Monte Carlo-based dosimetry of proposed bi-radionuclide (125I and 106Ru/106Rh) eye plaque: A feasibility study.

BACKGROUND: Combining the sharp dose fall off feature of beta-emitting 106Ru/106Rh radionuclide with larger penetration depth feature of photon-emitting125I radionuclide in a bi-radionuclide plaque, prescribed dose to the tumor apex can be delivered while maintaining the tumor dose uniformity and sparing the organs at risk. The potential advantages of bi-radionuclide plaque could be of interest in context of ocular brachytherapy. PURPOSE: The aim of the study is to evaluate the dosimetric advantages of a proposed bi-radionuclide plaque for two different designs, consisting of indigenous 125I seeds and 106Ru/106Rh plaque, using Monte Carlo technique. The study also explores the influence of other commercial 125I seed models and presence or absence of silastic/acrylic seed carrier on the calculated dose distributions. The study further included the calculation of depth dose distributions for the bi-radionuclide eye plaque for which experimental data are available. METHODS: The proposed bi-radionuclide plaque consists of a 1.2-mm-thick silver (Ag) spherical shell with radius of curvature of 12.5 mm, 20 µm-thick-106Ru/106Rh encapsulated between 0.2 mm Ag disk, and a 0.1-mm-thick Ag window, and water-equivalent gel containing 12 symmetrically arranged 125I seeds. Two bi-radionuclide plaque models investigated in the present study are designated as Design I and Design II. In Design I, 125I seeds are placed on the top of the plaque, while in Design II 106Ru/106Rh source is positioned on the top of the plaque. In Monte Carlo calculations, the plaque is positioned in a spherical water phantom of 30 cm diameter. RESULTS: The proposed bi-radionuclide eye plaque demonstrated superior dose distributions as compared to 125I or 106Ru plaque for tumor thicknesses ranges from 5 to 10 mm. Amongst the designs, dose at a given voxel for Design I is higher as compared to the corresponding voxel dose for Design II. This difference is attributed to the higher degree of attenuation of 125I photons in Ag as compared to beta particles. Influence of different 125I seed models on the normalized lateral dose profiles of Design I (in the absence of carrier) is negligible and within 5% on the central axis depth dose distribution as compared to the corresponding values of the plaque that has indigenous 125I seeds. In the presence of a silastic/acrylic seed carrier, the normalized central axis dose distributions of Design I are smaller by 3%-12% as compared to the corresponding values in the absence of a seed carrier. For the published bi-radionuclide plaque model, good agreement is observed between the Monte Carlo-calculated and published measured depth dose distributions for clinically relevant depths. CONCLUSION: Regardless of the type of 125I seed model utilized and whether silastic/acrylic seed carrier is present or not, Design I bi-radionuclide plaque offers superior dose distributions in terms of tumor dose uniformity, rapid dose fall off and lesser dose to nearby critical organs at risk over the Design II plaque. This shows that Design I bi-radionuclide plaque could be a promising alternative to 125I plaque for treatment of tumor sizes in the range 5 to 10 mm.

AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.

BACKGROUND: Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions. METHODS: Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups. RESULTS: By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category. CONCLUSION: Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.

Iodine-125 brachytherapy for choroidal melanoma by using Ocuprosta seeds with indigenous non-collimated plaques: Our initial experience.

