Mammography Techniques and Review

Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography

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

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

A new challenge in Radiology: Radiomics in breast cancer diagnostics

Breast cancer is one of the most common and widespread cancers that women can have. In order to prevent the occurrence of cancer, it is important to perform a preventive examination, which includes ultrasound and mammography. Radiology as a branch of medicine has seen rapid development in recent decades thanks to the development of technology, and artificial intelligence is increasingly used in radiology. Radiomics is a new method of radiological image processing that uses software programs to analyse tissue during diagnostic imaging. It is a combination of multiple imaging modalities with the aim of highlighting pathological formations that are not visible to the naked eye or are less significant. The aim of the paper is to introduce to the readers with radiomics and to explain in more detail how it works, and how it was integrated into certain radiological diagnostics and greatly facilitated the image processing process and the diagnosis of breast cancer. Many studies have confirmed that radiomics is a method with numerous advantages, but like any new field, it has its drawbacks. The main limitation is the computer system, which must be standardised so that radiomic data processing can be used in all institutions and so that these institutions can exchange information with each other without difficulty. The problem is also false positive findings, which greatly increase the costs of institutions and the time it takes for patients to reach a diagnosis. The solution to these allegations is the development of new computer algorithms and an increase in the sensitivity of computer detection of lesions. Radiomics will certainly play an important role in diagnostics and image analysis over a period of time. Given that artificial intelligence is still in the process of development, radiomics may not have an independent application, but it will certainly make the work of doctors easier in the analysis of radiological images

Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

Purpose: The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. Methods: A total of 496 patients (mean age, 52.5 +/- 10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. Results: In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. Conclusion: A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.11Nsciescopu

Deep learning in breast cancer screening

Breast cancer is the most common cancer form among women worldwide and the incidence is rising. When mammography was introduced in the 1980s, mortality rates decreased by 30% to 40%. Today all women in Sweden between 40 to 74 years are invited to screening every 18 to 24 months. All women attending screening are examined with mammography, using two views, the mediolateral oblique (MLO) view and the craniocaudal (CC) view, producing four images in total. The screening process is the same for all women and based purely on age, and not on other risk factors for developing breast cancer. Although the introduction of population-based breast cancer screening is a great success, there are still problems with interval cancer (IC) and large screen detected cancers (SDC), which are connected to an increased morbidity and mortality. To have a good prognosis, it is important to detect a breast cancer early while it has not spread to the lymph nodes, which usually means that the primary tumor is small. To improve this, we need to individualize the screening program, and be flexible on screening intervals and modalities depending on the individual breast cancer risk and mammographic sensitivity. In Sweden, at present, the only modality in the screening process is mammography, which is excellent for a majority of women but not for all. The major lack of breast radiologists is another problem that is pressing and important to address. As their expertise is in such demand, it is important to use their time as efficiently as possible. This means that they should primarily spend time on difficult cases and less time on easily assessed mammograms and healthy women. One challenge is to determine which women are at high risk of being diagnosed with aggressive breast cancer, to delineate the low-risk group, and to take care of these different groups of women appropriately. In studies II to IV we have analysed how we can address these challenges by using deep learning techniques. In study I, we described the cohort from which the study populations for study II to IV were derived (as well as study populations in other publications from our research group). This cohort was called the Cohort of Screen Aged Women (CSAW) and contains all 499,807 women invited to breast cancer screening within the Stockholm County between 2008 to 2015. We also described the future potentials of the dataset, as well as the case control subset of annotated breast tumors and healthy mammograms. This study was presented orally at the annual meeting of the Radiological Society of North America in 2019. In study II, we analysed how a deep learning risk score (DLrisk score) performs compared with breast density measurements for predicting future breast cancer risk. We found that the odds ratios (OR) and areas under the receiver operating characteristic curve (AUC) were higher for age-adjusted DLrisk score than for dense area and percentage density. The numbers for DLrisk score were: OR 1.56, AUC, 0.65; dense area: OR 1.31, AUC 0.60, percent density: OR 1.18, AUC, 0.57; with P < .001 for differences between all AUCs). Also, the false-negative rates, in terms of missed future cancer, was lower for the DLrisk score: 31%, 36%, and 39% respectively. This difference was most distinct for more aggressive cancers. In study III, we analyzed the potential cancer yield when using a commercial deep learning software for triaging screening examinations into two work streams – a ‘no radiologist’ work stream and an ‘enhanced assessment’ work stream, depending on the output score of the AI tumor detection algorithm. We found that the deep learning algorithm was able to independently declare 60% of all mammograms with the lowest scores as “healthy” without missing any cancer. In the enhanced assessment work stream when including the top 5% of women with the highest AI scores, the potential additional cancer detection rate was 53 (27%) of 200 subsequent IC, and 121 (35%) of 347 next-round screen-detected cancers. In study IV, we analyzed different principles for choosing the threshold for the continuous abnormality score when introducing a deep learning algorithm for assessment of mammograms in a clinical prospective breast cancer screening study. The deep learning algorithm was supposed to act as a third independent reader making binary decisions in a double-reading environment (ScreenTrust CAD). We found that the choice of abnormality threshold will have important consequences. If the aim is to have the algorithm work at the same sensitivity as a single radiologist, a marked increase in abnormal assessments must be accepted (abnormal interpretation rate 12.6%). If the aim is to have the combined readers work at the same sensitivity as before, a lower sensitivity of AI compared to radiologists is the consequence (abnormal interpretation rate 7.0%). This study was presented as a poster at the annual meeting of the Radiological Society of North America in 2021. In conclusion, we have addressed some challenges and possibilities by using deep learning techniques to make breast cancer screening programs more individual and efficient. Given the limitations of retrospective studies, there is a now a need for prospective clinical studies of deep learning in mammography screening

