Impaired mitochondrial quality control in fibromyalgia: Mechanisms involved in skeletal muscle alteration.

Fibromyalgia (FMS) is a persistent syndrome marked by widespread musculoskeletal pain and behavioural symptoms. Given the hypothesis linking FMS aetiology to mitochondrial dysfunction and oxidative stress, we examined the biochemical correlation among these factors by studying specific proteins associated with mitochondrial homeostasis in muscle. Additionally, this study investigated the role of Boswellia serrata gum resin extract (BS), known for its various functions, including the potent induction of antioxidant enzymes, in determining protective or reparative mechanisms in the muscle cells. Sprague-Dawley rats were injected with reserpine to induce FMS. These animals exhibited moderate changes in hind limb skeletal muscles, experiencing mobility difficulties. Additionally, there were noteworthy morphological and ultrastructural alterations, along with the expression of myogenin, mitochondrial enzymes and oxidative stress markers in the gastrocnemius muscle. Interestingly, BS demonstrated a reduction in spontaneous motor activity difficulties. Moreover, BS showed a positive impact on musculoskeletal morphostructural aspects, as well as a decrease in oxidative stress and mitochondrial alterations. In particular, BS restored the mRNA expression of citrate synthase and cytochrome-c oxidase subunit II and the activity of electron transfer chain complexes. BS also influenced mitochondrial biogenesis, upregulating PGC-1α expression and the related transcription factors (Nrf1, Tfam, Nrf2, FOXO3a, SIRT3, GCLC, NQO1, SOD2 and GPx4), oxidative stress (lipid peroxidation, GSH levels and GSH-Px activity) and mitochondrial dynamics and function (Mnf2 expression and CoQ10 levels). Overall, this study underlined the key role of the mitochondrial alteration in FMS and that BS had a very high antioxidant effect in these organelles and also in the cells.

Enviromental endocrine disruptor risks in the central nervous system: Neurotoxic effects of PFOS and glyphosate.

Endocrine disruptors (EDs) pose significant risks to human and environmental health, with potential implications for neurotoxicity. This study investigates the synergistic neurotoxic effects of perfluorooctane sulfonate (PFOS) and glyphosate (GLY), two ubiquitous EDs, using SHSY5Y neuronal and C6 astrocytic cell lines. While individual exposures to PFOS and glyphosate at non-toxic concentrations did not induce significant changes, their combination resulted in a marked increase in oxidative stress and neuroinflammatory responses. Specifically, the co-exposure led to elevated levels of interleukin-6, tumor necrosis factor alpha, and interferon gamma, along with reduced interleukin-10 expression, indicative of heightened neuroinflammatory processes. These findings underscore the importance of considering the synergistic interactions of EDs in assessing neurotoxic risks and highlight the urgent need for further research to mitigate the adverse effects of these compounds on neurological health.

Role of mitochondrial dysfunction and biogenesis in fibromyalgia syndrome: Molecular mechanism in central nervous system.

A critical role for mitochondrial dysfunction has been shown in the pathogenesis of fibromyalgia. It is a chronic pain syndrome characterized by neuroinflammation and impaired oxidative balance in the central nervous system. Boswellia serrata (BS), a natural polyphenol, is a well-known able to influence the mitochondrial metabolism. The objective of this study was to evaluate the mitochondrial dysfunction and biogenesis in fibromyalgia and their modulation by BS. To induce the model reserpine (1 mg/Kg) was subcutaneously administered for three consecutive days and BS (100 mg/Kg) was given orally for twenty-one days. BS reduced pain like behaviors in reserpine-injected rats and the astrocytes activation in the dorsal horn of the spinal cord and prefrontal cortex that are recognized as key regions associated with the neuropathic pain. Vulnerability to neuroinflammation and impaired neuronal plasticity have been described as consequences of mitochondrial dysfunction. BS administration increased PGC-1α expression in the nucleus of spinal cord and brain tissues, promoting the expression of regulatory genes for mitochondrial biogenesis (NRF-1, Tfam and UCP2) and cellular antioxidant defence mechanisms (catalase, SOD2 and Prdx 3). According with these data BS reduced lipid peroxidation and the GSSG/GSH ratio and increased SOD activity in the same tissues. Our results also showed that BS administration mitigates cytochrome-c leakage by promoting mitochondrial function and supported the movement of PGC-1α protein into the nucleus restoring the quality control of mitochondria. Additionally, BS reduced Drp1 and Fis1, preventing both mitochondrial fission and cell death, and increased the expression of Mfn2 protein, facilitating mitochondrial fusion. Overall, our results showed important mitochondrial dysfunction in central nervous system in fibromyalgia syndrome and the role of BS in restoring mitochondrial dynamics.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers.

