A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Diagnostic performance of image-guided vacuum-assisted breast biopsy after neoadjuvant therapy for breast cancer: prospective pilot study.

BACKGROUND: Image-guided vacuum-assisted breast biopsy (VABB) of the tumour bed, performed after neoadjuvant therapy, is increasingly being used to assess residual cancer and to potentially identify to identify pathological complete response (pCR). In this study, the accuracy of preoperative VABB specimens was assessed and compared with surgical specimens in patients with triple-negative or human epidermal growth factor receptor 2 (HER2)-positive invasive ductal breast cancer after neoadjuvant therapy. As a secondary endpoint, the performance of contrast-enhanced MRI of the breast and PET-CT for response prediction was assessed. METHODS: This single-institution prospective pilot study enrolled patients from April 2018 to April 2021 with a complete response on imaging (iCR) who subsequently underwent VABB before surgery. Those with a pCR at VABB were included in the primary analysis of the accuracy of VABB. The performance of imaging (MRI and PET-CT) was analysed for prediction of a pCR considering both patients with an iCR and those with residual disease at postneoadjuvant therapy imaging. RESULTS: Twenty patients were included in the primary analysis. The median age was 44 (range 35-51) years. At surgery, 18 of 20 patients showed a complete response (accuracy 90 (95 per cent exact c.i. 68 to 99) per cent). Only two patients showed residual ductal intraepithelial neoplasia of grade 2 and 3 respectively. In the secondary analysis, accuracy was similar for MRI and PET-CT (77 versus 78 per cent; P = 0.76). CONCLUSION: VABB in patients with an iCR might be a promising method to select patients for de-escalation of surgical treatment in triple-negative or HER2-positive breast cancer. The present results support such an approach and should inform the design of future trials on de-escalation of surgery.

Preoperative breast MRI positively impacts surgical outcomes of needle biopsy-diagnosed pure DCIS: a patient-matched analysis from the MIPA study.

OBJECTIVES: To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS). METHODS: The MIPA observational study database (7245 patients) was searched for patients aged 18-80 years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs). RESULTS: Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111). CONCLUSIONS: Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation. CLINICAL RELEVANCE STATEMENT: When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies. KEY POINTS: • Confounding factors cause imbalance when investigating the influence of preoperative MRI on surgical outcomes of pure DCIS. • When patient matching is applied to women with pure unilateral DCIS, reoperation rates are significantly reduced in women who underwent preoperative MRI. • The reduction of reoperations brought about by preoperative MRI is more than double the increase in overall mastectomies.

Screening and diagnostic breast MRI: how do they impact surgical treatment? Insights from the MIPA study.

OBJECTIVES: To report mastectomy and reoperation rates in women who had breast MRI for screening (S-MRI subgroup) or diagnostic (D-MRI subgroup) purposes, using multivariable analysis for investigating the role of MRI referral/nonreferral and other covariates in driving surgical outcomes. METHODS: The MIPA observational study enrolled women aged 18-80 years with newly diagnosed breast cancer destined to have surgery as the primary treatment, in 27 centres worldwide. Mastectomy and reoperation rates were compared using non-parametric tests and multivariable analysis. RESULTS: A total of 5828 patients entered analysis, 2763 (47.4%) did not undergo MRI (noMRI subgroup) and 3065 underwent MRI (52.6%); of the latter, 2441/3065 (79.7%) underwent MRI with preoperative intent (P-MRI subgroup), 510/3065 (16.6%) D-MRI, and 114/3065 S-MRI (3.7%). The reoperation rate was 10.5% for S-MRI, 8.2% for D-MRI, and 8.5% for P-MRI, while it was 11.7% for noMRI (p ≤ 0.023 for comparisons with D-MRI and P-MRI). The overall mastectomy rate (first-line mastectomy plus conversions from conserving surgery to mastectomy) was 39.5% for S-MRI, 36.2% for P-MRI, 24.1% for D-MRI, and 18.0% for noMRI. At multivariable analysis, using noMRI as reference, the odds ratios for overall mastectomy were 2.4 (p < 0.001) for S-MRI, 1.0 (p = 0.957) for D-MRI, and 1.9 (p < 0.001) for P-MRI. CONCLUSIONS: Patients from the D-MRI subgroup had the lowest overall mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). This analysis offers an insight into how the initial indication for MRI affects the subsequent surgical treatment of breast cancer. KEY POINTS: • Of 3065 breast MRI examinations, 79.7% were performed with preoperative intent (P-MRI), 16.6% were diagnostic (D-MRI), and 3.7% were screening (S-MRI) examinations. • The D-MRI subgroup had the lowest mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). • The S-MRI subgroup had the highest mastectomy rate (39.5%) which aligns with higher-than-average risk in this subgroup, with a reoperation rate (10.5%) not significantly different to that of all other subgroups.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome.

