Analysis of Local Recurrence Risk in Ductal Carcinoma In Situ and External Validation of the Memorial Sloan Kettering Cancer Center Nomogram.

BACKGROUND: Adjuvant radiotherapy and hormonotherapy after breast-conserving surgery (BCS) in ductal carcinoma in situ (DCIS) have been shown to reduce the risk of local recurrence. To predict the risk of ipsilateral breast tumor relapse (IBTR) after BCS, the Memorial Sloan Kettering Cancer Center (MSKCC) developed a nomogram to analyze local recurrence (LR) risk in our cohort and to assess its external validation. METHODS: A historical cohort study using data from 296 patients treated for DCIS at the Hospital Clínic of Barcelona was carried out. Patients who had had a mastectomy were excluded from the analysis. RESULTS: The mean age was 58 years (42-75), and the median follow-up time was 10.64 years. The overall local relapse rate was 13.04% (27 patients) during the study period. Actuarial 5- and 10-year IBTR rates were 5.8 and 12.9%, respectively. The external validation of the MSKCC nomogram was performed using a multivariate logistic regression analysis on a total of 207 patients, which did not reach statistical significance in the studied population for predicting LR (p = 0.10). The expression of estrogen receptors was significantly associated with a decreased risk of LR (OR: 0.25; p = 0.004). CONCLUSIONS: In our series, the LR rate was 13.4%, which was in accordance with the published series. The MSKCC nomogram did not accurately predict the IBTR in this Spanish cohort of patients treated for DCIS (p = 0.10).

Nonsentinel Axillary Lymph Node Status in Clinically Node-Negative Early Breast Cancer After Primary Systemic Therapy and Positive Sentinel Lymph Node: A Predictive Model Proposal.

BACKGROUND: In clinically node-negative (cN0) early stage breast cancer (EBC) undergoing primary systemic treatment (PST), post-treatment positive sentinel lymph node (SLN+) directs axillary lymph node dissection (ALND), with uncertain impacts on outcomes and increased morbidities. PATIENTS AND METHODS: We conducted an observational study on imaging-confirmed cN0 EBC, who underwent PST and breast surgery that resulted in SLN+ and underwent ALND. The association among baseline/postsurgical clinic-pathological factors and positive nonsentinel additional axillary lymph nodes (non-SLN+) was analyzed with logistic regression. LASSO regression (LR) identified variables to include in a predictive score of non-SLN+ (ALND-predict). The accuracy and calibration were assessed, an optimal cut-point was then identified, and in silico validation with bootstrap was undertaken. RESULTS: Non-SLN+ were detected in 22.2% cases after ALND. Only progesterone receptor (PR) levels and macrometastatic SLN+ were independently associated to non-SLN+. LR identified PR, Ki67, and type and number of SLN+ as the most efficient covariates. The ALND-predict score was built based on their LR coefficients, showing an area under the curve (AUC) of 0.83 and an optimal cut-off of 63, with a negative predictive value (NPV) of 0.925. Continuous and dichotomic scores had a good fit (p = 0.876 and p = 1.00, respectively) and were independently associated to non-SLN+ [adjusted odds ratio (aOR): 1.06, p = 0.002 and aOR: 23.77, p < 0.001, respectively]. After 5000 bootstrap-adjusted retesting, the estimated bias-corrected and accelerated 95%CI included the aOR. CONCLUSIONS: In cN0 EBC with post-PST SLN+, non-SLN+ at ALND are infrequent (~22%) and independently associated to PR levels and macrometastatic SLN. ALND-predict multiparametric score accurately predicted absence of non-SLN involvement, identifying most patients who could be safely spared unnecessary ALND. Prospective validation is required.

In vivo assignment of methylmalonic acid in breast tissue using 2D MRS and relationship with breast density, menopausal status and cancer risk.

BACKGROUND: Methylmalonic acid (MMA) is linked to progression and aggressiveness of tumours. A recent study showed that high levels of circulatory MMA directed genetic programs promoting cancer progression. PURPOSE: To evaluate in vivo two-dimensional correlated spectroscopy (2D COSY) data from women at elevated risk of breast cancer to determine if resonances consistent with MMA are present, and if so to correlate levels with breast density, menopausal status and risk categories. MATERIALS AND METHODS: With institutional review board approval, 106 women at elevated risk (mean age 47), including 46 participants at medium risk, 43 at high risk with no known mutation and 17 BRCA-mutation carriers, were recruited. Breast density was assessed using a T2 sequence. A T1 sequence was used to place the voxel for the 2D COSY data. Peak volumes were normalized to the methylene peak at (1.30, 1.30) ppm. Chi-squared and Mann-Whitney tests were used. RESULTS: Two resonances are assigned on the diagonal at 3.15 ppm and 3.19 ppm consistent with and denoted MMA1 and MMA2 respectively. MMA1 and MMA2 increased in parallel with increased risk. BRCA-mutation carriers recorded an increase in mean MMA1 of 120% (p = 0.033) and MMA2 of 127% (p = 0.020) in comparison with participants with no known mutation. BRCA-mutation carriers with dense breasts recorded a significant increase in mean MMA1 of 137% (p = 0.002) and in mean MMA2 of 143% (p = 0.004) compared with BRCA-mutation participants with low-density breast tissue. MMA1 and MMA2 were higher in premenopausal women with dense breasts compared with those with low-density tissue. The highest values of MMA were recorded in BRCA-mutation carriers. CONCLUSION: Two tentative assignments are made for MMA in breast tissue of women at elevated risk for cancer. BRCA-mutation carriers exhibited higher values of MMA than those with no known mutation. Premenopausal women with BRCA mutation and dense breasts recorded the highest levels of MMA compared with other categories.

Intrinsic subtypes and therapeutic decision-making in hormone receptor-positive/HER2-negative metastatic breast cancer with visceral crisis: A case report.

Background: CDK4/6 inhibitors (CDKi), namely, palbociclib, ribociclib, and abemaciclib, combined with either an aromatase inhibitor (AI) or fulvestrant are the standard first/second line for hormone receptor-positive(HR+)/HER2-negative(neg) metastatic breast cancer (MBC). However, the choice of one specific CDKi is arbitrary and based on the physician's experience with the drug, toxicity profile, and patient's preferences, whereas biomarkers for optimal patient selection have not been established so far. Moreover, upfront chemotherapy is still recommended in case of clinical presentation with visceral crisis, despite no evidence of superior benefit for chemotherapy regimens against CDKi-based regimens. Recent correlative biomarker analyses from pivotal trials of palbociclib and ribociclib showed that HR+/HER2-neg MBC might respond differently according to the molecular intrinsic subtype, with Luminal A and B tumors being sensitive to both CDKi, Basal-like being insensitive to endocrine therapy, irrespective of CDKi, and HER2-enriched tumors showing a benefit only with ribociclib-based therapy. Clinical case: We hereby present a paradigmatic clinical case of a woman affected by a relapsed HR+/HER2-neg MBC with bone and nodal lesions, presenting with a visceral crisis in the form of lymphangitis carcinomatosis and diagnosed with a molecularly HER2-enriched tumor, successfully treated with upfront ribociclib + fulvestrant. The patient experienced a complete symptomatic and radiologic remission of the lymphangitis with a partial response as best response, according to RECIST 1.1 criteria. The progression-free survival (PFS) was of 20 months, in line with the median PFS observed in the ribociclib + fulvestrant pivotal trial, where, however, patients with visceral crisis had been excluded. Conclusions: This clinical case confirms in the real-world setting that non-luminal subtypes can be found in HR+/HER2-neg disease and may have potential therapeutic implications in the metastatic setting. It also questions the recommendation of upfront chemotherapy in the case of a visceral crisis in the era of CDKi-based regimens. These issues merit further evaluation in prospective and larger studies.

Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination.

PURPOSE: The aim of this study is to assess the potential of quantitative image analysis and machine learning techniques to differentiate between malignant lymph nodes and benign lymph nodes affected by reactive changes due to COVID-19 vaccination. METHOD: In this institutional review board-approved retrospective study, we improved our previously published artificial intelligence model, by retraining it with newly collected images and testing its performance on images containing benign lymph nodes affected by COVID-19 vaccination. All the images were acquired and selected by specialized breast-imaging radiologists and the nature of each node (benign or malignant) was assessed through a strict clinical protocol using ultrasound-guided biopsies. RESULTS: A total of 180 new images from 154 different patients were recruited: 71 images (10 cases and 61 controls) were used to retrain the old model and 109 images (36 cases and 73 controls) were used to evaluate its performance. The achieved accuracy of the proposed method was 92.7% with 77.8% sensitivity and 100% specificity at the optimal cut-off point. In comparison, the visual node inspection made by radiologists from ultrasound images reached 69.7% accuracy with 41.7% sensitivity and 83.6% specificity. CONCLUSIONS: The results obtained in this study show the potential of the proposed techniques to differentiate between malignant lymph nodes and benign nodes affected by reactive changes due to COVID-19 vaccination. These techniques could be useful to non-invasively diagnose lymph node status in patients with suspicious reactive nodes, although larger multicenter studies are needed to confirm and validate the results.

Magnetic Resonance Imaging Assessment of Radial Scars/complex Sclerosing Lesions of the Breast.