This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (DCs), Odontogenic Keratocysts (OKCs), and Unicystic Ameloblastomas (UAs). This retrospective study involves 103 patients who underwent jaw lesion surgery in the Maxillofacial Surgery Unit of Federico II University Of Naples between January 2018 and January 2023. Nonparametric Wilcoxon-Mann-Whitney and Kruskal Wallis tests were used for continuous variables. Linear and non-logistic regression models (LRM and NLRM) were employed, along with machine learning techniques such as decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), to predict the outcomes. When individual inflammatory biomarkers were considered alone, their ability to differentiate between OKCs, UAs, and DCs was below 50 % accuracy. However, a linear regression model combining four inflammatory biomarkers achieved an accuracy of 95 % and an AUC of 0.96. The accuracy of single radiomics predictors was lower than that of inflammatory biomarkers, with an AUC of 0.83. The Fine Tree model, utilizing NLR, SII, and one radiomic feature, achieved an accuracy of 94.3 % (AUC = 0.95) on the training and testing sets, and a validation set accuracy of 100 %. The Fine Tree model demonstrated the capability to discriminate between OKCs, UAs, and DCs. However, the LRM utilizing four inflammatory biomarkers proved to be the most effective algorithm for distinguishing between OKCs, UAs, and DCs.

Correction: D'Amico et al. Toxic Exposure to Endocrine Disruptors Worsens Parkinson's Disease Progression through NRF2/HO-1 Alteration. Biomedicines 2022, 10, 1073.

Errors in Figures [...].

Translational Insights in the Landscape of Salivary Gland Cancers: Ready for a New Era?

Salivary gland carcinomas (SGCs) are rare neoplasms, representing less than 10% of all head and neck tumors, but they are extremely heterogeneous from the histological point of view, their clinical behavior, and their genetics. The guidelines regarding their treatment include surgery in most cases, which can also play an important role in oligometastatic disease. Where surgery cannot be used, systemic therapy comes into play. Systemic therapy for many years has been represented by polychemotherapy, but recently, with the affirmation of translational research, it can also count on targeted therapy, at least in some subtypes of SGCs. Interestingly, in some SGC histotypes, predominant mutations have been identified, which in some cases behave as "driver mutations", namely mutations capable of governing the carcinogenesis process. Targeting these driver mutations may be an effective therapeutic strategy. Nonetheless, it is not always possible to have drugs suitable for targeting driver mutations-and targeting driver mutations is not always accompanied by a clinical benefit. In this review, we will analyze the main mutations predominant in the various histotypes of SGCs.

Sentinel Lymph Node Biopsy (SLNB) for Early-Stage Head and Neck Squamous-Cell Carcinoma of the Tongue: Twenty Years of Experience at I.N.T. "G.Pascale".

Oral tongue squamous-cell carcinoma (OTSCC) is the most prevalent malignancy in the head and neck region. Lymphatic spread, particularly to cervical lymph nodes, significantly impacts 5-year survival rates, emphasizing the criticality of precise staging. Metastatic cervical lymph nodes can decrease survival rates by 50%. Yet, elective neck dissection (END) in T1-2 cN0 patients proves to be an overtreatment in around 80% of cases. To address this, sentinel lymph node biopsy (SLNB) was introduced, aiming to minimize postoperative morbidity. This study, conducted at the ENT and Maxillofacial Surgery department of the Istituto Nazionale Tumori in Naples, explores SLNB's efficacy in early-stage oral tongue squamous-cell carcinoma (OTSCC). From January 2020 to January 2022, 122 T1/T2 cN0 HNSCC patients were enrolled. Radioactive tracers and lymphoscintigraphy identified sentinel lymph nodes, aided by a gamma probe during surgery. Results revealed 24.6% SLN biopsy positivity, with 169 SLNs resected and a 21.9% positivity ratio. The study suggests SLNB's reliability for T1-2 cN0 OTSCC patient staging and early micrometastasis detection.

Calcified uterine leiomyoma from an 18th-century nunnery in North Italy.

