Label-free multiphoton imaging allows brain tumor recognition based on texture analysis-a study of 382 tumor patients

Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis

Significance

Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics.

Aim

We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65 mm and arbitrary core arrangement and inhomogeneous transmissivity.

Approach

Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes.

Results

For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2 dB and 0.20 to 23.4 dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6 dB and 0.97 with 14 fps computing speed.

Conclusions

With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility.

Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer

Brain cancer is one of those few cancers with very high mortality and low five-year survival rate. First and foremost reason for the woes is the difficulty in diagnosing and monitoring the progression of brain tumors both benign and malignant, noninvasively and in real time. This raises a need in this hour for a tool to diagnose the tumors in the earliest possible time frame. On the other hand, Raman spectroscopy which is well-known for its ability to precisely represent the molecular markers available in any sample given, including biological ones, with great sensitivity and specificity. This has led to a number of studies where Raman spectroscopy has been used in brain tumors in various ways. This review article highlights the fundamentals of Raman spectroscopy and its types including conventional Raman, SERS, SORS, SRS, CARS, etc. are used in brain tumors for diagnostics, monitoring, and even theragnostics, collating all the major works in the area. Also, the review explores how Raman spectroscopy can be even more effectively used in theragnostics and the clinical level which would make them a one-stop solution for all brain cancer needs in the future.

Ultrasonic Aspirator for Tissue Contact Detection: An Online Classification on Time-Series

The goal of neurosurgical tumor surgery is to remove the tumor completely without damaging healthy brain structures and thereby impairing the patient's neurological functions. This requires careful planning and execution of the operation by experienced neurosurgeons using the latest intraoperative technologies to achieve safe and rapid tumor reduction without harming the patient. To achieve this goal, a standard ultrasonic aspirator designed for tissue removal is equipped with additional intraoperative tissue detection using machine learning methods.Since decision-making in a clinical context must be fast, online contact detection is critical. Data are generated on three types of artificial tissue models in a CNC machine-controlled environment with four different ultrasonic aspirator settings. Contact classification on artificial tissue models is evaluated on four classification algorithms: change point detection (CPD), random forest (RF), recurrent neural network (RNN) and temporal convolutional network (TCN). Data preprocessing steps are applied, and their impacts are investigated. All methods are evaluated on five-fold cross-validation and provide generally good results with a performance of up to 0.977±0.007 in mean F1-score. Preprocessing the data has a positive effect on the classification processes for all methods and consistently improves the metrics. Thus, this work indicates in a first step that contact classification is feasible in an online context for an ultrasonic aspirator. Further research is necessary on different tissue types, as well as hand-held use to more closely resemble the intraoperative clinical conditions.

Label-free multiphoton microscopy enables histopathological assessment of colorectal liver metastases and supports automated classification of neoplastic tissue

As the state of resection margins is an important prognostic factor after extirpation of colorectal liver metastases, surgeons aim to obtain negative margins, sometimes elaborated by resections of the positive resection plane after intraoperative frozen sections. However, this is time consuming and results sometimes remain unclear during surgery. Label-free multimodal multiphoton microscopy (MPM) is an optical technique that retrieves morpho-chemical information avoiding all staining and that can potentially be performed in real-time. Here, we investigated colorectal liver metastases and hepatic tissue using a combination of three endogenous nonlinear signals, namely: coherent anti-Stokes Raman scattering (CARS) to visualize lipids, two-photon excited fluorescence (TPEF) to visualize cellular patterns, and second harmonic generation (SHG) to visualize collagen fibers. We acquired and analyzed over forty thousand MPM images of metastatic and normal liver tissue of 106 patients. The morphological information with biochemical specificity produced by MPM allowed discriminating normal liver from metastatic tissue and discerning the tumor borders on cryosections as well as formalin-fixed bulk tissue. Furthermore, automated tissue type classification with a correct rate close to 95% was possible using a simple approach based on discriminant analysis of texture parameters. Therefore, MPM has the potential to increase the precision of resection margins in hepatic surgery of metastases without prolonging surgical intervention.

The neurosurgical benefit of contactless in vivo optical coherence tomography regarding residual tumor detection: A clinical study

Purpose

In brain tumor surgery, it is crucial to achieve complete tumor resection while conserving adjacent noncancerous brain tissue. Several groups have demonstrated that optical coherence tomography (OCT) has the potential of identifying tumorous brain tissue. However, there is little evidence on human in vivo application of this technology, especially regarding applicability and accuracy of residual tumor detection (RTD). In this study, we execute a systematic analysis of a microscope integrated OCT-system for this purpose.

Experimental design

Multiple 3-dimensional in vivo OCT-scans were taken at protocol-defined sites at the resection edge in 21 brain tumor patients. The system was evaluated for its intraoperative applicability. Tissue biopsies were obtained at these locations, labeled by a neuropathologist and used as ground truth for further analysis. OCT-scans were visually assessed with a qualitative classifier, optical OCT-properties were obtained and two artificial intelligence (AI)-assisted methods were used for automated scan classification. All approaches were investigated for accuracy of RTD and compared to common techniques.

Results

Visual OCT-scan classification correlated well with histopathological findings. Classification with measured OCT image-properties achieved a balanced accuracy of 85%. A neuronal network approach for scan feature recognition achieved 82% and an auto-encoder approach 85% balanced accuracy. Overall applicability showed need for improvement.

Conclusion

Contactless in vivo OCT scanning has shown to achieve high values of accuracy for RTD, supporting what has well been described for ex vivo OCT brain tumor scanning, complementing current intraoperative techniques and even exceeding them in accuracy, while not yet in applicability.

Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring

Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.

Optical biopsy of laryngeal lesions using femtosecond multiphoton microscopy

Laryngeal squamous cell carcinoma (LSCC) is one of the most prevalent malignancy of the upper aerodigestive tract. Detection of early lesions in vivo could improve the survival rate significantly. In this study, we demonstrated that femtosecond multiphoton microscopy (MPM) is an effective tool to visualize the microscopic features within fixed laryngeal tissues, without sectioning, staining, or labeling. Accurate detection of lesions and determination of the tumor grading can be achieved, with excellent consistency with conventional histological examination. These results suggest that MPM may represent a powerful tool for in-vivo or fast ex-vivo diagnosis of laryngeal lesions at the point of care.

Confocal Laser Endomicroscopy Assessment of Pituitary Tumor Microstructure: A Feasibility Study

This is the first study to assess confocal laser endomicroscopy (CLE) use within the transsphenoidal approach and show the feasibility of obtaining digital diagnostic biopsies of pituitary tumor tissue after intravenous fluorescein injection. We confirmed that the CLE probe reaches the tuberculum sellae through the transnasal transsphenoidal corridor in cadaveric heads. Next, we confirmed that CLE provides images with identifiable histological features of pituitary adenoma. Biopsies from nine patients who underwent pituitary adenoma surgery were imaged ex vivo at various times after fluorescein injection and were assessed by a blinded board-certified neuropathologist. With frozen sections used as the standard, pituitary adenoma was diagnosed as “definitively” for 13 and as “favoring” in 3 of 16 specimens. CLE digital biopsies were diagnostic for pituitary adenoma in 10 of 16 specimens. The reasons for nondiagnostic CLE images were biopsy acquisition <1 min or >10 min after fluorescein injection (n = 5) and blood artifacts (n = 1). In conclusion, fluorescein provided sufficient contrast for CLE at a dose of 2 mg/kg, optimally 1–10 min after injection. These results provide a basis for further in vivo studies using CLE in transsphenoidal surgery.