Optical imaging for breast cancer prescreening

Assessing women's stated preferences for breast cancer screening: a systematic review and a meta-analysis

BACKGROUND

Breast cancer is the most common diagnosed disease, and the second leading cause of death among women. Thus, due to its importance, the current research is aimed at identifying the preferences of individuals for improving breast cancer screening programs and the related policies.

METHOD

A systematic search was applied on databases including - PubMed, Scopus, the Web of Science, Embase, Cochrane, SID- up to October 2022. The including articles were original or review papers that assessed individuals' willingness to pay. Also, articles including the effective variables or attributes for breast cancer screening program were included. Meta-analysis was applied to calculate Willingness to Pay (WTP) as a mean for breast cancer screening followed by vote-counting for identifying the variables and attributes correlated with screening.

RESULTS

A total of 721 articles were identified during the first phase. After the screening process, thirteen papers were chosen, out of which, nine assessed mammography as a breast cancer screening program. The results of random effect meta-analysis on the including studies indicated that the rate of willingness to pay for screening was 0.28% of GDP per capita (95%CI: 0.14-0.43), which was found to be statistically significant. The result of stratified meta- analysis indicated that the rate of willingness to pay for screening was 0.22% of GDP per capita (95%CI: 0.07-0.37), which was found to be statistically significant. Generally, income was the basic factor for receiving screening services, and cost was an effective attribute for participating in screening programs.

CONCLUSIONS

To increase women's participation in breast cancer screening programs; it is essential to provide legitimate information and eliminate the barriers to women's non-participation. Offering rapid tests at low costs in healthcare centers (both in terms of travel and screening time) delivered by female staff can lead to an increase in women's willingness to participate in breast cancer screening programs.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Objective

Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo.

Materials and methods

In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification.

Results

The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49 ± 0.01 %, and 98.53 ± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data.

Conclusions

External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

Differentiating Primary and Recurrent Lesions in Patients with a History of Breast Cancer: A Comprehensive Review

Breast cancer (BC) recurrence remains a concerning issue, requiring accurate identification and differentiation from primary lesions for optimal patient management. This comprehensive review aims to summarize and evaluate the current evidence on methods to distinguish primary breast tumors from recurrent lesions in patients with a history of BC. Also, we provide a comprehensive understanding of the different imaging techniques, including mammography, ultrasound, magnetic resonance imaging, and positron emission tomography, highlighting their diagnostic accuracy, limitations, and potential integration. In addition, the role of various biopsy modalities and molecular markers was explored. Furthermore, the potential role of liquid biopsy, circulating tumor cells, and circulating tumor DNA in differentiating between primary and recurrent BC was emphasized. Finally, it addresses emerging diagnostic modalities, such as radiomic analysis and artificial intelligence, which show promising potential in enhancing diagnostic accuracy. Through comprehensive analysis and review of the available literature, the current study provides an up-to-date understanding of the current state of knowledge, challenges, and future directions in accurately distinguishing between primary and recurrent breast lesions in patients with a history of BC.

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.

Method for tissue clearing: temporal tissue optical clearing

Light absorption and scattering in biological tissue are significant variables in optical imaging technologies and regulating them enhances optical imaging quality. Optical clearing methods can decrease light scattering and improve optical imaging quality to some extent but owing to their limited efficacy and the potential influence of optical clearing agents on tissue functioning, complementing approaches must be investigated. In this paper, a new strategy of optical clearing proposed as time-dependent or temporal tissue optical clearing (TTOC) is described. The absorption and scattering in light interaction with tissue are regulated in the TTOC technique by altering the pulse width. Here, the dependence of optical properties of matter on the pulse width in a gelatin-based phantom was investigated experimentally. Then, a semi-classical model was introduced to computationally study of Ultra-short laser/matter interaction. After studying phantom, the absorption and scattering probabilities in the interaction of the pulse with modeled human skin tissue were investigated using the proposed model for pulse widths ranging from 1µs to 10fs. The propagation of the pulse through the skin tissue was simulated using the Monte Carlo technique by computing the pulse width-dependent optical properties (absorption coefficient µ_a, scattering coefficient µ_s, and anisotropy factor g). Finally, the penetration depth of light into the tissue and reflectance for different pulse widths was found.

