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Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions.We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10.The best similarity with the patients was obtained with the polyacrylate inserts (55.6-90.2%), the worst with Catphan (15.7-19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3-100% at 120kVp, 75.7-97.9% at 100kVp), and observed a texture dependency in repeatability.Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies.

Purpose The assessment of low-contrast-details is a part of the quality control (QC) program in digital radiology. It generally consists of evaluating the threshold contrast (Cth) detectability details for different-sized inserts, appropriately located in dedicated QC test tools. This work aims to propose a simplified method, based on a statistical model approach for threshold contrast estimation, suitable for different modalities in digital radiology. Methods A home-made low-contrast phantom, made of a central aluminium insert with a step-wedge, was assembled and tested. The reliability and robustness of the method were investigated for Mammography, Digital Radiography, Fluoroscopy and Angiography. Images were analysed using our dedicated software developed on Matlab®. The Cth is expressed in the same unit (mmAl) for all studied modalities. Results This method allows the collection of Cth information from different modalities and equipment by different vendors, and it could be used to define typical values. Results are summarized in detail. For 0.5 diameter detail, Cth results are in the range of: 0.018–0.023 mmAl for 2D mammography and 0.26–0.34 mmAl DR images. For angiographic images, for 2.5 mm diameter detail, the Cth s median values are 0.55, 0.4, 0.06, 0.12 mmAl for low dose fluoroscopy, coronary fluorography, cerebral and abdominal DSA, respectively. Conclusions The statistical method proposed in this study gives a simple approach for Low-Contrast-Details assessment, and the typical values proposed can be implemented in a QA program for digital radiology modalities.

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2–G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45–58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.

Purpose To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. Methods T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. Results From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. Conclusion We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical “how-to” guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

The study aimed to evaluate the performance of radiomics features and one ultrasound CAD (computer-aided diagnosis) in the prediction of the malignancy of a breast lesion detected with ultrasound and to develop a nomogram incorporating radiomic score and available information on CAD performance, conventional Breast Imaging Reporting and Data System evaluation (BI-RADS), and clinical information. Data on 365 breast lesions referred for breast US with subsequent histologic analysis between January 2020 and March 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 255) and a validation test group (n = 110). A radiomics score was generated from the US image. The CAD was performed in a subgroup of 209 cases. The radiomics score included seven radiomics features selected with the LASSO logistic regression model. The multivariable logistic model incorporating CAD performance, BI-RADS evaluation, clinical information, and radiomic score as covariates showed promising results in the prediction of the malignancy of breast lesions: Area under the receiver operating characteristic curve, [AUC]: 0.914; 95% Confidence Interval, [CI]: 0.876–0.951. A nomogram was developed based on these results for possible future applications in clinical practice.

Background: Concerns about overdiagnosis and overtreatment of DCIS have led to an interest in de-escalating treatment for DCIS. However, accruing patients to active surveillance trials poses several issues, the most pressing of which is the high risk of synchronous invasion at the biopsy level (~26%). Since DCIS-associated stromal changes and influx of immune cells might be mediators of progression to invasive breast cancer, the present project will investigate whether radiogenomics is able to predict the upstaging in patients preoperatively diagnosed with DCIS with Vacuum-Assisted Breast Biopsy (VABB). Hypothesis: We hypothesize a different immune gene expression signature between pure DCIS (p-DCIS) and DCIS with an invasive component at final surgery (i-DCIS). -We hypothesize to be able to identify these different immune gene expression signatures in VABB tissue samples. -We hypothesize that a subsequent gene expression profiling-derived immunohistochemistry signature in DCIS diagnosed on VABB, integrated with other histopathological characteristics and preoperative imaging - Standard Mammography (SM), Digital Breast Tomosynthesis (DBT), Contrast-Enhanced Spectral Mammography (CESM), and Magnetic Resonance (MR) - will predict with higher accuracy the upstaging at final surgery. We hypothesize that the implementation of a radiogenomic approach will reproduce all this information with ultimate precision. Aims: Aim 1: to investigate the relationship between immune gene expression signature at diagnosis of DCIS at VABB and at final surgery outcomes. -Aim 2: to investigate if the association of imaging plus genomics is better than genomics alone. -Aim 3: to create and validate a gene expression profiling-derived immunohistochemistry signature. -Aim 4: to implement radiomic in DCIS management. Experimental Design: Starting from a cohort of more than 2000 consecutive patients with a diagnosis of DCIS on VABB and subsequently operated at the European Institute of Oncology, training and testing datasets will be created. VABB p-DCIS and i-DCIS will be profiled using a next-generation sequencing gene expression assay, targeting 395 genes associated with tumor-immune systems interactions and performed on RNA extracted from DCIS samples. Gene expression and the subsequently derived immunohistochemistry profiles will be integrated with preoperative imaging (SM, DBT, CESM, MR). Finally, we will perform a radiomic feature analysis by SM, DBT (when available), and CESM-detected DCIS at VABB. Impact on Cancer: Patients with features that place them at low risk may be more likely to forego additional therapy. Clinicians would distinguish patients who are candidates to surveillance only, because the "pure" DCIS has been completely removed, from patients going to active surveillance plus endocrine treatment because the residual lesion is with high probability a DCIS, from patients to be sent to surgery because most certainly the invasive component will be present. Citation Format: Matteo Lazzeroni, Luca Nicosia, Elena Guerini Rocco, Sara Gandini, Sara Raimondi, Massimo Barberis, Nicola Fusco, Marta Cremonesi, Daniela Origgi, Enrico Cassano, Aliana Guerrieri Gonzaga, Davide Serrano, Debora Macis, Harriet Johansson, Bernardo Bonanni. Radiogenomics for predicting underestimation of invasiveness in ductal carcinoma in situ (DCIS) diagnosed with vacuum assisted breast biopsy: study rationale and design [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr PR004.

