The complementary value of intraoperative fluorescence imaging and Raman spectroscopy for cancer surgery: combining the incompatibles

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

Precise tumor delineation in clinical tissues using a novel acidic tumor microenvironment activatable near-infrared fluorescent contrast agent

Tumor selective near-infrared (NIR) fluorescent contrast agents has the potential to greatly enhance the efficiency and precision of tumor surgery by enabling real-time tumor margin identification for tumor resection guided by imaging. However, the development of these agents is still challenging. In this study, based on the acidic tumor microenvironment (TME), we designed and synthesized a novel pH-sensitive NIR fluorescent contrast agent OBD from β-carboline. The fluorescence quantum yield of OBD exhibited a notable increase at pH 3.6, approximately 12-fold higher compared to its value at pH 7.4. After cellular uptake, OBD lighted up the cancer cells with high specificity and accumulated in the mitochondria. Spraying OBD emitted selective fluorescence in xenograft tumor tissues with tumor-to-normal tissue ratios (TNR) as high as 11.18, implying successful image-guided surgery. Furthermore, OBD was also shown to track metastasis in spray mode. After simple topical spray, OBD rapidly and precisely visualized the tumor margins of clinical colon and liver tissues with TNR over 4.2. Therefore, the small-molecule fluorescent contrast agent OBD has promising clinical applications in tumor identification during surgery.

Image-guided cancer surgery: a narrative review on imaging modalities and emerging nanotechnology strategies

Surgical resection is the cornerstone of solid tumour treatment. Current techniques for evaluating margin statuses, such as frozen section, imprint cytology, and intraoperative ultrasound, are helpful. However, an intraoperative assessment of tumour margins that is accurate and safe is clinically necessary. Positive surgical margins (PSM) have a well-documented negative effect on treatment outcomes and survival. As a result, surgical tumour imaging methods are now a practical method for reducing PSM rates and improving the efficiency of debulking surgery. Because of their unique characteristics, nanoparticles can function as contrast agents in image-guided surgery. While most image-guided surgical applications utilizing nanotechnology are now in the preclinical stage, some are beginning to reach the clinical phase. Here, we list the various imaging techniques used in image-guided surgery, such as optical imaging, ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine imaging, and the most current developments in the potential of nanotechnology to detect surgical malignancies. In the coming years, we will see the evolution of nanoparticles tailored to specific tumour types and the introduction of surgical equipment to improve resection accuracy. Although the promise of nanotechnology for producing exogenous molecular contrast agents has been clearly demonstrated, much work remains to be done to put it into practice.

Evaluation of the Cytotoxic Effect of Pd2Spm against Prostate Cancer through Vibrational Microspectroscopies

Regarding the development of new antineoplastic agents, with a view to assess the selective antitumoral potential which aims at causing irreversible damage to cancer cells while preserving the integrity of their healthy counterparts, it is essential to evaluate the cytotoxic effects in both healthy and malignant human cell lines. In this study, a complex with two Pd(II) centers linked by the biogenic polyamine spermine (Pd₂Spm) was tested on healthy (PNT-2) and cancer (LNCaP and PC-3) prostate human cell lines, using cisplatin as a reference. To understand the mechanisms of action of both cisplatin and Pd₂Spm at a molecular level, Fourier Transform Infrared (FTIR) and Raman microspectroscopies were used. Principal component analysis was applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug impact. The main changes were observed between the B-DNA native conformation and either Z-DNA or A-DNA, with a higher effect on lipids having been detected in the presence of cisplatin as compared to Pd₂Spm. In turn, the Pd-agent showed a more significant impact on proteins.

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders

Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how a sample's constituent molecules are arranged in space. Each tissue section/component is defined by a unique combination of spatial and spectral features, but given the high dimensionality of HSI datasets, extracting and utilising them to segment images is non-trivial. Here, we show how networks based on deep convolutional autoencoders (CAEs) can perform this task in an end-to-end fashion by first detecting and compressing relevant features from patches of the HSI into low-dimensional latent vectors, and then performing a clustering step that groups patches containing similar spatio-spectral features together. We showcase the advantages of using this end-to-end spatio-spectral segmentation approach compared to i) the same spatio-spectral technique not trained in an end-to-end manner, and ii) a method that only utilises spectral features (spectral k-means) using simulated HSIs of porcine tissue as test examples. Secondly, we describe the potential advantages/limitations of using three different CAE architectures: a generic 2D CAE, a generic 3D CAE, and a 2D convolutional encoder-decoder architecture inspired by the recently proposed UwU-net that is specialised for extracting features from HSI data. We assess their performance on IR HSIs of real colon samples. We find that all architectures are capable of producing segmentations that show good correspondence with HE stained adjacent tissue slices used as approximate ground truths, indicating the robustness of the CAE-driven spatio-spectral clustering approach for segmenting biomedical HSI data. Additionally, we stress the need for more accurate ground truth information to enable a precise comparison of the advantages offered by each architecture.

MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives

Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.