Detecting accurate tumor size across imaging modalities in breast cancer

2021 ◽  
pp. 1-6
Author(s):  
Olutayo Sogunro ◽  
Constance Cashen ◽  
Sami Fakir ◽  
Julie Stausmire ◽  
Nancy Buderer
Keyword(s):  
Breast Cancer ◽  
Tumor Size ◽  
Breast Imaging ◽  
Imaging Modality ◽  
Breast Cancer Diagnosis ◽  
Imaging Modalities ◽  
Clinical Staging ◽  
P Value ◽  
Pathology Report ◽  
Breast Cancers

BACKGROUND: Of the most common imaging modalities for breast cancer diagnosis – mammogram (MAM), ultrasound (US), magnetic resonance imaging (MRI) – it has not been well established which of these most accurately corresponds to the histological tumor size. OBJECTIVE: To determine which imaging modality (MAM, US, MRI) is most accurate for determining the histological tumor size of breast lesions. METHODS: A retrospective study of 76 breast cancers found in 73 female patients who received MAM, US, and/or MRI was performed. 239 charts were reviewed and 73 patients met inclusion criteria. Analysis was performed using signed rank tests comparing the reported tumor size on the imaging modality to the tumor size on pathology report. RESULTS: Mammography and ultrasonography underestimated tumor size by 3.5 mm and 4 mm (p-values < 0.002), respectively. MRI tends to overestimate tumor size by 3 mm (p-value = 0.0570). Mammogram was equivalent to pathological size within 1 mm 24% of the time and within 2 mm 35% of the time. CONCLUSIONS: No one single modality is the most accurate for detecting tumor size. When interpreting the size reported on breast imaging modalities, the amount of underestimation and overestimation in tumor size should be considered for both clinical staging and surgical decision-making.

ROLE OF CONTRAST ENHANCED COMPUTED TOMOGRAPHY SCAN IN DIAGNOSIS OF LOCAL SPREAD, DISTANT METASTASIS AND STAGING OF BREAST CARCINOMA

2021 ◽  
pp. 63-64
Author(s):  
Sangeeta Saxena ◽  
Suresh Kumar Saini ◽  
Dharm Raj Meena ◽  
Harsh Vardhan Khokhar
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Imaging Modality ◽  
Contrast Material ◽  
Stage Iv ◽  
Clinical Staging ◽  
Histopathological Correlation ◽  
Appropriate Treatment ◽  
Correlation Result ◽  
Enhanced Computed Tomography

Background: Breast cancer is the most common cause of cancer death for women worldwide. The accurate clinical staging of patients with breast cancer is important in determining the most appropriate treatment. The present study investigated the value of staging CECT in detecting asymptomatic distant (lung, liver and bone) metastases in patients with primary breast cancer. Material And Method: 30 patients with Breast Imaging Reporting and Data System category (BI-RADS) 4, 5 and 6 lesions underwent unenhanced breast CTand contrast material enhanced CTbefore histopathological correlation. Result And Discussion: In present study, 5(16.6%) cases shows metastasis into the lungs, 3(10%) cases shows metastasis into the liver, 3(10%) cases shows metastasis into the bones, 1(3.3%)case show metastasis into multiple site(lung and liver), 18(60%) cases shows no any evidence of metastasis. By contrast, 12 of 30 patients (40%) with stage III were upstaged to stage IV and 13 patients (43.3%) of those were originally stage IIIB or IIIC. Conclusion:CECTappears as an essential imaging modality to detect presence, extent and localisation of metastasis.

