Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study

• Published in: Cancer imaging Vol. 24; no. 1; pp. 32 - 10
• Main Authors: Park, Yae Won, Park, Ji Eun, Ahn, Sung Soo, Han, Kyunghwa, Kim, NakYoung, Oh, Joo Young, Lee, Da Hyun, Won, So Yeon, Shin, Ilah, Kim, Ho Sung, Lee, Seung-Koo
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.03.2024; BioMed Central; BMC
• Subjects: Artificial intelligence; Blood; Brain; Brain cancer; Brain metastases; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Brain tumors; Cancer patients; Cancer therapies; Clinical medicine; Confidence intervals; Deep Learning; Early Detection of Cancer; Humans; Lung cancer; Lung Neoplasms - diagnostic imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical imaging; Metastasis; Neural networks; Patients; Radiation therapy; Readers; Reading; Reproducibility; Reproducibility of Results; Retrospective Studies; Software; Tumors; Workflow; Workload; Workloads; Deep learning; Brain metastases; Magnetic resonance imaging; Brain tumors

To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having new...