PURPOSE: To describe the magnetic resonance characteristics of radial scars/complex sclerosing lesions (RS/CSL) of the breast using the current BI-RADS lexicon. To investigate the value of diffusion weighted imaging to predict malignancy. PATIENTS AND METHODS: From 2010 to 2017, we have found 25 women with architectural distortion at mammography who underwent surgical resection with a final hystopathologic report of RS/CSL. For the description of MRI findings, we adhered to BI-RADS classification (5th edition). RESULTS: The final pathological diagnosis was: "pure" RS/CSL in 7 cases (28%), RS/CSL with associated high risk lesions in 12 (48%) and 6 cases (24%) were associated with malignancy. Magnetic resonance findings: four of 25 negative or focus. Five of 25 mass enhancement: irregular, non circumscribed spiculated mass with heterogeneous or rim enhancement and most with type II curves. Sixteen of 25 non mass enhancement: focal or linear distribution and heterogeneous internal enhancement most with type I curves. Six of 25 had cancer associated with the complex sclerosing lesion. All six showed non-mass enhancement. Two cases with invasive breast carcinoma had ADC values under 1.15 x10-3 mm/s while most of the rest had the values above. CONCLUSION: Most RS/CSL showed enhancement at MR. The predominant pattern was a non-mass, focal, heterogeneous internal enhancement with type 1 curves. All cases with associated cancer showed non mass enhancement. Invasive breast cancers had ADC values < 1.15 10-3 s/mm2.

PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints.

We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.

SOLTI-1503 PROMETEO TRIAL: combination of talimogene laherparepvec with atezolizumab in early breast cancer.

New treatment strategies such as immune checkpoint inhibitors and oncolytic viruses are opening new possibilities in cancer therapy. Preliminary results in melanoma and other tumors showed that the combination of talimogene laherparepvec with an anti-PD-1/PD-L1 or anti-CTLA4 has greater efficacy than either therapy alone, without additional safety concerns beyond those expected for each agent. The presence of residual cancer after neoadjuvant chemotherapy in early breast cancer patients is an unmet medical need. SOLTI-1503 PROMETEO is a window of opportunity trial, which evaluates the combination of talimogene laherparepvec in combination with atezolizumab in women with operable HER2-negative breast cancer who present residual disease after neoadjuvant chemotherapy. The primary end point is the rate of residual cancer burden 0/1. Clinical Trial Registration: NCT03802604.

Computational Evaluation and In Vitro Validation of New Epidermal Growth Factor Receptor Inhibitors.

BACKGROUND: The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma. METHODS: We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,462 compounds in total) and the 3D structure of EGFR obtained from the Protein Data Bank (PDB code: 1M17). The docking results from this campaign were then ranked according to the theoretical binding affinity of these molecules to EGFR, and compared with the binding affinity of erlotinib, a well-known EGFR inhibitor. A total of 23 top-rated commercial compounds displaying potential binding affinities similar or even better than erlotinib were selected for experimental evaluation. In vitro assays in different cell lines were performed. A preliminary test was carried out with a simple and standard quick cell proliferation assay kit, and six compounds showed significant activity when compared to positive control. Then, viability and cell proliferation of these compounds were further tested using a protocol based on propidium iodide (PI) and flow cytometry in HCT116, Caco-2 and H358 cell lines. RESULTS: The whole six compounds displayed good effects when compared with erlotinib at 30 µM. When reducing the concentration to 10µM, the activity of the 6 compounds depends on the cell line used: the six compounds showed inhibitory activity with HCT116, two compounds showed inhibition with Caco-2, and three compounds showed inhibitory effects with H358. At 2 µM, one compound showed inhibiting effects close to those from erlotinib. CONCLUSION: Therefore, these compounds could be considered as potential primary hits, acting as promising starting points to expand the therapeutic options against a wide range of cancers.

Ultrasound Findings of Breast Adenomas.

OBJECTIVES: Breast adenomas are rare benign tumors. The aim of this study was to describe the ultrasound findings of breast adenomas, including less common worrisome features that can mimic malignancy. METHODS: From November 2014 to November 2019, we performed 1605 core needle biopsies guided by ultrasound in our department. In all, 229 corresponded to fibroadenomas and 12 to breast adenomas. RESULTS: Breast adenomas represented 0.75% of all core needle biopsies; 50% (6 of 12) were tubular adenomas; 17% (2 of 12) were ductal adenomas; and 33% (4 of 12) were lactating adenomas. Tubular and lactating adenomas occurred in young women and ductal adenomas in postmenopausal women. Lactating adenomas occurred in pregnant or breastfeeding women and were larger than the other adenomas. Ultrasound showed a mass with an oval shape, a parallel orientation, circumscribed margins, and hypoechoic and homogeneous patterns in most cases. However, up to one-third of them presented with suspicious findings. CONCLUSIONS: Breast adenomas are rare benign entities with no specific clinical and radiologic features. Sometimes, they appear as suspicious tumors on radiologic modalities, requiring a histopathologic assessment to rule out malignancy. Radiologists need to be familiar with this entity to determine concordance between imaging and the final pathologic analysis.

