Artificial intelligence in mammographic phenotyping of breast cancer risk: A narrative review

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman\u27s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies

Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.This project has been co-financed by the Spanish Government Grant PID2019-107790RB-C22, "Software development for a continuous PET crystal systems applied to breast cancer".Jiménez-Gaona, Y.; Rodríguez Álvarez, MJ.; Lakshminarayanan, V. (2020). Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences. 10(22):1-29. https://doi.org/10.3390/app10228298S1291022Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. CA: A Cancer Journal for Clinicians, 61(2), 69-90. doi:10.3322/caac.20107Gao, F., Chia, K.-S., Ng, F.-C., Ng, E.-H., & Machin, D. (2002). Interval cancers following breast cancer screening in Singaporean women. International Journal of Cancer, 101(5), 475-479. doi:10.1002/ijc.10636Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019). 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Automatic quality assessment in mammography screening:a deep learning based segmentation method

Abstract. Mammography is an imaging method used as a main tool to detect breast cancer at early stages. Images (mammograms) are examined by radiologists, who aim to identify cancerous findings. However, in order to do that, the mammograms need to be of diagnostic quality, which can sometimes be insufficient, and thus the quality of diagnosis also suffers. Radiology technicians (radiographers) are trained to take mammography images, but not in every healthcare center a strict quality control process is established, which may substantially affect the patients. The most common defects in mammograms are positioning defects, which are seen in the images as skin-foldings or non-imaged parts of the breast. The major issue at a process level is that the described positioning issues are noticed late, already at the diagnostic phase. If a radiologist decides that the mammogram is a non-diagnostic quality, the patient needs to revisit the imaging center. If quality control could be automated and standardized, unnecessary patient recalls could be avoided, thus, reducing the costs of the mammographic process. To date, there is a lack of automatic general quality control tools for mammography screening. Looking at the recent advances in artificial intelligence, it may be possible to automate this process. The goal of this thesis was to develop an automatic system for quality assessment of mammograms. The author used Deep learning to develop an automatic framework for automatic segmentation of defects in mammograms using a dataset of 512 mammographic images extracted from the Oulu University Hospital archive. The second stage of the developed method performed quality assessment by analyzing the presence and location of different tissues in the images from the predicted segmentations. The developed segmentation model yielded a Dice coefficient over 0.90 for the whole breast, breast, and pectoral muscle, and over 0.60 for skin-foldings and nipple. The developed method is the first to tackle automatic segmentation of all major positioning issues in mammography. Ultimately, the developed technology has a potential to improve the mammography workflows and, eventually, patient outcomes.Automaattinen laadunarviointi mammografian kuvauksessa : syväoppimispohjainen segmentointimenetelmä. Tiivistelmä. Mammografiaa on kuvantamismenetelmä, jota käytetään päävälineenä rintasyövän havaitsemiseksi varhaisessa vaiheessa. Radiologien on tutkittava mammogrammit ja päätettävä sitten, onko pahanlaatusia löydöksiä, ja tätä varten mammografiakuvien on oltava diagnostisesti laadukkaita. Ammattilaiset koulutetaan mammografiakuvien ottamiseksi, mutta ei kaikissa terveyskeskuksissa on otettu käyttöön tiukka laadunvalvontaprosessi, joka voi vaikuttaa merkittävästi potilaisiin. Kuvissa voi olla virheitä, jotka tekevät kuvista ei-diagnostisen laadukkaan mammogrammin, ja ne voivat vaikuttaa diagnostiikkatuloksiin. Yksi näistä vioista ovat paikannusvirheet, joissa näkyvät kuvissa ihon taitoksina ja jotkut rinnan osat eivät näy. Suurin ongelma prosessitasolla on, että kuvatut paikannusvirheet havaitaan myöhässä, jo diagnoosivaiheessa. Jos radiologit päättävät, että mammografiakuva ei ole diagnostisesti laadukas, potilaan on palattava kuvantamiskeskukseen ja tutkittava uudelleen, mikä voi lisätä kustannuksia ja työmäärää. Jos laadunvalvonta voidaan automatisoida ja standardoida, voidaan välttää tarpeetonta potilaan palauttamista ja vähentää siten mammografiaprosessin kustannuksia. Tähän mennessä mammografiaseulonnassa ei ole automaattista yleistä laadunvalvontaa. Kun tarkastellaan tekoälyn viimeaikaisia edistystä, tämän prosessin automatisointi voi olla mahdollista. Tämän projektin tarkoituksena oli todistaa diagnostisten ja ei-diagnostisten laatumammogrammien automaattisen erottamisen toteutettavuus. Kirjoittaja käytti syvää oppimista automatisoidun kehyksen luomisessa käyttämällä 512 mammografiakuvaa, jotka otettiin Oulun yliopistollisen sairaalan arkistosta. Automaattisen menetelmän ensimmäisessä vaiheessa suoritettiin rintakudosten ja ihon taittumien segmentointi. Toisessa vaiheessa suoritettiin laadunarviointi analysoimalla eri kudosten läsnäolo ja sijainti kuvissa. Kehitetyllä segmentointimallilla saavutettiin merkittäviä tuloksia, kun koko rinnan ja rintalihasten segmentoinnin onnistumisen hyvyttä mittaava Dice-kerroin oli yli 0,90, ja ihon taittumiselle ja nännille yli 0,60. Kehitetty menetelmä on ensimmäinen, joka käsittelee mammografian kaikkien tärkeimpien paikannusvirheiden automaattista segmentointia. Sillä on potentiaalia myötävaikuttaa mammografian työnkulkujen ja potilastulosten parantamiseen

Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance