Purpose: To compare the estimate of normalized glandular dose in mammography and breast CT imaging obtained using the actual glandular tissue distribution in the breast to that obtained using the homogeneous tissue mixture approximation. Methods: Twenty volumetric images of patient breasts were acquired with a dedicated breast CT prototype system and the voxels in the breast CT images were automatically classified into skin, adipose, and glandular tissue. The breasts in the classified images underwent simulated mechanical compression to mimic the conditions present during mammographic acquisition. The compressed thickness for each breast was set to that achieved during each patient's last screening cranio‐caudal (CC) acquisition. The volumetric glandular density of each breast was computed using both the compressed and uncompressed classified images, and additional images were created in which all voxels representing adipose and glandular tissue were replaced by a homogeneous mixture of these two tissues in a proportion corresponding to each breast's volumetric glandular density. All four breast images (compressed and uncompressed; heterogeneous and homogeneous tissue) were input into Monte Carlo simulations to estimate the normalized glandular dose during mammography (compressed breasts) and dedicated breast CT (uncompressed breasts). For the mammography simulations the x‐ray spectra used was that used during each patient's last screening CC acquisition. For the breast CT simulations, two x‐ray spectra were used, corresponding to the x‐ray spectra with the lowest and highest energies currently being used in dedicated breast CT prototype systems under clinical investigation. The resulting normalized glandular dose for the heterogeneous and homogeneous versions of each breast for each modality was compared. Results: For mammography, the normalized glandular dose based on the homogeneous tissue approximation was, on average, 27% higher than that estimated using the true heterogeneous glandular tissue distribution (Wilcoxon Signed Rank Testp = 0.00046). For dedicated breast CT, the overestimation of normalized glandular dose was, on average, 8% (49 kVp spectrum, p = 0.00045) and 4% (80 kVp spectrum, p = 0.000089). Only two cases in mammography and two cases in dedicated breast CT with a tube voltage of 49 kVp resulted in lower dose estimates for the homogeneous tissue approximation compared to the heterogeneous tissue distribution. Conclusions: The normalized glandular dose based on the homogeneous tissue mixture approximation results in a significant overestimation of dose to the imaged breast. This overestimation impacts the use of dose estimates in absolute terms, such as for risk estimates, and may impact some comparative studies, such as when modalities or techniques with different x‐ray energies are used. The error introduced by the homogeneous tissue mixture approximation in higher energy x‐ray modalities, such as dedicated breast CT, although statistically significant, may not be of clinical concern. Further...

Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

Photodynamic therapy (PDT) is a highly specific anticancer treatment modality for various cancers, particularly for recurrent cancers that no longer respond to conventional anticancer therapies. PDT has been under development for decades, but light-associated toxicity limits its clinical applications. To reduce the toxicity of PDT, we recently developed a targeted nanoparticle (NP) platform that combines a second-generation PDT drug, Pc 4, with a cancer targeting ligand, and iron oxide (IO) NPs. Carboxyl functionalized IO NPs were first conjugated with a fibronectin-mimetic peptide (Fmp), which binds integrin β1. Then the PDT drug Pc 4 was successfully encapsulated into the ligand-conjugated IO NPs to generate Fmp-IO-Pc 4. Our study indicated that both nontargeted IO-Pc 4 and targeted Fmp-IO-Pc 4 NPs accumulated in xenograft tumors with higher concentrations than nonformulated Pc 4. As expected, both IO-Pc 4 and Fmp-IO-Pc 4 reduced the size of HNSCC xenograft tumors more effectively than free Pc 4. Using a 10-fold lower dose of Pc 4 than that reported in the literature, the targeted Fmp-IO-Pc 4 NPs demonstrated significantly greater inhibition of tumor growth than nontargeted IO-Pc 4 NPs. These results suggest that the delivery of a PDT agent Pc 4 by IO NPs can enhance treatment efficacy and reduce PDT drug dose. The targeted IO-Pc 4 NPs have great potential to serve as both a magnetic resonance imaging (MRI) agent and PDT drug in the clinic.

Abstract. Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450-to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.