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Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. Methods: A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 −); (3) presence of the hormone receptor (HR + vs HR −); and (4) presence of luminal subtypes of breast cancer. Results: The best accuracy for discriminating HER2 + versus HER2 − breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR − breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). Conclusions: The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome. [ABSTRACT FROM AUTHOR]

Purpose: The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). Methods: GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. Results: A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. Conclusions: AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications. [ABSTRACT FROM AUTHOR]

Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs. [ABSTRACT FROM AUTHOR]

Background: Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. Purpose: To compare performance of the two packages on a single database of FFDM images. Methods: Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA's and OpenBreast's removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal–Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. Results: FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA's Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P < 0.05). No dependence on breast density or laterality has been found (Kruskal–Wallis test P > 0.05). Conclusion: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response. [ABSTRACT FROM AUTHOR]

Background: Radiology is an essential tool in the management of a patient. The aim of this manuscript was to build structured report (SR) Mammography based in Breast Cancer. Methods: A working team of 16 experts (group A) was composed to create a SR for Mammography Breast Cancer. A further working group of 4 experts (group B), blinded to the activities of the group A, was composed to assess the quality and clinical usefulness of the SR final draft. Modified Delphi process was used to assess level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency and to measure quality analysis according to the average inter-item correlation. Results: The final SR version was built by including n = 2 items in Personal Data, n = 4 items in Setting, n = 2 items in Comparison with previous breast examination, n = 19 items in Anamnesis and clinical context; n = 10 items in Technique; n = 1 item in Radiation dose; n = 5 items Parenchymal pattern; n = 28 items in Description of the finding; n = 12 items in Diagnostic categories and Report and n = 1 item in Conclusions. The overall mean score of the experts and the sum of score for structured report were 4.9 and 807 in the second round. The Cronbach's alpha (Cα) correlation coefficient was 0.82 in the second round. About the quality evaluation, the overall mean score of the experts was 3.3. The Cronbach's alpha (Cα) correlation coefficient was 0.90. Conclusions: Structured reporting improves the quality, clarity and reproducibility of reports across departments, cities, countries and internationally and will assist patient management and improve breast health care and facilitate research. [ABSTRACT FROM AUTHOR]