Simple Summary Radiomics aims to extract high-dimensional features from clinical images and associate them to clinical outcomes. These associations may be further investigated with machine learning models; however, guidelines on the most suitable method to support clinical decisions are still missing. To improve the reliability and the accuracy of radiomic features in the prediction of a binary variable in a lung cancer setting, we compared several machine learning classifiers and feature selection methods on simulated data. These account for important characteristics that may vary in real clinical datasets: sample size, outcome balancing and association strength between radiomic features and outcome variables. We were able to suggest the most suitable classifiers for each studied case and to evaluate the impact of method choices. Our work highlights the importance of these choices in radiomic analyses and provides guidelines on how to select the best models for the data at hand. Abstract Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features–outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large–medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.

Objectives: We aimed to determine whether radiomic features extracted from a highly homogeneous database of breast MRI could non-invasively predict pathological complete responses (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Methods: One hundred patients with breast cancer receiving NACT in a single center (01/2017–06/2019) and undergoing breast MRI were retrospectively evaluated. For each patient, radiomic features were extracted within the biopsy-proven tumor on T1-weighted (T1-w) contrast-enhanced MRI performed before NACT. The pCR to NACT was determined based on the final surgical specimen. The association of clinical/biological and radiomic features with response to NACT was evaluated by univariate and multivariable analysis by using random forest and logistic regression. The performances of all models were assessed using the areas under the receiver operating characteristic curves (AUC) with 95% confidence intervals (CI). Results: Eighty-three patients (mean (SD) age, 47.26 (8.6) years) were included. Patients with HER2+, basal-like molecular subtypes and Ki67 ≥ 20% presented a pCR to NACT more frequently, the clinical/biological model’s AUC (95% CI) was 0.81 (0.71–0.90). Using 136 representative radiomics features selected through cluster analysis from the 1037 extracted features, a radiomic score was calculated to predict the response to NACT, with AUC (95% CI): 0.64 (0.51–0.75). After combining the clinical/biological and radiomics models, the AUC (95% CI) was 0.83 (0.73–0.92). Conclusions: MRI-based radiomic features slightly improved the pre-treatment prediction of pCR to NACT, in addiction to biological characteristics. If confirmed on larger cohorts, it could be helpful to identify such patients, to avoid unnecessary treatment.

Purpose The Council Directive 2013/59/EURATOM considers interventional radiology to be a special practice involving high doses of radiation and requiring strict monitoring to ensure the best quality assurance programs. This work reports the early experience of managing dose data from patients undergoing angiography in a multicentre study. Materials and methods The study was based on a survey of about 15,200 sample procedures performed in 21 Italian hospitals centres involved on a voluntary basis. The survey concerned the collection of data related to different interventional radiology procedures: interventional cardiology, radiology, neuroradiology, vascular surgery, urology, endoscopy and pain therapy from a C-Arm and fixed units. The analysis included 11 types of procedures and for each procedure, air-kerma, kerma-area product and fluoroscopy time were collected. Results The duration and dose values of fluoroscopic exposure for each procedure is strongly dependent on individual clinical circumstances including the complexity of the procedure; the observed distribution of patient doses was very wide, even for a specified protocol. The median values of the parameters were compared with the diagnostic reference levels (DRL) proposed for some procedures in Italy (ISTISAN) or internationally. This work proposes local DRL values for three procedures. Conclusion This first data collection serves to take stock of the situation on patient’s dosimetry in several sectors and is the starting point for obtaining and updating DRL recalling that these levels are dependent on experience and technology available.

Background The aim of this study is to evaluate the dose delivered and the image quality of pre-treatment MVCT images with Hi-Art TomoTherapy system, varying acquisition and reconstruction parameters. Materials and methods Catphan 500 MVCT images were acquired with all acquisition pitch and reconstruction intervals; image quality was evaluated in terms of noise, uniformity, contrast linearity, contrast-to-noise ratio (CNR) and spatial resolution with the Modulation Transfer Function (MTF). Dose was evaluated as Multi Slice Average Dose (MSADw) and measurements were performed with the Standard TomoTherapy® Quality Assurance Kit composed by the TomoTherapy Phantom, the Exradin A1SL ion chamber and TomoElectrometer. For each pitch-reconstruction interval, acquisitions were repeated 5 times. Results Differences in noise and uniformity, though statistically significant in some cases, were very small: noise ranged from 2.3% for Coarse – 3 mm to 2.4% for Coarse – 6 mm, while uniformity passed from 99.5% for Coarse – 6 mm to 99.8% for Normal – 4 mm. No differences at all were found for CNR for high and low density inserts, while MTF was higher for pitch Coarse, even if no differences in spatial resolution were observed visually (spatial resolution was up to 4 lp/cm for all combinations of pitch and reconstruction interval). Dose was dependent on pitch, being 1.0 cGy for Coarse, 1.5 cGy for Normal and 2.85 cGy for Fine. Conclusions We observed negligible differences in image quality among different pitch and reconstruction interval, thus, considerations regarding pre-treatment imaging modalities should be based only on dose delivered and on the desired resolution along the cranio-caudal axis for image-guided radiotherapy and adaptive radiotherapy purposes.

