Detection Of Invasive Breast Cancer Research Articles

Radiation exposure and screening yield by digital breast tomosynthesis compared to mammography: results of the TOSYMA Trialbreast density related.

The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR). TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms. There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed. Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations. TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM. Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower andmore constant values. With the smallest average glandular dose differencein dense breasts, DBT plus SM had thehighest difference in invasive breast cancer detection rates.

Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology images

Accurate detection of invasive breast cancer (IC) can provide decision support to pathologists as well as improve downstream computational analyses, where detection of IC is a first step. Tissue containing IC is characterized by the presence of specific morphological features, which can be learned by convolutional neural networks (CNN). Here, we compare the use of a single CNN model versus an ensemble of several base models with the same CNN architecture, and we evaluate prediction performance as well as variability across ensemble based model predictions.Two in-house datasets comprising 587 whole slide images (WSI) are used to train an ensemble of ten InceptionV3 models whose consensus is used to determine the presence of IC. A novel visualisation strategy was developed to communicate ensemble agreement spatially. Performance was evaluated in an internal test set with 118 WSIs, and in an additional external dataset (TCGA breast cancer) with 157 WSI.We observed that the ensemble-based strategy outperformed the single CNN-model alternative with respect to accuracy on tile level in 89 % of all WSIs in the test set. The overall accuracy was 0.92 (DICE coefficient, 0.90) for the ensemble model, and 0.85 (DICE coefficient, 0.83) for the single CNN alternative in the internal test set. For TCGA the ensemble outperformed the single CNN in 96.8 % of the WSI, with an accuracy of 0.87 (DICE coefficient 0.89), the single model provides an accuracy of 0.75 (DICE coefficient 0.78).The results suggest that an ensemble-based modeling strategy for breast cancer invasive cancer detection consistently outperforms the conventional single model alternative. Furthermore, visualisation of the ensemble agreement and confusion areas provide direct visual interpretation of the results. High performing cancer detection can provide decision support in the routine pathology setting as well as facilitate downstream computational analyses.

Abstract PO2-13-08: Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ

Abstract Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( >1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.

In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

Breast MRI – The champion in the millimeter league: MIO breast MRI – The method of choice in women with dense breasts

We perform MRI of the breast as a first pass technique. We successfully established 10-minute-protocols (including T2 images) with a fixed dosage of 5 ml 1 M CM. A high spatial resolution of 526 × 526, better 672 × 672 or maximum (1.024 × 1.024, MIO MRI) is vital to achieve best results. We use fixation tools to avoid motion artifacts. Motion correction algorithms can, however, often eliminate such artifacts when they are present. In initial breast MRI exams, morphologic features are the most important criteria for lesion evaluation. If previous exams are available for comparison, the main criteria indicating a suspicious lesion are an increase in lesion size or the depiction of new lesions.High quality HR MRI of the breast is the method of choice in women with dense or extremely dense breasts in all cases (screening, assessment, follow up). In density type A or B, MRI can be helpful in defined constellations, e.g. when MX and US are limited or contraindicated.According to our experience, 95% or more of all carcinomas of the breast are detectable on MRI. The remaining 5% of MRI-occult lesions are intraductal tumors or very small invasive carcinomas depicted with mammography due to associated microcalcifications. MRI is, however, superior to all other imaging modalities in the detection of the clinically relevant DCIS (high risk DCIS, intermediate type).Consecutive MRI examinations in intervals of 12 to 24 months allow a reliable detection of invasive breast cancer with an average size of 7–8 mm. This corresponds to a rate of metastasis-free locoregional lymph nodes in >95% of cases. The rate of interval cancers is <2%. In conclusion, this strategy may increase the overall-lifetime survival of breast cancer patients to more than 95%. Inversely, mortality may be reduced to <5%.Taking these improvements in early breast cancer detection and survival that can be achieved through the implementation of QA HR MRI of the breast into account, it should be discussed to modify oncologic guidelines for the treatment of breast cancer.MRI is the best diagnostic tool we have and according to our experience, a first pass, quality-assured high-resolution breast MRI protocol provides best diagnostic results at minimal procedural effort.

