The global digital breast tomosynthesis (DBT) equipment market is estimated to be valued at USD 3.26 billion in 2025 and is projected to reach USD 7.66 billion by 2035, registering a compound annual growth rate (CAGR) of 8.9% over the forecast period.

Metric	Value
Industry Size (2025E)	USD 3.26 billion
Industry Value (2035F)	USD 7.66 billion
CAGR (2025 to 2035)	8.9%

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	7.5%

Country	CAGR (2025 to 2035)
Germany	6.0%

Country	CAGR (2025 to 2035)
China	9.2%

Country	CAGR (2025 to 2035)
India	8.6%

Country	CAGR (2025 to 2035)
Brazil	4.6%

Competitive Outlook

Company Name	Estimated Market Share (%)
Hologic, Inc.	23.5%
GE Healthcare	19.1%
Siemens Healthineers	13.4%
Fujifilm Holdings Corp.	9.2%
Canon Medical Systems	4.5%
Other Companies (combined)	30.1%

BillieCode/dbt dataset hosted on Hugging Face and contributed by the HF Datasets community

Overview

New York, NY – June 18, 2025 – Global Digital Breast Tomosynthesis Market Nsize is expected to be worth around US$ 10.2 Billion by 2034 from US$ 2.9 Billion in 2024, growing at a CAGR of 13.4% during the forecast period from 2025 to 2034. In 2024, North America led the market, achieving over 54.6% share with a revenue of US$ 1.5 Billion.

Digital Breast Tomosynthesis (DBT), also known as 3D mammography, is transforming breast cancer screening by providing clearer, more accurate imaging than traditional 2D mammography. This advanced imaging technique captures multiple X-ray images of the breast from different angles, creating a layered 3D reconstruction that enhances lesion visibility and reduces false positives.

The increasing global prevalence of breast cancer, which remains the most common cancer among women, has driven the adoption of DBT systems across hospitals, diagnostic centers, and specialty clinics. According to the World Health Organization (WHO), in 2022, over 2.3 million women were diagnosed with breast cancer worldwide. Early detection through advanced technologies such as DBT significantly improves survival rates and reduces treatment costs.

Government initiatives supporting breast cancer screening programs, especially in North America and Europe, are further accelerating DBT adoption. Additionally, technological advancements in detector sensitivity, image reconstruction software, and AI-based diagnostics are improving screening efficiency and clinical outcomes.

North America currently dominates the DBT market due to high screening awareness and established reimbursement frameworks. However, Asia-Pacific is projected to witness robust growth owing to rising healthcare infrastructure investments and growing awareness campaigns. As healthcare providers prioritize precision diagnostics, the digital breast tomosynthesis market is expected to expand steadily in the coming years, contributing to better patient outcomes and earlier cancer intervention.

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The size of the United States Mammography Market was valued at USD 1.05 Million in 2023 and is projected to reach USD 1.97 Million by 2032, with an expected CAGR of 9.39% during the forecast period. United States market in mammography is highly important for the health sector, as it has implications for early detection and diagnosis of breast cancer. Improving awareness of breast cancer and the significance of regular screening has dramatically increased the need for mammography services. The sector incorporates a number of different technologies, film mammography, digital mammography, and 3D mammography or tomosynthesis that improve imaging capabilities and yields higher detection rates. Industry is made through increased breast cancer awareness campaigns and new guidelines advising women above age 40 to have regular screening. Advances in technology, for example, through the use of artificial intelligence, enhance the analysis of images for better accuracy in diagnosis and reduced false positives. Increased outpatient imaging centers and mobile mammography units are making access easier for needy patients. While the outlook is positive, the mammography market will face challenges: unequal distribution of care; expensive new technology; and the fear of radiation exposure. In addition, the COVID-19 situation resulted in an interruption in the continuity of screening programs short-term, creating a mammogram backlog. Recovery in healthcare systems that focus on prevention should be well-entrenched in the US; the mammography market continues to grow, thereby enhancing outcomes and survival for patients with breast cancer. Recent developments include: In March 2022, iCAD, Inc. showcased its portfolio of Breast AI solutions, including ProFound AI for Digital Breast Tomosynthesis (DBT) in the iCAD booth at the 2022 Healthcare Information and Management Systems Society (HIMSS) Global Health Conference & Exhibition in Orlando, United States., In January 2022, ScreenPoint Medical expanded its presence in the United States and in 30 countries worldwide with the launch of Transpara an AI Breast Care system.. Key drivers for this market are: Growing Burden of Breast Cancer, Technological Advancements in the Field of Breast Imaging. Potential restraints include: Risk of Adverse Effects from Radiation Exposure. Notable trends are: Digital Systems are Expected to Hold Significant Share in Product Type Segment.