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.

Magnetic resonance imaging before breast cancer surgery: results of an observational multicenter international prospective analysis (MIPA).

OBJECTIVES: Preoperative breast magnetic resonance imaging (MRI) can inform surgical planning but might cause overtreatment by increasing the mastectomy rate. The Multicenter International Prospective Analysis (MIPA) study investigated this controversial issue. METHODS: This observational study enrolled women aged 18-80 years with biopsy-proven breast cancer, who underwent MRI in addition to conventional imaging (mammography and/or breast ultrasonography) or conventional imaging alone before surgery as routine practice at 27 centers. Exclusion criteria included planned neoadjuvant therapy, pregnancy, personal history of any cancer, and distant metastases. RESULTS: Of 5896 analyzed patients, 2763 (46.9%) had conventional imaging only (noMRI group), and 3133 (53.1%) underwent MRI that was performed for diagnosis, screening, or unknown purposes in 692/3133 women (22.1%), with preoperative intent in 2441/3133 women (77.9%, MRI group). Patients in the MRI group were younger, had denser breasts, more cancers ≥ 20 mm, and a higher rate of invasive lobular histology than patients who underwent conventional imaging alone (p < 0.001 for all comparisons). Mastectomy was planned based on conventional imaging in 22.4% (MRI group) versus 14.4% (noMRI group) (p < 0.001). The additional planned mastectomy rate in the MRI group was 11.3%. The overall performed first- plus second-line mastectomy rate was 36.3% (MRI group) versus 18.0% (noMRI group) (p < 0.001). In women receiving conserving surgery, MRI group had a significantly lower reoperation rate (8.5% versus 11.7%, p < 0.001). CONCLUSIONS: Clinicians requested breast MRI for women with a higher a priori probability of receiving mastectomy. MRI was associated with 11.3% more mastectomies, and with 3.2% fewer reoperations in the breast conservation subgroup. KEY POINTS: • In 19% of patients of the MIPA study, breast MRI was performed for screening or diagnostic purposes. • The current patient selection to preoperative breast MRI implies an 11% increase in mastectomies, counterbalanced by a 3% reduction of the reoperation rate. • Data from the MIPA study can support discussion in tumor boards when preoperative MRI is under consideration and should be shared with patients to achieve informed decision-making.

Comparative Diagnostic Efficacy of Abbreviated and Full Protocol Breast MRI: A Systematic Review and a Meta-Analysis.

