Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine -Based Approach to Overall Survival Estimation.

Objective: Glioblastoma multiforme (GBM), particularly the IDH-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: This study utilizes a Support Vector Machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (< 12 Months) and long (>=12 Months) survivors. A dataset comprising multi-parametric MRI (mpMRI) scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, FLAIR, and T1-Gd sequences. Low variance features were removed, and Recursive Feature Elimination (RFE) was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT promoter methylation status were integrated to enhance prediction accuracy. Results: The model showed reasonable results in terms of cross-validated AUC of 0.84 (95% CI: 0.80-0.90) with (p-value < 0.001) effectively categorizing patients into short and long survivors. Log-rank test (Chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen's d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value<0.0001. Conclusion: The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background and novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Methodology: Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results: Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Conclusion: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

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.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.

BACKGROUND AND MOTIVATION: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.