Development of a system to treat and online monitor photodynamic therapy of skin cancer using PpIX near-infrared fluorescence

Near-infrared phototheranostics of tumors with protoporphyrin IX and chlorin e6 photosensitizers

BACKGROUND

The study aims to develop a method for phototheranostics of tumors in the near-infrared (NIR) range using protoporphyrin IX (PpIX) and chlorin e6 (Ce6) photosensitizers (PSs) MATERIALS AND METHODS: Phototheranostics includes spectral fluorescence diagnostics of PS distribution and photodynamic therapy (PDT) using a single laser in the red spectral range. PpIX and Ce6 fluorescence were registered in the NIR range. PpIX and Ce6 photobleaching was determined during PDT by the change in PS fluorescence. NIR phototheranostics with PpIX and Ce6 were performed on optical phantoms and tumors of patients with oral leukoplakia and basal cell carcinoma.

RESULTS

NIR spectral fluorescence diagnostics of optical phantoms with PpIX or Ce6 is possible when fluorescence is excited by 635 or 660 nm lasers. Fluorescence intensity of PpIX and Ce6 was measured in the range of 725-780 nm. The highest values of signal-to-noise in the case of phantoms with PpIX were observed at λ_exc=635 nm, and for phantoms with Ce6 at λ_exc=660 nm. NIR phototheranostics provides the detection of tumor tissues with PpIX or Ce6 accumulation. The PSs photobleaching in the tumor during PDT occurs according to a bi-exponential law.

CONCLUSION

Phototheranostics of tumors containing PpIX or Ce6 allows fluorescent monitoring of PS distribution in the NIR range and measuring PSs photobleaching during light exposure that provides personalization of the photodynamic exposure duration to deeper tumors. Using a single laser for fluorescence diagnostics and PDT reduces patient treatment time.

HOSMI-LBP-BASED FEATURE EXTRACTION FOR MELANOMA DETECTION USING HYBRID DEEP LEARNING MODELS

“Melanoma is a serious form of skin cancer that begins in cells known as melanocytes and more dangerous due to its spreading ability to other organs more rapidly if it is not treated at an early stage”. This paper aims to propose a Melanoma detection methodology that includes four major phases: “(i) pre-processing (ii) segmentation (iii) the proposed feature extraction and (iv) classification”. Initially, pre-processing is performed, where the input image is subjected to processing like resizing and edge smoothening. Subsequently, segmentation is carried out by the Otsu thresholding process. In the feature extraction phase, the proposed Higher-Order Standardized Moment Induced-Local Binary Patterns (HOSMI-LBP)-based features are extracted. These features are then subjected to a classification process for classifying the disease. For this, it is planned to use a hybrid classification framework, where the Convolutional Neural Network (CNN) and the Neural Network (NN) are deployed. Two-phase of classification gets processed: the extracted features are subjected to NN; the input image is directly classified using an optimized CNN framework. Finally, the classified outputs from NN and optimized CNN are averaged and the final output is considered as detected output. Particularly, the weight and initial rate of CNN is optimized using the proposed algorithm known as the Sea Lion Integrated Grey Wolf Algorithm (SLI-GWO) method that hybrid the concepts of both Sea Lion Optimization (SLnO) and Grey Wolf Optimization (GWO) algorithm. At last, the proposed work performance is computed with traditional systems in terms of various measures.

The role of UVA radiation in ketoprofen-mediated BRAF-mutant amelanotic melanoma cells death - A study at the cellular and molecular level

Malignant melanoma is the cause of 80% of deaths in skin cancer patients. Treatment of melanoma in the 4th stage of clinical advancement, in which inoperable metastasis occur, does not provide sufficient effects. Ketoprofen has phototoxic properties and it can be used as a new treatment option for skin cancers as a part of photochemotherapy. The present study was designed to investigate whether ketoprofen in combination with UVA induces cytotoxic, anti-proliferative and pro-apoptotic effects on melanoma cells. It was stated that co-treatment with 1.0 mM ketoprofen and UVA irradiation disturbed homeostasis of C32 melanoma cells by lowering its vitality (decrease of GSH level). Contrary to C32 cells, melanocytes showed low sensitivity to ketoprofen and UVA radiation, pointing selectivity in the mode of action towards melanoma cells. Co-treatment with ketoprofen and UVA irradiation has cytotoxic and anti-proliferative and pro-apoptotic effect on C32. The co-treatment triggered the DNA fragmentation and changed the cell cycle in C32 cells. In conclusion, it could be stated that local application of ketoprofen in combination with UVA irradiation may be used to support the treatment of melanoma and creates the possibility of reducing the risk of cancer recurrence and metastasis.

A novel melanoma detection model: adapted K-means clustering-based segmentation process

Objectives

The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids.

Methods

This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection.

Results

Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively.

Conclusions

From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.

Photodynamic therapy, priming and optical imaging: Potential co-conspirators in treatment design and optimization - a Thomas Dougherty Award for Excellence in PDT paper

Photodynamic therapy is a photochemistry-based approach, approved for the treatment of several malignant and non-malignant pathologies. It relies on the use of a non-toxic, light activatable chemical, photosensitizer, which preferentially accumulates in tissues/cells and, upon irradiation with the appropriate wavelength of light, confers cytotoxicity by generation of reactive molecular species. The preferential accumulation however is not universal and, depending on the anatomical site, the ratio of tumor to normal tissue may be reversed in favor of normal tissue. Under such circumstances, control of the volume of light illumination provides a second handle of selectivity. Singlet oxygen is the putative favorite reactive molecular species although other entities such as nitric oxide have been credibly implicated. Typically, most photosensitizers in current clinical use have a finite quantum yield of fluorescence which is exploited for surgery guidance and can also be incorporated for monitoring and treatment design. In addition, the photodynamic process alters the cellular, stromal, and/or vascular microenvironment transiently in a process termed photodynamic priming, making it more receptive to subsequent additional therapies including chemo- and immunotherapy. Thus, photodynamic priming may be considered as an enabling technology for the more commonly used frontline treatments. Recently, there has been an increase in the exploitation of the theranostic potential of photodynamic therapy in different preclinical and clinical settings with the use of new photosensitizer formulations and combinatorial therapeutic options. The emergence of nanomedicine has further added to the repertoire of photodynamic therapy's potential and the convergence and co-evolution of these two exciting tools is expected to push the barriers of smart therapies, where such optical approaches might have a special niche. This review provides a perspective on current status of photodynamic therapy in anti-cancer and anti-microbial therapies and it suggests how evolving technologies combined with photochemically-initiated molecular processes may be exploited to become co-conspirators in optimization of treatment outcomes. We also project, at least for the short term, the direction that this modality may be taking in the near future.