OBJECTIVES: This meta-analysis aims to compare the efficacy, limitations, and clinical implications of Abbreviated Breast MRI (AB-MRI) and Full Protocol MRI (FP-MRI). It focuses on diagnostic accuracy across various populations and settings, extending the scope of prior meta-analyses by including studies conducted after 2019 in both screening and diagnostic contexts. METHODS: We conducted a systematic review (from 11/2019 to 12/2022). A bivariate model was used to calculate the summary estimates of sensitivity and specificity. Random effect models were used to calculate summary AUC and probability distributions for negative and positive predictive values were obtained. Subgroup analyses were conducted to investigate the differences between the AB-MRI and FP-MRI in terms of sensitivity, specificity, and AUC. RESULTS: The search across multiple databases yielded 11 eligible studies, including one prospective and ten retrospective studies. Statistical analysis revealed a significant difference in sensitivity between FP-MRI (95%) and AB-MRI (86%, p = 0.005), but not in specificity (p = 0.50). AB-MRI demonstrated a shorter acquisition time, suggesting potential for increased patient throughput. However, challenges remain in detecting small lesions and non-mass enhancements, with some studies suggesting the inclusion of additional sequences such as DWI with ADC mapping to enhance diagnostic performance. CONCLUSION: While FP-MRI remains the gold standard in breast cancer detection, AB-MRI presents as a viable, quicker alternative, especially useful in high-risk screening scenarios. Its lower sensitivity compared to FP-MRI, however, limits its utility as a standalone diagnostic tool. Future research should focus on optimizing AB-MRI protocols and investigating patient-specific factors to refine breast cancer screening and diagnostic strategies.

Radiological Features of Male Breast Neoplasms: How to Improve the Management of a Rare Disease.

The primary aim of our study was to assess the main mammographic and ultrasonographic features of invasive male breast malignancies. The secondary aim was to evaluate whether a specific radiological presentation would be associated with a worse receptor profile. Radiological images (mammography and/or ultrasound) of all patients who underwent surgery for male invasive breast cancer in our institution between 2008 and 2023 were retrospectively analyzed by two breast radiologists in consensus. All significant features of radiological presentation known in the literature were re-evaluated. Fifty-six patients were selected. The mean age at surgery of patients was 69 years (range: 35-81); in 82% of cases (46 patients), the histologic outcome was invasive ductal carcinoma. A total of 28 out of 56 (50%) patients had preoperative mammography; in 9/28 cases (32%), we found a mass with microcalcifications on mammography. The mass presented high density in 25 out of 28 patients (89%); the mass showed irregular margins in 15/28 (54%) cases. A total of 46 out of 56 patients had preoperative ultrasounds. The lesion showed a solid mass in 41/46 (89%) cases. In 5/46 patients (11%), the lesion was a mass with a mixed (partly liquid-partly solid) structure. We did not find any statistically significant correlation between major types of radiological presentation and tumor receptor arrangement. Knowledge of the main radiologic presentation patterns of malignant male breast neoplasm can help better manage this type of disease, which is rare but whose incidence is increasing.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.

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.

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography.

Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years' experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22−83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.

Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis.

We performed a systematic review and a meta-analysis of studies using MRI-radiomics for predicting the pathological complete response in breast cancer patients undergoing neoadjuvant therapy , and we evaluated their methodological quality using the radiomics-quality-score (RQS). Random effects meta-analysis was performed pooling area under the receiver operating characteristics curves. Publication-bias was assessed using the Egger's test and visually inspecting the funnel plot. Forty-three studies were included in the qualitative review and 34 in the meta-analysis. Summary area under the receiver operating characteristics curve was 0,78 (95%CI:0,74-0,81). Heterogeneity according to the I2 statistic was substantial (71%) and there was no evidence of publication bias (P-value = 0,2). The average RQS was 12,7 (range:-1-26), with an intra-class correlation coefficient of 0.93 (95%CI:0.61-0.97). Year of publication, field intensity and synthetic RQS score do not appear to be moderators of the effect (P-value = 0.36, P-value = 0.28 and P-value = 0.92, respectively). MRI-radiomics may predict response to neoadjuvant therapy in breast cancer patients but the heterogeneity of the current studies is still substantial.

Inter-Reader Agreement of Diffusion-Weighted Magnetic Resonance Imaging for Breast Cancer Detection: A Multi-Reader Retrospective Study.

