Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer

Deep learning implementation for extrahepatic bile duct detection during indocyanine green fluorescence-guided laparoscopic cholecystectomy: pilot study

BACKGROUND

A real-time deep learning system was developed to identify the extrahepatic bile ducts during indocyanine green fluorescence-guided laparoscopic cholecystectomy.

METHODS

Two expert surgeons annotated surgical videos from 113 patients and six class structures. YOLOv7, a real-time object detection model that enhances speed and accuracy in identifying and localizing objects within images, was trained for structures identification. To evaluate the model's performance, single-frame and short video clip validations were used. The primary outcomes were average precision and mean average precision in single-frame validation. Secondary outcomes were accuracy and other metrics in short video clip validations. An intraoperative prototype was developed for the verification experiments.

RESULTS

A total of 3993 images were extracted to train the YOLOv7 model. In single-frame validation, all classes' mean average precision was 0.846, and average precision for the common bile duct and cystic duct was 0.864 and 0.698 respectively. The model was trained to detect six different classes of objects and exhibited the best overall performance, with an accuracy of 94.39% for the common bile duct and 84.97% for the cystic duct in video clip validation.

CONCLUSION

This model could potentially assist surgeons in identifying the critical landmarks during laparoscopic cholecystectomy, thereby minimizing the risk of bile duct injuries.

Fluorescence-guided tumor visualization of colorectal cancer using tumor-initiating probe yellow in preclinical models

Fluorescence-guided surgery has emerged as an innovative technique with promising applications in the treatment of various tumors, including colon cancer. Tumor-initiating probe yellow (TiY) has been discovered for identifying tumorigenic cells by unbiased phenotypic screening with thousands of diversity-oriented fluorescence library (DOFL) compounds in a patient-derived lung cancer cell model. This study demonstrated the clinical feasibility of TiY for tumor-specific fluorescence imaging in the tissues of patients with colorectal cancer (CRC). To evaluate the efficacy of TiY in tumor imaging, surgical specimens were obtained, consisting of 36 tissues from 18 patients with CRC, for ex vivo molecular fluorescence imaging, histology, and immunohistochemistry. Orthotopic and chemically induced CRC mice models were administered TiY topically, and distinct tumor lesions were observed in 10 min by real-time fluorescence colonoscopy and ex vivo imaging. In a hepatic metastasis mouse model using splenic injection, TiY accumulation was detected in metastatic liver lesions through fluorescence imaging. Correlation analysis between TiY intensity and protein expression, assessed via immunohistochemistry and Western blotting, revealed a positive correlation between TiY and vimentin and Zeb1, which are known as epithelial-mesenchymal transition (EMT) markers of cancers. A comparative analysis of TiY with other FDA-approved fluorescence probes such as ICG revealed greater quantitative differences in TiY fluorescence intensity between tumor and normal tissues than those observed with ICG. Altogether, these results demonstrated that TiY has a strong potential for visualizing CRC by fluorescence imaging in various preclinical models, which can be further translated for clinical use such as fluorescence-guided surgery. Furthermore, our data indicate that TiY is preferentially uptaken by cells with EMT induction and progression, and overexpressing vimentin and Zeb1 in patients with CRC.

Radiomics in colorectal cancer

Colorectal cancer (CRC) is a global health challenge with high morbidity and mortality. Radiomics, an emerging field, utilizes quantitative imaging features extracted from medical images for CRC diagnosis, staging, treatment response assessment, and prognostication. This review highlights the potential of radiomics for personalized CRC management. Radiomics enables noninvasive tumor characterization, aiding in early detection and accurate diagnosis, and it can be used to predict tumor stage, lymph node involvement, and prognosis. Furthermore, radiomics guides personalized therapies by assessing the treatment response and identifying patients who could benefit. Challenges include standardizing imaging protocols and analysis techniques. Robust validation frameworks and user‐friendly software are needed for the integration of radiomics into clinical practice. Despite challenges, radiomics offers valuable insights into tumor biology, treatment response, and prognosis in CRC. Overcoming technical and clinical hurdles will unlock its full potential in CRC management.