Abstract
AbstractGiven the high prevalence of lung cancer, an accurate diagnosis is crucial. In the diagnosis process, radiologists play an important role by examining numerous radiology exams to identify different types of nodules. To aid the clinicians’ analytical efforts, computer-aided diagnosis can streamline the process of identifying pulmonary nodules. For this purpose, medical reports can serve as valuable sources for automatically retrieving image annotations. Our study focused on converting medical reports into nodule annotations, matching textual information with manually annotated data from the Lung Nodule Database (LNDb)—a comprehensive repository of lung scans and nodule annotations. As a result of this study, we have released a tabular data file containing information from 292 medical reports in the LNDb, along with files detailing nodule characteristics and corresponding matches to the manually annotated data. The objective is to enable further research studies in lung cancer by bridging the gap between existing reports and additional manual annotations that may be collected, thereby fostering discussions about the advantages and disadvantages between these two data types.
Funder
Ministry of Education and Science | Fundação para a Ciência e a Tecnologia
Publisher
Springer Science and Business Media LLC
Reference23 articles.
1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 71, 209–249 (2021).
2. Del Ciello, A. et al. Missed lung cancer: when, where, and why? Diagnostic and Interventional Radiology 23, 118–126 (2017).
3. Qadan, L., Ahmed, A. & Kapila, K. Thyroid ultrasound reports: deficiencies and recommendations. Medical Principles and Practice 28, 280–283 (2019).
4. Bruno, M. A., Walker, E. A. & Abujudeh, H. H. Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35, 1668–1676 (2015).
5. Onder, O. et al. Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review. Insights into Imaging 12, 1–21 (2021).