Development of a stiffness measurement accessory for ultrasound in breast cancer diagnosis

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.

Tissue engineering concept in the research of the tumor biology

Tumor is a heterogeneous complex, which lives in a three-dimensional environment flush with biopathophysiological and biomechanical signals. This signaling abundant extracellular milieu co-evolving from cell-cell and cell-host interaction guides the development and the generation of the tumor. There has been a recent surge of interest in studying the tumor biology that more closely mirror what happens in living organisms, especially in cancer research. Incorporating cancer cells in the 3D mimicking environment instead of monolayers is reasonable for maintaining in vivo cancer behaviors in spatial and temporal context. However, 3D culture for cancer still presents a challenge for researchers in this field. Tissue engineering, originally aiming at designing the artificial organs, provided a feasible approach to recreate such complex mechanical and biochemical interplay. Aside from reproducing bionic environment, tissue engineering has been routinely introduced into cancer study to build three dimensional structures not only to develop molecular therapeutics, but also to screen for toxic effects of drugs or radiotherapy sensitivity. In this article, we focused on the recent advances of the well-defined tissue-engineering biomaterials in the application in tumor biology. We also discussed the fabrications of the scaffolds from different materials, which might contribute to future cancer research.

Advanced spectral analyses for real-time automatic echographic tissue-typing of simulated tumor masses at different compression stages

Prototypal software algorithms for advanced spectral analysis of echographic images were developed to perform automatic detection of simulated tumor masses at two different pathological stages. Previously published works documented the possibility of characterizing macroscopic variation of mechanical properties of tissues through elastographic techniques, using different imaging modalities, including ultrasound (US); however, the accuracy of US-based elastography remains affected by the variable manual modality of the applied compression and several attempts are under investigation to overcome this limitation. Quantitative US (QUS), such as Fourier- and wavelet-based analyses of the RF signal associated with the US images, has been developed to perform a microscopic-scale tissue-type imaging offering new solutions for operator-independent examinations. Because materials able to reproduce the harmonic behavior of human liver can be realized, in this study, tissue-mimicking structures were US imaged and the related RF signals were analyzed using wavelet transform through an in-house-developed algorithm for tissue characterization. The classification performance and reliability of the procedure were evaluated on two different tumor stiffnesses (40 and 130 kPa) and with two different applied compression levels (0 and 3.5 N). Our results demonstrated that spectral components associated with different levels of tissue stiffness within the medium exist and can be mapped onto the original US images independently of the applied compressive forces. This wavelet-based analysis was able to identify different tissue stiffness with satisfactory average sensitivity and specificity: respectively, 72.01% ± 1.70% and 81.28% ± 2.02%.