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To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC). Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS. For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%. A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its ‘black box’ nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.

Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

Fabry disease (FD) is a rare X-linked disorder characterised by abnormal progressive lysosomal deposition of globotriaosylceramide in a large variety of cell types. The central nervous system (CNS) is often involved in FD, with a wide spectrum of manifestations ranging from mild symptoms to more severe courses related to acute cerebrovascular events. In this review we present the current knowledge on brain imaging for this condition, with a comprehensive and critical description of its most common neuroradiological imaging findings. Moreover, we report results from studies that investigated brain physiopathology underlying this disorder by using advanced imaging techniques, suggesting possible future directions to further explore CNS involvement in FD patients.Teaching Points• Conventional neuroradiological findings in FD are aspecific.• White matter hyperintensities represent the more consistent brain imaging feature of FD• Abnormalities of the vasculature wall of posterior circulation are also consistent features.• The pulvinar sign is not reliable as a finding pathognomonic for FD.• Advanced imaging techniques have increased our knowledge about brain involvement in FD.

The normal growth pattern of female C57BL/6J mice, from 5 to 30 weeks of age, has been investigated in a longitudinal study. Weight, body surface area (BS), and body mass index (BMI) were evaluated in forty mice. Lean mass and fat mass, bone mineral content (BMC), and bone mineral density (BMD) were monitored by dual energy X-ray absorptiometry (DEXA). Weight and BS increased linearly (16.15±0.64–27.64±1.42 g; 51.13±0.74–79.57±2.15 cm2, P<0.01), more markedly from 5 to 9 weeks of age (P<0.001). BMD showed a peak at 17 weeks (0.0548±0.0011 g/cm2* m, P<0.01). Lean mass showed an evident gain at 9 (15.8±0.8 g, P<0.001) and 25 weeks (20.5±0.3 g, P<0.01), like fat mass from 13 to 17 weeks (2.0±0.4–3.6±0.7 g, P<0.01). BMI and lean mass index (LMI) reached the highest value at 21 weeks (3.57±0.02–0.284±0.010 g/cm2, resp.), like fat mass index (FMI) at 17 weeks (0.057±0.009 g/cm2) (P<0.01). BMI, weight, and BS showed a moderate positive correlation (0.45–0.85) with lean mass from 5 to 21 weeks. Mixed linear models provided a good prediction for lean mass, fat mass, and BMD. This study may represent a baseline reference for a future comparison of wild-type C57BL/6J mice with models of altered growth.

Aquaporins (AQPs) are a family of membrane water channel proteins that control osmotically-driven water transport across cell membranes. Recent studies have focused on the assessment of fluid flux regulation in relation to the biological processes that maintain mesenchymal stem cell (MSC) physiology. In particular, AQPs seem to regulate MSC proliferation through rapid regulation of the cell volume. Furthermore, several reports have shown that AQPs play a crucial role in modulating MSC attachment to the extracellular matrix, their spread, and migration. Shedding light on how AQPs are able to regulate MSC physiological functions can increase our knowledge of their biological behaviours and improve their application in regenerative and reparative medicine.

Type 2 diabetes mellitus is a widespread disease, affecting millions of people globally. Although genetics and environmental factors seem to have a role, the cause of this metabolic disorder is largely unknown. Here we report a genetic flaw that markedly reduced the intracellular expression of the high mobility group A1 (HMGA1) protein, and adversely affected insulin receptor expression in cells and tissues from four subjects with insulin resistance and type 2 diabetes. Restoration of HMGA1 protein expression in subjects' cells enhanced INSR gene transcription, and restored cell-surface insulin receptor protein expression and insulin-binding capacity. Loss of Hmga1 expression, induced in mice by disrupting the Hmga1 gene, considerably decreased insulin receptor expression in the major targets of insulin action, largely impaired insulin signaling and severely reduced insulin secretion, causing a phenotype characteristic of human type 2 diabetes.

Aim: To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with ER+ HER2- invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm. Materials and Methods: Pre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS. Results: 248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable (n = 771), not variant (n = 82), and highly intercorrelated (n = 391), respectively. After the use of recursive feature elimination with logistic regression estimator and polynomial transformation, 92 features were finally selected. In the training set, the logistic regression classifier obtained an overall mean accuracy of 60%. In the test set, the accuracy of the ML classifier was 63%, with a sensitivity of 80%, specificity of 43%, and AUC of 66%. Conclusions: Radiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC.