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given

Locoregional stage assessment in clinically node negative breast cancer: Clinical, imaging, pathologic, and statistical methods

The locoregional staging remains an essential part of prognostication in breast cancer. Tumour size and biology, together with the number of lymph node metastases, guide the planning of appropriate treatments. Accurate clinical, imaging, pathologic, and statistical staging is needed as the surgical staging diminishes. In this study, 743 clinically lymph node negative breast cancer patients treated in 2009‒2017 were evaluated. Clinopathological factors were investigated in association with the number of lymph node metastases, the use of preoperative imaging methods and the surgical treatment method. A nomogram was developed and tested to predict the number of lymph node metastases after sentinel lymph node positivity. Three previously published models were validated to confirm their feasibility in the current population to predict nodal stage pN2a or pN3a. Tumour size, biologic subtype and proliferation associated with higher numbers of lymph node metastases. To predict stage pN2a or pN3a, the machine learning algorithms identified tumour size, invasive ductal histology, multifocality, lymphovascular invasion, oestrogen receptor status and the number of positive sentinel lymph nodes as risk factors. The nomograms performed well with favourable discrimination. Clinopathological factors seemed to guide preoperative magnetic resonance imaging (MRI) prior to more extensive surgery. MRI estimated the increasing tumour size more accurately than mammography or ultrasound. According to this study, clinopathological factors, additional preoperative MRI and modern statistics can be utilized in breast cancer staging without extensive surgical interference. The importance of non-surgical investigations in staging is growing in the planning of surgical, systemic and radiation treatments. Thus, maintaining the impressive survival outcomes of clinically node negative breast cancer patients can be achieved.Kliinisesti imusolmukenegatiivisen rintasyövän paikallislevinneisyyden arvioiminen. Kliiniset, kuvantamisen, patologian alan ja tilastotieteen menetelmät Kasvaimen paikallinen levinneisyys on tärkeä rintasyövän ennustetekijä. Kasvaimen koko ja biologia sekä imusolmukemetastaasien lukumäärä ohjaavat syöpähoitojen suunnittelua. Levinneisyyden selvittelyssä tarvitaan huolellista kliinistä tutkimusta sekä tarkkoja kuvantamisen, patologian alan ja tilastotieteen menetelmiä, kun kirurginen levinneisyysluokittelu vähenee. Tutkimuksessa arvioitiin vuosina 2009‒2017 hoidettujen 743 kliinisesti imusolmukenegatiivisen suomalaisen potilaan tietoja. Työssä selvitettiin kliinispatologisten tekijöiden ja kainaloimusolmukemetastaasien lukumäärän, leikkausta edeltävien kuvantamistutkimusten sekä leikkausmenetelmien yhteyttä. Ennustemalli kehitettiin ja koekäytettiin positiivisen vartijaimusolmuketutkimuksen jälkeisen imusolmukemetastaasien määrän arvioimiseksi. Kolme aiemmin julkaistua mallia validoitiin, jotta niiden käyttökelpoisuus imusolmukeluokan pN2a tai pN3a ennustamisessa varmistuisi tässä aineistossa. Kasvainkoko, biologinen alatyyppi ja jakautumisnopeus olivat yhteydessä suurempaan imusolmukemetastaasien määrään. Koneoppimisalgoritmit määrittivät levinneisyysluokan pN2a tai pN3a ennustamiseksi tarvittaviksi tekijöiksi kasvainkoon, invasiivisen duktaalisen histologian, monipesäkkeisyyden, suoni-invaasion, estrogeenireseptoristatuksen sekä positiivisten vartijaimusolmukkeiden määrän. Ennustemallit toimivat aineistossa hyvin osoittaen suotuisaa erotuskykyä. Kliinispatologiset tekijät näyttivät ohjaavan magneettikuvauspäätöstä ennen laajaa kirurgista hoitoa. Magneettikuvaus oli tarkin kuvantamismenetelmä suurenevan kasvainkoon arvioinnissa. Tämän tutkimuksen perusteella kliinispatologiset tekijät, leikkausta edeltävä täydentävä magneettikuvaus ja nykyaikaiset tilastotieteen menetelmät voivat hyödyttää rintasyövän levinneisyysluokittelua ilman laajoja kirurgisia toimenpiteitä. Kajoamattomien tutkimusten asema levinneisyysluokittelussa on vahvistumassa kirurgisten, lääkkeellisten ja sädehoitojen suunnittelun yhteydessä. Tarkka levinneisyysluokittelu edesauttaa kliinisesti imusolmukenegatiivisten rintasyöpäpotilaiden erinomaista ennustetta