PURPOSE: Brachytherapy is the gold-standard treatment for choroidal melanoma. This study evaluated iodine-125 brachytherapy by using Ocuprosta seeds with indigenous non-collimated plaques in Asian patients. METHODS: Retrospective single-center study in a tertiary care hospital of 12 eyes with choroidal melanoma in 12 Asian patients who underwent brachytherapy with Ocuprosta seeds fixed on non-collimated plaques and had a follow-up of at least 32 months (mean: 42.4 ± 9.5 months; median: 40 months). Radiotherapy was planned after developing the digital 3D model of the tumor within the eye by using radiological images and clinical pictures. Ocuprosta iodine-125 seeds were used on indigenous non-collimated gold plaques to deliver the radiation for precalculated time. "Successful outcome" was taken as a decrease in the volume of the tumor, and "unsuccessful outcome" was defined as no change in the tumor volume or increase in the tumor volume at 24 months after brachytherapy. RESULTS: The mean decrease in tumor volume was 21% (914.5 ± 912.2 mm3 to 495.7 ± 633.6 mm3) after brachytherapy, which correlated with the baseline volume of the tumor. Ten eyes (83.3%) showed a reduction in tumor volume, whereas two eyes showed an increase in the volume of the tumor after brachytherapy. One of the cases with a reduction in tumor size developed neovascular glaucoma. Enucleation was done in three eyes. A globe salvage rate of 75% and tumor regression rate of 83% were seen in the present study using Ocuprosta seeds. CONCLUSIONS: Iodine-125 brachytherapy with uncollimated indigenous gold plaques is an effective treatment modality for choroidal melanomas in Asian patients.

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.

BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT methylation biomarkers using multi-parametric MRI (mp-MRI) helps neuro-oncologists to analyze GBM and its treatment plan. METHOD: The hand-crafted radiomics feature extraction of GBM's subregions, such as edema(ED), tumor core (TC), and enhancing tumor (ET) in the machine learning (ML) framework, was investigated using support vector machine(SVM), K-Nearest Neighbours (KNN), random forest (RF), LightGBM, and extreme gradient boosting (XGB). For tissue-level analysis of the promotor genes in GBM, we used the deep residual neural network (ResNet-18) with 3D architecture, followed by EfficientNet-based investigation for variants as B0 and B1. Lastly, we analyzed the fused deep learning (FDL) framework that combines ML and DL frameworks. RESULT: Structural mp-MRI consisting of T1, T2, FLAIR, and T1GD having a size of 400 and 185 patients, respectively, for discovery and replication cohorts. Using the CV protocol in the ResNet-3D framework, MGMT methylation status prediction in mp-MRI gave the AUC of 0.753 (p < 0.0001) and 0.72 (p < 0.0001) for the discovery and replication cohort, respectively. We presented that the FDL is ∼7% superior to solo DL and ∼15% to solo ML. CONCLUSION: The proposed study aims to provide solutions for building an efficient predictive model of MGMT for GBM patients using deep radiomics features obtained from mp-MRI with the end-to-end ResNet-18 3D and FDL imaging signatures.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review.

BACKGROUND: Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding their effectiveness is challenging due to their complexity and diversity. Several reviews on brain tumor segmentation are available, but none of them describe a link between the threats due to risk-of-bias (RoB) in AI and its architectures. In our review, we focused on linking RoB and different AI-based architectural Cluster in popular DL framework. Further, due to variance in these designs and input data types in medical imaging, it is necessary to present a narrative review considering all facets of BLS. APPROACH: The proposed study uses a PRISMA strategy based on 75 relevant studies found by searching PubMed, Scopus, and Google Scholar. Based on the architectural evolution, DL studies were subsequently categorized into four classes: convolutional neural network (CNN)-based, encoder-decoder (ED)-based, transfer learning (TL)-based, and hybrid DL (HDL)-based architectures. These studies were then analyzed considering 32 AI attributes, with clusters including AI architecture, imaging modalities, hyper-parameters, performance evaluation metrics, and clinical evaluation. Then, after these studies were scored for all attributes, a composite score was computed, normalized, and ranked. Thereafter, a bias cutoff (AP(ai)Bias 1.0, AtheroPoint, Roseville, CA, USA) was established to detect low-, moderate- and high-bias studies. CONCLUSION: The four classes of architectures, from best-to worst-performing, are TL > ED > CNN > HDL. ED-based models had the lowest AI bias for BLS. This study presents a set of three primary and six secondary recommendations for lowering the RoB.

Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review.

