Imaging For Breast Cancer Detection Research Articles

Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening.

Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System "extremely dense" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test. Results The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 (P < .001) for AISmartDensity and the best-performing density model (age-adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best-performing density model (age-adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% (P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next-round screen-detected cancers, whereas the best-performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively. Conclusion AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Chang in this issue.

Microwave Imaging for Breast Cancer Detection- A Comprehensive review

Microwave Imaging for Breast Cancer Detection- A Comprehensive review

Enhanced Physics Modelling in Radar-Based Microwave Imaging for Breast Cancer Detection

Enhanced Physics Modelling in Radar-Based Microwave Imaging for Breast Cancer Detection

Unenhanced Breast MRI With Diffusion-Weighted Imaging for Breast Cancer Detection: Effects of Training on Performance and Agreement of Subspecialty Radiologists.

To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm² was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive. The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC). Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9), 90.8% (95% CI: 85.6-94.2), and 83.5% (95% CI: 78.6-87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52-0.63) before training and 0.68 (95% CI: 0.62-0.74) after training, with a difference of 0.11 (95% CI: 0.02-0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69-0.74) before training and 0.79 (95% CI: 0.76-0.80) after training (P = 0.002). Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

Holographic Terahertz Imaging for Breast Cancer Detection

Abstract Terahertz (THz) imaging is a promising technology that can accurately detect breast tumors during breast-conserving surgery. Researchers have studied THz imaging and spectroscopy techniques to improve breast tumor detection for the past 20 years. This paper presents the recent development of the holographic THz imaging (HTI) method for identifying breast tumors. To evaluate the effectiveness of this new approach, we have developed a numerical system that includes realistic breast models and an imaging processing model. Through various experiments, we have successfully used the proposed holographic THz imaging method to identify breast tumors. Our results have shown that this method can reconstruct high-quality breast images and accurately detect small tumor inclusions, providing the correct size and location information. Based on these promising results, further investigation is warranted to explore the potential of this approach for breast tumor detection in a faster and more cost-effective manner.

Microwave Imaging and Sensing Techniques for Breast Cancer Detection.

Medical imaging techniques, including X-ray mammography, ultrasound, and magnetic resonance imaging, play a crucial role in the timely identification and monitoring of breast cancer. However, these conventional imaging modalities have their limitations, and there is a need for a more accurate and sensitive alternative. Microwave imaging has emerged as a promising technique for breast cancer detection due to its non-ionizing, non-invasive, and cost-effective nature. Recent advancements in microwave imaging and sensing techniques have opened up new possibilities for the early diagnosis and treatment of breast cancer. By combining microwave sensing with machine learning techniques, microwave imaging approaches can rapidly and affordably identify and classify breast tumors. This manuscript provides a comprehensive overview of the latest developments in microwave imaging and sensing techniques for the early detection of breast cancer. It discusses the principles and applications of microwave imaging and highlights its advantages over conventional imaging modalities. The manuscript also delves into integrating machine learning algorithms to enhance the accuracy and efficiency of microwave imaging in breast cancer detection.

Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection.

In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.

Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for breast cancer detection: An update meta-analysis.

ObjectiveTo evaluate the diagnostic performance of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) for breast cancer identification.MethodsA comprehensive electronic systematic searching of Medline, Ovid, EMBASE, Web of Science, CNK, and Cochrane Library databases was performed up to 2 August 2021. Clinical studies associated with DCE‐MRI for breast cancer detection were screened and inlcuded in the meta‐analysis. The data of true positive(tp), false positive(fp), false negative(fn) and true negative(tn) was extracted from includded studies. The sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (SROC) were pooled under fixed or random effect models. Publication bias was evaluated by Deek's funnel plot.ResultsA final set of 15 studies with 1321 breast lesions were included in the present work. The pooled diagnostic sensitivity, specificity, and DOR were 0.87 (95% confidence interval [CI] 0.81–0.92), 0.74 (95% CI 0.68–0.80), and 18.83 (95% CI 9.07–36.54), respectively, and the area under the SROC was 0.86 (95% CI 0.82–0.88). Given a pretest probability of 50%, the positive post‐test probability was 77%, and the negative post‐test probability was 14%. Deek's funnel plot indicated low publication bias (p = 0.61).ConclusionDCE‐MRI is a noninvasive method of breast cancer diagnosis for suspected malignant breast lesions with relative high diagnostic sensitivity and specificity.

