Medicare Advantage

Medical Policy

Notification

Computer-Aided Detection (CAD) System for Use with Chest Radiographs

Notification Issued Date:

This policy has been reviewed and reissued to communicate the Company’s continuing position on Computer-Aided Detection (CAD) System for use with Chest Radiographs

Title:

Computer-Aided Detection (CAD) System for Use with Chest Radiographs

Policy #:

MA09.014b

Policy

In the absence of coverage criteria from applicable Medicare statutes, regulations, NCDs, LCDs, CMS manuals, or other Medicare coverage documents, this policy uses internal coverage criteria developed by the Company in consideration of peer-reviewed medical literature, clinical practice guidelines, and/or regulatory status.

The use of computer-aided detection (CAD) systems with chest radiographs is considered experimental/investigational and, therefore, not covered because the safety and/or effectiveness of this service cannot be established by review of the available published literature.*

Guidelines

There is no Medicare coverage criteria addressing this service; therefore, the Company policy is applicable.

BENEFIT APPLICATION

Subject to the terms and conditions of the applicable Evidence of Coverage, the use of computer-aided detection (CAD) systems with chest radiographs is not eligible for payment under the medical benefits of the Company’s Medicare Advantage products because the service is considered experimental/investigational and, therefore, not covered.

Services that are experimental/investigational are excluded for the Company’s Medicare Advantage products. Therefore, they are not eligible for reimbursement consideration.

US FOOD AND DRUG ADMINISTRATION (FDA) STATUS

There are numerous devices approved by the FDA for use as a computer-aided detection (CAD) system.

Description

A computer-aided detection (CAD) system is used as an adjunctive tool in assessing chest radiographs. The basic function of CAD is to provide radiologists with a computer algorithm that assists with interpreting radiological images. CAD is thought to improve the accuracy and consistency of radiological diagnosis by reducing the time it takes to interpret images.

The RapidScreen™ RS-2000 (Deus Technologies, Rockville, MD) is a CAD system that identifies and marks regions of interest on digitized frontal chest radiographs. It is believed to identify features associated with solitary pulmonary nodules from 9 mm to 30 mm in size, which could represent early-stage lung cancer. The device is intended for use as an aid only after a physician has performed a preliminary interpretation of the radiograph.

The published literature regarding CAD for chest X-rays consists primarily of the technical capabilities of CAD systems. High-quality, randomized trials examining the effect of CAD systems for chest X-rays on lung cancer morbidity and mortality are necessary to determine the true impact of this technology on health outcomes. There is a paucity of literature that supports the efficacy of this modality at this time.

References

Abe H, Macmahon H, Shiraishi J, et al. Computer-aided diagnosis in chest radiology. Semin Ultrasound CT MR. 2004;25(5):432-437.

Coppini G, Diciotti S, Falchini M, Neural networks for computer-aided diagnosis: Detection of lung nodules in chest radiograms. IEEE Trans Inf Technol Biomed. 2003;7(4):344-357.

Croswell JM, Baker SG, Marcus PM, et al. Cumulative incidence of false-positive test results in lung cancer screening. Ann Intern Med. 2010;152(8):505-512.

De Boo DW, Prokop M, Uffmann M, et al. Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs. Eur J Radiol. 2009;72(2):218-225.

de Hoop B, De Boo DW, Gietema HA et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology. 2010;257(2):532-540.

Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol. 2005;78 Spec No 1:S3-S19.

Freedman M. Improved small volume lung cancer detection with computer-aided detection: Database characteristics and imaging of response to breast cancer risk reduction strategies. Ann N Y Acad Sci. 2004;1020:175-189.

Freedman M. State-of-the-art screening for lung cancer (part1): the chest radiograph. Thorac Surg Clin. 2004;14(1):43-52.

Hayes, Inc. Evidence Analysis Research Brief. Computer-aided detection systems with chest radiographs for assessment of pulmonary nodules or masses. Hayes, Inc. [Hayes Web site]. 07/02/2024. Available at: Hayes Knowledge Center | symplr (hayesinc.com). [via subscription only]. Accessed September 21, 2025

Hocking WG, Oken MM, Winslow SD, et al. Lung cancer screening in the randomized prostate, lung, colorectal and ovarian (PLCO) cancer screening trial. J Natl Cancer Inst. 2010;102(10):722-31.