To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features.TFrom 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information.We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. Results: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. Conclusions: a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.

Radiomics focuses on extracting a large number of quantitative imaging features and testing both their correlation with clinical characteristics and their prognostic and predictive values. We propose a radiomic approach using magnetic resonance imaging (MRI) to decode tumor phenotype and local recurrence in oropharyngeal squamous cell carcinoma (OPSCC). The contrast-enhanced T1-weighted sequences from baseline MRI examinations of OPSCC patients treated between 2008 and 2016 were retrospectively selected. Radiomic features were extracted using the IBEX software, and hierarchical clustering was applied to reduce features redundancy. The association of each radiomic feature with tumor grading and stage, HPV status, loco-regional recurrence within 2 years, considered as main endpoints, was assessed by univariate analysis and then corrected for multiple testing. Statistical analysis was performed with SAS/STAT® software. Thirty-two eligible cases were identified. For each patient, 1286 radiomic features were extracted, subsequently grouped into 16 clusters. Higher grading (G3 vs. G1/G2) was associated with lower values of GOH/65Percentile and GOH/85Percentile features (p = 0.04 and 0.01, respectively). Positive HPV status was associated with higher values of GOH/10Percentile (p = 0.03) and lower values of GOH/90Percentile (p = 0.03). Loco-regional recurrence within 2 years was associated with higher values of GLCM3/4-7Correlation (p = 0.04) and lower values of GLCM3/2-1InformationMeasureCorr1 (p = 0.04). Results lost the statistical significance after correction for multiple testing. T stage was significantly correlated with 9 features, 4 of which (GLCM25/180-4InformationMeasureCorr2, Shape/MeanBreadth, GLCM25/90-1InverseDiffMomentNorm and GLCM3/6-1InformationMeasureCorr1) retained statistical significance after False Discovery Rate correction. MRI-based radiomics is a feasible and promising approach for the prediction of tumor phenotype and local recurrence in OPSCC. Some radiomic features seem to be correlated with tumor characteristics and oncologic outcome however, larger collaborative studies are warranted in order to increase the statistical power and to obtain robust and validated results.

To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients. This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007–23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster’s representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant. Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41–99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model. This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12. • No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12.

To evaluate quantification of iodine uptake in metastatic and non-metastatic lymph nodes (LNs) by dual-energy CT (DECT) and to assess if the distribution of iodine within LNs at DECT correlates with the pathological structure. Ninety LNs from 37 patients (23 with lung and 14 with gynaecological malignancies) were retrospectively selected. Information of LNs sent for statistical analysis included Hounsfield units (HU) at different energy levels; decomposition material densities fat–iodine, iodine–fat, iodine–water, water–iodine. Statistical analysis included evaluation of interobserver variability, material decomposition densities and spatial HU distribution within LNs. Interobserver agreement was excellent. There was a significant difference in iodine–fat and iodine–water decompositions comparing metastatic and non-metastatic LNs (p < 0.001); fat–iodine and water–iodine did not show significant differences. HU distribution showed a significant gradient from centre to periphery within non-metastatic LNs that was significant up to 20–30% from the centre, whereas metastatic LNs showed a more homogeneous distribution of HU, with no significant gradient. DECT demonstrated a lower iodine uptake in metastatic compared to non-metastatic LNs. Moreover, the internal iodine distribution showed an evident gradient of iodine distribution from centre to periphery in non-metastatic LNs, and a more homogeneous distribution within metastatic LNs, which corresponded to the pathological structure. • This study demonstrated a lower iodine uptake in metastatic than non-metastatic LNs. • Internal distribution of HU was different between metastatic and non-metastatic lymph nodes. • The intranodal iodine distribution disclosed a remarkable correlation with the histological LN structure.

Purpose To develop a phantom for methodological radiomic investigation on Magnetic Resonance (MR) images of female patients affected by pelvic cancer. Methods A pelvis-shaped container was filled with a MnCl2 solution reproducing the relaxation times (T1, T2) of muscle surrounding pelvic malignancies. Inserts simulating multi-textured lesions were embedded in the phantom. The relaxation times of muscle and tumour were measured on an MR scanner on healthy volunteers and patients; T1 and T2 of MnCl2 solutions were evaluated with a relaxometer to find the concentrations providing a match to in vivo relaxation times. Radiomic features were extracted from the phantom inserts and the patients’ lesions. Their repeatability was assessed by multiple measurements. Results Muscle T1 and T2 were 1128 (806–1378) and 51 (40–65) ms, respectively. The phantom reproduced in vivo values within 13% (T1) and 12% (T2). T1 and T2 of tumour tissue were 1637 (1396–2121) and 94 (79–101) ms, respectively. The phantom insert best mimicking the tumour agreed within 7% (T1) and 24% (T2) with in vivo values. Out of 1034 features, 75% (95%) had interclass correlation coefficient greater than 0.9 on T1 (T2)-weighted images, reducing to 33% (25%) if the phantom was repositioned. The most repeatable features on phantom showed values in agreement with the features extracted from patients’ lesions. Conclusions We developed an MR phantom with inserts mimicking both relaxation times and texture of pelvic tumours. As exemplified with repeatability assessment, such phantom is useful to investigate features robustness and optimise the radiomic workflow on pelvic MR images.