Terahertz Imaging for Breast Cancer Detection.

Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.

Contrast-enhanced spectral mammography in the radiological assessment of response to neoadjuvant chemotherapy in breast cancer

Accurate morphological assessment and measurement of the residual disease following neoadjuvant chemotherapy are vital for the effective surgical treatment in patients with breast cancer. Neoadjuvant chemotherapy response is measured by RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors), and the classification of the specific therapeutic responses is based on the difference in the tumour size prior to and after chemotherapy. There are currently a few methods of imaging used in the assessment of the neoadjuvant chemotherapy response. Conventional mammography remains the most popular method, whereas magnetic resonance imaging is considered the most effective ones. Nonetheless, the available methods tend to be imperfect and limited, and therefore, new methods are constantly investigated. Contrast-enhanced spectral mammography is a relatively new method used in breast cancer diagnosis, which involves the phenomenon of neoangiogenesis of cancerous tumours, allowing contrast enhancement in the areas of vessel proliferation in the background of the surrounding breast tissue. Contrast-enhanced spectral mammography presents sensitivity similar to magnetic resonance imaging in breast cancer detection, and can be an efficient method used in monitoring neoadjuvant chemotherapy response.

Potential of Infrared Imaging for Breast Cancer Detection: A Critical Evaluation

Abstract Screening for breast cancer to detect the disease in an early curable stage remains our most important tool to reduce breast cancer mortality, and routine screening mammography is recommended. Although the technique has been refined, screening mammography has significant shortfalls. A major drawback is related to the occurrence of “dense breast tissue” that obscures mammography images leading to missed cancer diagnoses. Adjunctive imaging technology has been used in this setting, and potential of infrared imaging (IRI) is evaluated to fulfill this role. This review provides a critical evaluation of IRI protocols and assessment techniques employed by previous researchers since the inception of this technology in 1956. The prevalence of empirical approach is identified as a major area of concern and recent scientific approaches utilizing mathematical modeling and analysis with patient-specific geometries are strongly advised. Such efforts and subsequent validation are essential before the IRI can be widely used in the mainstream breast cancer screening as an adjunctive tool.

Noncontrast-Enhanced MR-Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation.

There is increasing interest in noncontrast-enhanced MRI due to safety concerns for gadolinium contrast agents. To investigate the clinical feasibility of MR-based conductivity imaging for breast cancer detection and lesion differentiation. Prospective. One hundred and ten women, with 112 known cancers and 17 benign lesions (biopsy-proven), scheduled for preoperative MRI. Non-fat-suppressed T2-weighted turbo spin-echo sequence (T2WI), dynamic contrast-enhanced MRI and diffusion-weighted imaging (DWI) at 3T. Cancer detectability on each imaging modality was qualitatively evaluated on a per-breast basis: the conductivity maps derived from T2WI were independently reviewed by three radiologists (R1-R3). T2WI, DWI, and pre-operative digital mammography were independently reviewed by three other radiologists (R4-R6). Conductivity and apparent diffusion coefficient (ADC) measurements (mean, minimum, and maximum) were performed for 112 cancers and 17 benign lesions independently by two radiologists (R1 and R2). Tumor size was measured from surgical specimens. Cancer detection rates were compared using generalized estimating equations. Multivariable logistic regression analysis was performed to identify factors associated with cancer detectability. Discriminating ability of conductivity and ADC was evaluated by using the areas under the receiver operating characteristic curve (AUC). Conductivity imaging showed lower cancer detection rates (20%-32%) compared to T2WI (62%-71%), DWI (85%-90%), and mammography (79%-88%) (all P< 0.05). Fatty breast on MRI (odds ratio=11.8, P< 0.05) and invasive tumor size (odds ratio=1.7, P< 0.05) were associated with cancer detectability of conductivity imaging. The maximum conductivity showed comparable ability to the mean ADC in discriminating between cancers and benign lesions (AUC=0.67 [95% CI: 0.59, 0.75] vs. 0.84 [0.76, 0.90], P= 0.06 (R1); 0.65 [0.56, 0.73] vs. 0.82 [0.74, 0.88], P= 0.07 (R2)). Although conductivity imaging showed suboptimal performance in breast cancer detection, the quantitative measurement of conductivity showed the potential for lesion differentiation. 1 TECHNICAL EFFICACY: Stage 2.