Radial scar (RS) or complex sclerosing lesions (CSL) if > 10 mm is a benign lesion with an increasing incidence of diagnosis (ranging from 0.6 to 3.7%) and represents a challenge both for radiologists and for pathologists. The digital mammography and digital breast tomosynthesis appearances of RS are well documented, according to the literature. On ultrasound, variable aspects can be detected. Magnetic resonance imaging contribution to differential diagnosis with carcinoma is growing. As for the management, a vacuum-assisted biopsy (VAB) with large core is recommended after a percutaneous diagnosis of RS due to potential sampling error. According to the recent International Consensus Conference, a RS/CSL lesion, which is visible on imaging, should undergo therapeutic excision with VAB. Thereafter, surveillance is justified. The aim of this review is to provide a practical guide for the recognition of RS on imaging, illustrating radiological findings according to the most recent literature, and to delineate the management strategies that follow. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the role of automated breast volume scanner (ABVS) compared to handheld ultrasound (HHUS) and contrast-enhanced magnetic resonance imaging (CE-MRI) in the early detection of patients with locally advanced breast cancer who are more likely to reach a complete pathological response (pCR) during neoadjuvant chemotherapy (NAC). Methods: A single-institution prospective study was performed in patients with histological diagnosis of invasive breast cancer, eligible for NAC, and who were to undergo surgery in our Hospital. Imaging examinations with ABVS, HHUS and CE-MRI were performed at diagnosis (basal time) and after 3 months of chemotherapy (middle time). The tumor size of each lesion was measured at the basal and middle times, and the dimensional variation was reported. Based on this, patients were divided dichotomously by the median value, obtaining "good responders" (goodR) versus "poor responders" (poorR). The results were correlated with the histological assessment (pCR versus No-pCR) with the use of the intergroup comparison of categorical data (Fisher's exact test). Result: A total of 21 patients were included; 5 obtained a pCR (23%). Both the ABVS and the CE-MRI found all 5 patients with pCR in the group of goodR (10 patients), while none of the poorR (11 patients) obtained a pCR [correlation was statistically significant (p 0.01)]. In the HHUS, goodR (10 patients) 1 obtained a pCR while in the poorR (11 patients) 4 obtained a pCR [correlation not statistically significant (p 0.31)]. Conclusions: ABVS could be a useful tool, appearing to be more reliable than HHUS, and as accurate as CE-MRI, in early detection of patients who could reach a pCR after NAC. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop a radiomics model for predicting the Ki-67 proliferation index in patients with invasive ductal breast cancer through magnetic resonance imaging (MRI) preoperatively. Materials and methods: A total of 128 patients who were clinicopathologically diagnosed with invasive ductal breast cancer were recruited. This cohort included 32 negative Ki67 expression (Ki67 proliferation index < 14%) and 96 cases with positive Ki67 expression (Ki67 proliferation index ≥ 14%). All patients had undergone diffusion-weighted imaging (DWI) MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from apparent diffusion coefficient (ADC) maps which were obtained by DWI-MRI from patients with invasive ductal breast cancer. 80% of the patients were divided into training set to build radiomics model, and the rest into test set to evaluate its performance. The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features, and then, the logistic regression (LR) model was established using fivefold cross-validation to predict the Ki-67 index. The performance was evaluated by receiver-operating characteristic (ROC) analysis, accuracy, sensitivity and specificity. Results: Quantitative imaging features (n = 1029) were extracted from ADC maps, and 11 features were selected to construct the LR model. Good identification ability was exhibited by the ADC-based radiomics model, with areas under the ROC (AUC) values of 0.75 ± 0.08, accuracy of 0.71 in training set and 0.72, 0.70 in test set. Conclusions: The ADC-based radiomics model is a feasible predictor for the Ki-67 index in patients with invasive ductal breast cancer. Therefore, we proposed that three-dimensional imaging features from ADC maps could be used as candidate biomarker for preoperative prediction the Ki-67 index noninvasively. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate excision histology outcome of B3a lesions diagnosed at imaging-guided core biopsy and assess whether clinical and radiologic features may predict upgrade to malignancy.Methods: A total of 153 B3a lesions (74 papillomas, 51 radial scars and 28 fibro-epithelial lesions) that underwent surgical excision were assessed. PPV for malignancy in all B3a lesions and specific for each sub-category was evaluated. Multivariate analysis was conducted to identify association between clinical (age, family or personal history of breast cancer, symptoms), diagnostic findings (imaging modality, lesion size, final BI-RADS category) and final excision outcome.Results: Eleven (7%) of 153 B3a lesions were upgraded to malignancy. All carcinomas diagnosed on excision were non-high grade DCIS. Following features were significantly associated with malignancy: lesions identified only on mammography (3/21; 14%), both mammography and ultrasound (7/44; 16%), lesion size > 10 mm (10/74; 13.5%) and BI-RADS category 4-5 (8/29; 27.6%). The absence of residual microcalcification after biopsy was associated with decreased risk for malignancy (15/16[93.7%]; p = 0.0297). Lesion size > 10 mm (OR = 9.3832; 95%; p = 0.0398) and BI-RADS category 4-5 (OR = 12.6004; 95%; p = 0.0006) were found to be independent predictors of upgrade to malignancy.Conclusions: B3a lesions are associated with low risk of malignancy at excision. Lesion size > 10 mm and BI-RADS 4-5 category may represent useful predictors of upgrade to malignancy. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the agreement between multiparametric Magnetic Resonance Imaging (mpMRI), Partin tables (PT) and the Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) in assessing risk category in prostate cancer (PCa) patients referred to External Beam Radiotherapy (EBRT).Materials and methods: In this bicentric study, we prospectively enrolled 80 PCa patients who underwent pre-EBRT mpMRI on a 3.0T magnet with a multiparametric protocol including high-resolution, multiplanar T2-weighted sequences, diffusion-weighted imaging and dynamic contrast-enhanced imaging. National comprehensive cancer network risk categories were assessed using prostate-specific-antigen level, Gleason score and the T-stage as defined by mpMRI or nomograms. Cohen’s kappa statistic was used to calculate the agreement between mpMRI and nomograms in assessing the T-stage (organ-confined (OC) vs. non-organ-confined (nOC) disease) and risk category (≤ low risk vs. intermediate risk vs. ≥ high risk).Results: mpMRI showed poor agreement with PT and MSKCCn in assessing nOC versus OC (k = 0.16 for both), translating into an mpMRI-induced reclassification of PT- and MSKCCn-related risk category in 36.3% (k = 0.43) and 41.3% (k = 0.31) of cases, respectively, with most changes occurring towards intermediate risk category.Conclusions: mpMRI showed low agreement with nomograms as a tool to stratify PCa risk, leading to significant risk reclassification. Assuming that mpMRI is a more reliable surrogate standard of reference for pathology, this technique should refine or replace nomograms in risk classification before EBRT. [ABSTRACT FROM AUTHOR]

