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Pulmonary Health

Lung Cancer Screening

Epidemiology & Progression:

Lung cancer is the leading cause of cancer death globally [1]. Annual low-dose CT (LDCT) screening for high-risk individuals can detect cancers earlier, when they are more treatable. The National Lung Screening Trial (NLST) showed a 20% reduction in lung cancer mortality with LDCT screening versus chest radiography [2]. Approximately 10-15% of baseline LDCT scans show indeterminate nodules (Lung-RADS® 3/4), requiring follow-up; a small percentage represent malignancy [3, ACR Lung-RADS® v2022]. Consistent follow-up is crucial for realizing screening benefits. 

Clinical Value:

Systematic tracking ensures adherence to ACR Lung-RADS® guidelines, reducing delays in diagnosis and treatment initiation for suspicious findings. This improves patient outcomes and survival rates [2]. Failure to track screening exams and subsequent findings increases the risk of missed or delayed diagnoses, potentially leading to late-stage cancer discovery, poorer prognosis, increased treatment costs, and potential medical liability [4]. Proactive management enhances patient safety and care quality. 

Return on Investment (ROI):

Implementing a robust LCS tracking program like Thynk Health's can significantly increase downstream revenue through timely follow-up imaging, diagnostics (biopsies, PET), and interventions (surgery, oncology referrals). Based on proprietary modeling for a medium-sized hospital system, effective tracking can capture an estimated $1.6 million in additional net revenue over five years compared to standard follow-up rates (30% vs 70% capture), while improving adherence to quality measures [Thynk Health ROI Data, 2025]. 

Feature List:



    Lung-RADS® Integration: Automatically tracks Lung-RADS categories based on NLP findings. 





    Automated Follow-up Scheduling: Creates tasks/reminders based on Lung-RADS recommendations (e.g., 6-month LDCT for LR3, 3-month for LR4A). 





    Patient Cohort Management: Tracks eligible screening population and adherence rates. 





    Reporting & Analytics: Monitors screening volume, Lung-RADS distribution, follow-up completion rates, and cancer detection rates. 

Mobile Lung Cancer Screening

Epidemiology & Progression:

Mobile lung cancer screening units aim to improve access and reduce disparities in screening uptake, particularly in rural or underserved communities where lung cancer incidence and mortality rates can be higher [5]. Bringing LDCT screening directly to high-risk populations can increase participation rates. Progression risks for detected nodules follow standard Lung-RADS® categories, emphasizing the need for reliable follow-up regardless of screening location [ACR Lung-RADS® v2022].

Clinical Value:

Mobile units increase screening accessibility, facilitating earlier detection among hard-to-reach populations [5]. However, ensuring longitudinal tracking and follow-up for patients screened remotely presents unique challenges. Integrated platforms connecting mobile units with hospital systems are vital to guarantee appropriate management according to Lung-RADS® guidelines, preventing patients from being lost to follow-up after an abnormal screen [6].

Return on Investment (ROI):

While expanding market reach, mobile LCS requires efficient coordination to realize ROI. Thynk Health ensures findings from mobile units are integrated and tracked, facilitating the same downstream revenue opportunities as fixed-site screening (estimated $1.6 million net revenue gain over 5 years with 70% vs 30% capture). This prevents patient leakage and ensures appropriate follow-up care within the health system, maximizing the investment in mobile outreach [Thynk Health ROI Data, 2025].

Feature List:



    Data Integration: Seamlessly incorporates patient data and reports from mobile units into the central tracking system. 


    Cross-Platform Communication: Facilitates communication between mobile unit staff and hospital-based navigators/providers. 


    Geographic/Demographic Reporting: Tracks screening reach and identifies underserved areas. 

ILA (Interstitial Lung Abnormalities)

Epidemiology & Progression:

Interstitial Lung Abnormalities (ILA) are incidental findings on chest CT, present in 4-9% of smokers and 7-10% of older adults [7]. While often non-progressive, certain ILA patterns carry a significantly increased risk of progression to clinically significant interstitial lung disease (ILD) and are associated with increased mortality [8]. Progression rates vary, with estimates around 20-40% over 5 years for specific ILA subtypes [7, 8].

Clinical Value:

Tracking ILA allows for risk stratification and appropriate surveillance according to guidelines, enabling early detection of progression to ILD when interventions may be more effective [8]. Untracked ILA can lead to delayed diagnosis of serious conditions like idiopathic pulmonary fibrosis (IPF), resulting in irreversible lung damage and poorer outcomes. Systematic monitoring helps differentiate stable ILA from progressive ILD [ACR White Paper on Incidental ILD].

Return on Investment (ROI):

Proactive ILA tracking identifies patients needing pulmonology referral and specialized care earlier. Based on proprietary modeling for a medium-sized hospital system, effectively managing ILA follow-up can capture an estimated $15.2 million in additional net revenue over five years (70% vs 30% capture) through appropriate diagnostic workups (PFTs, specialized CTs) and management of progressive ILD [Thynk Health ROI Data, 2025]. 