This document aims to support the dental professional in choosing the adequate diagnostic technique, minimising the radiation dose in observance of the As Low As Reasonably Achievable (ALARA) principle (7). This principle states that the biological cost can only be justifed when the beneft, that is, the diagnosis, outweighs the risk related to radiation exposure. In this guideline paper, we report recommendations for radiologists, medical physicists, paediatrician, dentists and maxillofacial surgeons, with reference to the specifc felds.

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

Purpose To implement a Quality Assurance program for mammography integrating the dose-tracking software Radimetrics™ (Bayer) in a workflow for a quick assessment of equipment performance and patient dose reduction. Methods A setting was created in Radimetrics™ [1] to collect the acquisition parameters and dosimetric data of all the mammographies performed. However, to implement an optimization program, a deeper level of data stratification was needed. A tool was developed in Matlab®, to separately store the data exported from Radimetrics™ according to the acquisition type (2D/3D), control mode (manual/automatic), equipment, operator. The tool automatically produces reports and graphs synthesizing acquisition and dosimetric parameters (anode-filter coupling, kV, mAs, compression thickness, Entrance Dose ED, Average Glandular Dose AGD) for each mammography unit, highlighting possible outliers and/or changes of the equipment performance. Data of 70806 acquisitions were collected. Dosimetric data of manual acquisitions of prothesis using producer indications were compared to those obtained with parameters chosen by the technicians according to experience. Data from automatic acquisition with three different SenoEssential equipments (GE Healthcare) were compared in terms of acquisition settings (% of Mo-Mo, Mo-Rh and Rh-Rh, minimum–maximum values of kV and mAs) and dose, also comparing AGD with the achievable and acceptable levels reported in the European Guidelines [2] . Results For prothesis acquisitions, lower AGD values were delivered using parameters suggested by manufacturer for compression thickness During automatic acquisitions of 2D projections, the use of the bucky dedicated to tomosynthesis lead to thickness underestimation, and to a higher proportion of Mo-Rh acquisitions delivering AGD above the achievable threshold (Figure). Conclusions For prothesis acquisitions, new indications were provided to the technicians suggesting the optimal settings resulted for each compression thickness interval. According to our results, use of tomosynthesis bucky for 2D acquisitions should be discouraged.

Purpose To extract radiomic features from CT images of non small cells lung cancer (NSCLC) patients, assess their repeatability and reproducibility, and investigate their possible correlation with lymph node status (pN). Methods CT images of 481 patients affected by NSCLC were considered, selecting only images acquired at our Institution with 120 kV and 2.5 mm slice thickness. The 3D Regions Of Interest (ROIs) delineating the nodule were drawn by a Radiologist and uploaded in the IBEX tool with the CT images for radiomic features calculation (1419 for each ROI). Features repeatability was investigated with a phantom study. Only features exhibiting Coefficient Of Variation ANOVA was performed to identify the features significantly affected by acquisition parameters as pixel size, mAs, contrast medium type, or by ROI volume. Hierarchical clustering was performed to group the remaining repeatable and reproducible features into clusters, maximizing intra-cluster and minimizing inter-cluster correlation. Wilcoxon rank-sum test was performed to investigate correlation between each feature and pN. For each cluster, the feature yielding highest significant correlation (if present) was considered representative. For comparison, correlation was also investigated between pN and the not-radiomic Signal-to-Noise and Contrast-to-Noise Ratio parameters (SNR, CNR) calculated for each ROI. Results 203 of the 481 patients entered the Radiomic analysis; 520 out of the 1419 extracted features were selected after repeatability and robustness assessment. 15 clusters represented the original dataset without redundancy (Figure); 4 of them contained at least one feature with significant p value at Wilcoxon test (Table), indicating high probability to be correlated with pN. No significant correlation was found between non-radiomic parameters (SNR, CNR) and pN. Conclusion Despite preliminary, the present results evidence that – differently from standard quantitative parameters like SNR and CNR – radiomic features describing tumour texture and shape may predict information about lymph node status.

