Diagnosi di malattia da reflusso gastro esofageo con pH-impedenzometria: proposta di nuovi parametri diagnostici

Oggigiorno si ritiene che la ph-impedenzometria delle 24 ore sia la miglior tecnica per valutare gli episodi di reflusso gastro-esofageo. Il presente studio di tesi valuta l’idea di un nuovo parametro diagnostico che conferisca maggiore sensibilità e specificità al test pH-impedenzometrico attualmente in uso e che sia in grado di fare diagnosi di malattia da reflusso gastroesofageo(MRGE) in tempi molto più brevi rispetto alle 24 ore necessarie alla ph-impedenzometria classica. Il parametro diagnostico in questione è l’impedenza basale esofagea e, dai dati raccolti, sembra che il valore basale dell’impedenza esofagea calcolata nei primi 30 minuti sia in correlazione con i valori medi dell’impedenza basale nelle 24 ore e con i valori attualmente diagnostici per la MRGE

Radiomic applications on Digital breast Tomosynthesis of BI-RADS category 4 calcifications underwent on vacuum-assisted breast biopsy.

According to the Breast Imaging Reporting and Data System (BI-RADS) microcalcifications category 4 includes those findings that have from 2% to 95% chance to be a neoplasia. Regarding the detection of the microcalcifications, mammography is a test with high sensibility (about 95%) but low specificity (about 41%) with a positive predictive value (PPV) inferior to 30%. To date the definitive diagnosis of suspicious microcalcifications detected on imaging necessarily requires a histopathologic examination and, therefore, a biopsy sample. Considering that the majority of the suspicious microcalcifications addressed to biopsy (70-80%) turn out to be benign at the histopathological examination, it is clear that many of these biopsies could be avoided. An instrument capable of reducing the rate of false-positive findings is needed. This hypothetical tool should overcome the large use of biopsy which is an invasive, not risk-free, and expensive technique. Radiomics is a new re-search tool well suited to this scenario. Indeed, radiomics represents an advanced ana-lytic methodology that uses and studies the quantitative features extracted from bio-medical images to generate imaging biomarkers for better support in the clinical management of the patient. The study aims to investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications addressed to vacuum-assisted breast biopsy (VABB) in our center. According to micro-histopathology, the patients were divided into two groups: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted from the centering tomosynthesis of each patient and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by means of four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. The best performance was achieved using the RF classifier (AUC=0.59, 95% confidence interval 0.57–0.60; sensitivity=0.56, 95% CI 0.54–0.58; specificity=0.61, 95% CI 0.59–0.63; accuracy=0.58, 95% CI 0.57–0.59). Our study has some limitations: it was a retrospective single-center design; although we had not a small data set, it could be amplified in future studies; the clinical risk factors were not incorporated; we did not correlate the radiomic results with breast density which can influence the risk to develop breast cancer; for the manual segmentation of the process a single radiologist was involved, therefore, was not possible to evaluate the reliability of the intra- and inter-observer processes. In conclusion, DBT-based radiomic analysis seems to show only a limited potential in discriminating benign from malignant microcalcifications. Therefore, radiomic features alone are not able to define the clinical management of patients with BI-RADS category 4 microcalcifications. However, our results did not exclude that a further improved classification model can reduce the false-positive rate and adjust the radiologic cut-off for image-guided breast biopsy. We believe that with further large-scale studies capable of overcoming the limits of our work it might be possible to obtain a radiomic classification model as a supplement to the BI-RADS for a better selection of patients with suspicious microcalcifications that need VABB

Imaging of Adverse Events Related to Checkpoint Inhibitor Therapy

Immunotherapy with checkpoint inhibitors (ICIs) is becoming standard of practice for an increasing number of cancer types. ICIs enhance T-cell action against the cancer cells. By unbalancing the immune system ICIs may cause dysimmune toxicities, a series of disorders broadly defined immune-related adverse events (irAEs). IrAEs may affect any organ or apparatus and most frequently involve skin, colon, endocrine organs, liver, and lungs. Early identification and appropriate treatment of irAEs can improve patient outcome. The paper aims at reviewing mechanisms of the occurrence of irAEs, the importance of a proper diagnosis and the main pillars of therapy. To provide effective guidance to the comprehension of major irAEs imaging findings will be reviewed

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications

Can Magnetic Resonance Radiomics Analysis Discriminate Parotid Gland Tumors? A Pilot Study

Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy