bathonian) of the Mahajanga Basin, NW Madagascar. It was diagnosed on the basis of teeth and a fragmentary maxilla, but its affinities were uncertain. Here we describe new cranial remains (above all, an almost complete right premaxilla and a caudally incomplete left dentary) that greatly improve our knowledge on this enigmatic species and reveal its anatomy to be crocodylomorph. The right premaxilla indicates that the rostrum was deep, wide, and not pointed; it bears five teeth that are sub-vertical and just slightly curved lingually; the mesial teeth are U-shaped in cross-section and have serrated carinae on the lingual side; the aperturae nasi osseae (external bony nares) are confluent and face rostrally; and there is no lateral groove at the premaxillomaxillary suture for reception of a hypertrophied lower caniniform tooth. The preserved portion of the left dentary has an edentulous tip and bears eight large mandibular teeth of which the mesial (1-3) are the largest, but none is a hypertrophied caniniform tooth; the mandibular (dentary) symphysis extends caudally to the level of the third tooth; the splenial is not preserved, but its sutural marks on the dentary indicate that it contributed to the mandibular symphysis for at least 20% of the symphyseal length in dorsal aspect. On the basis of this new data, some previously uncertain features of the holotype maxilla-such as the margin of the suborbital fenestra, the contact surfaces for the palatine, the ectopterygoid, and the jugal-are now apparent. Testing of the phylogenetic position of the species within Crocodylomorpha indicates that R. sakalavae is a mesoeucrocodylian. It also represents one of the earliest events of exacerbated increase in body size along the evolutionary history of the group. In addition, it is by far the oldest notosuchian. A cranial reconstruction of this gigantic predator is also attempted here. The very robust jaw bones of R. sakalavae, coupled with its peculiar dentition, strongly suggest a diet that included hard tissue such as bone and tendon.

The decapod fauna (Axiidea, Anomura, Brachyura) from the Late Pleistocene of Trumbacà, Reggio Calabria (Calabria, southern Italy).

We report a rich faunal assemblage from the Tyrrhenian (Late Pleistocene) of Trumbacà, located in the southern area of Reggio Calabria (Calabria, southern Italy). The only brachyuran reported to date from this locality is Ranilia constricta (A. Milne Edwards, 1880) by Vazzana (2008). The studied specimens have been assigned, as follows: ?Corallianassa sp., Dardanus arrosor (Herbst, 1796), Dardanus substriatus (A. Milne Edwards, 1861), Paguristes cf. P. syrtensis de Saint Laurent 1970, Anapagurus sp., Ranilia constricta (A. Milne Edwards, 1880), Ranina propinqua Ristori, 1891, Ebalia cf. E. deshayesi Lucas, 1846, Ilia nucleus (Linnaeus, 1758), Medorippe lanata (Linnaeus, 1767), Calappa granulata (Linnaeus, 1758), Pisa armata (Latreille, 1803), Derilambrus cf. D. angulifrons (Latreille, 1825), Atelecyclus undecimdentatus (Herbst, 1783), Carcinus sp., Pilumnus hirtellus (Linnaeus, 1761), and Xantho cf. X. incisus (Leach, 1814). The studied assemblage enlarges our knowledge on the evolution of the Mediterranean decapod faunas.