Publications by authors named "Danielle Power"

Background: Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns.

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Introduction: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy.

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Article Synopsis
  • Recurrence of lung cancer after radiotherapy occurs in up to 36% of patients, highlighting the need for better prediction of who is at higher risk.
  • Researchers developed radiomic classification models using CT scans from over 900 patients with NSCLC to predict overall survival (OS), recurrence-free survival (RFS), and recurrence rates two years post-treatment.
  • The models showed promising results in predicting outcomes and could be used to create personalized surveillance strategies, potentially leading to improved patient care in future clinical trials.
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Article Synopsis
  • A study focused on non-small cell lung cancer (NSCLC) patients, aimed to develop and validate machine learning models using patient, tumor, and treatment data for predicting recurrence, recurrence-free survival (RFS), and overall survival (OS) following radiotherapy.
  • The research included 657 patients from 5 hospitals and involved various data pre-processing and machine learning techniques to create risk-stratification models, assessed through cross-validation and external testing.
  • Findings indicated that the machine learning models outperformed traditional TNM stage and performance status assessments in predicting recurrence and overall survival, with promising AUC scores demonstrating their effectiveness.
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It remains unclear whether targeted next-generation sequencing (tNGS) conveys a reliable estimate of tumor mutational burden (TMB). We sequenced 79 archival samples of immune checkpoint inhibitors (ICPIs) recipients (57% lung cancer, 43% melanoma) using Ion Ampliseq Cancer Hotspot Panel. Employing multiple cutoff values, we verified that TMB by tNGS did not correlate with response or survival following ICPI.

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Importance: Gut dysbiosis impairs response to immune checkpoint inhibitors (ICIs) and can be caused by broad-spectrum antibiotic (ATB) therapy.

Objective: To evaluate whether there is an association between ATB therapy administered concurrently (cATB) or prior (pATB) to ICI therapy and overall survival (OS) and treatment response to ICI therapy in patients with cancer treated with ICIs in routine clinical practice.

Design, Setting, And Participants: This prospective, multicenter, cohort study conducted at 2 tertiary academic referral centers recruited 196 patients with cancer who received ICI therapy between January 1, 2015, and April 1, 2018, in routine clinical practice rather than clinical trials.

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Purpose: The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC).

Patients And Methods: Pre-therapy PET scans from a total of 358 Stage I-III NSCLC patients scheduled for radiotherapy/chemo-radiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. A semi-automatic threshold method was used to segment the primary tumors.

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Five single nucleotide polymorphisms (SNPs) associated with thyroid cancer (TC) risk have been reported: rs2910164 (5q24); rs6983267 (8q24); rs965513 and rs1867277 (9q22); and rs944289 (14q13). Most of these associations have not been replicated in independent populations and the combined effects of the SNPs on risk have not been examined. This study genotyped the five TC SNPs in 781 patients recruited through the TCUKIN study.

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