Publications by authors named "Caroline Chung"

Article Synopsis
  • A retrospective study assessed the effectiveness of contrast-enhanced T1-weighted 3D SPACE MRI sequences versus 3D FLASH sequences in detecting brain metastases in patients at a single institution.
  • The evaluation involved a review of cases where both imaging techniques were used, with assessments on factors like the number of lesions detected, image quality, and contrast-to-noise ratio (CNR) by certified neuroradiologists.
  • Results showed that SPACE detected more metastatic lesions and had better image quality and fewer artifacts than FLASH, supporting SPACE as the superior method for brain metastasis detection.
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Background: The invasion of glioblastoma cells beyond the visible tumor margin depicted by conventional neuroimaging is believed to mediate recurrence and predict poor survival. Radiomic biomarkers that are associated with the direction and extent of tumor infiltration are, however, non-existent.

Methods: Patients from a single center with newly diagnosed glioblastoma ( = 7) underwent preoperative Q-space magnetic resonance imaging (QSI; 3T, 64 gradient directions, b = 1000 s/mm) between 2018 and 2019.

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We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data.

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Background: Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects.

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(1) Background: Myxopapillary ependymoma (MPE) is a rare tumor of the spine, typically slow-growing and low-grade. Optimal management strategies remain unclear due to limited evidence given the low incidence of the disease. (2) Methods: We analyzed data from 1197 patients with spinal MPE from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2020).

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Background: Glioblastoma (GBM) poses therapeutic challenges due to its aggressive nature, particularly for patients with poor functional status and/or advanced disease. Hypofractionated radiotherapy (RT) regimens have demonstrated comparable disease outcomes for this population while allowing treatment to be completed more quickly. Here, we report our institutional outcomes of patients treated with 2 hypofractionated RT regimens: 40 Gy/15fx (3w-RT) and 50 Gy/20fx (4w-RT).

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With improvements in survival for patients with metastatic cancer, long-term local control of brain metastases has become an increasingly important clinical priority. While consensus guidelines recommend surgery followed by stereotactic radiosurgery (SRS) for lesions >3 cm, smaller lesions (≤3 cm) treated with SRS alone elicit variable responses. To determine factors influencing this variable response to SRS, we analyzed outcomes of brain metastases ≤3 cm diameter in patients with no prior systemic therapy treated with frame-based single-fraction SRS.

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Purpose: A dedicated magnetic resonance imaging simulation (MRsim) for radiation treatment (RT) planning in patients with high-grade glioma (HGG) can detect early radiologic changes, including tumor progression after surgery and before standard of care chemoradiation. This study aimed to determine the effect of using postoperative magnetic resonance imaging (MRI) versus MRsim as the baseline for response assessment and reporting pseudoprogression on follow-up imaging at 1 month (FU1) after chemoradiation.

Methods And Materials: Histologically confirmed patients with HGG were planned for 6 weeks of RT in a prospective study for adaptive RT planning.

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Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g.

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We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model.

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Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs.

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Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases.

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The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques.

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Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives.

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Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality.

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Article Synopsis
  • - A 32-year-old male experienced severe visual issues, leading to the diagnosis of bilateral uveitis, retinal periphlebitis, and optic neuritis caused by a non-pineal central nervous system (CNS) germinoma.
  • - Upon examination, he showed several eye problems, including decreased visual acuity, optic disc edema, and abnormal imaging findings, which led to further investigation and a brain biopsy confirming the germinoma.
  • - Treatment with chemotherapy for the germinoma resulted in significant improvement of his ocular symptoms, highlighting the connection between CNS germinomas and eye conditions like optic neuritis and uveitis.
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Article Synopsis
  • * The symposium highlighted a significant shift towards integrating AI into clinical care, especially in radiation oncology, which produces a lot of digital data and is likely to see early transformations due to AI advancements.
  • * The report shares key insights from the event, focusing on data management and sharing, aiming to prepare radiation oncology for effective and safe adoption of AI and informatics technologies.
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Background: Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers.

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Purpose: The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

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Article Synopsis
  • Survivors of SARS-CoV-2 pneumonia, particularly cancer patients, often experience lasting respiratory symptoms and interstitial lung abnormalities (ILAs) following their infection, but the risk factors for these conditions are not well understood.
  • In a study of 140 patients from cancer centers, around 70% of participants had ILAs just 3 months after hospital discharge, with a notable percentage still experiencing symptoms at 6 months.
  • Higher pneumonia severity scores at hospital admission were linked to a greater likelihood of developing persistent ILAs, suggesting that both the severity of initial illness and age can influence respiratory recovery in these patients.
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