Assessment of deep (>50%) myometrial invasion by dual-energy CT (DECT) and Trans-Vaginal US (TVUS) in patients with endometrial cancer. We retrospectively enrolled patients with endometrial cancer who underwent DECT and TVUS for pre-surgical staging. Three sets of images were evaluated: 70 keV (routine CT images), 50 keV, and iodine-water reconstructions. The gold standard was pathology after surgery. The agreement between the different imaging modalities and the gold standard was estimated. Sensitivity, specificity and accuracy for each imaging modality were evaluated with 95% confidence intervals (CI). Thirty-nine patients were included. Median time from CT and TVUS to surgery was 23 and 18 days, respectively. The best agreement between evaluation of myometrial infiltration and the gold standard was 0.88 (0.72, 1.00) for the 50 keV images; the worst agreement was 0.43 (0.00, 0.88) for the 70 keV images. CT iodine reconstructions and US agreement were comparable. Specificity, sensitivity and accuracy were 0.91, 1.00, 0.94; 0.57, 0.86, 0.71; 0.82, 1.00, 0.87; 0.91, 0.77, 0.86 for 50 keV, 70 keV, iodine reconstructions and ultrasound, respectively. DECT is a promising tool for assessment of myometrial invasion in endometrial cancer patients, with a special focus on 50 keV images.

Purpose To assess the noise characteristics of the new adaptive statistical iterative reconstruction (ASiR‐V) in comparison to ASiR. Methods A water phantom was acquired with common clinical scanning parameters, at five different levels of CTDIvol. Images were reconstructed with different kernels (STD, SOFT, and BONE), different IR levels (40%, 60%, and 100%) and different slice thickness (ST) (0.625 and 2.5 mm), both for ASiR‐V and ASiR. Noise properties were investigated and noise power spectrum (NPS) was evaluated. Results ASiR‐V significantly reduced noise relative to FBP: noise reduction was in the range 23%–60% for a 0.625 mm ST and 12%–64% for the 2.5 mm ST. Above 2 mGy, noise reduction for ASiR‐V had no dependence on dose. Noise reduction for ASIR‐V has dependence on ST, being greater for STD and SOFT kernels at 2.5 mm. For the STD kernel ASiR‐V has greater noise reduction for both ST, if compared to ASiR. For the SOFT kernel, results varies according to dose and ST, while for BONE kernel ASIR‐V shows less noise reduction. NPS for CT Revolution has dose dependent behavior at lower doses. NPS for ASIR‐V and ASiR is similar, showing a shift toward lower frequencies as the IR level increases for STD and SOFT kernels. The NPS is different between ASiR‐V and ASIR with BONE kernel. NPS for ASiR‐V appears to be ST dependent, having a shift toward lower frequencies for 2.5 mm ST. Conclusions ASiR‐V showed greater noise reduction than ASiR for STD and SOFT kernels, while keeping the same NPS. For the BONE kernel, ASiR‐V presents a completely different behavior, with less noise reduction and modified NPS. Noise properties of the ASiR‐V are dependent on reconstruction slice thickness. The noise properties of ASiR‐V suggest the need for further measurements and efforts to establish new CT protocols to optimize clinical imaging.

Purpose The aim of this study is to collect Statistical Low Contrast Detectability (LCD) [1] data from different angiography using the same phantom and the same software for the analysis. Data from this study will define the reference value and the tolerance for quality control tests. Methods A phantom consisting of a square aluminum target and a 5-step-wedge (0.25–1.25 mmAl) was built. A Matlab software automatically calculates LCD values in terms of Pixel Value (PV) and contrast threshold (Cth); it also converts PV in mmAl, providing the LCD (mmAl). Phantoms and software have been distributed to the members of AIFM digital radiology working group. A simple protocol has been followed by all the physicists. Two different set up for Low Contrast Detectability measurement have been defined using, at least, two different attenuation phantoms: 16 cm PMMA phantom and 1.5 mmCu filter. Different acquisition protocols were used and LCD phantom has been employed in different modalities such as fluoroscopy, fluorography and DSA. Dosimetric data has been also collected. Results This method is able to collect low contrast detectability information from different modalities, clinical protocols and equipment by different vendors. Preliminary results have been reported in Table 1 . Conclusions The LCD phantom and statistical method can be easily implemented in angiography. Data from this study can be used to define the reference value and the tolerance for low contrast threshold quality control in digital angiography with flat panel detector. The same method can also be applied in mammography, CR and DR radiography.

Purpose To assess low contrast detectability (LCD) of two different iterative reconstruction (IR) algorithms: the ASiR and the new ASiR-V. Methods LCD was evaluated through a statistical method [1] . A water phantom was acquired with two different devices: a CT Discovery 750HD equipped with the ASiR and a CT Revolution equipped with the ASiR-V. Acquisitions were performed at CTDI vol levels ranging from 1 to 15 mGy and images were reconstructed with FBP and 3 different levels of IR (40%, 60% and 100%, for both algorithms). A 192 × 192 pixels ROI placed over 20 images was divided into subROIs from 2 × 2 to 24 × 24 pixels. Distribution of the mean pixel value of the subROIs was taken into account: normality was assessed using Jarque-Brera test and low contrast threshold ( LC th ) was calculated according to [1] and then averaged over the 20 slices. To take into account the intrinsic differences among the two scanners, LC th was normalized to the 4.4 mm insert with the FBP. Results LC th increases passing from FBP to ASiR-V depending on insert dimension and IR level. Considering the 4.4. mm insert, LC th improves of 11% and 27% for ASiR-V 40% and ASiR-V 100%, respectively. For a fixed level of IR, improvement is more evident for smaller objects, passing from 27% for the 2.2 mm insert to 8% for the 13.2 mm insert. ASiR-V presents an improvement in LC th for all doses and all levels of IR in comparison to ASiR. Improvement in detectability increases with dose for a fixed IR level; for a fixed dose level, LC th increases with increasing IR level. Conclusions ASiR-V improves low contrast detectability in comparison to ASiR.

