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Purpose. Latanoprost has become one of the most widely prescribed topical antihypertensive medications in recent years. Yet there have been few reports of secondary iris pigment epithelial (IPE) and ciliary body (CB) cyst formation to date and none, to our knowledge, reported in eyes predisposed to primary angle closure. Methods. We report the first documented case of bilateral IPE and CB cysts in a hypermetropic patient with prior laser peripheral iridotomies (LPIs) as a rare, delayed side effect of topical Latanoprost treatment. The cysts subsided with discontinuation of Latanoprost, thereby demonstrating a causal relationship. We discuss the pathogenesis of such cysts and advocate using of serial ultrasound biomicroscopic (UBM) images to monitor them. Results and Conclusions. Latanoprost may cause iris pigment epithelial and ciliary body cysts that remain clinically undetected. In eyes predisposed to angle closure, such cysts may become clinically detectable and masquerade as iris tumours. Prior laser peripheral iridotomies in these eyes may delay or prevent the detection of these cysts. Ultrasound biomicroscopy (UBM) imaging is therefore a valuable tool in diagnosing and monitoring these cysts.

Integrated PET/CT has emerged as an integral component of oncology management because of its unique potential of providing both functional and morphological images in a single imaging session. In this work, performance of the ′bismuth germinate (BGO) crystal′-based PET of a newly installed Discovery ST PET/CT was evaluated in 2D and 3D mode for whole-body scanning using National Electrical Manufacturers Association (NEMA) NU 2-2001 protocol and the recommended phantoms. During the entire measurements, the system operates with an energy window of 375-650 keV and 11.7 ns coincidence time window. The set of tests performed were spatial resolution, sensitivity, scatter fraction (SF) and counting rate performance. The average transaxial and axial spatial resolution measured as full width at half maximum (FWHM) of the point spread function at 1 cm (and 10 cm) off-axis was 0.632 (0.691) and 0.491 (0.653) cm in 2D and 0.646 (0.682) and 0.54 (0.601) cm in 3D respectively. The average sensitivity for the two radial positions ( R = 0 cm and R = 10 cm) was 2.56 (2.63) cps/kBq in 2D and 11.85 (12.14) cps/kBq in 3D. The average scatter fraction was 19.79% in 2D and 46.19% in 3D. The peak noise equivalent counting rate (NECR) evaluated with single random subtraction was 89.41 kcps at 49 kBq/cc in 2D and 60 kcps at 12 kBq/cc in 3D acquisition mode. The NECR with delayed random subtraction was 61.47 kcps at 40.67 kBq/cc in 2D and 45.57 kcps at 16.45 kBq/cc in 3D. The performance of the PET scanner was satisfactory within the manufacturer-specified limits. The test result of PET shows excellent system sensitivity with relatively uniform resolution throughout the FOV, making this scanner highly suitable for whole-body studies.

Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]

Byline: N. Purandare, C. Pramesh, J. Agarwal, A. Agrawal, S. Shah, K. Prabhash, G. Karimundackal, S. Jiwnani, S. Tandon, V. Rangarajan BACKGROUND: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) has become [...]

Byline: A. Mathew, J. Agarwal, A. Munshi, S. Laskar, C. Pramesh, G. Karimundackal, S. Jiwnani, K. Prabhash, V. Noronha, A. Joshi, V. Rangarajan, N. Purandare, N. Jambhekar, S. Tandon, A. [...]

Byline: N. Purandare, C. Pramesh, G. Karimundackal, S. Jiwnani, A. Agrawal, S. Shah, J. Agarwal, K. Prabhash, V. Noronha, A. Joshi, R. Kumar, V. Rangarajan Objectives: To study the utility [...]