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Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer.
Shafiee, Shayan; Jagtap, Jaidip; Zayats, Mykhaylo; Epperlein, Jonathan; Banerjee, Anjishnu; Geurts, Aron; Flister, Michael; Zhuk, Sergiy; Joshi, Amit.
Afiliação
  • Shafiee S; Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI 53226, USA.
  • Jagtap J; Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
  • Zayats M; IBM Research Europe, D15 HN66 Dublin, Ireland.
  • Epperlein J; IBM Research Europe, D15 HN66 Dublin, Ireland.
  • Banerjee A; Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
  • Geurts A; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
  • Flister M; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
  • Zhuk S; IBM Research Europe, D15 HN66 Dublin, Ireland.
  • Joshi A; Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Cancers (Basel) ; 15(5)2023 Feb 25.
Article em En | MEDLINE | ID: mdl-36900252
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.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article