Toward a Portable Cancer Diagnostic Tool Using a Disposable MEMS-Based Biochip

State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

Early detection of breast cancer is crucial for a better prognosis. Various studies have been conducted where tumor lesions are detected and localized on images. This is a narrative review where the studies reviewed are related to five different image modalities: histopathological, mammogram, magnetic resonance imaging (MRI), ultrasound, and computed tomography (CT) images, making it different from other review studies where fewer image modalities are reviewed. The goal is to have the necessary information, such as pre-processing techniques and CNN-based diagnosis techniques for the five modalities, readily available in one place for future studies. Each modality has pros and cons, such as mammograms might give a high false positive rate for radiographically dense breasts, while ultrasounds with low soft tissue contrast result in early-stage false detection, and MRI provides a three-dimensional volumetric image, but it is expensive and cannot be used as a routine test. Various studies were manually reviewed using particular inclusion and exclusion criteria; as a result, 91 recent studies that classify and detect tumor lesions on breast cancer images from 2017 to 2022 related to the five image modalities were included. For histopathological images, the maximum accuracy achieved was around 99 % , and the maximum sensitivity achieved was 97.29 % by using DenseNet, ResNet34, and ResNet50 architecture. For mammogram images, the maximum accuracy achieved was 96.52 % using a customized CNN architecture. For MRI, the maximum accuracy achieved was 98.33 % using customized CNN architecture. For ultrasound, the maximum accuracy achieved was around 99 % by using DarkNet-53, ResNet-50, G-CNN, and VGG. For CT, the maximum sensitivity achieved was 96 % by using Xception architecture. Histopathological and ultrasound images achieved higher accuracy of around 99 % by using ResNet34, ResNet50, DarkNet-53, G-CNN, and VGG compared to other modalities for either of the following reasons: use of pre-trained architectures with pre-processing techniques, use of modified architectures with pre-processing techniques, use of two-stage CNN, and higher number of studies available for Artificial Intelligence (AI)/machine learning (ML) researchers to reference. One of the gaps we found is that only a single image modality is used for CNN-based diagnosis; in the future, a multiple image modality approach can be used to design a CNN architecture with higher accuracy.

RapidET: a MEMS-based platform for label-free and rapid demarcation of tumors from normal breast biopsy tissues

The rapid and label-free diagnosis of malignancies in ex vivo breast biopsy tissues has significant utility in pathology laboratories and operating rooms. We report a MEMS-based platform integrated with microchips that performs phenotyping of breast biopsy tissues using electrothermal sensing. The microchip, fabricated on a silicon substrate, incorporates a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) as on-chip sensing elements. The microchips are integrated onto the platform using a slide-fit contact enabling quick replacement for biological measurements. The bulk resistivity (ρ B ), surface resistivity (ρ S ), and thermal conductivity (k) of deparaffinized and formalin-fixed paired tumor and adjacent normal breast biopsy samples from N = 8 patients were measured. For formalin-fixed samples, the mean ρ B for tumors showed a statistically significant fold change of 4.42 (P = 0.014) when the tissue was heated from 25 °C to 37 °C compared to the adjacent normal tissue, which showed a fold change of 3.47. The mean ρ S measurements also showed a similar trend. The mean k of the formalin-fixed tumor tissues was 0.309 ± 0.02 W m-1 K-1 compared to a significantly higher k of 0.563 ± 0.028 W m-1 K-1 for the adjacent normal tissues. A similar trend was observed in ρ B, ρ S, and k for the deparaffinized tissue samples. An analysis of a combination of ρ B , ρ S , and k using Fisher's combined probability test and linear regression suggests the advantage of using all three parameters simultaneously for distinguishing tumors from adjacent normal tissues with higher statistical significance.

