Leveraging artificial intelligence for timely detection and referral of incidental pulmonary nodules at a safety-net hospital.
Authors: Johnson Raymond , Patel Paras M. , Winter Cindy , Dean Nikki M. , Donepudi Jyotsna , Valladares Carlos M. , Schoppe Kurt , Narra Kalyani 
Published: September 2024

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Background:
Incidental pulmonary nodules (IPNs) are commonly reported through a multitude of radiologic exams. A small number of these IPNs can harbor lung cancer (LC). To aide LC detection rates, hospitals must identify and validate the completion of appropriate follow-up regarding IPNs in a timely manner. The problem with timely referral and completion of work up is found to be amplified in safety-net settings with limited resources. John Peter Smith (JPS) hospital, a safety-net hospital, has developed a method to identify, track, and refer patients presenting with IPNs.
Methods:
JPS has created an algorithm to classify all IPNs by means of Thynk Health, an Artificial Intelligence (AI) platform that uses Natural Language Processing (NLP) to identify and track incidental findings and lung cancer screening patients (LCS). The algorithm created a scoring system by combining Fleischner Society (2017) and the American College of Radiology (ACR) LCS (Lung-RADS) guideline recommendations. The scoring system was designed to mimic Lung-RADS established by the ACR (1, 2, 3, 4A, and 4B). The assigned score creates a risk stratification to assist providers in the patient’s care plan. Using NLP, the project queried radiologic exams performed at JPS searching for key terminology relating to IPNs. Findings were then reviewed by radiology staff for validity and assigned a score based on the algorithm. Program exclusions, when identified in the radiology report, included: patient < 35 years, patient has known cancer or under surveillance of oncology team, abnormalities favoring infectious process, LCS patients and when nodule size is not reported in the radiologist’s dictation.
Results:
A current state analysis was completed from 3/25/24 to 4/25/24. The AI platform analyzed 5,276 CT exams (2049 abdomen, 1347 head/neck, and 1880 chest) involving partial or complete imaging of the lungs. A total of 727 (13.8%) IPNs were flagged for review. After excluding exams falling outside of the set program parameters, a total of 178 (3.4%) IPNs were identified. 21 (11.8%) of the IPNs identified received a score of 4A or 4B. Patients found to have a score of 4A (11, 6.2%) and 4B (10, 5.6%) underwent additional review.
Conclusions:
Our team has created a unique system that can be used as a model for other large health systems. By leveraging technology, we are able to successfully identify key factors associated with IPNs to help ensure patients receive the recommended care in a timely manner.

Results of JPS incidental pulmonary nodule program: first monthly analysis.

JPS Pulmonary Nodule Score
4A
4B

Number of Patients (% of IPNs)
11 (6.2%)
10 (5.6%)

Median Age (years)
70
68.5

JPS Assigned PCP
1

7 (63.6%)
7 (70%)

Median Smoking History (PY)
2

6.4
24.5

Average Nodule Size (mm)
11.5
34.3

Pulmonary Referral
3/11 (27.3%)
7/10 (70%)

Positive for Cancer
0
3/4 (75%)

1
Primary care provider.

2
Pack years.