Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine. Issue 2 (February 2022)

• Record Type: Journal Article
• Authors: Kocher, Madison R.; Chamberlin, Jordan; Waltz, Jeffrey; Snoddy, Madalyn; Stringer, Natalie; Stephenson, Joseph; Kahn, Jacob; Mercer, Megan; Baruah, Dhiraj; Aquino, Gilberto; Kabakus, Ismail; Hoelzer, Philipp; Sahbaee, Pooyan; Schoepf, U. Joseph; Burt, Jeremy R.
• Is Part Of: Heliyon. Volume 8:Issue 2(2022)
• Publication Date: 2022-02
• Subjects: Breast cancer -- Chest CT -- Staging -- Artificial intelligence; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2022.e08962
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Background: Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. Objective: To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. Methods: Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. Results: 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07–1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). Conclusion: Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. Clinical impact: As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes. Highlights: Automated detection software can quantify lung metastases on initial staging chest CT in breast cancer patients. AI-detected lung metastases number and max diameter on CT at initial cancer staging were strong predictors of mortality. AI detection and segmentation tool contributes to accurate individualized prognostication in breast cancer patients. Abstract : Breast cancer, Chest CT, Staging, Artificial intelligence.