Fiber attenuated total reflection infrared spectroscopy of kidney tissue during live surgery

Toward cancer detection by label-free microscopic imaging in oncological surgery: Techniques, instrumentation and applications

This review examines the clinical application of label-free microscopy and spectroscopy, which are based on optical signals emitted by tissue components. Over the past three decades, a variety of techniques have been investigated with the aim of developing an in situ histopathology method that can rapidly and accurately identify tumor margins during surgical procedures. These techniques can be divided into two groups. One group encompasses techniques exploiting linear optical signals, and includes infrared and Raman microspectroscopy, and autofluorescence microscopy. The second group includes techniques based on nonlinear optical signals, including harmonic generation, coherent Raman scattering, and multiphoton autofluorescence microscopy. Some of these methods provide comparable information, while others are complementary. However, all of them have distinct advantages and disadvantages due to their inherent nature. The first part of the review provides an explanation of the underlying physics of the excitation mechanisms and a description of the instrumentation. It also covers endomicroscopy and data analysis, which are important for understanding the current limitations in implementing label-free techniques in clinical settings. The second part of the review describes the application of label-free microscopy imaging to improve oncological surgery with focus on brain tumors and selected gastrointestinal cancers, and provides a critical assessment of the current state of translation of these methods into clinical practice. Finally, the potential of confocal laser endomicroscopy for the acquisition of autofluorescence is discussed in the context of immediate clinical applications.

Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging

Kidney and renal pelvic cancer are a significant cause of cancer-related deaths, with the most common malignant kidney tumor being renal cell carcinoma (RCC). Chromophobe renal cell carcinoma is a rarer form of RCC that poses significant challenges to accurate diagnosis, as it shares many histologic features with Oncocytoma, a benign renal tumor. Biopsies for histopathological and immunohistochemical analysis have limitations in distinguishing chromophobe RCC from Oncocytoma. Syndromic cases may also have tumors with overlapping features. Techniques such as infrared (IR) spectroscopic imaging have shown promise as an alternative approach to tissue diagnostics. In this study, we propose a deep-learning-based framework for automating classification in kidney tumor tissue microarrays (TMAs) using an IR dataset. Feature selection algorithms reduce data dimensionality, followed by a deep learning classification approach. A classification accuracy of 91.3% was observed for validation data, even with the use of 13.6% of all wavelengths, thereby reducing training time by 21% compared to using the entire spectrum. Through the integration of scalable deep learning models coupled with feature selection, we have developed a classification pipeline with high predictive power, which could be integrated into a high-throughput real-time IR imaging system. This would create an advanced diagnostic tool for the detection and classification of renal tumors, namely chromophobe RCC and Oncocytoma. This may impact patient outcomes and treatment strategies.

Differentiation of Urothelial Carcinoma and Normal Bladder Tissues by Means of Fiber-Based ATR IR Spectroscopy

Surgical treatment is widely applied curative approach for bladder cancer. White light cystoscopy (WLC) is currently used for intraoperative diagnostics of malignant lesions but has relatively high false-negative rate. Here we represent an application of label free fiber-based attenuated total reflection infrared spectroscopy (ATR IR) for freshly resected human bladder tissue examination for 54 patients. Defined molecular spectral markers allow to identify normal and urothelial carcinoma tissues. While methods of statistical analysis (Hierarchical cluster analysis (HCA) and Principal component analysis (PCA)) used for spectral data treatment allow to discriminate tissue types with 91% sensitivity and 96-98% specificity. In the present study the described method was applied for tissue examination under ex vivo conditions. However, after method validation the equipment could be translated from laboratory studies to in situ or even in vivo studies in operating room.