Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

Intraoperative application of optical coherence tomography for lung tumor.

On-site instant determination of benign or malignant tumors for deciding the types of resection is crucial during pulmonary surgery. We designed a portable spectral-domain optical coherence tomography (SD-OCT) system to do real-time scanning intraoperatively for the distinction of fresh tumor specimens in the lung. A total of 12 ex vivo lung specimens from six patients were enrolled. Three patients were diagnosed with invasive adenocarcinoma (IA), while the others were benign. After OCT-imaged reconstruction, we compared the qualitative morphology of OCT and histology among malignant, benign, and normal tissues. In addition, through analysis of the quantitative data, a discrete difference in optical attenuation coefficients around the junctional surface was shown by our data processing. This study demonstrated a feasible OCT-assisted resection guide by a rapid on-site tumor diagnosis. The results indicate that future deep learning of OCT-captured image systems able to improve diagnostic and therapeutic efficiency is warranted.

Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet.

Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively. Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design. Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues. Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice. Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients.

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients. Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

Intelligent classification of platelet aggregates by agonist type.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the "intelligent platelet aggregate classifier" or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and "learned" to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method's accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient's heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.

Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging.

Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.

Virtual-freezing fluorescence imaging flow cytometry.

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

Neurosurgical brain tumor detection based on intraoperative optical intrinsic signal imaging technique: A case report of glioblastoma.

The delineation of brain tumor margins has been a challenging objective in neurosurgery for decades. Despite the development of various preoperative imaging techniques, the current methodology is still insufficient for clinical practice. We present an intraoperative optical intrinsic signal imaging system for brain tumor surgery and establish a data processing procedure model to localize tumors. From the experimental result of a glioblastoma patient, we observe a relative small oscillation of ΔHbD in tumor region and speculate that vessels in tumor region have poor ability to provide oxygen. We applied the same data processing procedure on the second time data and proclaimed a successful surgery. Figure: Merged ΔHbD image captured prior and posterior to tumor removal.

Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia.

Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 µL into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Altered near-infrared spectroscopy response to breath-holding in patients with fibromyalgia.

Fibromyalgia (FM) is a complex syndrome characterized by chronic widespread pain and a heightened response to pressure. Most medical researches pointed out that FM patients with endothelial dysfunction and arterial stiffness. A continuous-wave near-infrared spectroscopy (NIRS) system is used in present study to measure the hemodynamic changes elicited by breath-holding task in patients with FM. Each patient completed a questionnaire survey including demographics, characteristics of body pain, associated symptoms, headache profiles and Hospital Anxiety and Depression Scale. A total of 27 FM patients and 26 health controls were enrolled. In comparison with healthy controls, patients with FM showed lower maximal and averaged change of oxyhemoglobin concentration in both the left (1.634 ±0.890 and 0.810 ±0.525 µM) and the right (1.576 ±0.897 and 0.811 ±0.601 µM) prefrontal cortex than healthy controls (P < .05 for both sides) during the breath-holding task. In conclusion, FM is associated with altered cerebrovascular reactivity measured by NIRS and breath-holding task, which may reflect endothelial dysfunction or arterial stiffness. Oxygenated hemoglobin concentration changes of healthy controls and FM patients.

Near-infrared bone densitometry: A feasibility study on distal radius measurement.

Osteoporosis, defined as decreased bone mineral density (BMD), poses patients in dangers for fracture risk and has become a major public health problem worldwide because of is associated morbidity, mortality and costs. Without doubt, early detection and timely intervention are important to successfully manage osteoporosis and its associated complications. The dual-energy x-ray absorptiometry (DXA) is the most popular and standard method to measure BMD. However, limitations including radiation exposure and availability restrict its application for osteoporosis screening among general population. In this study, we developed a simple method to detect human distal radius bone density based on near infrared (NIR) image system. Among 10 volunteers (including 5 young and 5 elderly participants) receiving bone density measurement using our NIR image system at the ultradistal part of bilateral distal radius, we demonstrated a strong correlation between the optical attenuation and BMD measured with DXA, which may facilitate predicting bone density status. We hope our potential NIR image system may open a new avenue for development of osteoporosis screening facilities and help in prevention of osteoporosis related fracture and its associated complications in the near future. Pearson's correlations between BMD values from the DXA and light intensity of NIR system.

Reduced frontal activity during a verbal fluency test in fibromyalgia: A near-infrared spectroscopy study.

Fibromyalgia (FM) is a complex disorder characterized by widespread chronic pain and associated sleep problems and cognitive dysfunction. However, only few studies focusing on cognitive dysfunction in FM are available so far. In the present study, we aimed to use near infrared spectroscopy (NIRS) to evaluate the brain function in FM patients subjected to a verbal fluency test (VFT). A total of 11 primary FM patients and 13 healthy individuals (HC) underwent NIRS while performing a VFT. The Fibromyalgia Impact Questionnaire (FIQ) was used to evaluate the symptom severity of FM and Beck Depression Inventory II (BDI) was used to evaluate the severities depression symptoms in study participants. Five regions of interests (ROIs) were defined: the frontal-, bilateral inferior frontal gyrus (IFG), and temporal regions. Brain activities of ROIs between the two groups were compared. In addition, we investigated the relationship between clinical symptoms and brain cortical activity in FM patients. Our results showed that there were no significant differences between HC and FM patients in age, sex, and BDI scores. We found significantly reduced brain activity over the frontal regions during a VFT in FM patients (p = .026). In addition, we found decreased frontal activity was associated with BDI scores (rho = -0.755, p = .007). Furthermore, there were no significant correlations between frontal activity and FIQ subscales. In conclusion, our study demonstrated a reduced frontal cortical activity during VFT in FM patients, and that NIRS could be a potential tool for evaluating brain function in FM patients in clinical settings.

Diffuser-aided time-domain diffuse optical imaging: a phantom study.

We present the first experimental results of time-resolved diffuser-aided diffuse optical imaging (DADOI) method in this paper. A self-manufactured diffuser plate was inserted between the optode and the surface of a scattering medium. The diffuser was utilized to enhance the multiple scattering that destroys the image information for baseline measurement of turbid medium. Therefore, the abnormality can be detected with the modified optical density calculation. The time-domain DADOI method can provide better imaging contrast and simpler imaging than the conventional diffuse optical tomography measurement. Besides, it also reveals rich depth information with temporal responses. Therefore, the DADOI offers a great potential to detect the breast tumor and chemotherapy monitoring in clinical diagnosis.

Finite element modelling of implant designs and cortical bone thickness on stress distribution in maxillary type IV bone.

The aims of this study were to examine the effect of implant neck design and cortical bone thickness using 3D finite element analysis and to analyse the stability of clinical evidence based on micromotion and principal stress. Four commercial dental implants for a type IV bone and maxillary segments were created. Various parameters were considered, including the osseointegration condition, loading direction and cortical bone thickness. Micromotion and principal stresses were used to evaluate the failure of osseointegration and bone overloading, respectively. It was found that the maximum stress of the peri-implant bone decreased as cortical bone thickness increased. The micromotion level in full osseointegration is less than that in non-osseointegration and it also decreases as cortical bone thickness increases. The cortical bone thickness should be measured before surgery to help select a proper implant. In the early stage of implantation, the horizontal loading component induces stress concentration in bone around the implant neck more easily than does the vertical loading component, and this may result in crestal bone loss.

Dental optical coherence tomography.

This review paper describes the applications of dental optical coherence tomography (OCT) in oral tissue images, caries, periodontal disease and oral cancer. The background of OCT, including basic theory, system setup, light sources, spatial resolution and system limitations, is provided. The comparisons between OCT and other clinical oral diagnostic methods are also discussed.

Diffuser-aided diffuse optical imaging for breast tumor: a feasibility study based on time-resolved three-dimensional Monte Carlo modeling.

This study proposed diffuser-aided diffuse optical imaging (DADOI) as a new approach to improve the performance of the conventional diffuse optical tomography (DOT) approach for breast imaging. The 3-D breast model for Monte Carlo simulation is remodeled from clinical MRI image. The modified Beer-Lambert's law is adopted with the DADOI approach to substitute the complex algorithms of inverse problem for mapping of spatial distribution, and the depth information is obtained based on the time-of-flight estimation. The simulation results demonstrate that the time-resolved Monte Carlo method can be capable of performing source-detector separations analysis. The dynamics of photon migration with various source-detector separations are analyzed for the characterization of breast tissue and estimation of optode arrangement. The source-detector separations should be less than 4 cm for breast imaging in DOT system. Meanwhile, the feasibility of DADOI was manifested in this study. In the results, DADOI approach can provide better imaging contrast and faster imaging than conventional DOT measurement. The DADOI approach possesses great potential to detect the breast tumor in early stage and chemotherapy monitoring that implies a good feasibility for clinical application.

Tissue birefringence of hypercholesterolemic rat liver measured with polarization-sensitive optical coherence tomography.

We measure in vitro tissue birefringence in the liver of hypercholesterolemic rats with polarization-sensitive optical coherence tomography. Tissue birefringence is determined by measuring the phase retardation as a function of tissue depth. The birefringence of such a sample is usually due to the narrow fibrous structures that cannot be resolved by a standard optical coherence tomography system. Anisotropic structures are formed in the hypercholesterolemic rat liver, which is quite different from the isotropic nature of healthy liver. Birefringence is evaluated to give an order of magnitude of 4.48x10(-4) at 790 nm in hypercholesterolemic rat liver. The infiltration of macrophages and increased collagen deposition should be major causes for tissue birefringence in hypercholesterolemic liver.