Combined chemotherapy and intra-arterial chemotherapy of retinoblastoma

PurposeRetinoblastoma (RB) is the most common primary malignant intraocular tumor in children. Although systemic chemotherapy has been the primary treatment, intra-arterial chemotherapy (IAC) represents a new treatment option. Here, we performed alternate systemic chemotherapy and IAC and retrospectively reviewed the efficacy and safety of this approach.MethodsPatients diagnosed with intraocular RB between January 2000 and December 2011 at Severance Children's Hospital, Yonsei University, were reviewed. Before February 2010, the primary treatment for RB was chemotherapy (non-IAC/CTX). Since February 2010, the primary treatment for RB has been IAC (IAC/CTX). External beam radiotherapy or high-dose chemotherapy were used as "last resort" treatments just prior to enucleation at the time of progression or recurrence during primary treatment. Enucleation-free survival (EFS) and progression-free survival were assessed.ResultsWe examined 19 patients (median age, 11.9 months; range, 1.4 to 75.6 months) with a sum of 25 eyes, of which, 60.0% were at advanced Reese Ellsworth (RE) stages. The enucleation rate was 33.3% at early RE stages and 81.8% at advanced RE stages (P=0.028). At 36 months, EFS was significantly higher in the IAC/CTX group than in the non-IAC/CTX group (100% vs. 40.0%, P=0.016). All 5 patients treated with IAC achieved eye preservation, although most patients were at advanced RE stages (IV-V).ConclusionDespite the limitation of a small sample size, our work shows that an alternative combined approach using IAC and CTX may be safe and effective for eye preservation in advanced RB

A Comparison Between Optical Coherence Tomography Angiography and Fluorescein Angiography for the Imaging of Type 1 Neovascularization.

Purpose: To determine the sensitivity of the combination of optical coherence tomography angiography (OCTA) and structural optical coherence tomography (OCT) for detecting type 1 neovascularization (NV) and to determine significant factors that preclude visualization of type 1 NV using OCTA. Methods: Multicenter, retrospective cohort study of 115 eyes from 100 patients with type 1 NV. A retrospective review of fluorescein (FA), OCT, and OCTA imaging was performed on a consecutive series of eyes with type 1 NV from five institutions. Unmasked graders utilized FA and structural OCT data to determine the diagnosis of type 1 NV. Masked graders evaluated FA data alone, en face OCTA data alone and combined en face OCTA and structural OCT data to determine the presence of type 1 NV. Sensitivity analyses were performed using combined FA and OCT data as the reference standard. Results: A total of 105 eyes were diagnosed with type 1 NV using the reference. Of these, 90 (85.7%) could be detected using en face OCTA and structural OCT. The sensitivities of FA data alone and en face OCTA data alone for visualizing type 1 NV were the same (66.7%). Significant factors that precluded visualization of NV using en face OCTA included the height of pigment epithelial detachment, low signal strength, and treatment-naïve disease (P \u3c 0.05, respectively). Conclusions: En face OCTA and structural OCT showed better detection of type 1 NV than either FA alone or en face OCTA alone. Combining en face OCTA and structural OCT information may therefore be a useful way to noninvasively diagnose and monitor the treatment of type 1 NV

Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy

PURPOSE: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts. DESIGN: Retrospective multicenter study. PARTICIPANTS: Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022. METHODS: We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians\u27 experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python. MAIN OUTCOME MEASURES: Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models. RESULTS: The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17). CONCLUSIONS: We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Adalimumab, etanercept and ustekinumab for treating plaque psoriasis in children and young people: systematic review and economic evaluation

Background: Psoriasis is a chronic inflammatory disease that predominantly affects the skin. Adalimumab (HUMIRA®, AbbVie, Maidenhead, UK), etanercept (Enbrel®, Pfizer, New York, NY, USA) and ustekinumab (STELARA®, Janssen Biotech, Inc., Titusville, NJ, USA) are the three biological treatments currently licensed for psoriasis in children. Objective: To determine the clinical effectiveness and cost-effectiveness of adalimumab, etanercept and ustekinumab within their respective licensed indications for the treatment of plaque psoriasis in children and young people. Data sources: Searches of the literature and regulatory sources, contact with European psoriasis registries, company submissions and clinical study reports from manufacturers, and previous National Institute for Health and Care Excellence (NICE) technology appraisal documentation. Review methods: Included studies were summarised and subjected to detailed critical appraisal. A network meta-analysis incorporating adult data was developed to connect the effectiveness data in children and young people and populate a de novo decision-analytic model. The model estimated the cost-effectiveness of adalimumab, etanercept and ustekinumab compared with each other and with either methotrexate or best supportive care (BSC), depending on the position of the intervention in the management pathway. Results: Of the 2386 non-duplicate records identified, nine studies (one randomised controlled trial for each drug plus six observational studies) were included in the review of clinical effectiveness and safety. Etanercept and ustekinumab resulted in significantly greater improvements in psoriasis symptoms than placebo at 12 weeks’ follow-up. The magnitude and persistence of the effects beyond 12 weeks is less certain. Adalimumab resulted in significantly greater improvements in psoriasis symptoms than methotrexate for some but not all measures at 16 weeks. Quality-of-life benefits were inconsistent across different measures. There was limited evidence of excess short-term adverse events; however, the possibility of rare events cannot be excluded. The majority of the incremental cost-effectiveness ratios for the use of biologics in children and young people exceeded NICE’s usual threshold for cost-effectiveness and were reduced significantly only when combined assumptions that align with those made in the management of psoriasis in adults were adopted. Limitations: The clinical evidence base for short- and long-term outcomes was limited in terms of total participant numbers, length of follow-up and the absence of young children. Conclusions: The paucity of clinical and economic evidence to inform the cost-effectiveness of biological treatments in children and young people imposed a number of strong assumptions and uncertainties. Health-related quality-of-life (HRQoL) gains associated with treatment and the number of hospitalisations in children and young people are areas of considerable uncertainty. The findings suggest that biological treatments may not be cost-effective for the management of psoriasis in children and young people at a willingness-to-pay threshold of £30,000 per quality-adjusted life-year, unless a number of strong assumptions about HRQoL and the costs of BSC are combined. Registry data on biological treatments would help determine safety, patterns of treatment switching, impact on comorbidities and long-term withdrawal rates. Further research is also needed into the resource use and costs associated with BSC. Adequately powered randomised controlled trials (including comparisons against placebo) could substantially reduce the uncertainty surrounding the effectiveness of biological treatments in biologic-experienced populations of children and young people, particularly in younger children. Such trials should establish the impact of biological therapies on HRQoL in this population, ideally by collecting direct estimates of EuroQol-5 Dimensions for Youth (EQ-5D-Y) utilities. Study registration: This study is registered as PROSPERO CRD42016039494. Funding: The National Institute for Health Research Health Technology Assessment programme