Faculty Bibliography

Internal mammary lymph nodes (IMLNs) account for approximately 10%-40% of the lymphatic drainage of the breast. Internal mammary lymph nodes measuring up to 10 mm are commonly seen on high-risk screening breast MRI examinations in patients without breast cancer and are considered benign if no other suspicious findings are present. Benign IMLNs demonstrate a fatty hilum, lobular or oval shape, and circumscribed margins without evidence of central necrosis, cortical thickening, or loss of fatty hilum. In patients with breast cancer, IMLN involvement can alter clinical stage and treatment planning. The incidence of IMLN metastases detected on US, CT, MRI, and PET-CT ranges from 10%-16%, with MRI and PET-CT demonstrating the highest sensitivities. Although there are no well-defined imaging criteria in the eighth edition of the American Joint Committee on Cancer Staging Manual for Breast Cancer, a long-axis measurement of ≥ 5 mm is suggested as a guideline to differentiate benign versus malignant IMLNs in patients with newly diagnosed breast cancer. Abnormal morphology such as loss of fatty hilum, irregular shape, and rounded appearance (which can be quantified by a short-axis/long-axis length ratio greater than 0.5) also raises suspicion for IMLN metastases. MRI and PET-CT have good sensitivity and specificity for the detection of IMLN metastases, but fluorodeoxyglucose avidity can be seen in both benign conditions and metastatic disease. US is helpful for staging, and US-guided fine-needle aspiration can be performed in cases of suspected IMLN metastasis. Management of suspicious IMLNs identified on imaging is typically with chemotherapy and radiation, as surgical excision does not provide survival benefit and is performed only in rare cases.

An amendment to this paper has been published and can be accessed via the original article.

BACKGROUND:We assessed confidence in visualization of markers within metastatic axillary lymph nodes (LNs) on magnetic resonance imaging (MRI), which were placed post-ultrasound (US)-guided biopsy. METHODS:A retrospective review was performed on 55 MRI cases between May 2015 and October 2017. Twenty-two MRIs were performed before neoadjuvant therapy, and 33 MRIs were after its initiation. There were 34/55 HydroMARK®, 10/55 Tumark®, and 11/55 other marker types. Time interval between marker placement and MRI examination was 103 ± 81 days (mean ± standard deviation). Three readers with 1-30 years of experience independently assessed four axial sequences: unenhanced fat-suppressed three-dimensional T1-weighted spoiled gradient-recalled (SPGR), first contrast-enhanced fat-suppressed SPGR, T2-weighted water-only fast spin-echo (T2-WO), and T2-weighted fat-only fast-spin-echo (T2-FO). RESULTS:Markers were 5.2× more likely to be visualized on T2-WO than on unenhanced images (p = < 0.001), and 3.3× more likely to be visualized on contrast-enhanced than on unenhanced sequences (p = 0.009). HydroMARK markers demonstrated a 3× more likelihood of being visualized than Tumark (p = 0.003). Markers were 8.4× more likely to be visualized within morphologically abnormal LNs (p < 0.001). After 250 days post-placement, confidence in marker brightness of HydroMARK markers on T2-WO images was less than 50% (p < 0.001). Inter-rater agreement was excellent for T2-WO and contrast-enhanced SPGR, good for unenhanced SPGR, and poor for T2-FO images. CONCLUSION/CONCLUSIONS:T2-WO and contrast-enhanced images should be used for marker identification. HydroMARK was the best visualized marker. Markers were easier to identify when placed in abnormal LNs. The visibility of HydroMARK markers was reduced with time.

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. (i) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. (ii) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. (iii) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. (iv) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breastcancerclassifier.

Pseudoaneurysm (PSA) formation is a potential complication of breast biopsies. Ultrasound is the most common imaging modality used for evaluation and treatment of a PSA. Color Doppler images show a cystic structure with swirling flow inside in a "to-and-fro" pattern. Treatment options for PSA include observation, ultrasound-guided focused compression, thrombin injection, open surgical repair, and percutaneous embolization. The risks and benefits of these treatment options will be discussed in the following cases.

Architectural distortion on digital breast tomosynthesis (

This case highlights the role of molecular breast imaging (MBI) in evaluating persistent clinical concerns after a negative diagnostic mammogram and ultrasound. MBI is especially useful in the diagnosis of invasive lobular carcinoma due to its occult nature on conventional imaging modalities.