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ObjectiveThis study explores researches of dialectical behavior therapy (DBT) in mental health to provide an overview of current knowledge landscape and predict future development trends of DBT.MethodThe bibliometric approach was used in the study. Articles on DBT-related research were retrieved from the Web of Science Core Collection (WoSCC) database up to December 31, 2024, and analyzed using VOSviewer and CiteSpace.ResultsA total of 2,723 articles were analyzed. DBT research has grown significantly since the 1990s, with the United States leading in publication volume, citation impact, and academic collaboration. Research is primarily conducted in developed countries like the United States, the United Kingdom, and Germany, with limited contributions from emerging economies. Cognitive and Behavioral Practice is the most prolific journal in DBT research. Key topics include borderline personality disorder (BPD), suicide, adolescent interventions, forensic psychiatry, and family therapy. Recently, keywords such as “emotion dysregulation” and “mobile phone” have become research hotspots.ConclusionDBT research has evolved from early focus areas like BPD and suicide to studies on emotion dysregulation mechanisms and digital interventions. While the United States dominates the field, expanding participation from emerging countries and strengthening global collaboration could advance DBT research and improve mental health accessibility. This bibliometric analysis provides a global perspective and long-term trend insights, highlighting future directions in neurobiological mechanisms, methodological innovation, and technological integration.

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PurposeTo construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer.Materials and methods149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model’s stability was assessed through AUC, calibration curves, and DCA.ResultsSupport vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net.ConclusionThe fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

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T-SYNTH

T-SYNTH is a synthetic digital breast tomosynthesis (DBT) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) toolkit.

  Dataset Details

The dataset has the following characteristics:

Breast density: dense, heterogeneously dense, scattered, fatty Mass radius (mm): 5.00, 7.00, 9.00 Mass density: 1.0, 1.06, 1.1 (ratio of… See the full description on the dataset page: https://huggingface.co/datasets/didsr/tsynth.

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Table S1. Exome sequence data sets from the HapMap. Legend: Exome sequencing of (CEU) Utah residents with ancestry from Northern and Western Europe and of (CHB) Han Chinese in Beijing, China - CEPH – HapMap. Table S2. Coverage: Reads mapped per drDCM subject. Legend: Percentage and number of reads mapped before and after filtering for each subject in the drDCM case control study. Table S3. Alignment Summary (Italian). Legend: Mean coverage: the total number of targeted bases divided by the targeted region size. Target coverage at 1X: Percentage targets with coverage greater. Table S4. Alignment summary (Chinese). Legend: Mean coverage: the total number of targeted bases divided by the targeted region size. Target coverage at 1X: Percentage targets with coverage greater. Table S5. Alignment summary: HapMap project data set. Legend: Mean coverage: the total number of targeted bases divided by the targeted region size. Target coverage at 1X: Percentage targets with coverage greater than 1X. Target coverage at 10X: Percentage targets with coverage greater than 10X. Target coverage at 20X: Percentage targets with coverage greater than 20X. Target coverage at 50X: Percentage targets with coverage greater than 50X. Table S6. drDCM 131 variants in differentially expressed genes. Table S7. drDCM Pathways. Table S8. Variants found in a pathway. Table S9. RNA-Seq drDCM genotypes: ECHS1, DBT, and MCCC1. Legend: Ref: reference allele, Alt: alternative allele, DCM: dilated cardiomyopathy, CTR: control. ECHS1: enoyl-CoA hydratase, short chain, 1, mitochondrial, DBT: Dihydrolipoamide branched chain transacylase E2, and MCCC1: methyl crotonoyl-CoA carboxylase 1. Table S10. Variants in DCM pedigrees from Italy and China. Legend: Genotypes for DCM cases for the ECHS1, DBT, and MCCC1 genes. ECHS1: enoyl-CoA hydratase, short chain, 1, mitochondrial, DBT: Dihydrolipoamide branched chain transacylase E2, and MCCC1: methyl crotonoyl-CoA carboxylase 1. Table S11. Variant scanning in HapMap data set. Legend: Control genotypes for the ECHS1, DBT, and MCCC1 genes. ECHS1: enoyl-CoA hydratase, short chain, 1, mitochondrial, DBT: Dihydrolipoamide branched chain transacylase E2, and MCCC1: methyl crotonoyl-CoA carboxylase 1. Table S12. Population Genetics for the ECHS1:rs10466126 Putative Mutation. Legend: Population Code: “CHB: Han Chinese in Beijing, China, JPT:Japanese in Tokyo, Japan, CHS: Southern Han Chinese, CDX: Chinese Dai in Xishuangbanna, China, KHV: Kinh in Ho Chi Minh City, Vietnam, CEU: Utah Residents (CEPH) with Northern and Western European Ancestry, TSI: Toscani in Italia, FIN: Finnish in Finland, GBR: British in England and Scotland, IBS: Iberian Population in Spain, YRI: Yoruba in Ibadan, Nigeria, LWK: Luhya in Webuye, Kenya, GWD: Gambian in Western Divisions in the Gambia, MSL: Mende in Sierra Leone, ESN: Esan in Nigeria, ASW: Americans of African Ancestry in SW USA, ACB: African Caribbeans in Barbados, MXL: Mexican Ancestry from Los Angeles USA, PUR: Puerto Ricans from Puerto Rico, CLM: Colombians from Medellin, Colombia, PEL: Peruvians from Lima, Peru, GIH: Gujarati Indian from Houston, Texas, PJL: Punjabi from Lahore, Pakistan, BEB: Bengali from Bangladesh, STU: Sri Lankan Tamil from the UK, ITU: Indian Telugu from the UK.” (< http://www.internationalgenome.org/category/population/> ). Table S13. Novel ECHS1 c.41insT. Table S14. drDCM differentially expressed genes. Table S15. drDCM Diseases and Functions. Table S16. ECHS1:rs10466126 and ECHS1:rs1049951 pairwise linkage disequilibrium in 24 populations from the 1000 genomes project. Table S17. Data mining: IPA knowledge database, PALLD. Table S18. Chemicals that interact with the ECHS1 gene. Table S19. The effect of chemical interactions on the expression of the ECHS1 gene. Table S20. Chemicals associated with diseases that interfere with the ECHS1 gene. (XLSX 632 kb)