Abstract P2-09-01: An Emerging Technology for Breast Cancer Detection - Preliminary Data of Breast Cancer Detection using Novel Low Dose Positron Emission Mammography

Abstract Purpose: To investigate the feasibility of low-dose Positron Emission Mammography (PEM) to identify breast cancer. Materials and Methods: In an REB-approved ongoing clinical trial, which started in December 2019, all newly diagnosed women with breast cancer who had not undergone neoadjuvant chemotherapy and consented to the study were randomly assigned independently of their mammographic breast density, tumor size, and histopathology cancer subtype to perform PEM using a novel organ-targeted PET system(Radialis PET Imager, Radialis Inc., FDA cleared to image and measure the distribution of injected positron-emitting radiopharmaceuticals) with either 1mCi, 2mCi or 5 mCi of 18F-FDG. The PEM images acquired 1 and 4 hours after 18F-FDG administration were reviewed in consensus by two fellowship-trained breast radiologists blinded to cancer location. PEM imaging features of known malignancies and additional PEM findings were recorded and correlated with histopathology as the ground truth. Results: Our cohort comprised 22 women with a median age of 51 years (range 32-85) with 88 completed bilateral sets of images where 23 cancers (18 invasives, and 5 in situ) were present. The median invasive cancer size on surgical pathology was 28 mm (range: 3-120). Out of 18 invasive cancers, the only PEM images that did not visualize cancer (2 invasive lobular cancers and 2 in-situ cancers) were acquired with the lowest dose of 18F-FDG (1 mCi). A total of 6 (27.3%) subjects received 1mCi 18F-FDG with 5 invasive cancers, 7 (31.8%) of patients received 5 mCi 18F-FDG with 7 invasive cancers, and 9 (40.9%) of patients received 5 mCi 18F-FDG with 6 invasive cancers. A total of 3 false-positive images of benign findings were also present. No additional cancers were identified exclusively by PEM. The PEM performance was similar following an additional 3-hour interval for radiotracer uptake. Conclusion: This preliminary data results show the feasibility of invasive breast cancer detection with a decreased 18F-FDG dose, possibly due to the novel PEM system detectors increasing the sensitivity to radiotracer and the perceived spatial resolution by the visual assessment. Larger-scale clinical trials are required to consolidate our preliminary findings. Clinical Relevance: A novel organ-targeted PET system enables the detection of invasive breast cancer using low-dose PEM, potentially emerging as a promising novel imaging tool. Citation Format: Vivianne Freitas, Michael L. Waterston, Ken O. Olsen, Oleksandr Bubon, Shayna Parker, Brandon Baldassi, Borys Komarov, Samira Taeb, Alla Reznik. An Emerging Technology for Breast Cancer Detection - Preliminary Data of Breast Cancer Detection using Novel Low Dose Positron Emission Mammography [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-09-01.

Breast Density and Breast Cancer Screening with Digital Breast Tomosynthesis: A TOSYMA Trial Subanalysis.

Background Digital breast tomosynthesis (DBT) plus synthesized mammography (SM) reduces the diagnostic pitfalls of tissue superimposition, which is a limitation of digital mammography (DM). Purpose To compare the invasive breast cancer detection rate (iCDR) of DBT plus SM versus DM screening for different breast density categories. Materials and Methods An exploratory subanalysis of the TOmosynthesis plus SYnthesized MAmmography (TOSYMA) study, a randomized, controlled, multicenter, parallel-group trial recruited within the German mammography screening program from July 2018 to December 2020. Women aged 50-69 years were randomly assigned (1:1) to DBT plus SM or DM screening examination. Breast density categories A-D were visually assessed according to the Breast Imaging Reporting and Data System Atlas. Exploratory analyses were performed of the iCDR in both study arms and stratified by breast density, and odds ratios and 95% CIs were determined. Results A total of 49 762 women allocated to DBT plus SM and 49 796 allocated to DM (median age, 57 years [IQR, 53-62 years]) were included. In the DM arm, the iCDR was 3.6 per 1000 screening examinations in category A (almost entirely fatty) (16 of 4475 screenings), 4.3 in category B (102 of 23 534 screenings), 6.1 in category C (116 of 19 051 screenings), and 2.3 in category D (extremely dense breasts) (six of 2629 screenings). The iCDR in the DBT plus SM arm was 2.7 per 1000 screening examinations in category A (12 of 4439 screenings), 6.9 in category B (154 of 22 328 screenings), 8.3 in category C (156 of 18 772 screenings), and 8.1 in category D (32 of 3940 screenings). The odds ratio for DM versus DBT plus SM in category D was 3.8 (95% CI: 1.5, 11.1). The invasive cancers detected with DBT plus SM were most often grade 2 tumors; in category C, it was 58% (91 of 156 invasive cancers), and in category D, it was 47% (15 of 32 invasive cancers). Conclusion The TOmosynthesis plus SYnthesized MAmmography trial revealed higher invasive cancer detection rates with digital breast tomosynthesis plus synthesized mammography than digital mammography in dense breasts, relatively and absolutely most marked among women with extremely dense breasts. ClinicalTrials.gov registration no.: NCT03377036 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Surrogate endpoints in breast cancer screening trials – Authors' reply

