Assessment of Effectiveness and Adverse Effect of New Combination Chemotherapy (irinotecan, cisplatin, and dexamethasone) in Relapse and Refractory Hodgkin Lymphoma

Background: Chemotherapy with adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD regimen) cannot cure all patients with Hodgkin lymphoma. In this study, we evaluated the efficacy and adverse effect of a new regimen consist irinotecan, cisplatin, and dexamethasone (ICD) in relapsed and refractory Hodgkin lymphoma as the second to fifth line of treatment. Materials and Methods: We performed a retrospective study in 26 relapsed or refractory patients with Hodgkin lymphoma receiving at least the first-line chemotherapy regimen (ABVD) and (ICD) as salvage therapy in Thaleghany Hospital from 2012 to 2018. This regimen consisted of irinotecan 65mg/m2 D1, D8, cisplatin 30mg/m2 D1, D8, and dexamethasone 40mg D1, 2, 8, and 9 was administered every 3 weeks for 6 cycles.  Treatment was discontinued in cases of disease progression or severe toxicity. Response to treatment was evaluated after two cycles. Patients with complete and partial remission were candidate high dose chemotherapy and autologous stem cell transplantation. Twenty-four patients were enrolled in the study. The mean age of 22 patients was 31.5 (19-67) years. Seven patients (29.1%) were in the first recurrence, and 17 (70.8%) were in the second or subsequent recurrence. Results: According to this study, three patients (12.5%) had complete response, 13 (45%) had partial response, four (16.6%) had stable disease, and four (16.6%) had progressive disease. Nine patients (37.5%) received high-dose chemotherapy and autologous stem cell support after ICD regimen. None of the cycles of chemotherapy were delayed due to treatment-related adverse event. Overall survival after six months in all patients was 91%, and mortality rate was 8.3% at the end of the study. Conclusion: The goal of salvage chemotherapy in relapsed or refractory Hodgkin Lymphoma is achieving CR or PR preparation patients for stabilization with BMT. Thus, we recommend ICD as one of the most effective protocols with overall response rate of 66% in this population.  

Image steganalysis based on statistical moments of wavelet subband histogram of images with least significant bit planes

This paper proposed a new image Steganalysis scheme based on statistical moments of histogram of multi-level wavelet subbands in frequency domain. These wavelet subbands derived from an image that has some of least significant bits of the grey level test image and some of its most significant bit planes are removed. Then we decompose the image using threelevel Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LL0 subband).The Fourier transform of each subband histogram, is calculated. The first three statistical moments of each subband histogram are selected to form a 39-dimensional feature vector for Steganalysis. Support Vector Machines (SVM) classifier is then used to discriminate between stego-images and innocent images. We experiment our proposed scheme on LSB, Cox and QIM data hiding method. Experimental results show that the proposed method improves the detection rate especially for LSB steganography

Steganalysis of LSB-matching steganography by removing most significant bit planes

This paper proposed a new Steganalysis scheme of LSB-Matching steganography based on statistical moments of the DFT of histogram of multi-level wavelet subbands. Before deriving these wavelet subbands a pre-processing apply to images under the test. The pre-processing contains removing some most significant bit planes. Then we decompose the image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LL0 subband).The Fourier transform of each subband histogram, is calculated. Then it is divided into low and high frequency bands. The first three statistical moments of each band are selected to form a 78- dimensional feature vector for Steganalysis. Support Vector Machines (SVM) classifier is then used to discriminate between stego-images and innocent images

Detection of LSB±1 steganography based on co-occurrence matrix and bit plane clipping

Spatial LSB+/-1 steganography changes smooth characteristics between adjoining pixels of the raw image. We present a novel steganalysis method for LSB+/-1 steganography based on feature vectors derived from the co-occurrence matrix in the spatial domain. We investigate how LSB+/-1 steganography affects the bit planes of an image and show that it changes more least significant bit (LSB) planes of it. The co-occurrence matrix is derived from an image in which some of its most significant bit planes are clipped. By this preprocessing, in addition to reducing the dimensions of the feature vector, the effects of embedding were also preserved. We compute the co-occurrence matrix in different directions and with different dependency and use the elements of the resulting co-occurrence matrix as features. This method is sensitive to the data embedding process. We use a Fisher linear discrimination (FLD) classifier and test our algorithm on different databases and embedding rates. We compare our scheme with the current LSB+/-1 steganalysis methods. It is shown that the proposed scheme outperforms the state-of-the-art methods in detecting the LSB+/-1 steganographic method for grayscale images

LSB data hiding detection based on gray level co-occurrence matrix (GLCM)

In this paper we present a novel steganalysis method with feature vectors derived from gray level cooccurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. We consider several combinations of diagonal elements of GLCM as features and use SVM for classification. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking the LSB steganographic schemes applied to spatial domain. Our results also show that there are different between these features for stego and non-stego images and these features are convenient for steganalysis. With randomly selected 900 images for training and the remaining 900 images for testing, the proposed steganalysis system can achieve a correct classification rate of 98.1% for LSB (0.1 bpp) and 81.1 % for LSB Matching algorithm. For combination of algorithms we reach to 95.6% correct detection rate

Steganalysis of LSB matching based on co-occurrence matrix and removing most significat bit planes

In this paper we present a novel LSB matching steganalysis method based on feature vectors derived from co-occurrence matrix in spatial domain, which is sensitive to data embedding process. This matrix is derived from an image that some of its most significant bit planes are removed. By this preprocessing in addition to decrease the size of feature vector also preserve effects of embedding. We investigate how LSB matching embedding effect more least significant bits and obtain better case for steganalysis. We use SVM for classification and our experimental results have demonstrated that the proposed scheme can increase detection rate of stegnalysis technique in attacking the LSB Marching algorithm