Faculty Bibliography
Searched for:
person:naidid01
in-biosketch:true
Total Results:
260
How I Do It: An Algorithmic Approach to the Interpretation of Diffuse Lung Disease on Chest CT Imaging
PMID: 31704148
ISSN: 1931-3543
CID: 4184552
Lung Hyperlucency - A Clinical-Radiologic Algorithmic Approach to Diagnosis
Areas of diminished lung density are frequently identified both on routine chest radiographs and chest CT examinations. Colloquially referred to as "hyperlucent" foci of lung, a broad range of underlying pathophysiologic mechanisms and differential diagnoses account for these changes. Despite this, the spectrum of etiologies can be categorized into underlying parenchymal, airway and vascular related entities, respectively. The purpose of this review is to provide a practical diagnostic algorithmic approach to pulmonary hyperlucencies incorporating clinical history and characteristic imaging patterns to narrow the differential.
PMID: 31356811
ISSN: 1931-3543
CID: 4015192
Author Correction: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
PMID: 31253948
ISSN: 1546-170x
CID: 3964032
Approach to Peribronchovascular Disease on CT
Diseases that are predominantly peribronchovascular in distribution on computed tomography by definition involve the bronchi, adjacent vasculature, and associated lymphatics involving the central or axial lung interstitium. An understanding of diseases that can present with focal peribronchovascular findings is useful for establishing diagnoses and guiding patient management. This review will cover clinical and imaging features that may assist in differentiating amongst the various causes of primarily peribronchovascular disease.
PMID: 31200868
ISSN: 1558-5034
CID: 3930282
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States1. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines1-6. Existing challenges include inter-grader variability and high false-positive and false-negative rates7-10. We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
PMID: 31110349
ISSN: 1546-170x
CID: 3977392
CT screening for lung cancer: comparison of three baseline screening protocols
ISI:000483696900010
ISSN: 0938-7994
CID: 5323422
Is Spread of Tumor through Air Spaces a Concern for Interpreting Lung Nodules on CT Images?
PMID: 30179107
ISSN: 1527-1315
CID: 3271692
Low Dose Lung CT Screening in an Asian Population [Editorial]
PMID: 30017500
ISSN: 1878-4046
CID: 3202132
Are CT findings of pulmonary sarcoidosis ever sufficient for a presumptive diagnosis? [Letter]
PMID: 30007852
ISSN: 2213-2619
CID: 3202042
Journal of the American College of Radiology : JACR. 2018:15(8):1087-1096.DOI: 10.1016/j.jacr.2018.04.029
Managing Incidental Findings on Thoracic CT: Mediastinal and Cardiovascular Findings. A White Paper of the ACR Incidental Findings Committee
The ACR Incidental Findings Committee presents recommendations for managing incidentally detected mediastinal and cardiovascular findings found on CT. The Chest Subcommittee was composed of thoracic radiologists who developed the provided guidance. These recommendations represent a combination of current published evidence and expert opinion and were finalized by informal iterative consensus. The recommendations address the most commonly encountered mediastinal and cardiovascular incidental findings and are not intended to be a comprehensive review of all incidental findings associated with these compartments. Our goal is to improve the quality of care by providing guidance on how to manage incidentally detected thoracic findings.
PMID: 29941240
ISSN: 1558-349x
CID: 3162742