Automated breast ultrasound (ABUS) is a recently introduced ultrasonography technique, developed with the purpose to standardize breast ultrasonography and overcome some limitations of handheld ultrasound (HHUS), such as operator dependence and the considerable amount of medical time necessary to perform and interpret HHUS. This new ultrasonography technique separates the moment of image acquisition (that may be performed also by a technician) from that of its interpretation, increasing reproducibility, reducing operator-dependence and physician time. Moreover, multiplanar reconstructions, especially the coronal view, introduce new diagnostic information. ABUS, with those advantages, has the potential to be used as an adjunctive tool to screening mammography, especially in the dense breast, where mammography has a relatively low sensitivity. Women's awareness of risks related to breast density is a hot topic, especially in the USA where legislative breast density notification laws increase the demand for supplemental ultrasound screening. Therefore, ABUS might have the potential to respond to this need. The purpose of this article is to present a summary of current state-of-the-art of ABUS technology and applications, with an emphasis on breast cancer screening. This article discusses also how to overcome some ABUS limitations, in order to be familiar with the new technique. [ABSTRACT FROM AUTHOR]

Purpose: This study was done to evaluate test-retest reliability of liver diffusion tensor imaging (LDTI). Materials and methods: Ten healthy volunteers (median age 23 years) underwent two LDTI scans on a 3.0 T magnet during two imaging sessions separated by 2 weeks (session-1/-2, respectively). Fifteen gradient directions and b values of 0-1,000 s/mm were used. Two radiologists in consensus assessed liver apparent diffusion coefficient (ADC) and fraction of anisotropy (FA) values on ADC and FA maps at four reference levels, namely: right upper level (RUL), right lower level (RLL), left upper level (LUL) and left lower level (LLL). We then assessed (a) whether ADC and FA values overlapped when measured on different levels within the same imaging session or between different imaging sessions; (b) the degree of variability on an intra-session and inter-session basis, respectively, using the coefficient of variation (CV). Results: In sessions 1 and 2, the ADC/FA values were significantly larger in the left liver lobe (LUL/LLL) compared to right liver lobe (RUL/RLL) ( p < 0.05/6). Intra-session CVs were 9.51 % (session 1) and 9.73 % (session 2) for ADC, and 12.93 % (session 1) and 11.82 % (session 2) for FA, respectively. When comparing RUL, RLL, LUL and LLL on an inter-session basis, CVs were 6.52, 8.20, 6.52 and 11.06 % for ADC, and 15.42, 15.80, 15.42 and 6.80 % for FA, respectively. Conclusion: LDTI provides consistent and repeatable measurements. However, since larger left lobe ADC/FA values can be attributed to artefacts, right lobe values should be considered the most reliable measurements of water diffusivity within the liver. [ABSTRACT FROM AUTHOR]

Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of La Radiologia Medica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)