Incoherent Radar Imaging for Breast Cancer Detection and Experimental Validation against 3D Multimodal Breast Phantoms.

In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare them with classical coherent beam-forming. Those methods have the remarkable advantage of not requiring antenna characterization/compensation, which can be problematic in view of the close (to the breast) proximity set-up usually employed in breast imaging. Moreover, we proceed to an experimental validation of one of the incoherent methods, i.e., the I-MUSIC, using the multimodal breast phantom we have previously developed. While in a previous paper we focused on the phantom manufacture and characterization, here we are mainly concerned with providing the detail of the reconstruction algorithm, in particular for a new multi-step clutter rejection method that was employed and only barely described. In this regard, this contribution can be considered as a completion of our previous study. The experiments against the phantom show promising results and highlight the crucial role played by the clutter rejection procedure.

Experimental Validation on Tissue-Mimicking Phantoms of Millimeter-Wave Imaging for Breast Cancer Detection

Breast cancer is one of the leading causes of cancer death among women; to decrease the death rate for this disease, early detection plays a key role. Recently, microwave imaging systems have been proposed as an alternative to the current techniques, but they suffer from poor resolution due to the low frequencies involved. In this paper, for the first time, an innovative millimeter-wave imaging system for early-stage breast cancer detection is proposed and experimentally verified on different breast phantoms. This has the potential to achieve superior resolution for breasts with a high volumetric percentage of adipose tissue, and the merit to overcome the common misconception that millimeter-waves cannot achieve useful penetration depths for biological applications. Three phantoms were prepared according to the dielectric properties of human breast ex vivo tissues in the frequency range [0.5–50] GHz. Two cylindrical inclusions made by water and gelatin or agar, mimicking dielectric properties of neoplastic tissues, were embedded in the phantom at different depths up to 3 cm. Two double ridge waveguides, with mono-modal frequency band equal to [18–40] GHz, were used to synthetize a linear array of 24 elements in 28 positions, acquiring signals with a Vector Network Analyzer. The images were reconstructed by applying the Delay and Sum algorithm to calibrated data. The feasibility of a new imaging system with a central working frequency of about 30 GHz is experimentally demonstrated for the first time, and a target detection capability up to 3 cm within the phantom is shown. The presented results pave the way for a possible use of millimeter-waves to image non-superficial neoplasms in the breast.

Square Monopole Antenna Application in Localization of Tumors in Three Dimensions by Confocal Microwave Imaging for Breast Cancer Detection: Experimental Measurement

In this paper, application of a designed antenna for microwave imaging is evaluated. In previous research, application of an UWB antenna for microwave imaging was investigated. To the best of the author’s knowledge, the designed antenna was the smallest UWB antenna designed at the time of publication, for breast cancer detection. The smaller size antennas can cause more clutter in the image while, on the other hand, make it possible to use more antennas in the array at the same surface. Therefore, the smaller size of the antenna makes microwave imaging more challenging. Simulation results showed successful detection of 10 mm and 5 mm tumors by 16 UWB antennas with the hemispherical arrangement. For signal processing, the Delay-Multiply-and-Sum Algorithm is used for image reconstruction. In this paper results of two experimental microwave imaging is presented. Two tumors at different locations are placed and microwave imaging is performed. The experimental proves that the designed antenna in an array of 16 antennas is capable of detecting the 5 mm tumor in different location.

Near-Field Radar-Based Microwave Imaging for Breast Cancer Detection: A Study on Resolution and Image Quality

Radar-based microwave imaging (MWI) systems have shown encouraging results in early detection of breast cancer; however, there exist remaining challenges. Resolution is one of the most important issues not being addressed with clear theoretical relations in medical MWI systems. In this analytical study, we thoroughly clarify the relation of resolution to the most fundamental aspects of an MWI system, namely, limited-view versus full-view array geometry, monostatic versus multistatic configuration, single-frequency versus wideband operation, and near-field versus far-field imaging. This goal is achieved by expressing the inverse scattering problem in the spatial frequency domain and utilizing K-space representation. Besides, to analyze the MWI system more accurately, the sidelobe level (SLL) of its point-spread function (PSF) is taken into account. We show that generally, the resolution limits of a multistatic configuration are the same as its monostatic counterpart and, hence, have no superiority in terms of resolution. In addition, in imaging with a full-view array, the signal bandwidth has no effect on the resolution. Thus, the single-frequency operation provides the same resolution as the wideband operation. We also demonstrate that the effect of using multistatic configuration or wideband operation is to reduce the SLL of the PSF and, hence, improve the image quality.

