Label-free multiphoton excitation imaging as a promising diagnostic tool for breast cancer

Optical histopathology based on the nonlabeling analysis with multiphoton excitation imaging

Histopathological diagnosis is the definitive method for the evaluation of disease status; however, some problems need to be solved, such as invasiveness, time consumption, and difficulty in three-dimensional observation. To overcome these problems, a novel observation method, distinct from conventional histology, using tissue sections and glass slides is desirable. Fluorescence imaging of human tissues with multiphoton excitation imaging (MpEI), which was originally used for intravital imaging in biological research, is a promising method. Label-free MpEI, which requires only near-infrared excitation, can construct images with autofluorescent signals from fresh tissues, as well as nonlinear optical phenomena. It is possible to perform real-time three-dimensional imaging of human tissues without any tissue removal, fixation, or staining. This method has been reported to be useful for histopathological classification in multiple organs and tissues. Moreover, it is very compatible with quantitative image analyses, including artificial intelligence. Based on these characteristics, label-free MpEI has sufficient potential for clinical applications such as in endoscopy and intraoperative rapid diagnosis. The clinical application of label-free MpEI will bring changes not only to histopathology examination but also the clinical bedside and will contribute to the further development of histopathology.

Perspective on the use of fluorescence molecular imaging for peripheral and deep en face margin assessment

Significance

Current standard practice for margin assessment in solid tumor resection often leads to suboptimal results due to the inability to assess margins completely in a time-efficient manner. On the other hand, for small skin cancers, peripheral and deep en face margin assessment (PDEMA) offers 100% assessment of margins while sparing the utmost amount of normal surrounding tissues. Nonetheless, PDEMA is limited in its use owing to its lengthy tissue processing and imaging time as well as its requirement for high-quality frozen sections and real-time histologic analysis.

Aim

We aim to explore fluorescence molecular imaging (FMI) as a tool for resolving obstacles and integrating PDEMA into the surgeon-to-pathologist workflow for large solid tumors.

Approach

A review of recent pre-clinical and clinical studies using FMI to assess surgical margins was conducted to highlight promising fluorescence imaging technologies utilized in the surgical suite and laboratory.

Results

FMI techniques that provide macroscopic resolution are efficient in time and have a notable ability to identify true negative tissue yet have limited capability in identifying true positive tissues. Moreover, meso- and microscopic FMI methods require additional time to attain a higher resolution but deliver an enhanced sensitivity in detecting true positive tissues. In both cases, experts are still required to learn to interpret the FMI signals, which prohibits a seamless clinical integration.

Conclusions

Our proposed margin assessment platform (MAP) incorporates both macroscopic and, meso- or microscopic imaging with post-processing and machine learning for interpretation, to enable the application of PDEMA into solid tumor surgery. MAP leverages the advantages of each technique and thoroughly tackles the limitations of time and expertise to optimize the efficiency and accuracy of margin assessment and ultimately improve clinical outcomes.

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study

Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.

Elastin is responsible for the rigidity of the ligament under shear and rotational stress: a mathematical simulation study

BACKGROUND

An accurate understanding of the mechanical response of ligaments is important for preventing their damage and rupture. To date, ligament mechanical responses are being primarily evaluated using simulations. However, many mathematical simulations construct models of uniform fibre bundles or sheets using merely collagen fibres and ignore the mechanical properties of other components such as elastin and crosslinkers. Here, we evaluated the effect of elastin-specific mechanical properties and content on the mechanical response of ligaments to stress using a simple mathematical model.

METHODS

Based on multiphoton microscopic images of porcine knee collateral ligaments, we constructed a simple mathematical simulation model that individually includes the mechanical properties of collagen fibres and elastin (fibre model) and compared with another model that considers the ligament as a single sheet (sheet model). We also evaluated the mechanical response of the fibre model as a function of the elastin content, from 0 to 33.5%. Both ends of the ligament were fixed to a bone, and tensile, shear, and rotational stresses were applied to one of the bones to evaluate the magnitude and distribution of the stress applied to the collagen and elastin at each load.

RESULTS

Uniform stress was applied to the entire ligament in the sheet model, whereas in the fibre model, strong stress was applied at the junction between collagen fibres and elastin. Even in the same fibre model, as the elastin content increased from 0 to 14.4%, the maximum stress and displacement applied to the collagen fibres during shear stress decreased by 65% and 89%, respectively. The slope of the stress-strain relationship at 14.4% elastin was 6.5 times greater under shear stress than that of the model with 0% elastin. A positive correlation was found between the stress required to rotate the bones at both ends of the ligament at the same angle and elastin content.

CONCLUSIONS

The fibre model, which includes the mechanical properties of elastin, can provide a more precise evaluation of the stress distribution and mechanical response. Elastin is responsible for ligament rigidity during shear and rotational stress.