Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer

Abstract

Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.

Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art

“…1). Empirical evidence for this has been seen by several groups [63][64][65]. We ourselves have previously reported on in-vivo assessment of primary colorectal cancers following intravenous administration of ICG.…”

“…Delta like canonical notch ligand 4 (Dll4) affects angiogenesis within breast cancer and is a potential therapy target. ICG based NIR imaging has been shown to accurately predict DII4 levels with a sensitivity and specificity of > 90%, in a non-invasive manner via machine learning [64]. This concept has potential for stratifying patients for targeted therapies.…”

OSATS scoring confirms ICG enhancement of performance in laparoscopic radical gastrectomy: A post hoc analysis of a randomized controlled trial

“…The ICG fluorescence navigation technology correlates with the increase of the number of lymph node dissections, albeit with some additional economic costs. As a new surgical navigation technology, ICG NIR imaging technology has shown effective results in sentinel lymph node dissection and localization of tumors, such as breast cancer and nonsmall cell lung cancer 35 , 36 . In our study, the ICG group correlated with a significantly higher mean total number of lymph node dissections than did the non-ICG group, which was because ICG fluoresces at a longer wavelength and penetrates deeper into the lymphatic adipose tissue 37 .…”