Analysis of the Mechanism of Breast Metastasis Based on Image Recognition and Ultrasound Diagnosis

Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values <0.001). Mammography, ultrasound, and PET-CT examinations showed no difference in the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis. Breast MRI should be used as a routine examination for patients with axillary lymph node metastasis as the first diagnosis. The primary breast cancer in the first diagnosed patients with axillary lymph node metastasis is often presented as localized asymmetric compactness or calcification on X-ray; it often appears as small focal mass lesions and ductal lesions without three-dimensional space-occupying effect on ultrasound.

Thermo-optic measurements and their inter-dependencies for delineating cancerous breast biopsy tissue from adjacent normal

The histopathological diagnosis of cancer is the current gold standard to differentiate normal from cancerous tissues. We propose a portable platform prototype to characterize the tissue's thermal and optical properties, and their inter-dependencies to potentially aid the pathologist in making an informed decision. The measurements were performed on 10 samples from five subjects, where the cancerous and adjacent normal were extracted from the same patient. It was observed that thermal conductivity (k) and reduced-scattering-coefficient (μ'_s ) for both the cancerous and normal tissues reduced with the rise in tissue temperature. Comparing cancerous and adjacent normal tissue, the difference in k and μ'_s (at 940 nm) were statistically significant (p = 7.94e-3), while combining k and μ'_s achieved the highest statistical significance (6.74e-4). These preliminary results promise and support testing on a large number of samples for rapidly differentiating cancerous from adjacent normal tissues.

Cellphone enabled point-of-care assessment of breast tumor cytology and molecular HER2 expression from fine-needle aspirates

Management of breast cancer in limited-resource settings is hindered by a lack of low-cost, logistically sustainable approaches toward molecular and cellular diagnostic pathology services that are needed to guide therapy. To address these limitations, we have developed a multimodal cellphone-based platform—the EpiView-D4—that can evaluate both cellular morphology and molecular expression of clinically relevant biomarkers directly from fine-needle aspiration (FNA) of breast tissue specimens within 1 h. The EpiView-D4 is comprised of two components: (1) an immunodiagnostic chip built upon a “non-fouling” polymer brush-coating (the “D4”) which quantifies expression of protein biomarkers directly from crude cell lysates, and (2) a custom cellphone-based optical microscope (“EpiView”) designed for imaging cytology preparations and D4 assay readout. As a proof-of-concept, we used the EpiView-D4 for assessment of human epidermal growth factor receptor-2 (HER2) expression and validated the performance using cancer cell lines, animal models, and human tissue specimens. We found that FNA cytology specimens (prepared in less than 5 min with rapid staining kits) imaged by the EpiView-D4 were adequate for assessment of lesional cellularity and tumor content. We also found our device could reliably distinguish between HER2 expression levels across multiple different cell lines and animal xenografts. In a pilot study with human tissue (n = 19), we were able to accurately categorize HER2-negative and HER2-positve tumors from FNA specimens. Taken together, the EpiView-D4 offers a promising alternative to invasive—and often unavailable—pathology services and may enable the democratization of effective breast cancer management in limited-resource settings.

Hybrid Spectral-IRDx: Near-IR and Ultrasound Attenuation System for Differentiating Breast Cancer From Adjacent Normal Tissue

OBJECTIVE

While performing surgical excision for breast cancer (lumpectomy), it is important to ensure a clear margin of normal tissue around the cancer to achieve complete resection. The current standard is histopathology; however, it is time-consuming and labour-intensive requiring skilled personnel.

METHOD

We describe a Hybrid Spectral-IRDx - a combination of the previously reported Spectral-IRDx tool with multimodal ultrasound and NIR spectroscopy techniques. We show how this portable, cost-effective, minimal-contact tool could provide rapid diagnosis of cancer using formalin-fixed (FF) and deparaffinized (DP) breast biopsy tissues.

