A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors - Wu, Binlin; Mukherjee, Sushmita; Jain, Manu (SPIE, 2016)
Distinguishing chromophobe renal cell carcinoma (chRCC) from oncocytoma on hematoxylin and eosin images may be difficult and require time-consuming ancillary procedures. Multiphoton microscopy (MPM), an optical imaging modality, was used to rapidly generate sub-cellular histological resolution images from formalin-fixed unstained tissue sections from chRCC and oncocytoma. Tissues were excited using 780nm wavelength and emission signals (including second harmonic generation and autofluorescence) were collected in different channels between 390 nm and 650 nm. Granular structure in the cell cytoplasm was observed in both chRCC and oncocytoma. Quantitative morphometric analysis was conducted to distinguish chRCC and oncocytoma. To perform the analysis, cytoplasm and granules in tumor cells were segmented from the images. Their area and fluorescence intensity were found in different channels. Multiple features were measured to quantify the morphological and fluorescence properties. Linear support vector machine (SVM) was used for classification. Re-substitution validation, cross validation and receiver operating characteristic (ROC) curve were implemented to evaluate the efficacy of the SVM classifier. A wrapper feature algorithm was used to select the optimal features which provided the best predictive performance in separating the two tissue types (classes). Statistical measures such as sensitivity, specificity, accuracy and area under curve (AUC) of ROC were calculated to evaluate the efficacy of the classification. Over 80% accuracy was achieved as the predictive performance. This method, if validated on a larger and more diverse sample set, may serve as an automated rapid diagnostic tool to differentiate between chRCC and oncocytoma. An advantage of such automated methods are that they are free from investigator bias and variability. Copyright © 2016 SPIE.
Keywords: Artificial intelligence; Autofluorescence; Cancer diagnosis; Cells; Classification (of information); Computer aided diagnosis; Cytology; Diagnosis; Diseases; Fluorescence; Harmonic generation; Imaging techniques; Kidney cancer; Learning algorithms; Learning systems; Morphometric analysis; Multi-photon microscopy; Multiphoton microscopy; Nonlinear optics; Support vector machines; Tissue; Tumors; autofluorescence; cancer diagnosis; classification; dept.phy; imaging; kidney cancer; machine learning; morphometric analysis

Time reversal optical tomography: Detecting and locating tumors in an ex vivo model human breast - Wu, Binlin; Alrubaiee, M.; Gayen, S. K. (Optics and Laser Technology, 2016)
Time reversal optical tomography (TROT), a recently introduced diffuse optical imaging approach, is used to detect, locate, and obtain cross-section images of tumors inside a "model human breast." The model cancerous breast is assembled as a semi-cylindrical slab of uniform thickness using ex vivo human breast tissues with two pieces of tumors embedded in it. The experimental arrangement used a 750-nm light beam from a Ti:sapphire laser to illuminate an end face (source plane) of the sample in a multi-source probing scheme. A multi-detector signal acquisition scheme measured transmitted light intensity distribution on the other end face (detector plane). The perturbations in light intensity distribution in the detector plane were analyzed using TROT to obtain locations of the tumor pieces in three dimensions and estimate their cross sections. The estimated locations and dimensions of targets are in good agreement with the results of a corroborating magnetic resonance imaging experiment. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Biological tissues; Breast Cancer; Breast cancer; Histology; Image reconstruction; Infrared devices; Infrared imaging; Inverse image reconstruction; Light propagation; Light propagation in biological tissues; Location; Magnetic resonance imaging; Medical imaging; Near-infrared imaging; Optical tomography; Sapphire; Signal processing; Thermography (imaging); Time reversal; Tissue; Tumors; dept.phy