Purpose To assess the feasibility of a Computed Tomography Angiography (CTA) protocol with low kVp and the new iterative reconstruction (IR) algorithm ASiR-V. Methods 63 patients (43 men and 20 women) were scanned on a 256 slices CT Revolution in prospective axial mode, with 80 kVp and mA ranging from 350 to 600 depending on patient BMI. Images were reconstructed with Filtered Back-projection (FBP) and with ASiR-V ranging from 20% to 100%. Images were analyzed quantitatively, placing ROIs in the ascending aorta, aortic arch and descending aorta to evaluate noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratio for all patients and for all levels of ASiR-V. Qualitative image evaluation was performed by two radiologist (both >10 years of experience) using the LIKERT scale. For all patients effective dose (E) is calculated from DLP. In addition, a comparison with the former clinical protocol on a CT Discovery with ASiR instead of ASiR-V was performed. Results Passing from FBP to ASiR-V 100%, noise is reduced on average up to 31%, while SNR and CNR are improved on average up to 46%. Subjective analysis gives the highest LIKERT score to the images reconstructed with ASiR-V 60% (3.8 ± 0.3 for reader 1, 3.8 ± 0.6 for reader 2–k = 0.87). With this IR level, noise is reduced by 20%, while SNR and CNR are improved by 25%. In a subpopulation with BMI >30, ASiR-V 80% resulted in the best compromise between dose reduction and image quality. Considering the same 50% IR level for ASiR and ASiR-V, with ASiR-V we observe noise reduction (10–27%), CNR (12–40%) and SNR(6–32%) improvement, while dose decreases form 1.5 to 1 mSv. Conclusions The new proposed protocol at 80 kVp tube voltage and 60% ASiR-V allows high image quality and mean doses of only 1 mSv.

The National Lung Cancer Screening Trial has recently demonstrated that screening of high-risk populations with the use of low-dose computed tomography (LDCT) reduces lung cancer mortality[1]. Based on this encouraging result, the National Comprehensive Cancer Network guidelines recommended LDCT for selected patients at high risk of lung cancer[2]. This suggests that an increasing number of CT screening examinations will be performed. The LDCT technique is relatively simple but some CT parameters are important and should be accurately defined in order to achieve good diagnostic quality and minimize the delivered dose. In addition, LDCT examinations are not as easy to read as they may initially appear; different approaches and tools are available for nodule detection and measurement. Moreover, the management of positive results can be a complex process and can differ significantly from routine clinical practice. Therefore this paper deals with the LDCT technique, reading methods and interpretation in lung cancer screening, particularly for those radiologists who have little experience of the technique.

Background Multidetector CT (MDCT) scanners have con- tributed to the widespread use of CT in paediatric imaging. However, concerns are raised for the associated radiation exposure. Very few surveys on radiation exposure from MDCT studies in children are available. Objective The aim of this study was to outline the status of radiation exposure in children from MDCT practice in Italy. Materials and methods In this retrospective multicentre study we asked Italian radiology units with an MDCT scanner with at least 16 slices to provide dosimetric and acquisition param- eters of CT examinations in three age groups (1–5, 6–10, 11– 15 years) for studies of head, chest and abdomen. The dosi- metric results were reported in terms of third-quartile volu- metric CT dose index (CTDIvol) (mGy), size-specific dose estimate (SSDE) (mGy), dose length product (DLP) (mGy cm), and total DLP for multiphase studies. These results were compared with paediatric European and adult Italian published data. A multivariate analysis assessed the association of CTDIvol with patient characteristics and scanning modalities. Results We collected data from 993 MDCT examinations performed at 25 centres. For age groups 1–5 years, 6–10 years and 11–15 years, the CTDIvol, DLP and total DLP values were statistically significantly below the values observed in our analogous national survey in adults, although the difference decreased with increasing age. CTDIvol variability among centres was statistically significant (variance = 0.07; 95% confidence interval = 0.03–0.16; P < 0.001). Conclusions This study reviewed practice in Italian centres performing paediatric imaging with MDCT scanners. The variability of doses among centres suggests that the use of standardised CT protocols should be encouraged

In this paper a novel method for the extraction of magnetic resonance spectroscopy (MRS) parameters is presented. The method applies the traditional time-domain linear prediction singular value decomposition (LPSVD) on the set of orthonormal sub-signals obtained by wavelet packets (WP) decomposition of the original free induction decay (FID) signal. Using the properties of WP the desired, optimal, sub-band FID decomposition is obtained and used to progressively separate the different metabolic components in distinct sub-bands. A pseudo-optimal WP tree is obtained using the minimum description length (MDL) criteria. The proposed algorithm preserves all the advantages of the traditional LPSVD method, but the WP decomposition considerably improves the LPSVD performances in the presence of noise. The paper addresses this aspect in details by comparing the innovative sub-band and the traditional full-band approaches. Algorithms are tested on simulated signals that mimic real MRS data.

