Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

The aim of the study was to estimate the diagnostic accuracy of textural, morpho- logical and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were ana- lyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivari- ate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions

The role of Aurora-A kinase in the Golgi-dependent control of mitotic entry

During mitosis, the Golgi complex undergoes a multi-step fragmentation process that is instrumental to its correct partitioning into the daughter cells. To prepare for this segregation, the Golgi ribbon is initially separated into individual stacks during the G2 phase of the cell cycle. Then, at the onset of mitosis, these individual stacks are further disassembled into dispersed fragments. Inhibition of this Golgi fragmentation step results in a block or delay of G2/M transition, depending on the experimental approach. Thus, correct segregation of the Golgi complex appears to be monitored by a ‘Golgi mitotic checkpoint’. Using a microinjection-based approach, we recently identified the first target of the Golgi checkpoint, whereby a block of this Golgi fragmentation impairs recruitment of the mitotic kinase Aurora-A to, and its activation at, the centrosomes. Overexpression of Aurora-A can override this cell cycle block, indicating that Aurora-A is a major effector of the Golgi checkpoint. We have also shown that this block of Aurora-A recruitment to the centrosomes is not mediated by the known mechanisms of regulation of Aurora-A function. Here we discuss our findings in relation to the known functions of Aurora-A

PARP10 Mediates Mono-ADP-Ribosylation of Aurora-A Regulating G2/M Transition of the Cell Cycle

Intracellular mono-ADP-ribosyltransferases (mono-ARTs) catalyze the covalent attachment of a single ADP-ribose molecule to protein substrates, thus regulating their functions. PARP10 is a soluble mono-ART involved in the modulation of intracellular signaling, metabolism and apoptosis. PARP10 also participates in the regulation of the G1- and S-phase of the cell cycle. However, the role of this enzyme in G2/M progression is not defined. In this study, we found that genetic ablation, protein depletion and pharmacological inhibition of PARP10 cause a delay in the G2/M transition of the cell cycle. Moreover, we found that the mitotic kinase Aurora-A, a previously identified PARP10 substrate, is actively mono-ADP-ribosylated (MARylated) during G2/M transition in a PARP10-dependent manner. Notably, we showed that PARP10-mediated MARylation of Aurora-A enhances the activity of the kinase in vitro. Consistent with an impairment in the endogenous activity of Aurora-A, cells lacking PARP10 show a decreased localization of the kinase on the centrosomes and mitotic spindle during G2/M progression. Taken together, our data provide the first evidence of a direct role played by PARP10 in the progression of G2 and mitosis, an event that is strictly correlated to the endogenous MARylation of Aurora-A, thus proposing a novel mechanism for the modulation of Aurora-A kinase activity

Multidisciplinary Management of Retroperitoneal Sarcoma: Diagnosis, Prognostic Factors and Treatment

Retroperitoneal sarcomas (RPS) are rare cancers whose management can be challenging due to various presentation patterns, multiple organ involvement, and a high local and distant recurrence rate. Histopathology and prognostic factors analysis are essential to predict the behaviour of the disease and plan the best therapeutic strategy. To date, surgery is still the main therapeutic option that guarantees a chance of cure from the primary disease. While chemotherapy and radiotherapy seem to be good options for controlling metastatic and recurrent irresectable disease, their role in the treatment of primary RPS remains unclear. This literature review aims to provide a comprehensive overview of the multidisciplinary aspects of RPS management in high-volume centres, summarising the diagnostic path, the prognostic factors, and the most suitable therapeutic options

Safety and Activity of Metronomic Temozolomide in Second-Line Treatment of Advanced Neuroendocrine Neoplasms

Background. Platinum-based chemotherapy is the mainstay of front-line treatment of patients affected by pluri-metastatic intermediate/high grade NeuroEndocrine Neoplasms (NENs). However, there are no standard second-line treatments at disease progression. Previous clinical experiences have evidenced that temozolomide (TMZ), an oral analog of dacarbazine, is active against NENs at standard doses of 150 to 200 mg/mq per day on days 1 to 5 of a 28-day cycle, even if a significant treatment-related toxicity is reported. Methods. Metastatic NENs patients were treated at the ENETS (European NeuroEndocrine Tumor Society) center of excellence of Naples (Italy), from 2014 to 2017 with a second-line alternative metronomic schedule of TMZ, 75 mg/m2 per os “one week on/one week off”. Toxicity was graded with NCI-CTC criteria v4.0; objective responses with RECIST v1.1 and performance status (PS) according to ECOG. Results. Twenty-six consecutive patients were treated. Median age was 65.5 years. The predominant primary organs were pancreas and lung. Grading was G2 in 11 patients, G3 in 15. More than half of patients had a PS 2 (15 vs. 11 with PS 1). The median time-on-temozolomide therapy was 12.2 months (95% CI: 11.4–19.6). No G3/G4 toxicities were registered. Complete response was obtained in 1 patient, partial response in 4, stable disease in 19 (disease control rate: 92.3%), and progressive disease in 2. The median overall survival from TMZ start was 28.3 months. PS improved in 73% of patients. Conclusions. Metronomic TMZ is a suitable treatment for G2 and G3 NENs particularly in PS 2 patients. Prospective and larger trials are needed to confirm these results

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions

Malignant Sinonasal Tumors: Update on Histological and Clinical Management

Tumors of nasal cavity and paranasal sinuses (TuNSs) are rare and heterogeneous malignancies, presenting different histological features and clinical behavior. We reviewed the literature about etiology, biology, and clinical features of TuNSs to define pathologic features and possible treatment strategies. From a diagnostic point of view, it is mandatory to have high expertise and perform an immunohistochemical assessment to distinguish between different histotypes. Due to the extreme rarity of these neoplasms, there are no standard and evidence-based therapeutic strategies, lacking prospective and large clinical trials. In fact, most studies are retrospective analyses. Surgery represents the mainstay of treatment of TuNSs for small and localized tumors allowing complete tumor removal. Locally advanced lesions require more demolitive surgery that should be always followed by adjuvant radio- or chemo-radiotherapy. Recurrent/metastatic disease requires palliative chemo- and/or radiotherapy. Many studies emphasize the role of specific genes mutations in the development of TuNSs like mutations in the exons 4–9 of the TP53 gene, in the exon 9 of the PIK3CA gene and in the promoter of the TERT gene. In the near future, this genetic assessment will have new therapeutic implications. Future improvements in the understanding of the etiology, biology, and clinical features of TuNSs are warranted to improve their management

Major and ancillary features according to LI-RADS in the assessment of combined hepatocellular-cholangiocarcinoma

The aim of the study was to investigate the performance of the Liver Imaging Reporting and Data System (LI-RADS) v2018 for combined hepatocellular-cholangiocarcinoma (cHCC-CCA) identifying the features that allow an accurate characterization