| Title: |
Evaluation of a novel deep learning–based classifier for perifissural nodules |
| Authors: |
Han, D; Heuvelmans, M; Rook, M; Dorrius, M; van Houten, L; Price, NW; Pickup, LC; Novotny, P; Oudkerk, M; Declerck, J; Gleeson, F; van Ooijen, P; Vliegenthart, R |
| Publisher Information: |
Springer |
| Publication Year: |
2026 |
| Collection: |
Oxford University Research Archive (ORA) |
| Description: |
OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN). METHODS: Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN-CNN. Typical PFNs were considered as positive results, and atypical PFNs and non-PFNs were grouped as negative results. PFN-CNN performance was evaluated using the ROC curve, confusion matrix, and Cohen's kappa. RESULTS: Internal validation yielded a mean AUC of 91.9% (95% CI 90.6-92.9) with 78.7% sensitivity and 90.4% specificity. For the test set, the reader consensus rated 45/196 (23%) of nodules as typical PFN. The classifier-reader agreement (k = 0.62-0.75) was similar to the inter-reader agreement (k = 0.64-0.79). Area under the ROC curve was 95.8% (95% CI 93.3-98.4), with a sensitivity of 95.6% (95% CI 84.9-99.5), and specificity of 88.1% (95% CI 81.8-92.8). CONCLUSION: The PFN-CNN showed excellent performance in classifying typical PFNs. Its agreement with radiologically trained readers is within the range of inter-reader agreement. Thus, the CNN-based system has potential in clinical and screening settings to rule out perifissural nodules and increase reader efficiency. KEY POINTS: • Agreement between the PFN-CNN and radiologically trained readers is within the range of inter-reader agreement. • The CNN model for the classification of typical PFNs achieved an AUC of 95.8% (95% CI 93.3-98.4) with 95.6% (95% CI 84.9-99.5) ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1007/s00330-020-07509-x |
| Availability: |
https://doi.org/10.1007/s00330-020-07509-x; https://ora.ox.ac.uk/objects/uuid:a0252658-7492-4e82-921b-5eae6bd12e41 |
| Rights: |
info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| Accession Number: |
edsbas.DCC43CCA |
| Database: |
BASE |