We appreciate Ismail Jatoi's interest in the TOSYMA study. He challenges the choice of the primary endpoints—the invasive breast cancer detection rates at screening and the following invasive interval cancer rates—as used in this randomised controlled trial comparing digital breast tomosynthesis plus synthesised two-dimensional mammography with digital mammography.

Should tomosynthesis replace mammography for breast cancer screening?

A decade has passed since the first prospective trials showed that the mammography technology, digital breast tomosynthesis, increased the rate of cancer detection compared with digital mammography. 1 Ciatto S Houssami N Bernardi D et al. Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol. 2013; 14: 583-589 Summary Full Text Full Text PDF PubMed Scopus (608) Google Scholar , 2 Skaane P Bandos AI Gullien R et al. Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration. Eur Radiol. 2013; 23: 2061-2071 Crossref PubMed Scopus (170) Google Scholar A large body of evidence, comprising prospective non-randomised studies and retrospective studies, 3 Marinovich ML Hunter KE Macaskill P Houssami N Breast cancer screening using tomosynthesis or mammography: a meta-analysis of cancer detection and recall. J Natl Cancer Inst. 2018; 110: 942-949 Crossref PubMed Scopus (108) Google Scholar has since accumulated; however, evidence from randomised controlled trials is scarce. In The Lancet Oncology, Walter Heindel and colleagues report the results of the TOmosynthesis plus SYnthesised MAmmography Study (TOSYMA), 4 Heindel W Weigel W Gerß J et al. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 2022; (published online April 12.)https://doi.org/10.1016/S1470-2045(22)00194-2 Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar a multicentre trial embedded in the German population-wide mammography screening programme. TOSYMA is the largest of three randomised controlled trials providing evidence on breast tomosynthesis versus mammography screening. 4 Heindel W Weigel W Gerß J et al. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 2022; (published online April 12.)https://doi.org/10.1016/S1470-2045(22)00194-2 Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar , 5 Hofvind S Holen AS Aase HS et al. Two-view digital breast tomosynthesis versus digital mammography in a population-based breast cancer screening programme (To-Be): a randomised, controlled trial. Lancet Oncol. 2019; 20: 795-805 Summary Full Text Full Text PDF PubMed Scopus (38) Google Scholar , 6 Pattacini P Nitrosi A Giorgi RP et al. A randomized trial comparing breast cancer incidence and interval cancers after tomosynthesis plus mammography versus mammography alone. Radiology. 2022; (published online Feb 1.)https://doi.org/10.1148/radiol.211132 Crossref PubMed Scopus (3) Google Scholar Heindel and colleagues persevered with TOSYMA despite the COVID-19 pandemic stalling recruitment, resulting in a marginally lower sample size than planned in their adaptive design. 4 Heindel W Weigel W Gerß J et al. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 2022; (published online April 12.)https://doi.org/10.1016/S1470-2045(22)00194-2 Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trialThe results from this study indicate that the detection rate for invasive breast cancer was significantly higher with digital breast tomosynthesis plus s2D mammography than digital mammography alone. Evaluation of interval cancer rates in the follow-up study will further help to investigate incremental long-term benefits of digital breast tomosynthesis screening. Full-Text PDF

Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial.

