High correlation of double Debye model parameters in skin cancer detection

© 2014 IEEE. The double Debye model can be used to capture the dielectric response of human skin in terahertz regime due to high water content in the tissue. The increased water proportion is widely considered as a biomarker of carcinogenesis, which gives rise of using this model in skin cancer detection. Therefore, the goal of this paper is to provide a specific analysis of the double Debye parameters in terms of non-melanoma skin cancer classification. Pearson correlation is applied to investigate the sensitivity of these parameters and their combinations to the variation in tumor percentage of skin samples. The most sensitive parameters are then assessed by using the receiver operating characteristic (ROC) plot to confirm their potential of classifying tumor from normal skin. Our positive outcomes support further steps to clinical application of terahertz imaging in skin cancer delineation

A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study

A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency

In vitro direct rhizogenesis from Gerbera jamesonii Bolus leaf

The present report describes an original protocol for in vitro direct induction of roots from leaf explants of gerbera for the first time. Since gerbera has immense potential as a premium cut-flower, the major attempts were made on in vitro mass propagation chiefly through in vitro multiple shoot proliferation or callus regeneration. Nevertheless, rhizogenesis could be impending an unattempted method with its yet-to-be known advantages. In our study, the optimum conditions for direct root induction from leaf explants were assessed employing tissue culture technique. Leaves were inoculated to MS medium containing no or variable auxin sources and concentrations namely, 2,4-dichlorophenoxyacetic acid, indole-3-acetic acid (IAA), indole-3-butyric acid or α-naphthaleneacetic acid for root induction. It was evident that the maximum root induction (with a frequency of 92.6 %) occurred on MS media fortified with 1.5 mg l−1 IAA, wherein root induction was observed as early as 11 days of culture and an average of ~19 roots with ~13 mm length was obtained from 4 cm2 leaf segment after 45 days of culture. Stereo microscopic observation revealed the induction of roots and gradual developmental stages of rhizogenesis. The efficiency of direct root induction without any interim growth stages (such as, callus or shoots) in our study offers a reproducible system that could provide a model protocol for more comprehensive developmental studies on root growth

Breast Cancer classification using extracted parameters from a terahertz dielectric model of human breast tissue

© 2015 IEEE. Our previous study proposed a dielectric model for human breast tissue and provided initial analysis of classification potential of the eight model parameters and their multiparameter combinations with the support vector machine (SVM). A combination of three model parameters could achieve a leave-one-out cross validation accuracy of 93.2%. However, the SVM approach fails to exploit the combinations of more than three model parameters for classification improvement. Thus, the Bayesian neural network (BNN) method is employed to overcome this problem based on its advantages of handling our small data and high complexity of the multiparamter combinations. The BNN successfully classifies the data using the combinations of four model parameters with an accuracy, estimated by leave-one-out cross validation, of 97.3%. Overall performance assessed by leaveone- out and repeated random-subsampling cross validations for all examined combinations is also remarkably improved by BNN. The results indicate the advance of BNN as compared to SVM in utilising the model parameters for detecting tumour from normal breast tissue

The Potential of the Double Debye Parameters to Discriminate between Basal Cell Carcinoma and Normal Skin

� 2015 IEEE. The potential of terahertz imaging for improving the efficiency of Mohs's micrographic surgery in terms of tumor margin detection was previously studied. Thanks to high water content of human skin, its dielectric response to terahertz radiation can be described by the double Debye model which uses five parameters to fit experimental data. Skin tumors typically have a higher water content than normal tissues do, and this should be apparent in the parameters. The goal of this paper is to apply statistical methods to these parameters to test their power to differentiate skin cancer from normal tissue. Based on the prediction accuracy estimated using a cross-validation method, we found the best classifier was the static permittivity at low frequency (εs). By combining the most relevant parameters, we obtained a classification accuracy of 95.7%, confirming the classification capability of the parameters, thereby supporting their application to improve terahertz imaging for the purpose of skin cancer delineation

A dielectric model of human breast tissue in terahertz regime

© 2014 IEEE. The double Debye model has been used to understand the dielectric response of different types of biological tissues at terahertz (THz) frequencies but fails in accurately simulating human breast tissue. This leads to limited knowledge about the structure, dynamics, and macroscopic behavior of breast tissue, and hence, constrains the potential of THz imaging in breast cancer detection. The first goal of this paper is to propose a new dielectric model capable of mimicking the spectra of human breast tissue's complex permittivity in THz regime. Namely, a non-Debye relaxation model is combined with a single Debye model to produce a mixture model of human breast tissue. A sampling gradient algorithm of nonsmooth optimization is applied to locate the optimal fitting solution. Samples of healthy breast tissue and breast tumor are used in the simulation to evaluate the effectiveness of the proposed model. Our simulation demonstrates exceptional fitting quality in all cases. The second goal is to confirm the potential of using the parameters of the proposed dielectric model to distinguish breast tumor from healthy breast tissue, especially fibrous tissue. Statistical measures are employed to analyze the discrimination capability of the model parameters while support vector machines are applied to assess the possibility of using the combinations of these parameters for higher classification accuracy. The obtained analysis confirms the classification potential of these features