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection

Diffuse optical tomography (DOT) non-invasively measures the functional characteristics of breast lesions using near infrared light to probe tissue optical properties. This study aimed to evaluate a new digital breast tomosynthesis (DBT)/DOT fusion imaging technique and obtain preliminary data for breast cancer detection. Twenty-eight women were prospectively enrolled and underwent both DBT and DOT examinations. DBT/DOT fusion imaging was created after acquisition of both examinations. Two breast radiologists analyzed DBT and DOT images independently, and then finally evaluated the fusion images. The diagnostic performance of each reading session was compared and interobserver agreement was assessed. The technical success rate was 96.4%, with one failure due to an error during DOT data storage. Among the 27 women finally included in the analysis, 13 had breast cancer. The areas under the receiver operating characteristic curve (AUCs) for DBT were 0.783 and 0.854 for readers 1 and 2, respectively. DOT showed comparable diagnostic performance to DBT for both readers. The AUCs were significantly improved (P = 0.004) when the DBT/DOT fusion images were used. Interobserver agreements were highest for the DBT/DOT fusion images. In conclusion, this study suggests that DBT/DOT fusion imaging technique appears to be a promising tool for breast cancer diagnosis.

Review of Microwaves Techniques for Breast Cancer Detection.

Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

In this review article, a detailed chronological account of the research related to photoacoustic imaging for the management of breast cancer is presented. Performing a detailed analysis of the breast cancer detection related photoacoustic imaging studies undertaken by different research groups, this review attempts to present the clinical evidence in support of using photoacoustic imaging for breast cancer detection. Based on the experimental evidence obtained from the clinical studies conducted so far, the performance of photoacoustic imaging is compared with that of conventional breast imaging modalities. While we find that there is enough experimental evidence to support the use of photoacoustic imaging for breast cancer detection, additional clinical studies are required to be performed to evaluate the diagnostic potential of photoacoustic imaging for identifying different types of breast cancer. To establish the utility of photoacoustic imaging for breast cancer screening, clinical studies with high-risk asymptomatic patients need to be done.

Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection.

To estimate the performance of diffusion-weighted imaging (DWI) for breast cancer detection. Consecutive breast magnetic resonance imaging examinations performed from January to September 2016 were retrospectively evaluated. Examinations performed before/after neoadjuvant therapy, lacking DWI sequences or reference standard were excluded; breasts after mastectomy were also excluded. Two experienced breast radiologists (R1, R2) independently evaluated only DWI. Final pathology or > 1-year follow-up served as reference standard. Mc Nemar, χ2, and κ statistics were applied. Of 1,131 examinations, 672 (59.4%) lacked DWI sequence, 41 (3.6%) had no reference standard, 30 (2.7%) were performed before/after neoadjuvant therapy, and 10 (0.9%) had undergone bilateral mastectomy. Thus, 378 women aged 49 ± 11years (mean ± standard deviation) were included, 51 (13%) with unilateral mastectomy, totaling 705 breasts. Per-breast cancer prevalence was 96/705 (13.6%). Per-breast sensitivity was 83/96 (87%, 95% confidence interval 78-93%) for both R1 and R2, 89/96 (93%, 86-97%) for double reading (DR) (p = 0.031); per-lesion DR sensitivity for cancers ≤ 10mm was 22/31 (71%, 52-86%). Per-breast specificity was 562/609 (93%, 90-94%) for R1, 538/609 (88%, 86-91%) for R2, and 526/609 (86%¸ 83-89%) for DR (p < 0.001). Inter-observer agreement was substantial (κ = 0.736). Acquisition time varied from 3:00 to 6:22min:s. Per-patient median interpretation time was 46s (R1) and 51s (R2). DR DWI showed a 93% sensitivity and 88% specificity, with 71% sensitivity for cancers ≤ 10mm, pointing out a potential for DWI as stand-alone screening method.