Contrast-Enhanced Digital Mammography

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.

Breast cancer in dense breasts: comparative diagnostic merits of contrast-enhanced mammography and diffusion-weighted breast MRI

Background

The study was done to compare the value of contrast-enhanced mammography and diffusion-weighted breast MRI in dense breast screening and accurate detection of the breast cancer with correlation of the findings to the histopathological results.

The study included 32 female patients having suspicious breast lesions and underwent digital mammography then scheduled for CESM and MRI DW imaging technique. The imaging findings were correlated to the histopathological findings.

Results

The study was conducted on 40 breast lesions in 32 female patients having dense breasts; they were classified by the digital mammography into ACR C (59.4%) and ACR D (40.6%). By CESM, there were twenty three lesions (57.5%) as mass lesions and thirteen lesions (32.5%) as non-mass lesions. Four lesions (10%) showed no contrast enhancement. According to the lesion characteristics in diffusion-weighted imaging, the breast lesions were classified into thirty three lesions (82.5%) with restricted diffusion and seven lesions (17.5%) with non-restricted diffusion. The study showed a cutoff ADC value to detect the malignant lesions in the dense breasts ≤ 1.1 × 10-3 s/mm2 at b value of 1000 s/mm2 with a sensitivity of 96.77%, specificity of 66.67%, PPV of 96.77%, NPV of 55.55%, and an overall total accuracy of 92.5%.

On comparing the diagnostic accuracy of the CESM to that of the DW MRI, the sensitivity of DW MRI (96.77%) was higher than that of CESM (90.32%). The specificity of DW MRI (66.67%) was higher than that of CESM (33.33%). Total accuracy of DW MRI was higher than that of CESM; they were 90% and 77.5%, respectively. Also, PPV and NPV of DW MRI were 90.91 and 85.71% as compared with 82.35 and 50.00% in CESM, respectively. When comparing the sensitivity of CESM to DW MRI in the detection of multiple breast lesions, they were 88.8 and 100%, respectively.

Conclusion

CESM is a useful technique in identification of hidden lesions in mammographically dense breasts. DW MRI is a fast, unenhanced modality that can be used as a breast cancer screening modality. CESM and DWI demonstrated good overall diagnostic accuracy in dense breast patients; however, DW MRI has a higher diagnostic accuracy than CESM for the detection of malignant breast lesions and their multiplicity.

Primary breast edema on contrast-enhanced digital mammography: a preliminary experience

Background

Primary breast edema can cause marked increase in skin thickness, breast density and echogenicity due to dense breast tissue filled with fluid and so causes subsequent significant attenuation of both the x-ray and ultrasound beams. The study aim is to assess the value of contrast-enhanced digital mammography (CEDM) in assessment and characterization of the obscured underlying breast lesions in cases of primary breast edema.

Results

Fifty five female participants were evaluated, of median age 51 years old and IQR 21. CEDM shows high sensitivity and specificity in the lesion detection as well as local extension delineation in cases associated with primary breast edema. It was accurate in detection of multifocal/multi-centric disease. CEDM is considered as a good negative test in cases of metastatic axillary lymph nodes to exclude and assess any associated obscured breast lesions, as it is good in delineating breast masses obscured by condensed parenchymal tissue. The calculated sensitivity of DM & CEDM was 87.5%, 95.8%, specificity was 55.5%, 72%, the PPV and NPV were 91, 93.6% and 45%, 77.8%, respectively.

Conclusions

CEDM has an important additional diagnostic value in the assessment, characterization and better delineation of breast lesions in primary edematous breast cases.

A review of various modalities in breast imaging: technical aspects and clinical outcomes

Background

Nowadays, breast cancer is the second cause of death after cardiovascular diseases. In general, about one out of eight women (about 12%) suffer from this disease during their life in the USA and European countries. If breast cancer is detected at an early stage, its survival rate will be very high. Several methods have been introduced to diagnose breast cancer with their clinical advantages and disadvantages.

Main text

In this review, various methods of breast imaging have been introduced. Furthermore, the sensitivity and specificity of each of these methods have been investigated. For each of the imaging methods, articles that were relevant to the past 10 years were selected through electronic search engines, and then the most relevant papers were selected. Finally, about 40 articles were studied and their results were categorized and presented in the form of a report as follows. Various breast cancer imaging techniques were extracted as follows: mammography, contrast-enhanced mammography, digital tomosynthesis, sonography, sonoelastography, magnetic resonance imaging, magnetic elastography, diffusion-weighted imaging, magnetic spectroscopy, nuclear medicine, optical imaging, and microwave imaging.

Conclusion

The choice of these methods depends on the patient’s state and stage, the age of the individual and the density of the breast tissue. Hybrid imaging techniques appear to be an acceptable way to improve detection of breast cancer. This review article can be useful in choosing the right method for imaging in people suspected of breast cancer.

High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography

Contrast-enhanced digital mammography (CEDM) is an alternative to conventional X-ray mammography for imaging dense breasts. However, conventional approaches to CEDM require a double exposure of the patient, implying higher dose, and risk of incorrect image registration due to motion artifacts. A novel approach is presented, based on hyperspectral imaging, where a detector combining positional and high-resolution spectral information (in this case based on Cadmium Telluride) is used. This allows simultaneous acquisition of the two images required for CEDM. The approach was tested on a custom breast-equivalent phantom containing iodinated contrast agent (Niopam 150®). Two algorithms were used to obtain images of the contrast agent distribution: K-edge subtraction (KES), providing images of the distribution of the contrast agent with the background structures removed, and a dual-energy (DE) algorithm, providing an iodine-equivalent image and a water-equivalent image. The high energy resolution of the detector allowed the selection of two close-by energies, maximising the signal in KES images, and enhancing the visibility of details with the low surface concentration of contrast agent. DE performed consistently better than KES in terms of contrast-to-noise ratio of the details; moreover, it allowed a correct reconstruction of the surface concentration of the contrast agent in the iodine image. Comparison with CEDM with a conventional detector proved the superior performance of hyperspectral CEDM in terms of the image quality/dose tradeoff.

Optimization of contrast-enhanced breast imaging: Analysis using a cascaded linear system model

PURPOSE

Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction.

METHODS

We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds.

RESULTS

Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (f_h ) results in the minimization of quantum noise. Both subtraction weighting factor (w_Sub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration.

CONCLUSIONS

We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, f_h , and w_Sub as well as guiding the understanding of their effects on image contrast and noise.