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Intraoperative detection of liver tumors aided by a fluorescence goggle system and multimodal imaging.

ABSTRACT: Real-time image guidance in the operating room is needed to improve instantaneous surgical decisions. Toward this goal, we utilized a new fluorescence goggle system and a near-infrared fluorescent dye approved for human use, indocyanine green, to demonstrate the feasibility of detecting liver tumors intraoperatively. The fluorescence goggle provided successful imaging of multifocal breast cancer metastases in mouse liver. Diffused tumor deposits as small as 0.8 mm in diameter were detected, which were not obvious without the fluorescence goggle. A combination of surface-weighted fluorescence imaging and deep tissue-sensitive ultrasound imaging allowed comprehensive image guidance with the fluorescence goggle system for tumor resection in a rabbit VX2 liver metastasis model. This multimodal detection and guided surgical intervention strategy using ultrasonic imaging and real-time intraoperative fluorescence guidance is a promising and innovative technology platform for improving surgical outcome of human patients with primary or metastatic liver cancer.

SUBMITTER: Liu Y

PROVIDER: S-EPMC3650133 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Intraoperative detection of liver tumors aided by a fluorescence goggle system and multimodal imaging.

Liu Yang Y Akers Walter J WJ Bauer Adam Q AQ Mondal Suman S Gullicksrud Kyle K Sudlow Gail P GP Culver Joseph P JP Achilefu Samuel S

The Analyst 20130401 8

Real-time image guidance in the operating room is needed to improve instantaneous surgical decisions. Toward this goal, we utilized a new fluorescence goggle system and a near-infrared fluorescent dye approved for human use, indocyanine green, to demonstrate the feasibility of detecting liver tumors intraoperatively. The fluorescence goggle provided successful imaging of multifocal breast cancer metastases in mouse liver. Diffused tumor deposits as small as 0.8 mm in diameter were detected, whic ...[more]

PMID: 23467534

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Project description:Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34-82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F[Formula: see text] score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of [Formula: see text], 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

Dataset Information

Intraoperative detection of liver tumors aided by a fluorescence goggle system and multimodal imaging.

Publications

Intraoperative detection of liver tumors aided by a fluorescence goggle system and multimodal imaging.

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