BACKGROUND: Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-performance and poor clinical delivery. The main objective is to understand the nature of risk-of-bias (RoB) in ML and non-ML studies for CVD risk prediction. METHODS: PRISMA model was used to shortlisting 117 studies, which were analyzed to understand the RoB in ML and non-ML using 46 and 32 attributes, respectively. The mean score for each study was computed and then ranked into three ML and non-ML bias categories, namely low-bias (LB), moderate-bias (MB), and high-bias (HB), derived using two cutoffs. Further, bias computation was validated using the analytical slope method. RESULTS: Five types of the gold standard were identified in the ML design for CAD/CVD risk prediction. The low-moderate and moderate-high bias cutoffs for 24 ML studies (5, 10, and 9 studies for each LB, MB, and HB) and 14 non-ML (3, 4, and 7 studies for each LB, MB, and HB) were in the range of 1.5 to 1.95. BiasML< Biasnon-ML by ∼43%. A set of recommendations were proposed for lowering RoB. CONCLUSION: ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

Utilization of Chemical Deposition Technique for Preparation of Miniature 170Tm Sources and Preliminary Quality Assessment for Potential Use in Brachytherapy.

OBJECTIVE: This article describes the preparation of a 170Tm source by chemical deposition technique, its encapsulation in a titanium holder, and preliminary quality evaluation for potential utility as a brachytherapy source. METHODS: The procedure consisted of electrodeposition of Ni on a Cu wire followed by chemical deposition of 170Tm on it. Influence of feed solution pH, carrier Tm concentration, and reaction time were studied for optimum deposition of 170Tm on substrate. After sealing the source core in a titanium capsule, quality control tests were performed. Distribution of 170Tm on substrate was evaluated by autoradiography. Inactive Tm source was characterized by scanning electron microscope (SEM) and energy-dispersive X-ray (EDS) analyses. RESULTS: Under optimized conditions (pH 5, 10 µg Tm carrier, 5 h), 170Tm source core could be prepared by deposition of >95% of 170Tm radioactivity on substrate. Swipe tests and immersion tests on encapsulated sources confirmed that removable radioactivity and radioactivity leakage levels were within stipulated limits. Autoradiography of 170Tm source confirmed uniformity of radioactivity distribution. While SEM analysis confirmed good adhesion of Tm on substrate, EDS analysis confirmed elemental constituents of the Tm-deposited substrate. CONCLUSION: The objective of preparing a 170Tm source by chemical deposition for potential brachytherapy applications could be successfully achieved.

Preparation of Radioactive Skin Patches Using Polyhydroxamic Acid-Grafted Cellulose Films Toward Applications in Treatment of Superficial Tumors.

The primary objective of this investigation is the development of a strategy for the synthesis of polyhydroxamic acid (PHA)-grafted cellulose film, its characterization, and evaluation of its usefulness for the preparation of 177Lu skin patches for superficial brachytherapy applications. PHA-grafted cellulose films were synthesized and characterized by Fourier transformed infrared spectrometer analysis and visual color test with Fe(III) solution. Uptake of 177Lu on the PHA-grafted cellulose was investigated by varying the experimental conditions such as the pH of feed solution, amount of nonradioactive Lu carrier, time, and temperature of the reaction. Under the optimized conditions, >95% loading of 177Lu on the PHA-cellulose film could be achieved. Autoradiography studies of 177Lu-PHA-cellulose film confirmed the uniform distribution of 177Lu on the surface. Energy dispersive X-ray analysis of nonradioactive Lu-PHA-cellulose film confirmed the loading of Lu on PHA-cellulose film. The utility of PHA-functionalized cellulose films for the fabrication of radioactive sources for superficial brachytherapy applications could be successfully demonstrated.

Determination of surface dose rate of indigenous (32)P patch brachytherapy source by experimental and Monte Carlo methods.