Two dimensional (2D) full-field digital mammography is the current standard of breast cancer screening. Digital breast tomosynthesis generates pseudo-three dimensional datasets of the breast from which synthesised 2D (s2D) mammograms can be reconstructed. This innovative approach reduces the likelihood of overlapping breast tissues that can conceal features of malignancy. We aimed to compare digital breast tomosynthesis plus s2D mammography with digital screening mammography for the detection of invasive breast cancer. TOSYMA was a randomised, open-label, superiority trial done at 17 screening units in two federal states of Germany. Eligible participants were women aged 50-69 years who had been invited to participate in a population-wide, quality-controlled mammography screening programme. Women were randomly assigned (1:1) to digital breast tomosynthesis plus s2D mammography or digital mammography alone using block randomisation (block size of 32), stratified by site. The primary endpoints were the detection rate of invasive breast cancer and invasive interval cancer rate at 24 months, analysed in the modified full analysis set, which included all randomly assigned participants who underwent either type of screening examination. Ten examinations, corresponding to a second study participation, were excluded. Analyses were done according to the intention-to-treat principle. Interval cancer rates will be reported in the follow-up study. Safety was assessed in the as-treated population, which included all participants who were randomly assigned. This trial is registered with ClinicalTrials.gov, NCT03377036, and is closed to accrual. Between July 5, 2018, and Dec 30, 2020, 99 689 women were randomly assigned to digital breast tomosynthesis plus s2D mammography (n=49 804) or digital mammography (n=49 830). Invasive breast cancers were detected in 354 of 49 715 women with evaluable primary endpoint data in the digital breast tomosynthesis plus s2D group (detection rate 7·1 cases per 1000 women screened) and in 240 of 49 762 women in the digital mammography group (4·8 cases per 1000 women screened; odds ratio 1·48 [95% CI 1·25-1·75]; p<0·0001). Adverse events and device deficiencies were rare (six adverse events in each group; 23 device deficiencies in the digital breast tomosynthesis plus s2D group vs five device deficiencies in the digital mammography group) and no serious adverse events were reported. The results from this study indicate that the detection rate for invasive breast cancer was significantly higher with digital breast tomosynthesis plus s2D mammography than digital mammography alone. Evaluation of interval cancer rates in the follow-up study will further help to investigate incremental long-term benefits of digital breast tomosynthesis screening. Deutsche Forschungsgemeinschaft (German Research Foundation).

Magnetic Resonance Imaging Features in Different Types of Invasive Breast Cancer: A Systematic Review of the Literature.

Breast cancer is the most common malignancy affecting women worldwide, and early diagnosis of breast cancer is the key to its successful and effective treatment. Traditional imaging techniques such as mammography and ultrasound are used to detect and configure breast abnormalities; unfortunately, these modalities have low sensitivity and specificity, particularly in young patients with dense breast tissue, breast implants, or post-surgical scar/architecture distortions. Therefore, breast magnetic resonance imaging (MRI) has been superior in the characterization and detection of breast cancer, especially that with invasive features. This review article explores the importance of breast MRI in the early detection of invasive breast cancer versus traditional tools, including mammography and ultrasound, while also analyzing the use of MRI as a screening tool for high-risk women. We will also discuss the different MRI features for invasive ductal carcinoma and lobular carcinoma and the role of breast MRI in the detection of ductal carcinoma in situ with a focus on the utilization of new techniques, including MR spectroscopy and diffusion-weighted imaging.

Up-to-date breast cancer imaging

Introduction. Breast cancer is a major health and economic problem in Serbia. On the one hand, most patients see a the doctor at an advanced stage of the disease, and on the other hand, screening response rate is low. Early detection increases the chances of survival. Digital mammography is the gold standard in breast cancer imaging, together with supplemental ultrasound and tomosynthesis. Mammography combined with tomosynthesis has a greater detection rate regardless of breast density, although the difference is more pronounced in high-density lesions, and it has fewer falsenegative results than mammography alone. Ultrasound, apart from being a complementary method, is ideal for ultrasound guided breast biopsy. Magnetic resonance mammography is an imaging method with the highest sensitivity in the detection of invasive breast cancer and has an important role in the evaluation of the post-therapeutic response and in preoperative preparation. Magnetic resonance spectroscopy measures the choline levels and helps to differentiate malignant from benign lesions. It is also a promising method for early assessment of the post-therapeutic response. Positron emission tomography is likely to gain more significant roles in breast cancer imaging in the near future, especially in measuring the treatment response.

