Steganography plays a vital role in securing sensitive information within digital communication channels. It has the ability to hide data and protect information effectively without causing disruptions in medical data. The current hospital management systems primarily focus on embedding text and visual in medical images, which presents several challenges, while the integration of audio information remains largely overlooked. This research addresses the security gap by exploring the embedding of audio data in medical images to enhance patient record protection and information integrity. The Proposed work introduced an innovative Steganography approach for medical image data by embedding Audio files aiming to addresses this research gap. This method integrate audio data into medical images, specifically DICOM-standard images without compromising their visual integrity. It also preserves the integrity of DICOM metadata, ensuring compliance with medical imaging standards. The Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) steganography techniques are used to embed audio file data into medical images. The study illustrates the effectiveness of LSB and DCT techniques on a computed tomography(CT) Images. The LSB method is renowned for its simplicity and its ability to extract data without introducing errors. Additionally, the method offers efficient insertion and extraction time during the watermark embedding and retrieval process,making it highly practical for real-time applications. An algorithm employs DCT technique for Audio integration within DICOM images are more robust and imperceptible. Both techniques have been proven to be highly precise and supported by empirical evidence such as PSNR, SSIM, and MSE analysis. Robustness analysis was conducted on various image quality variation scenarios that includes compression level, resolution and noise tolerance. The proposed DCT approach achieves a PSNR of 57.89 dB,SSIM of 0.9997, and MSE of 0.013, demonstrating minimal distortion and superior structural integrity. Robustness analysis shows that DCT outperforms the LSB method, consistently maintaining PSNR above 47 dB, MSE approximately zero, and SSIM at 0.99 across varying compression levels, image resolutions, and noise conditions. After conducting an experiment,the DCT technique is the most suitable approach for secure communication of medical data with capacity of 2.03 bits per pixel (bpp). The overall analysis of proposed work significantly improves result interms of the security and robustness under various attacks.

Objectives: To investigate the value of DTI in differentiation of renal allograft rejection from well-functioning stable allograft, using fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values. Methods: In this prospective study, 22 transplant recipients with well-functioning stable allograft (group A) and 20 patients with renal allograft rejection (group B + C) were recruited over a period of 19 months from January 2018 to July 2019. DTI-MRI was performed in all the patients, and FA and ADC values were measured in cortical and medullary regions of the transplanted kidney. On biopsy, graft rejection was classified as acute (group B) (n = 7) and chronic graft rejection (group C) (n = 13) based on the BANNF scoring system. Statistical analysis was performed using STATA v.14.0. Results: Statistically significant difference between group A and group B + C was noted for cortical (p < 0.001), and medullary (p = 0.003) FA values, and cortical (p = 0.020), and medullary (p = 0.046) ADC values. Cortical(p < 0.001) and Medullary(p = 0.020) FA values showed statistically significant difference between group A and group C, and cortical FA value(p = 0.012) also showed statistically significant difference between group B and group C. AUC (to differentiate between renal allograft rejection and well-functioning stable allograft) for cortical, and medullary FA values and cortical and medullary ADC values were 0.853(p < 0.001), 0.757(p = 0.004), 0.709(p = 0.021) and 0.736(p = 0.009), respectively. Conclusion and Advances in knowledge: DTI is a promising functional MRI technique for the non-invasive assessment of renal allograft function. Diffusion parameters, such as FA and ADC values, can be useful in the differentiation of renal allograft rejection from well-functioning stable allograft.

To validate the detection of clinically significant prostate cancer (Gleason’s score ≥7) by PI-RADS v2 and to assess the ability of quantitative MRI parameters to detect clinically significant prostate cancer (CSPCa) in Indian men. Adult men (n = 95) with serum PSA >4 ng/ml were prospectively evaluated with multiparametric MRI (mpMRI) followed by histopathological evaluation using systematic 12-core prostate biopsy in 69 patients and prostatectomy specimens in 26 patients, performed within six weeks of mpMRI. The imaging and the pathology were divided into 12 sectors per prostate. For the validation of PI-RADS v2, a cut-off of PI-RADS v2 score ≥ 3 and PI-RADS v2 score ≥ 4 were compared to histopathology as a reference standard. Further, quantitative parameters, apparent diffusion coefficient (ADC), K^trans, and K_ep were correlated with the Gleason score and evaluated for their ability to distinguish between sectors with CSPCa and sectors without CSPCa. PI-RADS score ≥ 4 showed higher specificity (89%) than PI-RADS score ≥ 3 (72.2%) at the cost of mild but not significant reduction of sensitivity (sensitivity–87.6% vs 91.9), (n = 1,140 sectors, 95 patients). PI-RADS v2 and quantitative parameters demonstrated the ability to discriminate sectors positive vs negative for CSPCa: AUC (area under the curve) for ADC was 0.928, PI-RADS v2 was 0.903, K^trans was 0.897 and K_ep was 0.695. Gleason score correlated well with PI-RADS (r = 0.74), ADC (r = −0.73) and K^trans (r = 0.69). PI-RADS v2 is a reliable method for the detection and localization of clinically significant prostate cancer in Indian men, suggesting applicability beyond European or American demographics. Quantitative mpMRI parameters can detect clinically significant prostate cancer with similar test characteristics as PI-RADS v2.

Multiple endocrine neoplasia type 1 (MEN1) or Wermer’s syndrome is a genetic disease characterized by involvement of multiple endocrine glands, primarily involving parathyroid, pancreas, and pituitary. Other additional features include foregut carcinoids; non-functioning adrenal tumors; and skin lesions such as lipomas, collagenomas, and angiofibromas. Here, we describe our experience in managing a patient who presented to us with spontaneous episodes of hypoglycemia and was diagnosed with insulinoma. Detailed clinical and biochemical evaluation unraveled the diagnosis of MEN1 in the patient and her family members who constituted a large kindred. This case highlights the importance for evaluation of MEN1 in a patient or his/her family members in the setting of clinical and biochemical suspicion. In addition, we have also discussed the utility of the latest diagnostic and therapeutic modalities for management of MEN1.