Background. Hyperspectral imaging (HSI) is a relatively new method used in image-26 guided and precision surgery, which has shown promising results for characterization 27 of tissues and assessment of physiologic tissue parameters. Previous methods used 28 for analysis of preconditioning concepts in patients and animal models have shown 29 several limitations of application. The aim of this study was to evaluate HSI for the 30 measurement of ischemic conditioning effects during esophagectomy. 31 Methods. Intraoperative hyperspectral images of the gastric tube through the mini-32 thoracotomy were recorded from n=22 patients, 14 of whom underwent laparoscopic 33 gastrolysis and ischemic conditioning of the stomach with two-step transthoracic 34 esophagectomy and gastric pull-up with intrathoracic anastomosis after 3-7 days. 35 The tip of the gastric tube (later esophago-gastric anastomosis) was measured with 36 HSI. Analysis software provides a RGB image and 4 false color images representing 37 physiologic parameters of the recorded tissue area intraoperatively. These parameters contain tissue oxygenation (StO2), perfusion-(NIR Perfusion Index), 1 organ hemoglobin-(OHI) and tissue water index (TWI). 2 Results. Intraoperative HSI of the gastric conduit was possible in all patients and did 3 not prolong the regular operative procedure due to its quick applicability. In particular, 4 the tissue oxygenation of the gastric conduit was significantly higher in patients who 5 underwent ischemic conditioning (StO2Precond. = 78%; StO2NoPrecond. = 66%; p = 0.03). Conclusions. HSI is suitable for contact-free, non-invasive and intraoperative 7 evaluation of physiological tissue parameters within gastric conduits. Therefore HSI is 8 a valuable method for evaluating ischemic conditioning effects and may contribute to 9 reduce anastomotic complications. Additional studies are needed to establish normal 10 values and thresholds of the presented parameters for the gastric conduit 11 anastomotic site.

Significance: Hyperspectral imaging (HSI) can support intraoperative perfusion assessment, the identification of tissue structures, and the detection of cancerous lesions. The practical use of HSI for minimal-invasive surgery is currently limited, for example, due to long acquisition times, missing video, or large setups. Aim: An HSI laparoscope is described and evaluated to address the requirements for clinical use and high-resolution spectral imaging. Approach: Reflectance measurements with reference objects and resected human tissue from 500 to 1000 nm are performed to show the consistency with an approved medical HSI device for open surgery. Varying object distances are investigated, and the signal-to-noise ratio (SNR) is determined for different light sources. Results: The handheld design enables real-time processing and visualization of HSI data during acquisition within 4.6 s. A color video is provided simultaneously and can be augmented with spectral information from push-broom imaging. The reflectance data from the HSI system for open surgery at 50 cm and the HSI laparoscope are consistent for object distances up to 10 cm. A standard rigid laparoscope in combination with a customized LED light source resulted in a mean SNR of 30 to 43 dB (500 to 950 nm). Conclusions: Compact and rapid HSI with a high spatial-and spectral-resolution is feasible in clinical practice. Our work may support future studies on minimally invasive HSI to reduce intraand postoperative complications.

The HSI method provides a non-contact, non-invasive, intraoperative imaging procedure without the use of a contrast medium, which enables a real-time analysis of physiological anastomotic parameters, which may contribute to determine the "ideal" anastomotic region. In light of this, the establishment of this methodology in the field of visceral surgery, enabling the generation of normal or cut off values for different gastrointestinal anastomotic types, is an obvious necessity.

There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models.

No abstract