Multimodality approach to the nipple-areolar complex : a pictorial review and diagnostic algorithm

The anatomic and histologic characteristics of the nipple-areolar complex make this breast region special. The nipple-areolar complex can be affected by abnormal development and a wide spectrum of pathological conditions, many of which have unspecific clinical and radiological presentations that can present a challenge for radiologists. The nipple-areolar complex requires a specific imaging workup in which a multimodal approach is essential. Radiologists need to know the different imaging modalities used to study the nipple-areolar complex, as well as their advantages and limitations. It is essential to get acquainted with the acquisition technique for each modality and the spectrum of findings for the different conditions. This review describes and illustrates a combined clinical and radiological approach to evaluate the nipple-areolar complex, emphasizing the findings for the normal morphology, developmental abnormalities, and the most common benign and malignant diseases that can affect this region. We also present a diagnostic algorithm that enables a rapid, practical approach to diagnosing condition involving the nipple-areolar complex

The added value of quantitative multi-voxel MR spectroscopy in breast magnetic resonance imaging

To determine whether quantitative multivoxel MRS improves the accuracy of MRI in the assessment of breast lesions. Twenty-five consecutive patients with 26 breast lesions a parts per thousand yen1 cm assessed as BI-RADS 3 or 4 with mammography underwent quantitative multivoxel MRS and contrast-enhanced MRI. The choline (Cho) concentration was calculated using the unsuppressed water signal as a concentration reference. ROC analysis established the diagnostic accuracy of MRI and MRS in the assessment of breast lesions. Respective Cho concentrations in 26 breast lesions re-classified by MRI as BI-RADS 2 (n = 5), 3 (n = 8), 4 (n = 5) and 5 (n = 8) were 1.16 +/- 0.43 (mean +/- SD), 1.43 +/- 0.47, 2.98 +/- 2.15 and 4.94 +/- 3.10 mM. Two BI-RADS 3 lesions and all BI-RADS 4 and 5 lesions were malignant on histopathology and had Cho concentrations between 1.7 and 11.8 mM (4.03 +/- 2.72 SD), which were significantly higher (P = 0.01) than that in the 11 benign lesions (0.4-1.5 mM; 1.19 +/- 0.33 SD). Furthermore, Cho concentrations in the benign and malignant breast lesions in BI-RADS 3 category differed (P = 0.01). The accuracy of combined multivoxel MRS/breast MRI BI-RADS re-classification (AUC = 1.00) exceeded that of MRI alone (AUC = 0.96 +/- 0.03). These preliminary data indicate that multivoxel MRS improves the accuracy of MRI when using a Cho concentration cut-off a parts per thousand currency sign1.5 mM for benign lesions. Key Points aEuro cent Quantitative multivoxel MR spectroscopy can improve the accuracy of contrast-enhanced breast MRI. aEuro cent Multivoxel-MRS can differentiate breast lesions by using the highest Cho-concentration. aEuro cent Multivoxel-MRS can exclude patients with benign breast lesions from further invasive diagnostic procedures

Sensitivity of imaging for multifocal-multicentric breast carcinoma

<p>Abstract</p> <p>Background</p> <p>This retrospective study aims to determine: 1) the sensitivity of preoperative mammography (Mx) and ultrasound (US), and re-reviewed Mx to detect multifocal multicentric breast carcinoma (MMBC), defined by pathology on surgical specimens, and 2) to analyze the characteristics of both detected and undetected foci on Mx and US.</p> <p>Methods</p> <p>Three experienced breast radiologists re-reviewed, independently, digital mammography of 97 women with MMBC pathologically diagnosed on surgical specimens. The radiologists were informed of all neoplastic foci, and blinded to the original mammograms and US reports. With regards to Mx, they considered the breast density, number of foci, the Mx characteristics of the lesions and their BI-RADS classification. For US, they considered size of the lesions, BI-RADS classification and US pattern and lesion characteristics. According to the histological size, the lesions were classified as: index cancer, 2nd lesion, 3rd lesion, and 4th lesion. Any pathologically identified malignant foci not previously described in the original imaging reports, were defined as undetected or missed lesions. Sensitivity was calculated for Mx, US and re-reviewed Mx for detecting the presence of the index cancer as well as additional satellite lesions.</p> <p>Results</p> <p>Pathological examination revealed 13 multifocal and 84 multicentric cancers with a total of 303 malignant foci (282 invasive and 21 non invasive). Original Mx and US reports had an overall sensitivity of 45.5% and 52.9%, respectively. Mx detected 83/97 index cancers with a sensitivity of 85.6%. The number of lesions <it>un</it>detected by original Mx was 165/303. The Mx pattern of breasts with undetected lesions were: fatty in 3 (1.8%); scattered fibroglandular density in 40 (24.3%), heterogeneously dense in 91 (55.1%) and dense in 31 (18.8%) cases. In breasts with an almost entirely fatty pattern, Mx sensitivity was 100%, while in fibroglandular or dense pattern it was reduced to 45.5%. Re-reviewed Mx detected only 3 additional lesions. The sensitivity of Mx was affected by the presence of dense breast tissue which obscured lesions or by an incorrect interpretation of suspicious findings.</p> <p>US detected 73/80 index cancers (sensitivity of 91.2%), US missed 117 malignant foci with a mean tumor diameter of 6.5 mm; the sensitivity was 52.9%</p> <p>Undetected lesions by US were those smallest in size and present in fatty breast or in the presence of microcalcifications without a visible mass.</p> <p>US sensitivity was affected by the presence of fatty tissue or by the extent of calcification.</p> <p>Conclusion</p> <p>Mx missed MMBC malignant foci more often in dense or fibroglandular breasts. US missed small lesions in mainly fatty breasts or when there were only microcalcifications. The combined sensitivity of both techniques to assess MMBC was 58%. We suggest larger studies on multimodality imaging.</p