Feature List:



    NLP for ILA Patterns: Identifies specific ILA descriptors (e.g., reticulation, honeycombing, traction bronchiectasis) from radiology reports. 


    Risk Stratification Logic: Applies logic based on ILA pattern and extent to flag higher-risk patients per guidelines (e.g., Fleischner Society ILA White Paper). 


    Pulmonology Referral Workflow: Triggers automated tasks or notifications for pulmonology consultation based on findings. 

Pulmonary Nodule Tracking

Epidemiology & Progression:

Incidental pulmonary nodules (IPNs) are found in up to 35% of CT scans [9]. The vast majority are benign, but malignancy risk depends on size, morphology, patient risk factors, and growth rate. Fleischner Society guidelines stratify risk and recommend follow-up intervals [10]. Malignancy rates range from <1% for small, stable nodules to >15-25% for larger, growing, or spiculated nodules in high-risk patients [10].

Clinical Value:

Adherence to Fleischner Society guidelines via systematic tracking ensures appropriate surveillance for IPNs, balancing early cancer detection against unnecessary radiation exposure and procedures [10]. Failure to track IPNs is a major source of diagnostic delays and potential litigation [4]. Automated tracking minimizes missed follow-ups, facilitates timely intervention for growing nodules, and provides reassurance for stable findings.

Return on Investment (ROI):

Efficiently managing IPN follow-up significantly impacts downstream revenue and reduces liability. Based on proprietary modeling for a medium-sized hospital system, Thynk Health's automated tracking can drive an estimated $12.8 million in additional net revenue over five years (70% vs 30% capture) through guideline-appropriate imaging, biopsies, and early-stage cancer treatment, preventing patient leakage [Thynk Health ROI Data, 2025, 11]. 

Feature List:



    Fleischner Guideline Integration: Applies Fleischner criteria based on nodule size, type (solid, subsolid), and patient risk factors extracted via NLP. 


    Longitudinal Nodule Comparison: Tracks nodule size/characteristics over time across multiple scans. 


    Growth Rate Calculation (Potential Feature): Flags nodules meeting growth criteria for urgent review. 


    Automated Worklist Generation: Creates lists of patients due for follow-up scans. 

Important Note on ROI: The Return on Investment (ROI) figures presented are derived from Thynk Health's proprietary performance data analysis. These calculations are based on models simulating a medium-sized hospital system processing approximately 600,000 CT scans annually through emergency departments and utilizing multispecialty guidelines for follow-up care pathways and associated downstream revenue capture. Actual ROI may vary based on specific institutional factors, patient populations, and payer mix.  

References: 