The dataset includes computational digital breast phantoms derived from high-resolution 3D clinical breast images for the use in virtual clinical trials in 2D and 3D X-ray breast imaging. Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 60 clinical 3D breast images acquired via a dedicated CT scanner at UC Davis (California, USA). The uncompressed phantoms are submitted in a parallel dataset and present relate naming. Each image voxel was classified in one out of the four main materials presented in the field of view: fibro-glandular tissue, adipose tissue, skin tissue and air. Each of the classified materials is represented by one out of four values: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue and 3 for the skin tissue. For the image classification, a semi-automatic software was developed. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and elastic deformation of the breast, taking into account the tissue's elastic coefficients.

Background/Aims: We performed a network meta-analysis (NMA) to investigate and compare the diagnostic value of 19 different imaging methods used for breast cancer (BC). Methods: Cochrane Library, PubMed and EMBASE were searched to collect the relevant literature from the inception of the study until November 2016. A combination of direct and indirect comparisons was performed using an NMA to evaluate the combined odd ratios (OR) and draw the surface under the cumulative ranking curves (SUCRA) of the diagnostic value of different imaging methods for BC. Results: A total of 39 eligible diagnostic tests regarding 19 imaging methods (mammography [MG], breast-specific gamma imaging [BSGI], color Doppler sonography [CD], contrast-enhanced magnetic resonance imaging [CE-MRI], digital breast tomosynthesis [DBT], fluorodeoxyglucose positron-emission tomography/computed tomography [FDG PET/CT], fluorodeoxyglucose positron-emission tomography [FDG-PET], full field digital mammography [FFDM], handheld breast ultrasound [HHUS], magnetic resonance imaging [MRI], automated breast volume scanner [ABUS], magnetic resonance mammography [MRM], scintimammography [SMM], single photon emission computed tomography scintimammography [SPECT SMM], ultrasound elastography [UE], ultrasonography [US], mammography + ultrasonography [MG + US], mammography + scintimammography [MG + SMM], and ultrasound elastography + ultrasonography [UE + US]) were included in the study. According to this network meta-analysis, in comparison to the MG method, the CE-MRI, MRI, MRM, MG + SMM and UE + US methods exhibited relatively higher sensitivity, and the specificity of the FDG PET/CT method was higher, while the BSGI and MRI methods exhibited higher accuracy. Conclusion: The results from this NMA indicate that the diagnostic value of the BSGI, MG + SMM, MRI and CE-MRI methods for BC were relatively higher in terms of sensitivity, specificity and accuracy.