Elasto-Mammography: Elastic Property Reconstruction in Breast Tissues

2005 ◽  
Vol 874 ◽  
Author(s):  
Z. Wang ◽  
Y. Liu ◽  
L.Z. Sun ◽  
G. Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Imaging Modality ◽  
Breast Cancer Diagnosis ◽  
Reconstruction Method ◽  
Breast Cancers ◽  
Primary Method ◽  
Conventional Mammography ◽  
Breast Tissues ◽  
Higher Sensitivity

AbstractMammography is the primary method for screening and detecting breast cancers. However, it frequently fails to detect small tumors and is not quite specific in terms of tumor benignity and malignancy. The objective of this paper is to develop a new imaging modality called elastomammography that generates the modulus elastograms based conventional mammographs. A new elastic reconstruction method is described based on elastography and mammography for breast tissues. Elastic distribution can be reconstructed through the measurement of displacement provided by mammographic projection. It is shown that the proposed elasto-mammography provides higher sensitivity and specificity than the conventional mammography on its own for breast cancer diagnosis.

scholarly journals Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1511 ◽  
Author(s):  
Ella F. Jones ◽  
Deep K. Hathi ◽  
Rita Freimanis ◽  
Rita A. Mukhtar ◽  
A. Jo Chien ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Therapy ◽  
Breast Imaging ◽  
Imaging Modality ◽  
Neoadjuvant Treatment ◽  
Specific Treatment ◽  
Response To Treatment ◽  
Breast Cancers ◽  
Treatment Approaches ◽  
Imaging Markers

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Subtle signs of breast cancer as an important pitfall in mammographic interpretation. Establishing an accurate diagnosis in an equivocal case

2021 ◽  
pp. 1-6
Author(s):  
Nikolaos S. Salemis ◽  
Eleni Mourtzoukou ◽  
Michail Angelopoulos
Keyword(s):  
Breast Cancer ◽  
Imaging Modality ◽  
Benign Lesion ◽  
Imaging Features ◽  
Breast Cancers ◽  
Breast Clinic ◽  
Delay In Diagnosis ◽  
Delay Of Treatment ◽  
Equivocal Case ◽  
Related Mortality

Mammogram is the standard imaging modality for the early detection of breast cancer, and it has been shown to reduce disease-related mortality by up to 30%. Mammogram, however, has its limitations. It is reported that 10–30% of breast cancers may be missed on a mammogram. Delay in the diagnosis and treatment may adversely affect the prognosis of patients with breast cancer. We present a case of multifocal invasive early breast carcinoma, which was misinterpreted twice as intramammary lymph nodes, thus resulting in a delay in diagnosis for eighteen months. The tumors were detected incidentally after the patient presented to our Breast clinic for symptoms related to a concomitant benign lesion involving the same breast. We describe the tumors’ imaging features and discuss the possible reasons that likely led to repeated misinterpretation. Awareness of possible causes for missed breast cancer is necessary to avoid delay of treatment initiation that may adversely affect prognosis.

scholarly journals Diabetes, Obesity, and Inflammation: Impact on Clinical and Radiographic Features of Breast Cancer

2021 ◽  
Vol 22 (5) ◽  
pp. 2757
Author(s):  
Braden Miller ◽  
Hunter Chalfant ◽  
Alexandra Thomas ◽  
Elizabeth Wellberg ◽  
Christina Henson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Imaging Techniques ◽  
Growth Factor Receptor ◽  
Breast Cancer Diagnosis ◽  
Screening Mammography ◽  
Imaging Modalities ◽  
Conventional Imaging ◽  
Obese Women ◽  
Detection Rates ◽  
Therapeutic Decision Making

Obesity, diabetes, and inflammation increase the risk of breast cancer, the most common malignancy in women. One of the mainstays of breast cancer treatment and improving outcomes is early detection through imaging-based screening. There may be a role for individualized imaging strategies for patients with certain co-morbidities. Herein, we review the literature regarding the accuracy of conventional imaging modalities in obese and diabetic women, the potential role of anti-inflammatory agents to improve detection, and the novel molecular imaging techniques that may have a role for breast cancer screening in these patients. We demonstrate that with conventional imaging modalities, increased sensitivity often comes with a loss of specificity, resulting in unnecessary biopsies and overtreatment. Obese women have body size limitations that impair image quality, and diabetes increases the risk for dense breast tis-sue. Increased density is known to obscure the diagnosis of cancer on routine screening mammography. Novel molecu-lar imaging agents with targets such as estrogen receptor, human epidermal growth factor receptor 2 (HER2), pyrimi-dine analogues, and ligand-targeted receptor probes, among others, have potential to reduce false positive results. They can also improve detection rates with increased resolution and inform therapeutic decision making. These emerg-ing imaging techniques promise to improve breast cancer diagnosis in obese patients with diabetes who have dense breasts, but more work is needed to validate their clinical application.

Breast cancer ER, PR, and HER2 expression variance by germline cancer predisposition genes.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10526-10526
Author(s):  
Grace Wei ◽  
Marilin Rosa ◽  
Maxine Chang ◽  
Brian J. Czerniecki ◽  
Xia Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Cancer Predisposition ◽  
Tumor Evolution ◽  
Breast Cancers ◽  
Her2 Expression ◽  
Expression Levels ◽  
Genetic Test Results ◽  
Predisposition Genes

10526 Background: The association between breast cancer characteristics and survival with estrogen receptor (ER) and progesterone receptor (PR) expression has been primarily studied via binomial categories, ER-positive and ER-negative. In order to better characterize germline genetic influences on these markers, we investigated their IHC expression semi-quantitatively in cancer predisposition germline pathogenic variant (PV) carriers of the following genes: BRCA1, BRCA2, PALB2, TP53, PTEN, CDH1, ATM, CHEK2, and Lynch syndrome genes. The HER2 expression was also analyzed. Methods: We conducted a retrospective chart review of patients with germline panel genetic testing for cancer predisposition genes at Moffitt Cancer Center’s GeneHome clinic. Inclusion criteria included 1) women ≥18 years old, 2) breast cancer diagnosis, 3) cancer predisposition germline panel genetic test results, 4) available ER and PR expression levels, and 5) available HER expression and/or amplification status. ER, PR, and HER2 status were compared between PV carriers and non-PV carriers via Mann-Whitney U at p>0.05. Results: A total of 847 cases were reviewed for the study. Among 658 patients with a breast cancer diagnosis and complete ER PR data, 365 cases (55.5%) were non-PV carriers and 293 cases (44.5%) carried a PV in at least one of the genes listed above. Among 635 cases with available HER2 expression/amplification status, 355 (55.9%) cases were non-PV carriers and 288 (45.4%) cases were PV-carriers. When compared with non-PV carrier controls, BRCA1 PV carriers’ breast tumors had significantly lower ER and/or PR expression. Further, BRCA2 and TP53 PV tumors also displayed moderately lower ER expression. Contrarily, CHEK2 tumors displayed higher ER and PR expression compared to controls. Further, BRCA1 and BRCA2 PV carriers were more likely to have HER2- breast cancers. Conclusions: Differences in ER, PR, HER2 expression levels were observed in germline PV carrier breast cancers, signaling differential impacts by germline PVs on the tumor evolution process. It is likely that tumor differences in PV carriers influence responses to therapies, including hormone therapy, anti-HER2 therapy, and subsequent survival.[Table: see text]

scholarly journals 900 Effects Of COVID-19 Pandemic Restrictions on the 2ww Breast Referrals in A UK Specialist Breast Service

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
O Tokode ◽  
S Rastall
Keyword(s):  
Breast Cancer ◽  
Waiting Times ◽  
Age Groups ◽  
Breast Cancer Diagnosis ◽  
Hospital Staff ◽  
Breast Cancers ◽  
Telephone Triage ◽  
Telephone Consultation ◽  
Mean Waiting Time ◽  
Delays In Diagnosis

