Evaluation of tomosynthesis elastography in a breast-mimicking phantom

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

To establish a predictive model incorporating conventional ultrasound, strain elastography and clinicopathological features for Ki-67 expression in small breast cancer (SBC) which defined as maximum diameter less than2 cm. In this retrospective study, 165 SBC patients from our hospital were allocated to a high Ki-67 group (n = 104) and a low Ki-67 group (n = 61). Multivariate regression analysis was performed to identify independent indicators for developing predictive models. The area under the receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff value were compared. Conventional ultrasound parameters (spiculated margin, absence of posterior shadowing and Adler grade 2-3), strain elastic scores and clinicopathological information (HER2 positive) were significantly correlated with high expression of Ki-67 in SBC (all p < .05). Model 2, which incorporated conventional ultrasound features and strain elastic scores, yielded good diagnostic performance (AUC = 0.774) with better sensitivity than model 1, which only incorporated ultrasound characteristics (78.85%vs. 55.77%, p = .000), with specificities of 77.05% and 62.30% (p = .035), respectively. Model 3, which incorporated conventional ultrasound, strain elastography and clinicopathological features, yielded better performance (AUC = 0.853) than model 1 (AUC = 0.694) and model 2 (AUC = 0.774), and the specificity was higher than model 1 (86.89% vs. 77.05%, p = .001). The predictive model combining conventional ultrasound, strain elastic scores and clinicopathological features could improve the predictive performance of Ki-67 expression in SBC.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer

Noncancerous breast tissue and cancerous breast tissue have different elastic properties. In particular, cancerous breast tumors are stiff when compared to the noncancerous surrounding tissue. This difference in elasticity can be used as a means for detection through the method of elastographic tomosynthesis by means of physical modulation. This paper deals with a method to visualize elasticity of soft tissues, particularly breast tissues, via x-ray tomosynthesis. X-ray tomosynthesis is now used to visualize breast tissues with better resolution than the conventional single-shot mammography. The advantage of X-ray tomosynthesis over X-ray CT is that fewer projections are needed than CT to perform the reconstruction, thus radiation exposure and cost are both reduced. Two phantoms were used for the testing of this method, a physical phantom and an in silico phantom. The standard root mean square error in the tomosynthesis for the physical phantom was 2.093 and the error in the in silico phantom was negligible. The elastographs were created through the use of displacement and strain graphing. A Gaussian Mixture Model with an expectation–maximization clustering algorithm was applied in three dimensions with an error of 16.667%. The results of this paper have been substantial when using phantom data. There are no equivalent comparisons yet in 3D x-ray elastographic tomosynthesis. Tomosynthesis with and without physical modulation in the 3D elastograph can identify feature groupings used for biopsy. The studies have potential to be applied to human test data used as a guide for biopsy to improve accuracy of diagnosis results. Further research on this topic could prove to yield new techniques for human patient diagnosis purposes.

[Future of mammography-based imaging]

Mammography is the central diagnostic method for clinical diagnostics of breast cancer and the breast cancer screening program. In the clinical routine complementary methods, such as ultrasound, tomosynthesis and optional magnetic resonance imaging (MRI) are already combined for the diagnostic procedure. Future developments will utilize investigative procedures either as a hybrid (combination of several different imaging modalities in one instrument) or as a fusion method (the technical fusion of two or more of these methods) to implement fusion imaging into diagnostic algorithms. For screening there are reasonable hypotheses to aim for studies that individualize the diagnostic process within the screening procedure. Individual breast cancer risk prediction and individualized knowledge about sensitivity and specificity for certain diagnostic methods could be tested. The clinical implementation of these algorithms is not yet in sight.

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis.

Pilot Study on the Detection of Simulated Lesions Using a 2D and 3D Digital Full-Field Mammography System with a Newly Developed High Resolution Detector Based on Two Shifts of a-Se

Purpose: Experimental study of a new system for digital 2D and 3D full-field mammography (FFDM) using a high resolution detector based on two shifts of a-Se. Material and Methods: Images were acquired using the new FFDM system Amulet® (FujiFilm, Tokio, Japan), an a-Se detector (receptor 24 × 30 cm², pixel size 50 µm, memory depth 12 bit, spatial resolution 10 lp/mm, DQE > 0.50). Integrated in the detector is a new method for data transfer, based on optical switch technology. The object of investigation was the Wisconsin Mammographic Random Phantom, Model 152A (Radiation Measurement Inc., Middleton, WI, USA) and the same parameters and exposure data (Tungsten, 100 mAs, 30 kV) were consistently used. We acquired 3 different pairs of images in the c-c and ml planes (2D) and in the c-c and c-c planes with an angle of 4 degrees (3D). Five radiologists experienced in mammography (experience ranging from 3 months to more than 5 years) analyzed the images (monitoring) which had been randomly encoded (random generator) with regard to the recognition of details such as specks of aluminum oxide (200-740 µm), nylon fibers (0.4-1.6 mm) and round lesions/masses (diameters 5-14 mm), using special linear glasses for 3D visualization, and compared the results. Results: A total of 225 correct positive decisions could be detected: we found 222 (98.7 %) correct positive results for 2D and 3D visualization in each case. Conclusion: The results of this phantom study showed the same detection rates for both 2D and 3D imaging using full field digital mammography. Our results must be confirmed in further clinical trials.