PURPOSE: In order to evaluate the use of un-enhanced magnetic resonance imaging (MRI) for detecting breast cancer, we evaluated the accuracy and the agreement of diffusion-weighted imaging (DWI) through the inter-reader reproducibility between expert and non-expert readers. MATERIAL AND METHODS: Consecutive breast MRI performed in a single centre were retrospectively evaluated by four radiologists with different levels of experience. The per-breast standard of reference was the histological diagnosis from needle biopsy or surgical excision, or at least one-year negative follow-up on imaging. The agreement across readers (by inter-reader reproducibility) was examined for each breast examined using Cohen's and Fleiss' kappa (κ) statistics. The Wald test was used to test the difference in inter-reader agreement between expert and non-expert readers. RESULTS: Of 1131 examinations, according to our inclusion and exclusion criteria, 382 women were included (49.5 ± 12 years old), 40 of them with unilateral mastectomy, totaling 724 breasts. Overall inter-reader reproducibility was substantial (κ = 0.74) for expert readers and poor (κ = 0.37) for non- expert readers. Pairwise agreement between expert readers and non-expert readers was moderate (κ = 0.60) and showed a statistically superior agreement of the expert readers over the non-expert readers (p = 0.003). CONCLUSIONS: DWI showed substantial inter-reader reproducibility among expert-level readers. Pairwise comparison showed superior agreement of the expert readers over the non-expert readers, with the expert readers having higher inter-reader reproducibility than the non-expert readers. These findings open new perspectives for prospective studies investigating the actual role of DWI as a stand-alone method for un-enhanced breast MRI.

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future.

Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer's molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

Role of breast MR imaging for predicting malignancy of histologically borderline lesions diagnosed at core needle biopsy: prospective evaluation.

PURPOSE: To prospectively determine the diagnostic performance of breast magnetic resonance (MR) imaging in predicting the malignancy of breast lesions classified as borderline at core needle biopsy (CNB). MATERIALS AND METHODS: This prospective study was approved by the local ethics committee, and all patients provided written informed consent. Between February 2007 and October 2009, 193 patients underwent ultrasonography (US)-guided CNB. Thirty-two lesions in 32 patients were classified as "borderline" ("lesions of uncertain malignant potential" according to the United Kingdom National Health Service Breast Screening program or those that are "probably benign" according to the Breast Imaging Reporting and Data System [BI-RADS] lexicon) at pathologic examination and were evaluated. All 32 patients underwent contrast agent-enhanced breast MR imaging 1-3 weeks after CNB; surgical biopsy was performed within 60 days of MR imaging. Results were compared with histologic findings. Sensitivity, specificity, accuracy, and positive and negative predictive values were calculated. RESULTS: Findings from surgical biopsy confirmed the diagnosis of seven atypical ductal hyperplasia lesions, four atypical lobular hyperplasia (ALH) lesions, five papillary lesions, two fibroepithelial lesions, three radial scars, three lobular carcinomas in situ, three ductal carcinomas in situ (DCIS), four invasive ductal carcinomas, and one invasive lobular carcinoma. With regard to malignancy, breast MR imaging helped correctly classify 22 of the 32 lesions as negative and seven as positive. Two of the lesions confirmed to be ALH were incorrectly classified as suspicious (BI-RADS category IV; false-positive finding), whereas one small DCIS was incorrectly classified as nonsuspicious (BI-RADS category II; false-negative finding). The sensitivity, specificity, accuracy, and positive and negative predictive values for the correct demonstration of malignancy at MR imaging were 88% (seven of eight lesions), 92% (22 of 24 lesions), 91% (29 of 32 lesions), 78% (seven of nine lesions), and 96% (22 of 23 lesions), respectively. CONCLUSION: Breast MR imaging can improve the evaluation of lesions classified as borderline at CNB. In cases of nonsuspicious enhancement or no enhancement at breast MR imaging, follow-up rather than surgical biopsy might be performed.

How we provided appropriate breast imaging practices in the epicentre of the COVID-19 outbreak in Italy.