High‐speed Intraoperative Assessment of Breast Tumor Margins by Multimodal Ultrasound and Photoacoustic Tomography

Conventional methods for breast tumor margins assessment need a long turnaround time, which may lead to re‐operation for patients undergoing lumpectomy surgeries. Photoacoustic tomography (PAT) has been shown to visualize adipose tissue in small animals and human breast. Here, we demonstrate a customized multimodal ultrasound and PAT system for intraoperative breast tumor margins assessment using fresh lumpectomy specimens from 66 patients. The system provides the margin status of the entire excised tissue within 10 minutes. By subjective reading of three researchers, the results show 85.7% [95% confidence interval (CI), 42.0% ‐ 99.2%] sensitivity and 84.6% (95% CI, 53.7% ‐ 97.3%) specificity, 71.4% (95% CI, 30.3% ‐ 94.9%) sensitivity and 92.3% (95% CI, 62.1% ‐ 99.6%) specificity, and 100% (95% CI, 56.1% ‐ 100%) sensitivity and 53.9% (95% CI, 26.1% ‐ 79.6%) specificity respectively when cross‐correlated with post‐operational histology. Furthermore, a machine learning‐based algorithm is deployed for margin assessment in the challenging ductal carcinoma in situ tissues, and achieved 85.5% (95% CI, 75.2% ‐ 92.2%) sensitivity and 90% (95% CI, 79.9% ‐ 95.5%) specificity. Such results present the potential of using mutlimodal ultrasound and PAT as a high‐speed and accurate method for intraoperative breast tumor margins evaluation

Breast and Axilla Treatment in Ductal Carcinoma In Situ

Ductal carcinoma in situ (DCIS) represents a challenge for the breast unit team, beginning from its difficult radiological detection and continuing with its controversial multimodal treatment and management. With the introduction of the mammographic screening, DCIS has become a common diagnosis. In fact, today DCIS is mostly identified by mammography or magnetic resonance imaging (MRI). The increased prevalence of DCIS diagnosis, in the past, raised the problem of the therapeutic management. In this chapter, the breast and axillary surgery in case of DCIS and the most controversial aspects regarding DCIS management are reviewed based on international guidelines and on the current literature

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI

Advancing combined radiological and optical scanning for breast-conserving surgery margin guidance

Breast cancer is one of the most common types of cancer worldwide, and standard-of-care for early-stage disease typically involves a lumpectomy or breast-conserving surgery (BCS). BCS involves the local resection of cancerous tissue, while sparring as much healthy tissue as possible. State-of-the-art methods for intraoperatively evaluating BCS margins are limited. Approximately 20% of BCS cases result in a tissue resection with cancer at or near the resection surface (i.e., a positive margin). A two-fold increase in ipsilateral breast cancer recurrence is associated with the presence of one or more positive margins. Consequently, positive margins often necessitate costly re-excision procedures to achieve a curative outcome. X-ray micro-computed tomography (CT) is emerging as a powerful ex vivo specimen imaging technology, as it provides robust three-dimensional sensing of tumor morphology rapidly. However, X-ray attenuation lacks contrast between soft tissues that are important for surgical decision making during BCS. Optical structured light imaging, including spatial frequency domain imaging and active line scan imaging, can act as adjuvant tools to complement micro-CT, providing wide field-of-view, non-contact sensing of relevant breast tissue subtypes on resection margins that cannot be differentiated by micro-CT alone. This thesis is dedicated to multimodal imaging of BCS tissues to ultimately improve intraoperative BCS margin assessment, reducing the number of positive margins after initial surgeries and thereby reducing the need for costly follow-up procedures. Volumetric sensing of micro-CT is combined with surface-weighted, sub-diffuse optical reflectance derived from high spatial frequency structured light imaging. Sub-diffuse reflectance plays the key role of providing enhanced contrast to a suite of normal, abnormal benign, and malignant breast tissue subtypes. This finding is corroborated through clinical studies imaging BCS specimen slices post-operatively and is further investigated through an observational clinical trial focused on combined, intraoperative micro-CT and optical imaging of whole, freshly resected BCS tumors. The central thesis of this work is that combining volumetric X-ray imaging and sub-diffuse optical scanning provides a synergistic multimodal imaging solution to margin assessment, one that can be readily implemented or retrofitted in X-ray specimen imaging systems and that could meaningfully improve surgical guidance during initial BCS procedures

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study

Purpose: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs). Material and methods: Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B). Results: A total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively. Conclusion: AI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers