Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections

Abstract

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Figure 1

QCL based IR imaging of whole slice colorectal cancer tissue thin sections. Thin tissue section images of colorectal cancer marked by H&E in A and label-free index color images in B and C taken by QCL based IR microscope and analyzed by an improved new classifier. (A) H&E stained image of colorectal cancer thin section, showing morphological structures of diseased colon wall. Cancerous regions are prominently illustrated in the upper part by heavy purple hematoxylin staining patters. Below is the muscularis propria surrounded by connective tissue. Infiltrating inflammatory cells are distributed over the whole tissue sample. (B) index color image of the first RF classifier with the following tissue classes: red: pathological region; yellow: infiltrating inflammatory cells; white: muscle; green: connective tissue; cyan: crypts; blue: lumen. The morphological architecture is well represented by the index colors and agrees nicely with the annotation of two clinical pathologists. (C) index color image showing the tumor class only identified in the second classifier. The red pixels represent the cancerous region of the sample matching the annotation by the clinical pathologists.

Figure 2

Detailed tissue classification with IR imaging in 100 seconds. Colorectal cancer tissue annotation provided by QCL based imaging: Cancerous (A) and tumor-free (B) tissue sections stained with H&E (top row), QCL based classification, showing the first RF (second row), the second RF (third row), and the overlay with the tumor class (A) and the infiltrating inflammatory cells (B) of the second RF analysis (fourth row). The QCL imaging tissue classification takes 100 seconds for each sample shown here. (A) The analysis of cancerous tissue sections, showing the tumor cells invading the connective tissue. second row: red, pathological regions; yellow, infiltrating inflammatory cells; white, muscularis mucosae and muscularis propria; green, connective tissue. Cell debris and blood are sorted out and presented in the background color black. All regions marked in red using the first level RF were further analyzed by the second level RF. third row: red, tumor cells; magenta, necrotic regions; yellow, infiltrating inflammatory cells. The inflammatory cells originate from the first level RF. The overlay of tumor class (red) and HE underlines the high spatial accuracy of detection (bottom row). (B) The analysis of tumor-free tissue sections. second row: cyan, healthy crypts; blue, lumen; white, thin muscular layer between the crypts and the submucosal connective tissue; green, connective tissue. The representative pathological, second level RF analysis is presented in the third row image: yellow, infiltrating inflammatory cells; magenta, necrosis; red, tumor cells. No tumor cells can be detected here. Following, the HE overlay is shown for the infiltrating inflammatory cells.

Figure 3

Transferability of the label-free tissue classification between different instruments. Two consecutive measurements of the same tissue section performed using two Spero QT microscopes. (A) Tissue section analysis using the first QCL based instrument. (B) Tissue section analysis using the second QCL based instrument: red, pathological regions; yellow, inflammatory cells; green, connective tissue; blue, lumen; cyan, crypts; white, musculature. (C) and (D) second RF for tumor detection with the first and the second QCL based instrument: red, tumor; yellow, infiltrating inflammatory cells; magenta, necrosis; green, inflammation. Only subtle are visible. Overall the detection matches very well.

Figure 4

Suggested diagnostic workflow. The overview image was taken at amid I (1656 cm⁻¹), showing tissue morphological characteristics. Upper right, a large lymph follicle; center, glandular tumor lesion; yellow rectangle, region of interest. Bottom, single field analyses. Left, H&E staining; middle, first-level RF analysis; red, pathological regions; yellow, the invading inflammatory cells; white, the surrounding muscles; green, connective tissue. Right, second level RF analysis. All regions marked red using the first level RF were further analyzed by the second level RF of the selected, single-field, region: red, tumor cells; magenta, necrotic regions; yellow, infiltrating inflammatory cells.