Molecular Photoacoustic Imaging

Medicine has gone through several challenges to make it much more accurate and thus prolong the human being's life. A large part of this challenge is diseased, so early detection can help carry out treatment on time. There is a technology that allows detecting an abnormality within the body without using an invasive method. Ultrasound is a diagnostic test used to scan organs and tissues through sound waves. Although this technique has been widely used, the results are not desired because the images generated are not high resolution. On the other hand, X-rays are used because it presents an image with a much higher resolution than other techniques based on light waves or ultrasound; despite this, they are harmful to cells. In consequence of this problem, another method called molecular photoacoustic imaging has been implemented. This technique bridges the traditional depth limits of ballistic optical imaging and diffuse optical imaging's resolution limits, using the acoustic waves generated in response to laser light absorption, which has now shown potential for molecular imaging, allowing the visualization of biological processes in a non-invasive way. The purpose of this article is to give a critically scoped review of the physical, chemical, and biochemical characteristics of existing photoacoustic contrast agents, highlighting the pivotal applications and current challenges for molecular photoacoustic imaging.

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5th, 10th, 15th & 20th, were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain. The spectra were then pre-processed, augmented for a 10-fold increase in the data strength, and subjected to wavelet packet transformation for feature extraction and selection using MATLAB software. In the present study, the top 10 features from all the time point groups under study were selected based on their prediction ranking values using the mRMR algorithm. The chosen features of all the time-point groups were then subjected to multi-class Support Vector Machine (SVM) algorithms for learning and classifying into respective time point groups under study. The analysis demonstrated accuracy values of 95.2%, 99.5%, and 80.3% with SVM- Radial Basis Function (SVM-RBF), SVM-Polynomial & SVM-Linear, respectively. The serum metabolomic levels during tumor progression complemented photoacoustic patterns of tumor progression, depicting breast cancer pathophysiology.

The Pimpled Gold Nanosphere: A Superior Candidate for Plasmonic Photothermal Therapy

BACKGROUND

The development of highly efficient nanoparticles to convert light to heat for anti-cancer applications is quite a challenging field of research.

METHODS

In this study, we synthesized unique pimpled gold nanospheres (PGNSs) for plasmonic photothermal therapy (PPTT). The light-to-heat conversion capability of PGNSs and PPTT damage at the cellular level were investigated using a tissue phantom model. The ability of PGNSs to induce robust cellular damage was studied during cytotoxicity tests on colorectal adenocarcinoma (DLD-1) and fibroblast cell lines. Further, a numerical model of plasmonic (COMSOL Multiphysics) properties was used with the PPTT experimental assays.

RESULTS

A low cytotoxic effect of thiolated polyethylene glycol (SH-PEG400-SH-) was observed which improved the biocompatibility of PGNSs to maintain 89.4% cell viability during cytometry assays (in terms of fibroblast cells for 24 hrs at a concentration of 300 µg/mL). The heat generated from the nanoparticle-mediated phantom models resulted in ΔT=30°C, ΔT=23.1°C and ΔT=21°C for the PGNSs, AuNRs, and AuNPs, respectively (at a 300 µg/mL concentration and for 325 sec). For the in vitro assays of PPTT on cancer cells, the PGNS group induced a 68.78% lethality (apoptosis) on DLD-1 cells. Fluorescence microscopy results showed the destruction of cell membranes and nuclei for the PPTT group. Experiments further revealed a penetration depth of sufficient PPTT damage in a physical tumor model after hematoxylin and eosin (H&E) staining through pathological studies (at depths of 2, 3 and 4 cm). Severe structural damages were observed in the tissue model through an 808-nm laser exposed to the PGNSs.

CONCLUSION

Collectively, such results show much promise for the use of the present PGNSs and photothermal therapy for numerous anti-cancer applications.