OBJECTIVE: To develop a differential diagnosis of a mass retrieved alongside skeletal remains in the crypt of the church of Santissima Annunziata of Valenza (Province of Alessandria, Northern Italy). MATERIAL: A calcified mass measuring 40 × 39 mm and 17.62 × 16.3817.62 × 16.38 mm. METHOD: The analysis utilized macroscopic assessment and histologic examination (including histochemical and immunohistochemical analyses). RESULTS: Morphological traits include an irregular and spongy external surface. Holes of different sizes lead toward the inner part of the object. A section of the mass shows an "intertwined bundle" pattern, confirmed by microscopic examination. CONCLUSIONS: Differential diagnosis determined the mass to be consistent with calcified leiomyoma. SIGNIFICANCE: Identifying uterine leiomyoma adds to the paucity of paleopathological literature on the condition and to calcified tumors more broadly. It also allows for an important discussion of women's gynecological health in the past and potentially among nulliparous women. LIMITATIONS: Neither histochemical staining nor immunohistochemical analysis demonstrated the certain muscular nature of the specimens due to the rehydration and decalcification processes, for which there are no gold standards. SUGGESTIONS FOR FURTHER RESEARCH: Calcified masses are common in the clinical literature but remain rare in paleopathological literature. Careful excavation and improved recognition of apparently calcified masses are necessary to improve recognition, diagnosis, and interpretation.

Oxidative stress and mitochondrial dysfunction in brain of vinclozolin exposed animals.

Vinclozolin (VCZ) is a widely used fungicide in agriculture, especially in fruits and wine. Various studies have detailed the effects of VCZ exposure on different organs, but no information is available on its effects on brain tissues. This paper investigated the effects of VCZ exposure on the oxidative stress and mitochondrial dysfunction in brain tissue. C57BL/6 mice were exposed to VCZ (100 mg/kg) by oral gavage for 28 days. Mitochondrial homeostasis, often known as mitochondrial quality control, involves a range of processes, including mitochondrial biogenesis, mitochondrial fusion and fission, mitophagy and autophagy. VCZ administration modified the mRNA expression levels of Sirt1, Sirt3, PGC-1α, TFAM, Nrf1, VDAC-1 and Cyt c in brain tissue, as compared to control animals (CTR). The analyses also showed increased oxidative stress, in particular VCZ administration reduced SOD and CAT activities and GSH levels while increased T-AOC levels and lipid peroxidation. Additionally, brain tissues from VCZ group showed DNA oxidation (increased PARP-1 immunostaining) and apoptosis (increased TUNEL+ cells, increased expression of Bax mRNA level and reduced Bcl-2 levels). Western blot and immunohistochemical analyses showed increased mitophagic pathway with the accumulation of PINK1 and Parkin in mitochondria. Additionally, autophagic pathway was also increased with the increased expression and colocalization of LC3 with Neun and GFAP. Overall, this study showed that chronic VCZ exposure impaired mitochondrial homeostasis and increased oxidative stress in brain tissues.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine.

PURPOSE: To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard. METHODS: Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee. A comprehensive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software. RESULTS: Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization. CONCLUSIONS: AI solution represents an efficacy and useful toll degenerative pathologies of lumbar spine to improve radiologist workflow.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.

The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.

PURPOSE: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. METHODS: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. RESULTS: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. CONCLUSIONS: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

An overview on multimodal imaging for the diagnostic workup of pleural mesothelioma.

Pleural mesothelioma (PM) is an aggressive disease that has a strong causal relationship with asbestos exposure and represents a major challenge from both a diagnostic and therapeutic viewpoint. Despite recent improvements in patient care, PM typically carries a poor outcome, especially in advanced stages. Therefore, a timely and effective diagnosis taking advantage of currently available imaging techniques is essential to perform an accurate staging and dictate the most appropriate treatment strategy. Our aim is to provide a brief, but exhaustive and up-to-date overview of the role of multimodal medical imaging in the management of PM.

Elongated styloid process of an autopsied skull from the Cemetery of Santa Maria Maggiore in Vercelli, 18th-19th century (Piedmont, northern Italy).

We discuss the coexistence of a postmortem cut and a pathological alteration, recorded on a skeleton belonging to an adult man that was discovered during the archaeological investigations of the cemetery of the Church of Santa Maria Maggiore in Vercelli (northern Italy, 18th-19th century). The skull presents an oblique cleft, which from the top of the frontal bone bends towards the occipital, and the left styloid process is elongated compared to normal values (48 mm). The elongated styloid process is due to the ossification of the styloid ligament which has several possible causes. To increase the knowledge about this pathological condition in the past, it was necessary to compare all the data present in the literature today and consider the few cases published in the paleopathological field. In this paper, our main goals are: i) to investigate the reasons for which the craniotomy was performed; ii) to examine the possible cause of the ossification of the styloid process, described as Eagle's syndrome; iii) to enrich the archaeological literature of elongated styloid process cases and iv) to investigate the presence of a hypothetical relationship between the autopsy cut and the diagnosed Eagle's syndrome on this skull.