Imaging spontaneous MMTVneu transgenic murine mammary tumors: targeting metabolic activity versus genetic products.

INTRODUCTION: Despite the great strides made in imaging breast cancer (BC) in humans, the current imaging modalities miss up to 30% of BC, do not distinguish malignant lesions from benign ones, and require histologic examinations for which invasive biopsy must be performed. Annually in the United States, approximately 5.6 million biopsies find benign lesions. More than 50% of human BCs overexpress cyclin D1, and all BCs exhibit VPAC1 oncogene products. Together, these gene products may provide an excellent biomarker for the early and accurate detection of BC. We have evaluated 4 biologically active peptide analogs that have high affinity for VPAC1. The transgenic MMTVneu mice spontaneously develop BC and metastatic lesions that overexpress cyclin D1 and VPAC1 biomarkers. The MMTVneu mouse, therefore, provides an excellent animal model that mimics the pathogenesis of human BC. The objective of this investigation was to determine the ability of 1 of the peptide analogs, (64)Cu-TP3805, to detect BC in MMTVneu mice using (18)F-FDG as a gold standard. METHODS: The transgenic MMTVneu mouse colony was maintained. Offspring were screened for transgenic status by reverse transcriptase polymerase chain reaction (RT-PCR). Nine mice with visible, palpable, or unknown metastatic lesions were entered into the protocol. (18)F-FDG (6,475 +/- 1,628 kBq [175 +/- 44 microCi]) PET served as a control, followed by a CT scan and 24-48 h later by PET with (64)Cu-TP3805 (4,588 +/- 962 kBq [124 +/- 26 microCi]). RT-PCR on excised tumors determined VPAC1 expression, and histology ascertained the pathology. RESULTS: Ten tumors were detected by PET. Four tumors were detected both by (18)F-FDG and by (64)Cu-TP3805. Additionally, 4 tumors were imaged with (64)Cu-TP3805 only. These 8 tumors overexpressed VPAC1 receptors and were malignant by histology. The 2 remaining tumors were visualized with (18)F-FDG only. These tumors did not express the VPAC1 oncogene product and had benign histology. The standard uptake value ranged from 3.1 to 18.3 for (64)Cu-TP3805 and 0.9 to 1.4 for (18)F-FDG. CONCLUSION: (64)Cu-TP3805 identified all malignant lesions unequivocally that overexpressed the VPAC1 oncogene surface product. The 2 benign tumors that did not express the VPAC1 receptor were not imaged. (64)Cu-TP3805 promises to have the potential for the early and accurate imaging of primary and metastatic BC

Targeting apoptosis for optical imaging of infection

PURPOSE: Infection is ubiquitous and a major cause of morbidity and mortality. The most reliable method for localizing infection requires radiolabeling autologous white blood cells ex vivo. A compound that can be injected directly into a patient and can selectively image infectious foci will eliminate the drawbacks. The resolution of infection is associated with neutrophil apoptosis and necrosis presenting phosphatidylserine (PS) on the neutrophil outer leaflet. Targeting PS with intravenous administration of a PS-specific, near-infrared (NIR) fluorophore will permit localization of infectious foci by optical imaging. METHODS: Bacterial infection and sterile inflammation were induced in separate groups (n = 5) of mice. PS was targeted with a NIR fluorophore, PSVue(®)794 (2.7 pmol). Imaging was performed (ex = 730 nm, em = 830 nm) using Kodak Multispectral FX-Pro system. The contralateral normal thigh served as an individualized control. Confocal microscopy of normal and apoptotic neutrophils and bacteria confirmed PS specificity. RESULTS: Lesions, with a 10-s image acquisition, were unequivocally visible at 5 min post-injection. At 3 h post-injection, the lesion to background intensity ratios in the foci of infection (6.6 ± 0.2) were greater than those in inflammation (3.2 ± 0.5). Image fusions confirmed anatomical locations of the lesions. Confocal microscopy determined the fluorophore specificity for PS. CONCLUSIONS: Targeting PS presented on the outer leaflet of apoptotic or necrotic neutrophils as well as gram-positive microorganism with PS-specific NIR fluorophore provides a sensitive means of imaging infection. Literature indicates that NIR fluorophores can be detected 7-14 cm deep in tissue. This observation together with the excellent results and the continued development of versatile imaging devices could make optical imaging a simple, specific, and rapid modality for imaging infection

Three Dimensional Projection Environment for Molecular Design and Surgical Simulation

Poster presented at Medicine Meets Virtual Reality conference February 8-12, 2011 in Newport Beach, California. Conclusions: Turning 2D CT/PET slices into 3D objects assists in understanding the topology surrounding tumor masses. Incorporating the visual and physical characteristics of a patient’s anatomy will provide surgeons with an informative pre-operative tool to plan and practice the operation before the first incision. Including haptic feedback provides a familiar \u27feel\u27 to surgeons as they palpate the target organ, trying to locate the tumor and determine how large a margin of resection will be needed. The development of genetic PET imaging and contrast CT into a combined visual will further improve the surgeons’ knowledge by more accurately pinpointing malignant tissue and any hidden blood vessels

Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman’s rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets

Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization

Background and purpose: Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulate small and multi-institutional studies and study the responsiveness of the models to feature selection, machine learning algorithms, centre-effect harmonization and increased dataset sizes. Materials and methods: 562 patients histologically confirmed and treated for locally advanced head-and-neck cancer (LA-HNC) from two public and two private datasets; one private dataset exclusively reserved for validation. Clinical contours of primary tumours were not recontoured and were used for Pyradiomics based feature extraction. ComBat harmonization was applied, and LASSO-Logistic Regression (LR) and Support Vector Machine (SVM) models were built. 95% confidence interval (CI) of 1000 bootstrapped area-under-the-Receiver-operating-curves (AUC) provided predictive performance. Responsiveness of the models’ performance to the choice of feature selection methods, ComBat harmonization, machine learning classifier, single and pooled data was evaluated. Results: LASSO and SelectKBest selected 14 and 16 features, respectively; three were overlapping. Without ComBat, the LR and SVM models for three institutional data showed AUCs (CI) of 0.513 (0.481–0.559) and 0.632 (0.586–0.665), respectively. Performances following ComBat revealed AUCs of 0.559 (0.536–0.590) and 0.662 (0.606–0.690), respectively. Compared to single cohort AUCs (0.562–0.629), SVM models from pooled data performed significantly better at AUC = 0.680. Conclusions: Multi-institutional retrospective data accentuates the existing variabilities that affect radiomics. Carefully designed prospective, multi-institutional studies and data sharing are necessary for clinically relevant head-and-neck cancer prognostication models