PAPIRI

Con la parola papiro si indica sia la pianta, sia il materiale che ne deriva, utilizzato anticamente per la scrittura. Il volume ripercorre a grandi linee le caratteristiche botaniche ed ecologiche della pianta, con note sull'utilizzo tecnologico e la raffigurazione nell'arte; prosegue con accenni alla scrittura, alla storia del supporto scrittorio, alle piante papirifere, alle proto-carte e conclude con una riflessione sull'uso sostenibile dei materiali cartacei e sulla differenziazione dei rifiuti e il riciclaggio

A Simple Ultrasound Based Classification Algorithm Allows Differentiation of Benign from Malignant Breast Lesions by Using Only Quantitative Parameters.

PURPOSE: We hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not. PROCEDURES: One hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with B-mode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse-ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWVmax, SWVinterm, and SWVmin) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chi-squared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm. RESULTS: Sixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWVmax), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWVmax ≤ or > 3.16; if SWVmax ≤ 3.16 then RI ≤ 0.66, 0.66-0.77 or > 0.77; if RI ≤ 0.66 then SWVmax ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95 % CI 0.818-0.937, p < 0.0001), a sensitivity of 98.46 % (95 % CI 91.7-100 %), and a specificity of 61.02 % (95 % CI 47.4-73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases. CONCLUSIONS: A simple classification algorithm incorporating two quantitative US parameters (SWVmax and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60 % of all cases, avoiding any subjective interpretation bias

Virtual Touch IQ elastography reduces unnecessary breast biopsies by applying quantitative "rule-in" and "rule-out" threshold values.

Our purpose was to evaluate Virtual Touch IQ (VTIQ) elastography and identify quantitative "rule-in" and "rule-out" thresholds for the probability of malignancy, which can help avoid unnecessary breast biopsies. 189 patients with 196 sonographically evident lesions were included in this retrospective, IRB-approved study. Quantitative VTIQ images of each lesion measuring the respective maximum Shear Wave Velocity (SWV) were obtained. Paired and unpaired, non-parametric statistics were applied for comparisons as appropriate. ROC-curve analysis was used to analyse the diagnostic performance of VTIQ and to specify "rule-in" and "rule-out" thresholds for the probability of malignancy. The standard of reference was either histopathology or follow-up stability for >24 months. 84 lesions were malignant and 112 benign. Median SWV of benign lesions was significantly lower than that of malignant lesions (p 98% with a concomitant significant (p = 0.032) reduction in false positive cases of almost 15%, whereas a "rule-in" threshold of 6.5 m/s suggested a probability of malignancy of >95%. In conclusion, VTIQ elastography accurately differentiates malignant from benign breast lesions. The application of quantitative "rule-in" and "rule-out" thresholds is feasible and allows reduction of unnecessary benign breast biopsies by almost 15%

Breast lesion detection and characterization with contrast-enhanced magnetic resonance imaging: Prospective randomized intraindividual comparison of gadoterate meglumine (0.15 mmol/kg) and gadobenate dimeglumine (0.075 mmol/kg) at 3T.

BACKGROUND: Contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast is highly sensitive for breast cancer detection. Multichannel coils and 3T scanners can increase signal, spatial, and temporal resolution. In addition, the T1 -reduction effect of a gadolinium-based contrast agent (GBCA) is higher at 3T. Thus, it might be possible to reduce the dose of GBCA at 3T without losing diagnostic information. PURPOSE: To compare a three-quarter (0.075 mmol/kg) dose of the high-relaxivity GBCA gadobenate dimeglumine, with a 1.5-fold higher than on-label dose (0.15 mmol/kg) of gadoterate meglumine for breast lesion detection and characterization at 3T CE-MRI. STUDY TYPE: Prospective, randomized, intraindividual comparative study. POPULATION: Eligible were patients with imaging abnormalities (BI-RADS 0, 4, 5) on conventional imaging. Each patient underwent two examinations, 24-72 hours apart, one with 0.075 mmol/kg gadobenate and the other with 0.15 mmol/kg gadoterate administered in a randomized order. In all, 109 patients were prospectively recruited. FIELD STRENGTH/SEQUENCE: 3T MRI with a standard breast protocol (dynamic-CE, T2 w-TSE, STIR-T2 w, DWI). ASSESSMENT: Histopathology was the standard of reference. Three blinded, off-site breast radiologists evaluated the examinations using the BI-RADS lexicon. STATISTICAL TESTS: Lesion detection, sensitivity, specificity, and diagnostic accuracy were calculated per-lesion and per-region, and compared by univariate and multivariate analysis (Generalized Estimating Equations, GEE). RESULTS: Five patients were excluded, leaving 104 women with 142 histologically verified breast lesions (109 malignant, 33 benign) available for evaluation. Lesion detection with gadobenate (84.5-88.7%) was not inferior to gadoterate (84.5-90.8%) (P ≥ 0.165). At per-region analysis, gadobenate demonstrated higher specificity (96.4-98.7% vs. 92.6-97.3%, P ≤ 0.007) and accuracy (96.3-97.8% vs. 93.6-96.1%, P ≤ 0.001) compared with gadoterate. Multivariate analysis demonstrated superior, reader-independent diagnostic accuracy with gadobenate (odds ratio = 1.7, P < 0.001 using GEE). DATA CONCLUSION: A 0.075 mmol/kg dose of the high-relaxivity contrast agent gadobenate was not inferior to a 0.15 mmol/kg dose of gadoterate for breast lesion detection. Gadobenate allowed increased specificity and accuracy. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1157-1165

MRI Insights in Breast Imaging

In the world, breast cancer is the most commonly diagnosed cancer among women. Currently, MRI is the most sensitive breast imaging method for detecting breast cancer, although false positive rates are still an issue. To date, the accuracy of breast MRI is widely recognized across various clinical scenarios, in particular, staging of known cancer, screening for breast cancer in high-risk women, and evaluation of response to neoadjuvant chemotherapy. Since technical development and further clinical indications have expanded over recent years, dedicated breast radiologists need to constantly update their knowledge and expertise to remain confident and maintain high levels of diagnostic performance in breast MRI. This review aims to detail current and future applications of breast MRI, from technological requirements and advances to new multiparametric and abbreviated protocols, and ultrafast imaging, as well as current and future indications

The potential of predictive and prognostic breast MRI (P2-bMRI)

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria