A ‘machine learning’ technique for discriminating captive-reared from wild Atlantic bluefin tuna, Thunnus thynnus (Osteichthyes: Scombridae), based on differential fin spine bone resorption

The Atlantic bluefin tuna (ABFT) fishery is regulated by the International Commission for the Conservation of Atlantic Tunas (ICCAT), which establishes the allowable annual yield and the minimum capture size, and allocates capture quotas to the Contracting Parties. Despite fishery monitoring, a considerable amount of captures escapes ICCAT control. In the Mediterranean Sea, the purse seine fishery supports ABFT farming, a capture-based aquaculture activity that involves catching fish from the wild and rearing them in sea cages for a few months. The first spine of the cranial dorsal fin undergoes a continuous bone remodeling process consisting in old bone (primary bone) resorption and new bone (secondary bone) apposition. A marked increase of spine bone resorption was shown in captive-reared ABFT with respect to wild specimens. In this paper, the Random Forest (RF), a Computer Aided Detection system, was applied to distinguish captive-reared from wild ABFT based on fish age, fish fork length, total surface of spine cross section, and surface of remodeled bone tissue in the spine cross section (sum of reabsorbed bone tissue and secondary cancellous bone). The RF system was also compared to the Logistic Regression method (LR). The percentages of properly classified animals, either wild or captive-reared, with respect to the overall number of animals, i.e. accuracy, was 95.3 ± 2.6% and 79.0 ± 5.1% for RF and LR, respectively. The percentages of the properly classified captive-reared specimens, i.e. sensitivity, were 93.5 ± 3.1% and 75.8 ± 5.3% for RF and LR, respectively. The percentages of the properly classified wild specimens was 96.7 ± 2.2% and 81.4 ± 4.9%, for RF and LR, respectively. The proposed technique appears to be a reliable investigation tool anytime the suspicion arises that illegally caught ABFT are sold as aquaculture products

Advancement study of CancerMath model as prognostic tools for predicting Sentinel lymph node metastasis in clinically negative T1 breast cancer patients

Purpose: Sentinel lymph node biopsy (SLNB) is an invasive surgical procedure and although it has fewer complications and is less severe than axillary lymph node dissection, it is not a risk-free procedure. Large prospective trials have documented SLNB that it is considered non-therapeutic in early stage breast cancer. Methods: Web-calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumour size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor. We collected 595 patients referred to our Institute resulting clinically negative T1 breast cancer characterized by sentinel lymph node status, prognostic factors defined by CM and also HER2 and Ki-67. We have compared classification performances obtained by online CM application with those obtained after training its algorithm on our database. Results: By training CM model on our dataset and using the same feature, adding HER2 or ki67 we reached a sensitivity median value of 71.4%, 73%, 70.4%, respectively, whereas the online one was equal to 61%, without losing specificity. The introduction of the prognostic factors Her2 and Ki67 could help improving performances on the classification of particularly type of patients. Conclusions: Although the training of the model on the sample of T1 patients has brought a significant improvement in performance, the general performance does not yet allow a clinical application of the algorithm. However, the experimental results encourage future developments aimed at introducing features of a different nature in the CM model

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used

An Analysis of Poverty in Italy through a Fuzzy Regression Model

Over recent years, and related in particular to the significant recent international economic crisis, an increasingly worrying rise in poverty levels has been observed both in Italy, as well as in other countries. Such a phenomenon may be analysed from an objective perspective (i.e. in relation to the macro and micro-economic causes by which it is determined) or, rather, from a subjective perspective (i.e. taking into consideration the point of view of individuals or families who locate themselves as being in a condition of hardship). Indeed, the individual “perception” of a state of being allows for the identification of measures of poverty levels to a much greater degree than would the assessment of an external observer. For this reason, experts in the field have, in recent years, attempted to overcome the limitations of traditional approaches, focusing instead on a multidimensional approach towards social and economic hardship, equipping themselves with a wide range of indicators on living conditions, whilst simultaneously adopting mathematical tools which allow for a satisfactory investigation of the complexity of the phenomenon under examination. The present work elaborates on data revealed by the EUSILC survey of 2006 regarding the perception of poverty by Italian families, through a fuzzy regression model, with the aim of identifying the most relevant factors over others in influencing such perceptions

Immune-Based Combinations versus Sorafenib as First-Line Treatment for Advanced Hepatocellular Carcinoma: A Meta-Analysis

Recent years have observed the emergence of novel therapeutic opportunities for advanced hepatocellular carcinoma (HCC), such as combination therapies including immune checkpoint inhibitors. We performed a meta-analysis with the aim to compare median overall survival (OS), median progression-free survival (PFS), complete response (CR) rate, and partial response (PR) rate in advanced HCC patients receiving immune-based combinations versus sorafenib. A total of 2176 HCC patients were available for the meta-analysis (immune-based combinations = 1334; sorafenib = 842) and four trials were included. Immune-based combinations decreased the risk of death by 27% (HR, 0.73; 95% CI, 0.65–0.83; p p p p < 0.03), respectively. The current study further confirms that first-line immune-based combinations have a place in the management of HCC. The CR rate observed in HCC patients receiving immune-based combinations appears more than twelve times higher compared with sorafenib monotherapy, supporting the long-term benefit of these combinatorial strategies, with even the possibility to cure advanced disease

Alzheimer’s disease diagnosis based on the Hippocampal Unified Multi-Atlas Network (HUMAN) algorithm

Abstract Background Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer’s disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance. Materials and methods The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available. Results HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC1 = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC2 = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans. Conclusions The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer’s disease and become a useful tool for other neuroimaging applications

La biopsia ecoguidata Elite con sistema TruVac e sonda da 13 G: risultati preliminari

OBIETTIVO: La tipizzazione di noduli mammari mediante prelievi con ago sottile è talora complicata, per varie ragioni da esiti inconclusivi. Queste problematiche dilatano i tempi diagnostici rendendo necessarie ulteriori biopsie con risultati spesso discordanti. Per questo, in molti centri si stanno utilizzando aghi di calibro maggiore sostituendo la citologia con la microistologia: i sistemi di biopsia vuoto-assistita (vacuum-assisted breast biopsy, VABB) evidenziano performance superiori, risultando, tuttavia, spesso meno maneggevoli e più costosi. Lo scopo di questo studio è di valutare le performance di sistema del prelievo microistologico con ago 13 G e tecnologia VABB senza cavi con una maneggiabilità vicina a quella di un ago tranciante. METODI: Da gennaio 2016 a febbraio 2018, due operatori hanno eseguito complessivamente 86 prelievi microistologici con ago 13 G Elite su lesioni BIRADS 3, 4 e 5, delle quali 30 ripetute dopo precedenti prelievi cito-istologici inconclusivi. Sono state biopsiate lesioni tra 5 e 43 mm di cui 70 noduli, 12 aree di alterazione ecostrutturale non-mass like e tre cisti complex. RISULTATI: Il sistema 13 G ha evidenziato 3,53% casi B1, 41,17% B2, 17,64% B3 e 37,64% B5. Nello stesso periodo i prelievi con ago tranciante 14-16 G con i medesimi operatori hanno evidenziato i seguenti risultati: 2,65% B1, 44,33% B2, 9% B3, 0,48% B4, 43,49% B5. Il prelievo da 13 G Elite ha permesso un cambio di classe istologica nell’83,33% delle procedure ripetute dopo prelievo non dirimente. CONCLUSIONI: La procedura bioptica con sistema TruVac si è dimostrata affidabile e potrebbe essere utilizzata per ridurre i casi con esito cito-istologico non dirimente