Purpose	Detection and characterization of incidental pulmonary nodules on chest radiographs (CXR). These are nodules that are detected on CXRs performed for other reasons than lung cancer screening.
Tag(s)
Panel	Thoracic
Define-AI ID	08190004
Originator	Thoracic Panel
Panel Chair	Warren B. Gefter, MD; Eric J. Stern, MD
Panel Reviewers	Thoracic Panel
License	Creative Commons 4.0
Status	Published
RadElement Set	RDES94

Value Proposition

Lung cancer, the leading cause of cancer-related deaths in both women and men, frequently presents as a pulmonary nodule on chest radiographs (CXRs) or CT scans. While low-dose CT is utilized for lung cancer screening, chest radiography, being among the most highly utilized diagnostic imaging procedures worldwide, is the most common thoracic imaging study in which incidental lung cancers are discovered. Nonetheless, interpretation of chest radiographs is challenging and prone to many reading errors. Thus nodules are frequently missed on CXRs, with studies showing approximately 20-30% (even up to 90%) seen only in retrospect. The causes for these frequent errors are multifactorial, including: overlapping anatomic structures such as the ribs, clavicles, thoracic spine, pulmonary vessels, heart, mediastinum and diaphragms; errors in visual search, lesion recognition or decision-making; and suboptimal image quality. Small, ill-defined nodules with low attenuation and conspicuity are particularly susceptible to being overlooked. As early detection of lung cancer reduces mortality, missed or delayed diagnosis due to these CXR errors may negatively impact patient survival.

Furthermore, such errors carry significant medicolegal risks, being the second most common cause (after breast cancer) for malpractice litigation in radiology. Algorithms based upon machine learning therefore offer an important use case to assist radiologists in more accurate detection, characterization, and any communication and recommendation for further study of these nodules. This may be particularly true for less experienced radiologists or in places without access to radiology expertise. These algorithms show promise in improving upon traditional CAD (computer-assisted detection) systems.

Narrative(s)

A 45-year-old man with cough and fever has a CXR for evaluation of possible pneumonia. Algorithm evaluates the lungs and detects a non-calcified, irregular-shaped nodule at the right lung apex partially obscured by the anterior first rib. Lesion is highlighted on annotated image, so as not to be overlooked by the radiologist. Radiologist confirms that this is a new finding compared with older CXRs and recommends further evaluation with a chest CT scan. Appropriate communication with the referring clinician is made.

A 40-year-old woman, never-smoker, undergoes a CXR for a newly positive PPD. Algorithm evaluates the lungs and detects a subtle smooth, round, non-calcified nodule in the retrocardiac region of the left lung. Lesion is highlighted on image annotated by algorithm. No prior CXRs are available. Radiologist recommends further evaluation with a chest CT. Appropriate communication with the referring physician is made.

A 69-year-old man with a long history of cigarette smoking has a CXR to evaluate a chronic cough. Algorithm evaluates the lungs and detects a subtle, juxtavascular nodule adjacent to the right hilum. The lesion is highlighted on an image annotated by the algorithm so as not to be missed by the radiologist. In retrospect the lesion can be seen on an earlier CXR and has enlarged in the interim. The radiologist recommends further evaluation with a chest CT and assures appropriate communication with the referring physician.

Workflow Description

CXR sent to PACS and the AI engine. Image analyzed by AI algorithm, which detects and characterizes the incidental pulmonary nodule(s). Annotated image(s) highlighting each incidental pulmonary nodule with associated nodule characteristics sent to PACS. Icon indicating AI nodule detection may appear on worklist for prioritization.

Procedures	CXR, CR, DR, dual-energy, and bone-suppression CXRs
Views	PA, lateral, AP, apical lordotic, obliques
Age	≥ 18 years old
Sex at birth	Male, Female
Nodule Validation	CT within 1 month of CXR. Corresponding nodule location on CXR confirmed by chest radiologists.
Nodule attenuation based on CT confirmation	solid, part-solid, groundglass, internal fat density, calcification, cavitary
Size (in mm)	[5,40]
Shape	round, oval, triangular, lobular, irregular
Margin	smooth, irregular, spiculated
Location	broad sampling of lung regions, apex to base, central to peripheral
Comorbidities	Smokers, non-smokers, COPD, travel/exposure history, other primary malignancy or history of primary malignancy, bronchitis, bronchiolitis, pneumonia, tuberculosis, fungal and other pulmonary infections, focal inflammatory lesions, usual interstitial pneumonia and other diffuse lung diseases, pleural effusion.
Other Considerations	Range of CXR technologies (CR, DR, dual-energy, bone suppression) and patient population demographics. Include normal CXRs without nodules, as well as those with single and multiple nodules. Range of nodule conspicuity. Datasets should be enriched with more challenging nodules prone to human error, including small lesions < 1 cm; lesions located in the apices/upper lobes, retrocardiac, perihilar and retrophrenic areas; and nodules with low conspicuity

Inputs

DICOM Study

Procedure	XRAY, Chest
Views	CXR: PA, lateral, AP, apical lordotic, obliques CR, DR, dual-energy, and bone-suppression CXRs
Data Type	DICOM
Modality	XRAY
Body Region	Chest
Anatomic Focus	Lung
Pharmaceutical	N/A
Scenario	N/A

 

Primary Outputs

Detection of nodule

 

Nodule attenuation

 

Nodule size

 

Nodule shape

 

Nodule margin

 

Nodule location

 

Secondary Outputs

Nodule growth

 

Probability of malignancy

 

Tracking of follow-up imaging

Automated tracking of whether recommended additional imaging studies have been ordered, scheduled, and performed and reminders to ordering clinicians or patients

Probability of Malignancy

Algorithm may output probability of malignancy of detected nodule(s) based upon CXR imaging features, smoking history and other clinical parameters.

No known related public datasets at this time, please alert us if you know of any.