Abstract Aim Recommendations were issued to the hospital Trusts to configure service delivery to balance cancer care with patient and hospital staff safety during the COVID-19 pandemic. It was felt the service restrictions might lead to delays in diagnosis and treatment of cancer patients. We conducted an audit to compare 2ww breast referrals in our center between May to July of 2019 and 2020. Method We triaged all referrals to face-face consultation or telephone consultation in our center during the pandemic. Patients with suspicious symptoms were offered face-face consultation after the telephone triage. Results Data analysis showed that the referrals fell by 28.3% (N 1569 versus N 1125). The largest reduction was noted in May (34.4% versus 24.2%). Mean waiting time in 2019 was 19.86 (± 7.14) and 11.43 (± 3.48) in 2020. The proportion of patients referred for suspected breast cancers increased across all age groups in 2020 (range +10.4% to 16.2%). Significantly more breast cancers were diagnosed in 2020 (7.1% versus 5.1%). No breast cancer was diagnosed in under 25 patients. 29.1% of the 522 patients telephoned were discharged, and others were seen in the clinic. Conclusions The COVID-19 infection’s management caused a fall in 2ww referrals and shortened waiting times but increased breast cancer diagnosis. Many 2ww referrals during the COVID-19 infection were unnecessary. The telephone consultation reduced waiting times but may have deferred clinic visitation for most patients.

Analysis of Low Estrogen Receptor (ER+) Breast Carcinomas in a Large Community Breast Cancer Center in Northern California

2021 ◽  
Vol 156 (Supplement_1) ◽  
pp. S26-S27
Author(s):  
G Bulusu ◽  
K Duncan ◽  
A Wheeler
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Histological Grade ◽  
Statistical Significance ◽  
Cancer Center ◽  
Pathology Report ◽  
Breast Cancers ◽  
Breast Carcinomas ◽  
Ki 67 ◽  
Er Positive

Abstract Introduction/Objective Estrogen Receptor (ER) expression in breast cancers is a crucial factor for endocrine therapy in patients with tumors expressing ER in ≥1% of tumor cells. The 2019 guidelines published by ASCO/CAP states that breast cancers that have a 1% to 10% of cells staining Estrogen Receptor (ER) positive should be reported as ER Low Positive cases. This study aims to address this subset of low-positive ER tumors and compare the clinical features to other known breast cancer subtypes. Methods/Case Report We conducted a retrospective review of a prospectively maintained breast cancer registry from 2013 to 2021 at Mills-Peninsula Medical Center, a Sutter Health Affiliate. The study reviewed patient charts with respect to the pathology report, operative report, chemotherapy regimen, and clinical outcomes. Statistical analyses were conducted using R Project for Statistical Coding, with The Student’s T-test used to compare continuous variables. Two-sided P values less than 0.05 indicate statistical significance. Results (if a Case Study enter NA) Our study identified 1316 cases of invasive breast carcinomas, of which 29 (2.16%) demonstrated ER Low-Positive expression. We aimed to evaluate the clinical and pathological features, such as histological grade, ER, PR, HER-2, Ki-67%, and patient age for these tumors. We found that ER Low-Positive tumors demonstrated higher mean histological grade morphology (2.5 out of 3, p&lt;0.001) that was similar to that of Triple Negative Breast Cancers (TNBC) (3 of 3, p&lt;0.001) than to High ER-Positive (1.6 of 3, p&lt;0.001) cancers. Further observations, through examining proliferation rates by utilizing the Ki-67 index, indicate comparative trends between the ER Low-Positive cohort and the TNBC cohort. Conclusion The results suggest that the ER Low-Positive carcinomas, despite reported as ER-positive cases, present with similar clinicopathological features to those of ER-negative tumors. Through this study and future research, we would like to emphasize a stricter set of guidelines that can be adopted to reduce variability for reporting biomarkers. This standardization will allow oncologists to provide more appropriate treatment options and improve the quality of patient care.

COVID-related anxiety is prevalent and accurately reflects serious COVID risks in breast cancer patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 12053-12053
Author(s):  
Marisa C. Weiss ◽  
Stephanie Kjelstrom ◽  
Meghan Buckley ◽  
Adam Leitenberger ◽  
Melissa Jenkins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
P Value ◽  
Breast Cancer Patients ◽  
Chi Square ◽  
Liver And Kidney ◽  
Chi Square Test ◽  
A Current

12053 Background: A current cancer diagnosis is a risk factor for serious COVID-19 complications (CDC). In addition, the pandemic has caused major disruptions in medical care and support networks, resulting in treatment delays, limited access to doctors, worsening health disparities, social isolation; and driving higher utilization of telemedicine and online resources. Breastcancer.org has experienced a sustained surge of new and repeat users seeking urgent information and support. To better understand these unmet needs, we conducted a survey of the Breastcancer.org Community. Methods: Members of the Breastcancer.org Community were invited to complete a survey on the effects of the COVID-19 pandemic on their breast cancer care, including questions on demographics, comorbidities (including lung, heart, liver and kidney disease, asthma, diabetes, obesity, and other chronic health conditions); care delays, anxiety due to COVID-related care delays, use of telemedicine, and satisfaction with care during COVID. The survey was conducted between 4/27/2020-6/1/2020 using Survey Monkey. Results were tabulated and compared by chi square test. A p-value of 0.05 is considered significant. Data were analyzed using Stata 16.0 (Stata Corp., Inc, College Station, TX). Results: Our analysis included 568 breast cancer patients of whom 44% had ≥1 other comorbidities associated with serious COVID-19 complications (per CDC) and 37% had moderate to extreme anxiety about contracting COVID. This anxiety increased with the number of comorbidities (p=0.021), age (p=0.040), and with a current breast cancer diagnosis (p=0.011) (see table). Anxiety was significantly higher in those currently diagnosed, ≥65, or with ≥3 other comorbidities, compared to those diagnosed in the past, age <44, or without other comorbidities. Conclusions: Our survey reveals that COVID-related anxiety is prevalent at any age regardless of overall health status, but it increased with the number of other comorbidities, older age, and a current breast cancer diagnosis. Thus, reported anxiety is proportional to the risk of developing serious complications from COVID. Current breast cancer patients of all ages—especially with other comorbidities—require emotional support, safe access to their providers, and prioritization for vaccination.[Table: see text]

Automatic detection of various patterns of calcification distribution on mammograms: a graph convolution network approach (Preprint)

2021 ◽  
Author(s):  
Melissa Min-Szu Yao ◽  
Hao Du ◽  
Mikael Hartman ◽  
Wing P. Chan ◽  
Mengling Feng
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Automatic Detection ◽  
Ground Truth ◽  
Architectural Distortion ◽  
Breast Cancers ◽  
Convolutional Network ◽  
Mammographic Images

UNSTRUCTURED Purpose: To develop a novel artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 200 patients classified as Category 4 or 5 according to the American College of Radiology Breast Imaging Reporting and Database System, which showed calcifications according to the mammographic reports and diagnosed breast cancers. The calcification distributions were classified as either diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer as a single or combined characterization such as a mass, asymmetry, or architectural distortion with or without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph convolutional network-based model was developed. 401 mammographic images from 200 cases of breast cancer were divided based on calcification distribution pattern: diffuse (n = 24), regional (n = 111), group (n = 201), linear (n = 8) or segmental (n = 57). The classification performances were measured using metrics including precision, recall, F1 score, accuracy and multi-class area under receiver operating characteristic curve. The proposed achieved precision of 0.483 ± 0.015, sensitivity of 0.606 (0.030), specificity of 0.862 ± 0.018, F1 score of 0.527 ± 0.035, accuracy of 60.642% ± 3.040% and area under the curve of 0.754 ± 0.019, finding method to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. Conclusion: The proposed deep neural network framework is an AI solution to automatically detect and classify calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.

Sign in / Sign up

Export Citation Format

Share Document