ObjectiveTo estimate the cumulative radiation exposure and lifetime attributable risk of cancer incidence associated with lung cancer screening using annual low dose computed tomography (CT).DesignSecondary analysis of data from a lung cancer screening trial and risk-benefit analysis.Setting10 year, non-randomised, single centre, low dose CT, lung cancer screening trial (COSMOS study) which took place in Milan, Italy in 2004-15 (enrolment in 2004-05). Secondary analysis took place in 2015-16.ParticipantsHigh risk asymptomatic smokers aged 50 and older, who were current or former smokers (≥20 pack years), and had no history of cancer in the previous five years.Main outcome measuresCumulative radiation exposure from low dose CT and positron emission tomography (PET) CT scans, calculated by dosimetry software; and lifetime attributable risk of cancer incidence, calculated from the Biological Effects of Ionizing Radiation VII (BEIR VII) report.ResultsOver 10 years, 5203 participants (3439 men, 1764 women) underwent 42 228 low dose CT and 635 PET CT scans. The median cumulative effective dose at the 10th year of screening was 9.3 mSv for men and 13.0 mSv for women. According to participants' age and sex, the lifetime attributable risk of lung cancer and major cancers after 10 years of CT screening ranged from 5.5 to 1.4 per 10 000 people screened, and from 8.1 to 2.6 per 10 000 people screened, respectively. In women aged 50-54, the lifetime attributable risk of lung cancer and major cancers was about fourfold and threefold higher than for men aged 65 and older, respectively. The numbers of lung cancer and major cancer cases induced by 10 years of screening in our cohort were 1.5 and 2.4, respectively, which corresponded to an additional risk of induced major cancers of 0.05% (2.4/5203). 259 lung cancers were diagnosed in 10 years of screening; one radiation induced major cancer would be expected for every 108 (259/2.4) lung cancers detected through screening.ConclusionRadiation exposure and cancer risk from low dose CT screening for lung cancer, even if non-negligible, can be considered acceptable in light of the substantial mortality reduction associated with screening.

The authors sought to evaluate the effect of model-based iterative reconstruction (MBIR) on the sensitivity of ground-glass nodule (GGN) detection at different dose levels.Fifty-four artificial GGN were randomly divided into three sets, each positioned in an anthropomorphic phantom. The three sets were evaluated on standard-dose (SD, 350 mA), low-dose (LD, 35 mA) and ultra-low-dose (ULD, 10 mA) CT scans (100 kV, 64 × 0.625 mm, 0.5 s), and each scan was reconstructed twice with filtered back projection (FBP) and MBIR. Three radiologists independently evaluated the scans for GGN presence and size. SD + FBP was considered the reference standard. A region of interest (ROI) was used to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio normalised to dose (CNRD). McNemar's test, Bland-Altman analysis and t test were used for statistical assessment (p 0.05).The mean diameter of the 54 GGNs was 9.2 mm (range 3.7-17.3 mm). For the three readers, no statistically significant differences were observed in the sensitivity of GGN detection between LD + MBIR, ULD + MBIR and SD + FBP (p 0.05). Bland-Altman analysis showed a good reader agreement (±1.5 mm) for GGN size between SD + FBP and ULD + MBIR. For low dose and ultra-low dose, the SNR and CNRD were significantly higher with MBIR (p 0.0001). The effective dose was 97.1 % lower with ultra-low dose (0.15 mSv) than standard dose (5.15 mSv).The detection of GGN with MBIR at low-dose and ultra-low-dose CT does not differ significantly from standard-dose CT with FBP in an anthropomorphic phantom.

To evaluate the radiation dose in routine multidetector computed tomography (MDCT) examinations in Italian population.This was a retrospective multicentre study included 5,668 patients from 65 radiology departments who had undergone common CT protocols: head, chest, abdomen, chest–abdomen–pelvis (CAP), spine and cardiac. Data included patient characteristics, CT parameters, volumetric CT dose index (CTDIvol) and dose length product (DLP) for each CT acquisition phase. Descriptive statistics were calculated, and a multi-regression analysis was used to outline the main factors affecting exposure.The 75th percentiles of CTDIvol (mGy) and DLP (mGy cm) for whole head were 69 mGy and 1,312 mGy cm, respectively; for chest, 15 mGy and 569 mGy cm; spine, 42 mGy and 888 mGy cm; cardiac, 7 mGy and 131 mGy cm for calcium score, and 61 mGy and 1,208 mGy cm for angiographic CT studies. High variability was present in the DLP of abdomen and CAP protocols, where multiphase examinations dominated (71 % and 73 % respectively): for abdomen, 18 mGy, with 555 and 920 mGy cm in abdomen and abdomen–pelvis acquisitions respectively; for CAP, 17 mGy, with 508, 850 and 1,200 mGy cm in abdomen, abdomen–pelvis and CAP acquisitions respectively.The results of this survey could help in the definition of updated diagnostic reference levels (DRL).• Radiation dose associated with multidetector CT (MDCT) is an important health issue. • This national survey assessed dose exposures of 5,668 patients undergoing MDCT. • Dose indices correlate with BMI, voltage, rotation time, pitch and tube current. • These results may contribute to an update of national diagnostic reference levels.