Italy has one of the highest COVID-19 clinical burdens in the world and Lombardy region accounts for more than half of the deaths of the country. Since COVID-19 is a novel disease, early impactful decisions are often based on experience of referral centres.We report the re-organisation which our institute (IEO, European Institute of Oncology), a cancer referral centre in Lombardy, went through to make our breast-imaging division pandemic-proof. Using personal-protective-equipment and innovative protocols, we provided essential breast-imaging procedures during COVID-19 pandemic without compromising cancer outcomes.The emergency management and infection-control-measures implemented in our division protected both the patients and the staff, making this experience useful for other radiology departments dealing with the pandemic.

MRI-guided vacuum-assisted breast biopsy: experience of a single tertiary referral cancer centre and prospects for the future.

MRI-guided vacuum-assisted breast biopsy (VABB) is used for suspicious breast cancer (BC) lesions which are detectable only with MRI: because the high sensitivity but limited specificity of breast MRI it is a fundamental tool in breast imaging divisions. We analyse our experience of MRI-guided VABB and critically discuss the potentialities of diffusion-weighted imaging (DWI) and artificial intelligence (AI) in this matter. We retrospectively analysed a population of consecutive women underwent VABB at our tertiary referral BC centre from 01/2011 to 01/2019. Reference standard was histological diagnosis or at least 1-year negative follow-up. McNemar, Mann-Whitney and χ2 tests at 95% level of significance were used as statistical exams. 217 women (mean age = 52, 18-72 years) underwent MRI-guided VABB; 11 were excluded and 208 MRI-guided VABB lesions were performed: 34/208 invasive carcinomas, 32/208 DCIS, 8/208 LCIS, 3/208 high-risk lesions and 131/208 benign lesions were reported. Accuracy of MRI-guided VABB was 97%. The predictive features for malignancy were mass with irregular shape (OR 8.4; 95% CI 0.59-31.6), size of the lesion (OR 4.4; 95% CI 1.69-9.7) and mass with irregular/spiculated margins (OR 5.4; 95% CI 6.8-31.1). Six-month follow-up showed 4 false-negative cases (1.9%). Invasive BC showed a statistically significant higher hyperintense signal at DWI compared to benign lesions (p = 0.03). No major complications occurred. MR-guided VABB showed high accuracy. Benign-concordant lesions should be followed up with breast MRI in 6-12 months due to the risk of false-negative results. DWI and AI applications showed potential benefit as support tools for radiologists.

Contrast-enhanced MR mammography: improved lesion detection and differentiation with gadobenate dimeglumine.

OBJECTIVE: The objective of our study was to intraindividually compare 0.1 mmol/kg doses of gadobenate dimeglumine and gadopentetate dimeglumine for contrast-enhanced breast MRI. SUBJECTS AND METHODS: Forty-seven women (mean age +/- SD, 50.8 +/- 12.9 years) with breast lesions classified as BI-RADS category 3, 4, or 5 for suspicion of malignancy underwent two identical MR examinations at 1.5 T separated by 48-72 hours. T1-weighted gradient-echo images were acquired before contrast administration and at 2-minute intervals after the randomized injection of gadopentetate dimeglumine or gadobenate dimeglumine at 2 mL/s. Two blinded readers evaluated randomized image sets for lesion detection and differentiation as benign or malignant compared with histology. The McNemar exact test and the generalized estimating equation (GEE) were used to compare lesion detection rates and diagnostic performance in terms of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: Histopathology data were available for 78 lesions. Significantly more lesions overall (75/78 [96%] vs 62/78 [79%], respectively; p = 0.0002) and significantly more malignant lesions (49/50 [98%] vs 38/50 [76%]; p = 0.0009) were detected with gadobenate dimeglumine than gadopentetate dimeglumine. All detected malignant lesions were correctly diagnosed with both agents. More detected benign lesions were correctly diagnosed with gadobenate dimeglumine than with gadopentetate dimeglumine (20/26 [77%] vs 17/24 [71%], respectively). Differentiation of lesions was significantly (p = 0.0001) better with gadobenate dimeglumine. Significantly better diagnostic performance was noted with gadobenate dimeglumine than with gadopentetate dimeglumine, respectively, for sensitivity (98.0% vs 76.0%; p = 0.0064), accuracy (88.5% vs 69.2%; p = 0.0004), PPV (86.0% vs 76.0%; p = 0.0321), and NPV (95.2% vs 57.1%; p = 0.0003). CONCLUSION: Lesion detection and malignant-benign differentiation is significantly better with 0.1 mmol/kg gadobenate dimeglumine than 0.1 mmol/kg gadopentetate dimeglumine.