RESULTS

Using this new tool, measurements were performed on cancerous/fibroadenoma and its adjacent normal tissues from the same patients (N = 14). The acoustic attenuation coefficient (α) and reduced scattering coefficient (µ'_s) (at 850, 940, and 1060 nm) for the cancerous/fibroadenoma tissues were reported to be higher compared to adjacent normal tissues, a basis of delineation. Comparing FF cancerous and adjacent normal tissue, the difference in µ'_s at 850 nm and 940 nm were statistically significant (p = 3.17e-2 and 7.94e-3 respectively). The difference in α between the cancerous and adjacent normal tissues for DP and FF tissues were also statistically significant (p = 2.85e-2 and 7.94e-3 respectively). Combining multimodal parameters α and µ'_s (at 940 nm) show highest statistical significance (p = 6.72e-4) between FF cancerous/fibroadenoma and adjacent normal tissues.

CONCLUSION

We show that Hybrid Spectral-IRDx can accurately delineate between cancerous and adjacent normal breast biopsy tissue.

SIGNIFICANCE

The results obtained establish the proof-of-principle and large-scale testing of this multimodal breast cancer diagnostic platform for core biopsy diagnosis.

Towards a Portable Platform Integrated With Multispectral Noncontact Probes for Delineating Normal and Breast Cancer Tissue Based on Near-Infrared Spectroscopy

Currently, the confirmation of diagnosis of breast cancer is made by microscopic examination of an ultra-thin slice of a needle biopsy specimen. This slice is conventionally formalin-fixed and stained with hematoxylin-eosin and visually examined under a light microscope. This process is labor-intensive and requires highly skilled doctors (pathologists). In this paper, we report a novel tool based on near-infrared spectroscopy (Spectral-IRDx) which is a portable, non-contact, and cost-effective system and could provide a rapid and accurate diagnosis of cancer. The Spectral-IRDx tool performs absorption spectroscopy at near-infrared (NIR) wavelengths of 850, 935, and 1060 nm. We measure normalized detected voltage (V_dn) with the tool in 10 deparaffinized breast biopsy tissue samples, 5 of which were cancer (C) and 5 were normal (N) tissues. The difference in V_dn at 935 nm and 1060 nm between cancer and normal tissues is statistically significant with p-values of 0.0038 and 0.0022 respectively. Absorption contrast factor (N/C) of 1.303, 1.551, and 1.45 are observed for 850, 935, and 1060 nm respectively. The volume fraction contrast (N/C) of lipids and collagens are reported as 1.28 and 1.10 respectively. Higher absorption contrast factor (N/C) and volume fraction contrast (N/C) signifies higher concentration of lipids in normal tissues as compared to cancerous tissues, a basis for delineation. These preliminary results support the envisioned concept for noninvasive and noncarcinogenic NIR-based breast cancer diagnostic platform, which will be tested using a larger number of samples.

Viscoelastic Properties of Human Autopsy Brain Tissues as Biomarkers for Alzheimer's Diseases

OBJECTIVE

The present study investigates viscoelastic properties of human autopsy brain tissue via nanoindentation to find feasible biomarkers for Alzheimer's disease (AD) in ex vivo condition and to understand the mechanics of the human brain better, especially on the difference before and after progression of AD.

METHODS

Viscoelastic properties of paraformaldehyde-fixed, paraffin-embedded thin (8 [Formula: see text]) sectioned normal and AD affected human autopsy brain tissue samples are investigated via nanoindentation with a combined loading profile of a linear preloading and a sinusoidal loading at various loading frequencies from 0.01 to 10 [Formula: see text]. In 1200 indentation tests for ten human autopsy brain tissue samples from ten different subjects (five AD cases and five normal controls), viscoelastic properties such as Young's modulus, storage modulus, loss modulus, and loss factor of both gray and white matter brain tissues samples from normal and AD affected tissues were measured experimentally.

RESULTS

We found that the normal brain tissues have higher Young's modulus values than the AD affected brain tissues by 23.5 % and 27.9 % on average for gray and white matter, respectively, with statistically significant differences ( ) between the normal and AD affected brain tissues. Additionally, the AD affected brain tissues have much higher loss factor than the normal brain tissues on lower loading frequencies.