Through negative regulation of gene expression, microRNAs (miRNAs) can function in cancers as oncosuppressors, and they can show altered expression in various tumor types. Here we have investigated medulloblastoma tumors (MBs), which arise from an early impairment of developmental processes in the cerebellum, where Notch signaling is involved in many cell-fate-determining stages. MBs occur bimodally, with the peak incidence seen between 3-4 years and 8-9 years of age, although it can also occur in adults. Notch regulates a subset of the MB cells that have stem-cell-like properties and can promote tumor growth. On the basis of this evidence, we hypothesized that miRNAs targeting the Notch pathway can regulated these phenomena, and can be used in anti-cancer therapies. In a screening of MB cell lines, the miRNA miR-199b-5p was seen to be a regulator of the Notch pathway through its targeting of the transcription factor HES1. Down-regulation of HES1 expression by miR-199b-5p negatively regulates the proliferation rate and anchorage-independent growth of MB cells. MiR-199b-5p over-expression blocks expression of several cancer stem-cell genes, impairs the engrafting potential of MB cells in the cerebellum of athymic/nude mice, and of particular interest, decreases the MB stem-cell-like (CD133+) subpopulation of cells. In our analysis of 61 patients with MB, the expression of miR-199b-5p in the non-metastatic cases was significantly higher than in the metastatic cases (P = 0.001). Correlation with survival for these patients with high levels of miR-199b expression showed a positive trend to better overall survival than for the low-expressing patients. These data showing the down-regulation of miR-199b-5p in metastatic MBs suggest a potential silencing mechanism through epigenetic or genetic alterations. Upon induction of de-methylation using 5-aza-deoxycytidine, lower miR-199b-5p expression was seen in a panel of MB cell lines, supported an epigenetic mechanism of regulation. Furthermore, two cell lines (Med8a and UW228) showed significant up-regulation of miR-199b-5p upon treatment. Infection with MB cells in an induced xenograft model in the mouse cerebellum and the use of an adenovirus carrying miR-199b-5p indicate a clinical benefit through this negative influence of miR-199b-5p on tumor growth and on the subset of MB stem-cell-like cells, providing further proof of concept. Despite advances in our understanding of the pathogenesis of MB, one-third of these patients remain incurable and current treatments can significantly damage long-term survivors. Here we show that miR-199b-5p expression correlates with metastasis spread, identifying a new molecular marker for a poor-risk class in patients with MB. We further show that in a xenograft model, MB tumor burden can be reduced, indicating the use of miR199b-5p as an adjuvant therapy after surgery, in combination with radiation and chemotherapy, for the improvement of anti-cancer MB therapies and patient quality of life. To date, this is the first report that expression of a miRNA can deplete the tumor stem cells, indicating an interesting therapeutic approach for the targeting of these cells in brain tumors.

Over the last few years, radiation therapy (RT) techniques have evolved very rapidly, with the aim of conforming high-dose volume tightly to a target. Although to date CT is still considered the imaging modality for target delineation, it has some known limited capabilities in properly identifying pathologic processes occurring, for instance, in soft tissues. This limitation, along with other advantages such as dose reduction, can be overcome using magnetic resonance imaging (MRI), which is increasingly being recognized as a useful tool in RT clinical practice. This review has a two-fold aim of providing a basic introduction to the physics of MRI in a narrative way and illustrating the current knowledge on its application “from head to toe” (i.e., different body sites), in order to highlight the numerous advantages in using MRI to ensure the best therapeutic response. We provided a basic introduction for residents and non-radiologist on the physics of MR and reported evidence of the advantages and future improvements of MRI in planning a tailored radiotherapy treatment “from head to toe”.Critical relevance statementThis review aims to help understand how MRI has become indispensable, not only to better characterize and evaluate lesions, but also to predict the evolution of the disease and, consequently, to ensure the best therapeutic response.Key PointsMRI is increasingly gaining interest and applications in RT planning.MRI provides high soft tissue contrast resolution and accurate delineation of the target volume.MRI will increasingly become indispensable for characterizing and evaluating lesions, and to predict the evolution of disease.