Infante M, Lutman FR, Cavuto S, et al. Lung cancer screening with spiral CT: baseline results of the randomized DANTE trial. Lung Cancer. 2008;59(3):355-363.

Infante M, Cavuto S, Lutman FR, et al. A randomized study of lung cancer screening with spiral computed tomography: three-year results from the DANTE Trial. Am J Respir Crit Care Med. 2009;180(5):445-53.

Kakeda S, Moriya J, Sato H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am J Roentgenol. 2004;182(2):505-510.

Li F, Engelmann R, Metz CE, et al. Lung cancers missed on chest radiographs: Results obtained with a commercial computer-aided detection program. Radiology. 2008;246(1):273-280.

Mazzone PJ, Obuchowski N, Phillips M et al. Lung cancer screening with computer aided detection chest radiography: design and results of a randomized, controlled trial. PLoS One. 2013;8(3):e59650.

Mohammed TH, Chowdhry A, Reddy GP, et al.; Expert Panel on Thoracic Imaging. ACR Appropriateness Criteria® screening for pulmonary metastases. [online publication]. Reston, VA: American College of Radiology (ACR); 2010.

Qin J, Bai H, Liu C, et al. Application of computer-aided diagnosis in early detection of pulmonary nodules based on digital chest radiograph. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2014;31(5):1117-1120.

Shi Z, Ma J, Feng Y, et al. Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection software. Pak J Pharm Sci. 2015;28(6 Suppl):2311-2316.

Shiraishi J, Abe H, Engelmann R, Doi K. Effect of high sensitivity in a computerized scheme for detecting extremely subtle solitary pulmonary nodules in chest radiographs: Observer performance study. Acad Radiol. 2003;10(11):1302-1311.

Shiraishi J, Li F, Doi K. et al. Computer-aided diagnosis for improved detection of lung nodules by use of posterior-anterior and lateral chest radiographs. Acad Radiol. 2007;14(1):28-37.

Suzuki K, Shiraishi J, Abe H, et al. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol. 2005;12(2):191-201.

US Food and Drug Administration (FDA). Center for Devices and Radiological Health. Premarket approval letter. RapidScreen™ RS-2000. [FDA Web site]. 07/12/01. Available at: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P000041. Accessed September 21, 2025

Van Iersel CA, de Koning HJ, Draisma G, et al. Risk-based selection from the general population in a screening trial: selection criteria, recruitment and power for the Dutch-Belgian randomized lung cancer multi-slice CT screening trial (NELSON). Int J Cancer. 2007;120(4):868-74.

Van Klaveren RJ, Oudkerk M, Prokop M, et al. Management of lung nodules detected by volume CT screening. N Engl J Med. 2009;361(23):2221-2229.

Way T, Chan HP, Hadjiiski L, et al. Computer-aided diagnosis of lung nodules on CT scans: ROC study of its effect on radiologists' performance. Acad Radiol. 2010;17(3):323-332.

White CS, Flukinger T, Jeudy J et al. Use of a computer-aided detection system to detect missed lung cancer at chest radiography. Radiology. 2009;252(1):273-281.

Yamada Y, Shiomi E. Hashimoto M, et al. Value of computer-aided detection system based on chest tomosynthesis imaging for the detection for pulmonary nodules. Radiology. 2018;287(1): 333-339.

Yanagawa M, Honda O, Yoshida S, Ono Y, Inoue A, Daimon T, et al. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: Preliminary study of 48 cases. Acad Radiol. 2009;16:924-933.

Coding

CPT Procedure Code Number(s)

0174T, 0175T

ICD - 10 Procedure Code Number(s)

N/A

ICD - 10 Diagnosis Code Number(s)

N/A

HCPCS Level II Code Number(s)

N/A

Revenue Code Number(s)

N/A

MPMiscCodesNarrativesPub

Coding and Billing Requirements

Cross Reference

Policy History

Version Effective Date:

9/9/2024

Version Issued Date:

9/9/2024

Version Reissued Date:

10/29/2025