Monitoring and evaluation of breast cancer screening programmes: selecting candidate performance indicators

BackgroundIn the scope of the European Commission Initiative on Breast Cancer (ECIBC) the Monitoring and Evaluation (M&E) subgroup was tasked to identify breast cancer screening programme (BCSP) performance indicators, including their acceptable and desirable levels, which are associated with breast cancer (BC) mortality. This paper documents the methodology used for the indicator selection.MethodsThe indicators were identified through a multi-stage process. First, a scoping review was conducted to identify existing performance indicators. Second, building on existing frameworks for making well-informed health care choices, a specific conceptual framework was developed to guide the indicator selection. Third, two group exercises including a rating and ranking survey were conducted for indicator selection using pre-determined criteria, such as: relevance, measurability, accurateness, ethics and understandability. The selected indicators were mapped onto a BC screening pathway developed by the M&E subgroup to illustrate the steps of BC screening common to all EU countries.ResultsA total of 96 indicators were identified from an initial list of 1325 indicators. After removing redundant and irrelevant indicators and adding those missing, 39 candidate indicators underwent the rating and ranking exercise. Based on the results, the M&E subgroup selected 13 indicators: screening coverage, participation rate, recall rate, breast cancer detection rate, invasive breast cancer detection rate, cancers > 20 mm, cancers ≤10 mm, lymph node status, interval cancer rate, episode sensitivity, time interval between screening and first treatment, benign open surgical biopsy rate, and mastectomy rate.ConclusionThis systematic approach led to the identification of 13 BCSP candidate performance indicators to be further evaluated for their association with BC mortality.

Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image.

The detection of the region of interests (ROIs) on Whole Slide Images (WSIs) is one of the primary steps in computer-aided cancer diagnosis and grading. Early and accurate identification of invasive cancer regions in WSI is critical in the improvement of breast cancer diagnosis and further improvements in patient survival rates. However, invasive cancer ROI segmentation is a challenging task on WSI because of the low contrast of invasive cancer cells and their high similarity in terms of appearance, to non-invasive regions. In this paper, we propose a CNN based architecture for generating ROIs through segmentation. The network tackles the constraints of data-driven learning and working with very low-resolution WSI data in the detection of invasive breast cancer. Our proposed approach is based on transfer learning and the use of dilated convolutions. We propose a highly modified version of U-Net based auto-encoder, which takes as input an entire WSI with a resolution of 320×320. The network was trained on low-resolution WSI from four different data cohorts and has been tested for inter as well as intra- dataset variance. The proposed architecture shows significant improvements in terms of accuracy for the detection of invasive breast cancer regions.

Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening

Improved screening methods for women with dense breasts are needed because of their increased risk of breast cancer and of failed early diagnosis by screening mammography. To compare the screening performance of abbreviated breast magnetic resonance imaging (MRI) and digital breast tomosynthesis (DBT) in women with dense breasts. Cross-sectional study with longitudinal follow-up at 48 academic, community hospital, and private practice sites in the United States and Germany, conducted between December 2016 and November 2017 among average-risk women aged 40 to 75 years with heterogeneously dense or extremely dense breasts undergoing routine screening. Follow-up ascertainment of cancer diagnoses was complete through September 12, 2019. All women underwent screening by both DBT and abbreviated breast MRI, performed in randomized order and read independently to avoid interpretation bias. The primary end point was the invasive cancer detection rate. Secondary outcomes included sensitivity, specificity, additional imaging recommendation rate, and positive predictive value (PPV) of biopsy, using invasive cancer and ductal carcinoma in situ (DCIS) to define a positive reference standard. All outcomes are reported at the participant level. Pathology of core or surgical biopsy was the reference standard for cancer detection rate and PPV; interval cancers reported until the next annual screen were included in the reference standard for sensitivity and specificity. Among 1516 enrolled women, 1444 (median age, 54 [range, 40-75] years) completed both examinations and were included in the analysis. The reference standard was positive for invasive cancer with or without DCIS in 17 women and for DCIS alone in another 6. No interval cancers were observed during follow-up. Abbreviated breast MRI detected all 17 women with invasive cancer and 5 of 6 women with DCIS. Digital breast tomosynthesis detected 7 of 17 women with invasive cancer and 2 of 6 women with DCIS. The invasive cancer detection rate was 11.8 (95% CI, 7.4-18.8) per 1000 women for abbreviated breast MRI vs 4.8 (95% CI, 2.4-10.0) per 1000 women for DBT, a difference of 7 (95% CI, 2.2-11.6) per 1000 women (exact McNemar P = .002). For detection of invasive cancer and DCIS, sensitivity was 95.7% (95% CI, 79.0%-99.2%) with abbreviated breast MRI vs 39.1% (95% CI, 22.2%-59.2%) with DBT (P = .001) and specificity was 86.7% (95% CI, 84.8%-88.4%) vs 97.4% (95% CI, 96.5%-98.1%), respectively (P < .001). The additional imaging recommendation rate was 7.5% (95% CI, 6.2%-9.0%) with abbreviated breast MRI vs 10.1% (95% CI, 8.7%-11.8%) with DBT (P = .02) and the PPV was 19.6% (95% CI, 13.2%-28.2%) vs 31.0% (95% CI, 17.0%-49.7%), respectively (P = .15). Among women with dense breasts undergoing screening, abbreviated breast MRI, compared with DBT, was associated with a significantly higher rate of invasive breast cancer detection. Further research is needed to better understand the relationship between screening methods and clinical outcome. ClinicalTrials.gov Identifier: NCT02933489.