An increase has been observed not only in the absolute number of CT examinations but also in the length of coverage and number of scanning phases, with the result that exposure to ionising radiation from CT is becoming an increasingly serious problem. The extent of the problem is not entirely known and cannot be adequately addressed without proper knowledge of all the phases that leads to the effective dose calculation. In light of the growing awareness of the issue of ionising radiation dose and the possible risk for the individual and the population, there is a need for radiologists, medical physicists and radiographers to play an active role in dose management. In this review, the authors try to delineate the problem in a consequential and multifaceted way: radiation–patient interaction, possible mechanisms of damage, main CT dose units, risk and its quantification in the population, with the aim of optimising the acquisition dose without diagnostic drawbacks. For an “up-to-date” use of CT, radiologists must know the dose concerns for the single patient and population, and use the CT apparatus with the best dose care; substitute CT with other diagnostic techniques when possible, especially in children; reduce the number/extension of scans and phases, and the dose in single scans and single examinations.

The goal of establishing prompt localization of the malignant spread or recurrence of a tumor has found a powerful solution in the definition of follow-up protocols, which include the indication for CT scans on an annual or semiannual basis. In the case of long-surviving patients, however, this approach will lead to a considerable integrated dose level over a period of several years after recovery from the illness. Pathologies treated primarily by surgery and/or chemotherapy have been considered, not taking into account cancers treated with adjuvant or radical radiotherapy. Given that the most likely protocols for these cancers often call for total body scans, an estimation of the consequent effective and organ doses can be performed with acceptable accuracy. The data acquired from five centers have been collected and the related effective and organ doses calculated by means of IMPACT software. Use of the effective dose concept, however, has lately become the subject of criticism, and the recently proposed Effective Risk Model has therefore also been applied. The evaluated absolute additional risk of second tumor induction ranges between 0.1% and 10%, depending primarily on age and pathology. These results depict this additional risk as an issue of significant importance for clinical practice. A revision of follow-up and scan parameter protocols, as well as the introduction of new algorithms for dose reduction, could significantly improve the risk-benefit ratio for all the pathologies studied.

Performance of Automatic Exposure control in several digital mammography systems (both CR and DR) were evaluated. The test procedure applied was that proposed by the European Guidelines, which requires measurement of contrast-to-noise ratio (CNR) produced by 0.2 mm Al superimposed on variable polymethyl-methacrylate (PMMA) thicknesses. PMMA layers were exposed by full automatic techniques and average glandular dose (AGD) determined. Results demonstrated that AGD values keep below the acceptable limits for all CR and DR systems at almost all equivalent breast thicknesses. CNRabsolute values per each thickness can significantly vary, depending on characteristics of each type of equipment. The application of limits suggested by the European Guidelines for CNRvariation with reference to 50 mm PMMA causes systematic failure for equivalent breast thickness above 50 mm; this may be due to the contradiction between the principles followed by the manufacturers to designed the AEC (signal constancy) and those proposed by the European protocol (CNRconstancy).

The objective of this study is to compare radiation doses given to ovarian cancer patients by different computed tomographies (CTs) and to evaluate association between doses and subjective and objective image quality. CT examinations included were performed either on a 16-slice CT, equipped with automatic z-axis tube current modulation, or on a 64-slice CT, equipped with z-axis, xy-axis modulation, and adaptive statistical iterative algorithm (ASIR). Evaluation of dose included the following dose descriptors: volumetric CT dose index (CTDIvol), dose length product (DLP), and effective dose (E). Objective image noise was evaluated in abdominal aorta and liver. Subjective image quality was evaluated by assessment of image noise, spatial resolution and diagnostic acceptability. Mean and median CTDIvol, DLP, and E; correlation between CTDIvol and DLP and patients’ weight; comparison of objective noise for the 2 scanners; association between dose descriptors and subjective image quality. The 64-slice CT delivered to patients 24.5% lower dose (P

Introduction The aim was to assess the value of magnetic resonance mammography (MRM) in the detection of recurrent breast cancer on the prior lumpectomy site in patients with previous conservative surgery and radiotherapy. Methods Between April 1999 and July 2003, 93 consecutive patients with breast cancer treated with conservative surgery and radiotherapy underwent MRM, when a malignant lesion on the site of lumpectomy was suspected by ultrasound and/or mammography. MRM scans were evaluated by morphological and dynamic characteristics. MRM diagnosis was compared with histology or with a 36-month imaging follow-up. Enhancing areas independent of the prior lumpectomy site, incidentally detected during the MRM, were also evaluated. Results MRM findings were compared with histology in 29 patients and with a 36-month follow-up in 64 patients. MRM showed 90% sensitivity, 91.6% specificity, 56.3% positive predictive value and 98.7% negative predictive value for detection of recurrence on the surgical scar. MRM detected 13 lesions remote from the scar. The overall sensitivity, specificity, positive predictive value and negative predictive value of MRM for detection of breast malignancy were 93.8%, 90%, 62.5% and 98.8%, respectively. Conclusion MRM is a sensitive method to differentiate recurrence from post-treatment changes at the prior lumpectomy site after conservative surgery and radiation therapy. The high negative predictive value of this technique can avoid unnecessary biopsies or surgical treatments.