Isotope production and Application Division of Bhabha Atomic Research Center developed (32)P patch sources for treatment of superficial tumors. Surface dose rate of a newly developed (32)P patch source of nominal diameter 25 mm was measured experimentally using standard extrapolation ionization chamber and Gafchromic EBT film. Monte Carlo model of the (32)P patch source along with the extrapolation chamber was also developed to estimate the surface dose rates from these sources. The surface dose rates to tissue (cGy/min) measured using extrapolation chamber and radiochromic films are 82.03±4.18 (k=2) and 79.13±2.53 (k=2) respectively. The two values of the surface dose rates measured using the two independent experimental methods are in good agreement to each other within a variation of 3.5%. The surface dose rate to tissue (cGy/min) estimated using the MCNP Monte Carlo code works out to be 77.78±1.16 (k=2). The maximum deviation between the surface dose rates to tissue obtained by Monte Carlo and the extrapolation chamber method is 5.2% whereas the difference between the surface dose rates obtained by radiochromic film measurement and the Monte Carlo simulation is 1.7%. The three values of the surface dose rates of the (32)P patch source obtained by three independent methods are in good agreement to one another within the uncertainties associated with their measurements and calculation. This work has demonstrated that MCNP based electron transport simulations are accurate enough for determining the dosimetry parameters of the indigenously developed (32)P patch sources for contact brachytherapy applications.

Studies on the development of ¹69Yb-brachytherapy seeds: New generation brachytherapy sources for the management of cancer.

This paper describes development of (169)Yb-seeds by encapsulating 0.6-0.65 mm (ϕ) sized (169)Yb2O3 microspheres in titanium capsules. Microspheres synthesized by a sol-gel route were characterized by XRD, SEM/EDS and ICP-AES. Optimization of neutron irradiation was accomplished and (169)Yb-seeds up to 74 MBq of (169)Yb could be produced from natural Yb2O3 microspheres, which have the potential for use in prostate brachytherapy. A protocol to prepare (169)Yb-brachytherapy sources (2.96-3.7 TBq of (169)Yb) with the use of enriched targets was also formulated.

Bioevaluation of (125) I Ocu-Prosta seeds for application in prostate cancer brachytherapy.

BACKGROUND & OBJECTIVES: In recent years, brachytherapy involving permanent radioactive seed implantation has emerged as an effective modality for the management of cancer of prostate. 125 I-Ocu-Prosta seeds were indigenously developed and studies were carried out to assess the safety of the indigenously developed 125 I-Ocu-Prosta seeds for treatment of prostate cancer. METHODS: Animal experiments were performed to assess the likelihood of in vivo release of 125 I from radioactive seeds and migration of seeds implanted in the prostate gland of the rabbit. In vivo release of 125 I activity was monitored by serial blood sampling from the auricular vein and subsequent measurement of 125 I activity. Serial computed tomography (CT) scans were done at regular intervals till 6 months post implant to assess the physical migration of the seeds. RESULTS: The laser welded seeds maintained their hermeticity and prevented the in vivo release of 125 I activity into the blood as no radioactivity was detected during follow up blood measurements. Our study showed that the miniature 125 I seeds were clearly resolved in CT images. Seeds remained within the prostate gland during the entire study period. Moreover, the seed displacement was minimal even within the prostate gland. INTERPRETATION & CONCLUSIONS: Our findings have demonstrated that indigenously developed 125 I-Ocu-Prosta seeds may be suitable for application in treatment of prostate cancer.

On the application of Nafion membrane for the preparation of (90)Y skin patches, quality control, and biological evaluation for treatment of superficial tumors.