Characteristics of Invasive Breast Cancer Detected by Digital Breast Tomosynthesis on Screening and Diagnostic Mammograms.

To determine whether there is added benefit for 3D mammography in the context of screening and diagnostic imaging, particularly relating to known prognostic characteristics, including histopathology, receptor status, and axillary lymph node involvement. An institutional review board-approved retrospective review was performed of our mammography and pathology databases from October 2012 to May 2015 to identify biopsy-proven invasive breast carcinoma detected on screening and diagnostic mammograms by 2D plus 3D (2D + 3D) imaging. Percentages of cancer detection by 2D and 3D were compared. Correlation with histopathology and lymph node status was analyzed. Of 53 cancers diagnosed on 12 543 screening mammograms, 36 (67.9%) were better visualized on 3D (not visualized, equivocal, or only seen in retrospect on 2D). Of the 62 cancers diagnosed on 4090 diagnostic mammograms, 24 (38.7%) cancers were better detected on 3D. A statistically significant greater number of cancers were better detected on 3D in the screening compared to the diagnostic mammograms (67.9% vs 38.7%, P < .05). A significantly higher frequency of less aggressive tumors (grade I and grade II, positive estrogen/progesterone receptor, Her2 negative) was detected by 3D, with higher significance in the screening population. Additionally, there was a higher frequency of positive axillary lymph nodes in cancers detected by 3D in the screening group. Three-dimension increases invasive breast cancer detection, particularly pathologically less aggressive tumors, in both screening and diagnostic mammograms with more benefit for the screening population. Three-dimensional mammography detected more breast cancer associated with metastatic axillary lymph nodes in the screening population.

Intensive Surveillance with Biannual Dynamic Contrast-Enhanced Magnetic Resonance Imaging Downstages Breast Cancer in BRCA1 Mutation Carriers.

To establish a cohort of high-risk women undergoing intensive surveillance for breast cancer.Experimental Design: We performed dynamic contrast-enhanced MRI every 6 months in conjunction with annual mammography (MG). Eligible participants had a cumulative lifetime breast cancer risk ≥20% and/or tested positive for a pathogenic mutation in a known breast cancer susceptibility gene. Between 2004 and 2016, we prospectively enrolled 295 women, including 157 mutation carriers (75 BRCA1, 61 BRCA2); participants' mean age at entry was 43.3 years. Seventeen cancers were later diagnosed: 4 ductal carcinoma in situ (DCIS) and 13 early-stage invasive breast cancers. Fifteen cancers occurred in mutation carriers (11 BRCA1, 3 BRCA2, 1 CDH1). Median size of the invasive cancers was 0.61 cm. No patients had lymph node metastasis at time of diagnosis, and no interval invasive cancers occurred. The sensitivity of biannual MRI alone was 88.2% and annual MG plus biannual MRI was 94.1%. The cancer detection rate of biannual MRI alone was 0.7% per 100 screening episodes, which is similar to the cancer detection rate of 0.7% per 100 screening episodes for annual MG plus biannual MRI. The number of recalls and biopsies needed to detect one cancer by biannual MRI were 2.8 and 1.7 in BRCA1 carriers, 12.0 and 8.0 in BRCA2 carriers, and 11.7 and 5.0 in non-BRCA1/2 carriers, respectively. Biannual MRI performed well for early detection of invasive breast cancer in genomically stratified high-risk women. No benefit was associated with annual MG screening plus biannual MRI screening.See related commentary by Kuhl and Schrading, p. 1693.