SIGNIFICANCE

AD is one of the leading causes of death in America and continues to affect a growing population. The challenges of recognizing the early pathological changes in brain tissue due to AD and diagnosing a patient has led to much research focused on finding biomarkers for the disease. In this regard, understanding the mechanics of brain tissues is increasingly recognized to play an important role in diagnosing brain diseases.

Micromanipulation: A Challenge for Actuation

Manipulating micro objects has become an important task in several applications. Actuation is a crucial aspect of micromanipulation because there are physical restrictions which affect actuators’ performances at the micro or nano scale. One way of getting rid of these limitations is the use of an appropriate mechanical structure which enhances the elasticity of the material or provides mechanical advantage. This Special Issue of Actuators, which is dedicated to micromanipulation, offers a contribution to the development of some promising methods to actuate a microsystem for micromanipulation.

Predicting Invasive Disease-Free Survival for Early-stage Breast Cancer Patients Using Follow-up Clinical Data

OBJECTIVE

Chinese women are seriously threatened by breast cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare an appropriate treatment plan that may prolong patient survival time. An alternative prognosis model framework to predict Invasive Disease-Free Survival (iDFS) for early-stage breast cancer patients, called MP4Ei, is proposed. MP4Ei framework gives an excellent performance to predict the relapse or metastasis breast cancer of Chinese patients in 5 years.

METHODS

MP4Ei is built based on statistical theory and gradient boosting decision tree framework. 5246 patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, with early-stage (stage I-III) breast cancer are eligible for inclusion. Stratified feature selection, including statistical and ensemble methods, is adopted to select 23 out of the 89 patient features about the patient' demographics, diagnosis, pathology and therapy. Then 23 selected features as the input variables are imported into the XGBoost algorithm, with Bayesian parameter tuning and cross validation, to find out the optimum simplified model for 5-year iDFS prediction.

RESULTS

For eligible data, with 4196 patients (80%) for training, and with 1050 patients (20%) for testing, MP4Ei achieves comparable accuracy with AUC 0.8451, which has a significant advantage (p < 0.05).

CONCLUSION

This work demonstrates the complete iDFS prognosis model with very competitive performance.

SIGNIFICANCE

The proposed method in this paper could be used in clinical practice to predict patients' prognosis and future surviving state, which may help doctors make treatment plan.

Electromechanical Coupling Factor of Breast Tissue as a Biomarker for Breast Cancer

GOAL

This research aims to validate a new biomarker of breast cancer by introducing electromechanical coupling factor of breast tissue samples as a possible additional indicator of breast cancer. Since collagen fibril exhibits a structural organization that gives rise to a piezoelectric effect, the difference in collagen density between normal and cancerous tissue can be captured by identifying the corresponding electromechanical coupling factor.

METHODS

The design of a portable diagnostic tool and a microelectromechanical systems (MEMS)-based biochip, which is integrated with a piezoresistive sensing layer for measuring the reaction force as well as a microheater for temperature control, is introduced. To verify that electromechanical coupling factor can be used as a biomarker for breast cancer, the piezoelectric model for breast tissue is described with preliminary experimental results on five sets of normal and invasive ductal carcinoma (IDC) samples in the 25-45 temperature range.

CONCLUSION

While the stiffness of breast tissues can be captured as a representative mechanical signature which allows one to discriminate among tissue types especially in the higher strain region, the electromechanical coupling factor shows more distinct differences between the normal and IDC groups over the entire strain region than the mechanical signature. From the two-sample -test, the electromechanical coupling factor under compression shows statistically significant differences ( 0.0039) between the two groups.

SIGNIFICANCE

The increase in collagen density in breast tissue is an objective and reproducible characteristic of breast cancer. Although characterization of mechanical tissue property has been shown to be useful for differentiating cancerous tissue from normal tissue, using a single parameter may not be sufficient for practical usage due to inherent variation among biological samples. The portable breast cancer diagnostic tool reported in this manuscript shows the feasibility of measuring multiple parameters of breast tissue allowing for practical application.