This article describes the preparation, quality control, and biological evaluation of (90)Y-skin patches based on Nafion(®) membrane as a viable treatment modality for superficial skin tumors such as melanoma. To arrive at the conditions for optimum uptake of (90)Y on the membrane, influence of various experimental parameters, such as pH of the feed solution, inactive yttrium carrier concentration, reaction volume, contact time, and temperature, was systematically investigated. Under the optimized conditions, >95% of the (90)Y activity (37-185 MBq) could be incorporated in the Nafion membranes to prepare (90)Y-skin patches. Quality control tests were carried out to ensure nonleachability, uniform distribution of activity, and stability of the (90)Y-patches. Mice bearing transplanted melanoma tumors that were treated with two doses of 74 MBq (90)Y-Nafion membrane sources showed complete tumor regression. Histopathological examination of the treated area showed absence of tumor. The results of the study indicate the potential of (90)Y-Nafion membrane sources for treatment of superficial skin tumors.

Indigenous (125)I brachytherapy source for the management of intraocular melanomas in India.

Episcleral plaque brachytherapy using (125)I seed is a viable option for the management of intraocular cancer with minimal invasiveness and surgical complications. This article describes the fabrication of (125)I seeds and initial experience on their use for the management of intraocular choroidal melanomas. The process of (125)I seed fabrication includes immobilization of (125)I into palladium-coated silver wires, its encapsulation in titanium capsules using Nd: YAG laser and quality control to assure safety. Plaque preparation consists of the assignment of seeds to slots on the plaque to achieve a desired dose rate distribution. The clinical study reported here includes the retrospective review of 9 eyes of 9 patients who underwent ophthalmic brachytherapy between May 2008 and June 2011. The average apical diameter before brachytherapy was 7.6 mm and the average largest basal diameter was 12.1 mm, which reduced to 3.3 and 7.2 mm, respectively, after the procedure at an average follow-up of 24 months. Patients in our studies experienced good local tumor control. The results of this study represent a significant step forward in the management of intraocular tumors in India.

Episcleral plaque brachytherapy using 'BARC I-125 Ocu-Prosta seeds' in the treatment of intraocular tumors: a single-institution experience in India.

CONTEXT: To analyze the results of episcleral plaque brachytherapy using indigenous Bhabha Atomic Research Centre (BARC) Iodine-125 Ocu-Prosta seeds for the management of intraocular tumors from a single institute. AIM: To report our initial experience and learning curve on the use of 'BARC I-125 Ocu-Prosta seeds' for the management of intraocular tumors such as choroidal melanomas, retinoblastomas and vasoproliferative tumors (VPT). MATERIALS AND METHODS: We retrospectively reviewed 13 eyes of 13 patients who underwent ophthalmic brachytherapy between May 2008 to March 2012. Nine cases had choroidal melanomas; three had retinoblastomas while one case had VPT. RESULTS: For choroidal melanomas the average apical diameter before brachytherapy was 7.6 mm and average largest basal diameter was 12.1 mm, respectively, which reduced to 4.2 mm and 7.7 mm after the procedure at an average follow-up of 24 months (range 10-43 months). Retinoblastoma and VPT also showed good regression after brachytherapy. CONCLUSION: Plaque radiotherapy using 125 I seeds can be performed under peribulbar anesthesia and provides a viable option for the management of intraocular cancer with minimal invasiveness and surgical complications. Patients in our studies experienced excellent local tumor control. With the availability of indigenous 'BARC I-125 Ocu-Prosta seeds' locally, cost effective ophthalmic brachytherapy can be performed in India.

Nafion-zirconium phosphate composite membrane: a new approach to prepare (32)P patches for superficial brachytherapy applications.

This article describes a method for the preparation of (32)P patch for the treatment of skin cancer. It is based on the surface modification of a Nafion film by treatment with ZrOCl(2) solution, impregnation of a predicted quantity of (32)P into the film, and its subsequent immobilization into a nonleachable matrix by lamination. The effect of variations of critical parameters on the incorporation of (32)P into the membrane, such as solution pH, contact time, reaction volume, inactive carrier concentration of the feed, reaction temperature, and so on, was investigated to arrive at the conditions resulting in optimum retention of (32)P activity. The morphology of the membrane was evaluated by scanning electron microscope and energy dispersive spectral analyses. Quality control tests were carried out to ensure nonleachability, uniform distribution of activity, and stability of the patches.