Predictors of invasive breast cancer and lymph node involvement in ductal carcinoma in situ initially diagnosed by vacuum-assisted breast biopsy: experience of 733 cases.

OBJECTIVE: To predict presence of invasive component and nodal involvement in women diagnosed preoperatively with ductal carcinoma in situ (DCIS) by vacuum-assisted breast biopsy (VABB). MATERIALS AND METHODS: We retrospectively analyzed 733 patients with preoperatively diagnosed DCIS, investigating the association of clinical-radiological variables with invasive component and nodal involvement. RESULTS: Mammographic size >20 mm and residual lesion on post-VABB mammogram were related to invasive component (both p < 0.0001) and nodal involvement (p = 0.001, p = 0.03). Age <40 years was associated with presence of invasive component (p = 0.003). By multivariate analysis residual disease was associated with invasive component, and mammographic tumor size >20 mm with nodal involvement, both highly significant. CONCLUSIONS: Older age, lesion <20 mm, and no residual lesion predict absence of invasion and no nodal involvement in VABB-diagnosed DCIS. However it would be imprudent to routinely forego sentinel node biopsy in such patients as non-negligible proportions of them have invasive disease.

The challenge of imaging dense breast parenchyma: is magnetic resonance mammography the technique of choice? A comparative study with x-ray mammography and whole-breast ultrasound.

PURPOSE: To establish the value of magnetic resonance imaging (MRI) of the breast in comparison to x-ray mammography and ultrasound for breast cancer evaluation in women with dense breast parenchyma. MATERIALS AND METHODS: Two hundred thirty-eight women with dense breast parenchyma who were suspicious for breast cancer or inconclusive for the presence of breast lesions based on clinical examination, ultrasound or x-ray mammography, and who underwent breast MRI at 1.5 T before and after administration of 0.1 mmol/kg gadobenate dimeglumine were evaluated. Lesions considered malignant (Breast Imaging Reporting and Data System (BI-RADS) 4 or 5) on x-ray mammography and/or ultrasound and as BI-RADS 3, 4, or 5 on MRI were evaluated histologically. Other lesions were followed up at 6 and/or 18 months. The diagnostic performance (sensitivity, specificity, accuracy, and positive and negative predictive values) of each technique was determined and compared using a general linear mixed model with appropriate correction for multiplicity. RESULTS: At final diagnosis 121 of 238 (50.8%) women had one or more confirmed malignant lesions, whereas 117 (49.2%) had benign lesions or no lesions. Among 97 women who underwent all 3 techniques more lesions (malignant and benign) were detected with breast MRI (n = 135) than with x-ray mammography (n = 85) or ultrasound (n = 107) and diagnostic confidence was greater. In terms of patient-based diagnostic accuracy breast MRI was significantly (P[r] < 0.0001) superior to both x-ray mammography and ultrasound (96.9% accuracy for MRI vs. 60.8% for mammography and 66.0% for US). Malignant lesions were histologically confirmed in 55 of 97 women who underwent all 3 techniques. Breast MRI detected more cases of multifocal, multicentric, and contralateral disease and fewer misdiagnoses occurred. Overall, breast MRI led to a modification of the surgical approach for 28 (23.1%) of the 121 women with diagnosed malignant disease. CONCLUSION: Breast MRI should be considered for routine breast cancer evaluation in women with dense breast parenchyma.