Quantitative Ultrasound Image Analysis of Axillary Lymph Nodes to Diagnose Metastatic Involvement in Breast Cancer.

This study aimed to assess the potential of state-of-the-art ultrasound analysis techniques to non-invasively diagnose axillary lymph nodes involvement in breast cancer. After exclusion criteria, 105 patients were selected from two different hospitals. The 118 lymph node ultrasound images taken from these patients were divided into 53 cases and 65 controls, which made up the study series. The clinical outcome of each node was verified by ultrasound-guided fine needle aspiration, core needle biopsy or surgical biopsy. The achieved accuracy of the proposed method was 86.4%, with 84.9% sensitivity and 87.7% specificity. When tested on breast cancer patients only, the proposed method improved the accuracy of the sonographic assessment of axillary lymph nodes performed by expert radiologists by 9% (87.0% vs 77.9%). In conclusion, the results demonstrate the potential of ultrasound image analysis to detect the microstructural and compositional changes that occur in lymph nodes because of metastatic involvement.

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks.

Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

Breast-lesion Segmentation Combining B-Mode and Elastography Ultrasound.

Breast ultrasound (BUS) imaging has become a crucial modality, especially for providing a complementary view when other modalities (i.e., mammography) are not conclusive in the task of assessing lesions. The specificity in cancer detection using BUS imaging is low. These false-positive findings often lead to an increase of unnecessary biopsies. In addition, increasing sensitivity is also challenging given that the presence of artifacts in the B-mode ultrasound (US) images can interfere with lesion detection. To deal with these problems and improve diagnosis accuracy, ultrasound elastography was introduced. This paper validates a novel lesion segmentation framework that takes intensity (B-mode) and strain information into account using a Markov Random Field (MRF) and a Maximum a Posteriori (MAP) approach, by applying it to clinical data. A total of 33 images from two different hospitals are used, composed of 14 cancerous and 19 benign lesions. Results show that combining both the B-mode and strain data in a unique framework improves segmentation results for cancerous lesions (Dice Similarity Coefficient of 0.49 using B-mode, while including strain data reaches 0.70), which are difficult images where the lesions appear with blurred and not well-defined boundaries.

Breast Density Analysis Using an Automatic Density Segmentation Algorithm.

Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density.

Shear-wave elastography and immunohistochemical profiles in invasive breast cancer: evaluation of maximum and mean elasticity values.

PURPOSE: To evaluate the correlations of maximum stiffness (Emax) and mean stiffness (Emean) of invasive carcinomas on shear-wave elastography (SWE) with St. Gallen consensus tumor phenotypes. METHODS: We used an ultrasound system with SWE capabilities to prospectively study 190 women with 216 histologically confirmed invasive breast cancers. We obtained one elastogram for each lesion. We correlated Emax and Emean with tumor size, histologic type and grade, estrogen and progesterone receptors, HER2 expression, the Ki67 proliferation index, and the five St. Gallen molecular subtypes: luminal A, luminal B without HER2 overexpression (luminal B HER2-), luminal B with HER2 overexpression (luminal B HER2+), HER2, and triple negative. RESULTS: Lesions larger than 20 mm had significantly higher Emax (148.04 kPa) and Emean (118.32 kPa) (P=0.005) than smaller lesions. We found no statistically significant correlations between elasticity parameters and histologic type and grade or molecular subtypes, although tumors with HER2 overexpression regardless whether they expressed hormone receptors (luminal B HER2+ and HER2 phenotypes) and triple-negative tumors had lower Emax and Emean than the others. We assessed the B-mode ultrasound findings of the lesions with some of the Emax or Emean values less than or equal to 80 kPa; only four of these had ultrasound findings suggestive of a benign lesion (two with luminal A phenotype and two with HER2 phenotype). CONCLUSIONS: We were unable to demonstrate statistically significant differences among the subtypes of invasive tumors, although there appears to be a trend toward lower Emax and Emean in the aggressive phenotypes.

Computerized detection of breast lesions using deformable part models in ultrasound images.

Ultrasound imaging is considered an important complementary technique for the screening of dense breasts. Detection of lesions at an early stage is a key step in which computerized lesion detection systems could play an important role in the analysis of US images. In this article, we propose adaptation of a generic object detection technique, deformable part models, to detect lesions in breast US images. The data set used in this study included 326 images, all from different patients (54 malignant lesions, 109 benign lesions and 163 healthy breasts). In terms of lesion detection, our proposal outperformed some of the most relevant approaches described in the literature; we obtained a sensitivity of 86% with 0.28 false-positive detection per image and an Az value of 0.975. In the detection of malignant lesions, our proposed approached had an Az value of 0.93 and a sensitivity of 78% at a 1.15 false-positive detections per image.