The mammography screening detection of ductal carcinoma in situ and invasive breast cancer according to women's characteristics: is it the same?

Detection of ductal carcinoma in situ (DCIS) has increased with the mammography dissemination. Given the potential role of DCIS as a precursor of invasive breast cancer (IBC), we aimed to assess whether women's characteristics have a different effect on the DCIS compared to IBC detection rate. This study included 3,609,569 screening mammograms performed from 2002 to 2015 in our organized breast cancer screening program, which actively invites women 50-69years of age. The association between women's characteristics and the DCIS detection rate, the IBC detection rate and the odds ratio of DCIS among screen-detected cancers was assessed by logistic regression and generalized estimating equations with independent correlation matrix and sandwich estimator. A total of 4173 DCIS and 15,136 IBC were screen-detected. Increasing women's age, current hormone replacement therapy use and higher body mass index were less associated with the DCIS than with IBC detection rates (p value for the odds of DCIS among screen-detected cancers of, respectively, < 0.0001, 0.0244 and < 0.0001). In contrast, having a previous breast aspiration or biopsy and increasing breast density were more strongly associated with DCIS than with IBC detection rates (p value of, respectively, 0.0050 and < 0.0001). The results suggest that some women's characteristics could be playing a role in the initiation and other in the progression from in situ to invasive breast cancer. These characteristics can also affect the screening sensitivity, and this effect may differ depending on whether screen-detected cases were DCIS or IBC.

Ultrasound for Breast Cancer Detection in Low-Resource Settings: Systematic Review and Meta-Analysis

Purpose Of breast cancer–related deaths worldwide, 62.1% occur in low- and middle-income countries (LMICs). Mammography, the gold standard for screening, is largely infeasible in limited-resource settings, predominantly because of unavailability. Recent data show a dismal 0 to 12 mammography units per one million women age 50 to 69 years in most of sub-Saharan Africa. Breast ultrasound, which is used in high-resource settings to supplement mammography in certain clinical scenarios, offers a potentially viable alternative for early breast cancer detection in resource-limited areas because it is portable, economical, and a versatile technology. Methods We conducted a review of the literature to assess the potential for ultrasound alone in the detection of breast cancer. PubMed and Scopus queries with terms “breast neoplasms” and “diagnostic imaging” were systematically filtered to n = 33. Inclusion criteria were peer-reviewed studies in human participants, retrospective or prospective reviews, and a reference standard of biopsy/histopathology. Each paper was required to have extractable data with which to calculate true positives, false positives, true negatives, and false negatives. Breast imagers reviewed each paper using QUADAS and reached consensus for inclusion. Results Heterogeneity analyses confirmed a high diversity of all included studies. Pooled weighted analyses of all included studies demonstrated that the sensitivity of ultrasound for the detection of breast cancer ranged from 17% to 100%, with a 69% mean. This is higher than the known reported sensitivity of mammography for breast cancer detection of 67.8%. The specificity of ultrasound ranged from 28% to 100%, with a pooled total specificity of 97%, which is higher than the known specificity of mammography of 75%. In addition, ultrasound performed with superior sensitivity to mammography in the studies performed in LMICs that directly compared ultrasound with mammography (six of 11 studies). In the remaining five studies in LMICs, the sensitivity of ultrasound for breast cancer detection was high and ranged from 62% to 100%. Stratified analyses by age, breast density, presenting symptoms, and high-risk factors maintained the high sensitivities and specificities of ultrasound. Conclusion Sensitivities and specificities of ultrasound are comparable to mammography for the detection of invasive breast cancer. Handheld ultrasound may offer a viable alternative where mammography is not widely available as a primary detection modality for breast cancer. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc . Susan Harvey Honoraria: Hologic Inc, IBM Watson Imaging Consulting or Advisory Role: IBM Watson, Hologic Inc Research Funding: IBM Watson

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.