[1] GLOBOCAN 2020 / Cancer.org 
[2] National Lung Screening Trial Research Team. (2011). Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. NEJM. 
[3] Pinsky, P. F., Gierada, D. S., Black, W., Munden, R., Nath, H., Aberle, D., & Kazerooni, E. (2015). Performance of Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Annals of internal medicine. 
[4] Lee, C. I., et al. (2013). Diagnostic CT scans: assessment of patient, physician, and radiologist awareness of incidental findings. Radiology. 
[5] Eberth, J. M., et al. (2019). Use of Mobile Computed Tomography Clinics for Lung Cancer Screening. JAMA Internal Medicine. 
[6] Thynk Health White Paper: Mobile Lung Cancer Screening Coordination. [Placeholder] 
[7] Putman, R. K., et al. (2016). Association Between Interstitial Lung Abnormalities and All-Cause Mortality. JAMA. 
[8] Hatabu, H., et al. (2020). Interstitial Lung Abnormalities Detected Incidentally on CT: A White Paper. Recommendations from the Fleischner Society. Radiology. 
[9] Gould, M. K., et al. (2013). Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 
[10] MacMahon, H., et al. (2017). Guidelines for management of incidental pulmonary nodules detected on CT scans: from the Fleischner Society 2017. Radiology. 
[11] Thynk Health White Paper: Incidental Findings Management. (Referenced in memory) 
[12] Hecht, H. S., et al. (2017). Coronary Artery Calcium Scanning: Clinical and Prognostic Impact. JACC: Cardiovascular Imaging. 
[13] Lehmann, N., et al. (2014). Coronary artery calcium progression: a landmark study. JACC: Cardiovascular Imaging. 
[14] ACR–SPR–SSR Practice Parameter for the Performance and Interpretation of Cardiac Computed Tomography (CT). 
[15] Upchurch Jr, G. R., & Schaub, T. A. (2006). Abdominal aortic aneurysm. American family physician. 
[16] Chaikof, E. L., et al. (2018). The Society for Vascular Surgery practice guidelines on the care of patients with an abdominal aortic aneurysm. Journal of vascular surgery. 
[17] ACR Appropriateness Criteria® Pulsatile Abdominal Mass Suspected Abdominal Aortic Aneurysm. 
[18] Messika-Zeitoun, D., et al. (2007). Assessment and clinical implications of aortic valve calcification measured by electron-beam computed tomography. Circulation. 
[19] Osnabrugge, R. L., et al. (2013). Aortic stenosis in the elderly: disease prevalence and number of candidates for transcatheter aortic valve replacement: a meta-analysis and modeling study. JACC: Cardiovascular Interventions. 
[20] Otto, C. M., et al. (2021). 2020 ACC/AHA guideline for the management of patients with valvular heart disease. JACC. 
[21] Healthcare Financial Management Association (HFMA) / Published hospital chargemasters. [General reference for procedure costs] 
[22] Elta, G. H., et al. (2018). ACG clinical guideline: diagnosis and management of pancreatic cysts. American Journal of Gastroenterology. 
[23] Berland, L. L., et al. (2017). Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee. Journal of the American College of Radiology. (Covers Pancreas) 
[24] Scheiman, J. M., et al. (2007). Screening for pancreatic cancer in high-risk individuals. Gastroenterology. 
[25] Corwin, M. T., et al. (2011). Incidental gallbladder polyps: when to resect and when to observe. American Journal of Roentgenology. 
[26] Wiles, R., et al. (2017). Management of gallbladder polyps. World Journal of Gastroenterology. / Foley, K. G., et al. (2021). ESGAR consensus statement on the imaging and management of gallbladder polyps. European Radiology. 
[27] Hundal, R., & Shaffer, E. A. (2014). Gallbladder cancer: epidemiology and outcome. Clinical epidemiology. 
[28] Ghonge, N. P., et al. (2010). Incidental splenic lesions. Indian Journal of Radiology and Imaging. 
[29] Berland, L. L., et al. (2010). Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee. Journal of the American College of Radiology. (Covers Spleen, Liver, Adrenal, Renal) 
[30] Jones, M. P. (2015). Evaluation and treatment of hiatal hernias. Surgical Clinics. 
[31] Kohn, G. P., et al. (2013). Guidelines for the management of hiatal hernia. SAGES. 
[32] Strate, L. L., & Morris, A. M. (2019). Epidemiology, pathophysiology, and treatment of diverticulitis. Gastroenterology. 
[33] Peery, A. F., et al. (2015). AGA technical review on the management of acute diverticulitis. Gastroenterology. 
[34] Gore, R. M., et al. (2011). Incidental liver lesions: pattern-based classification on CT. Cancer Imaging. 
[35] Marrero, J. A., et al. (2018). Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology. 
[36] Younossi, Z. M., et al. (2016). Global epidemiology of nonalcoholic fatty liver disease—meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 
[37] Rinella, M. E., et al. (2023). AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 
[38] Hoang, J. K., et al. (2015). Managing incidental thyroid nodules detected on imaging: white paper of the ACR Incidental Thyroid Findings Committee. Journal of the American College of Radiology. 
[39] Tessler, F. N., et al. (2017). ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. Journal of the American College of Radiology. 
[40] Mayo-Smith, W. W., et al. (2017). Management of incidental adrenal masses: a white paper of the ACR Incidental Findings Committee. Journal of the American College of Radiology. 
[41] Fassnacht, M., et al. (2016). Management of adrenal incidentalomas: European Society of Endocrinology Clinical Practice Guideline in collaboration with the European Network for the Study of Adrenal Tumors. European journal of endocrinology. 
[42] Silverman, S. G., et al. (2017). Management of the incidental renal mass. Radiology. / Israel, G. M., et al. (2017). ACR Appropriateness Criteria® Renal Mass-Solid and Cystic Lesions. Journal of the American College of Radiology. 
[43] Ljungberg, B., et al. (2016). EAU guidelines on renal cell carcinoma: 2014 update. European urology. 
[44] Turkbey, B., et al. (2019). PI-RADS 2.1: 2019 Update of Prostate Imaging Reporting and Data System. Radiology. 
[45] Sickles, E. A., et al. (2013). ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA, American College of Radiology. 
[46] Levine, D., et al. (2010). Management of asymptomatic ovarian and other adnexal cysts imaged at US: Society of Radiologists in Ultrasound Consensus Conference Statement. Radiology. / Patel, M. D., et al. (2018). ACR Appropriateness Criteria® Clinically Suspected Adnexal Mass. Journal of the American College of Radiology. 
[47] American College of Obstetricians and Gynecologists. (2016). Practice Bulletin No. 174: Evaluation and Management of Adnexal Masses. Obstetrics & Gynecology. 
[48] Stewart, E. A., et al. (2017). Epidemiology of uterine fibroids: a systematic review. BJOG: An International Journal of Obstetrics & Gynaecology. 
[49] Thompson, R. J., et al. (2011). Incidental findings on CT scans in the emergency department. Emergency Medicine International. 
[50] Dalal, A. K., et al. (2014). Closing the loop: ensuring effective communication of critical imaging results. Journal of the American Medical Informatics Association. 

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