Mathematical and numerical challenges in optical screening of female breast

Diffuse optical tomography (DOT) is an emerging imaging technique which uses light for diagnostic purposes in a non-invasive and non-ionizing way. In this paper, we focus on DOT application to female breast screening, where the surface of the breast is illuminated by light sources and the outgoing light is collected on the surface. The comparison of measured light data with the equivalent field obtained from a relevant mathematical model yields the DOT inverse problem whose solution provides an estimate of the optical coefficients of the tissue. These latter, in turn, can be related to clinical markers for cancer detection. The goal of this work is to propose a mathematical and computational approach tailored to the concept of a DOT imaging device able to perform fast and accurate screenings at an affordable cost. Namely, we address two original points about the crucial issue of the solution of the severely ill-conditioned DOT inverse problem: (a) a computational approach based on Green's functions which do not require the exact knowledge of the tissue geometry, proposed here in the declination of the Method of Fundamental Solutions, which allows to enforce correct boundary conditions; (b) the elastic net regularization technique that shares the desirable properties of both the ℓ₂ - and ℓ₁ -norm penalization approaches and opens the possibility for sparsity recognition in the optical coefficients field and refinement procedures.

Recalcitrant Issues and New Frontiers in Nano-Pharmacology

Packaging of old pharma drugs into new packaging "nanoparticles" is called nano-pharmacology and the products are called nano-based drugs. The inception of nano-pharmacology research and development (R&D) is marked by the approval of the first nano-based drug Doxil® in 1995 by the Food and Drug Administration. However, even after more than two decades, today, there are only ∼20 nano-based drugs in the market to treat cancers and brain diseases. In this article we share the perspectives of nanotechnology scientists, engineers, and clinicians on the roadblocks in nano-pharmacology R&D. Also, we share our opinion on new frontiers in the field of nano-pharmacology R&D that may allow rapid and efficient transfer of nano-pharma technologies from R&D to market.

[Omics biomarkers and early diagnostics]

One of main goals for omics sciences, such as transcriptomics, proteomics and metabolomics, in medicine is biomarker discovery for diagnostics of common non-infectious diseases. The opinion paper discusses diagnostic parameters, which limit the use of the biomarkers, as well as a positive predictive value, and conditions providing possible application of the biomarkers for early diagnostics. Using some examples from proteomics, it is stated that omics technologies, which measure gene expression products, are more often used to discover prognostic and predictive biomarkers. These biomarkers help to classify already diagnosed patients to groups with different disease management.

Near-Infrared Visual Differentiation in Normal and Abnormal Breast Using Hemoglobin Concentrations

Introduction: Near-infrared (NIR) optical imaging is a non-ionizing modality that is emerging as a diagnostic/prognostic tool for breast cancer according to NIR differentiation of hemoglobin (Hb) concentration. Methods: The transmission values of LED-sourced light at 625 nm were measured by power meter to evaluate the optical properties of Hb in breast phantom containing major and minor vessels. For the simulation of blood variations in cancerous breast condition, we prepared 2 concentrations of pre-menopausal Hb and 4 concentrations of post-menopausal Hb and, for comparison with normal tissue, one concentration of Hb injected inside the phantom's vessels. Imaging procedure on the phantom was also conducted by LED source and CCD camera. The images from the experiments were compared with the results obtained from the images analyzed by MATLAB software. Finally, mammography of phantom including various concentration of Hb was prepared. Results: The transmitting intensities of NIR in blood containing 1, 2 and 4 concentrations of Hb in the major vessels were 52.83±2.85, 43.00±3.11 and 31.17±2.27 µW, respectively, and in minor vessels containing similar Hb concentrations were 73.50±2.43, 60.08±5.09 and 42.42±4.86 µW, respectively. The gray-scale levels on the major vessel were about 96, 124, 162 and on the minor vessel about 72, 100, 130 measured for 1, 2 and 4 Hb concentrations, respectively. The sensitivity and specificity of NIR imaging differentiation were 97.4% and 91.3%, respectively. Conclusion: Significant differences in transmitting intensity, optical imaging as well as software analysis of images were observed for 1, 2 and 4 concentrations of Hb in major and minor breast phantom vessels. Differentiation capability of minor vessels was higher than major vessels for Hb concentrations. Despite a good detection for location of vessels by mammography, it could not show differences between vessels with various concentrations. However, NIR optical imaging demonstrated a good image contrast for showing vessels in terms of concentration. This study recommends NIR optical imaging for prescreening breast cancer due to its potential for early diagnosis.