Deep learning-based detection of patients with bone metastasis from Japanese radiology reports.
Bone metastasis
Deep learning
Long short-term memory
Natural language processing
Radiology report
Journal
Japanese journal of radiology
ISSN: 1867-108X
Titre abrégé: Jpn J Radiol
Pays: Japan
ID NLM: 101490689
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
received:
16
11
2022
accepted:
07
03
2023
medline:
23
10
2023
pubmed:
30
3
2023
entrez:
29
3
2023
Statut:
ppublish
Résumé
Deep learning (DL) is a state-of-the-art technique for developing artificial intelligence in various domains and it improves the performance of natural language processing (NLP). Therefore, we aimed to develop a DL-based NLP model that classifies the status of bone metastasis (BM) in radiology reports to detect patients with BM. The DL-based NLP model was developed by training long short-term memory using 1,749 free-text radiology reports written in Japanese. We adopted five-fold cross-validation and used 200 reports for testing the five models. The accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristics curve (AUROC) were used for the model evaluation. The developed model demonstrated classification performance with mean ± standard deviation of 0.912 ± 0.012, 0.924 ± 0.029, 0.901 ± 0.014, 0.898 ± 0.012, and 0.968 ± 0.004 for accuracy, sensitivity, specificity, precision, and AUROC, respectively. The proposed DL-based NLP model may help in the early and efficient detection of patients with BM.
Identifiants
pubmed: 36988827
doi: 10.1007/s11604-023-01413-2
pii: 10.1007/s11604-023-01413-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
900-908Subventions
Organisme : Japan Society for the Promotion of Science
ID : JP18K15567
Informations de copyright
© 2023. The Author(s) under exclusive licence to Japan Radiological Society.
Références
Savova GK, Danciu I, Alamudun F, Miller T, Lin C, Bitterman DS, et al. Use of natural language processing to extract clinical cancer phenotypes from electronic medical records. Cancer Res. 2019;79(21):5463–70.
doi: 10.1158/0008-5472.CAN-19-0579
pubmed: 31395609
pmcid: 7227798
Bao Y, Deng Z, Wang Y, Kim H, Armengol VD, Acevedo F, et al. Using machine learning and natural language processing to review and classify the medical literature on cancer susceptibility genes. JCO Clin Cancer Inform. 2019;3:1–9.
doi: 10.1200/CCI.19.00042
pubmed: 31545655
Hughes KS, Zhou J, Bao Y, Singh P, Wang J, Yin K. Natural language processing to facilitate breast cancer research and management. Breast J. 2020;26(1):92–9.
doi: 10.1111/tbj.13718
pubmed: 31854067
Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17(5):507–13.
doi: 10.1136/jamia.2009.001560
pubmed: 20819853
pmcid: 2995668
Dang NC, Moreno-García MN, De la Prieta F. Sentiment analysis based on deep learning: a comparative study. Electronics. 2020;9(3):483.
doi: 10.3390/electronics9030483
Yim WW, Yetisgen M, Harris WP, Kwan SW. natural language processing in oncology: a review. JAMA Oncol. 2016;2(6):797–804.
doi: 10.1001/jamaoncol.2016.0213
pubmed: 27124593
Fu S, Wyles CC, Osmon DR, Carvour ML, Sagheb E, Ramazanian T, et al. Automated detection of periprosthetic joint infections and data elements using natural language processing. J Arthroplasty. 2021;36(2):688–92.
doi: 10.1016/j.arth.2020.07.076
pubmed: 32854996
Zhang K, Demner-Fushman D. Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations. J Am Med Inform Assoc. 2017;24(4):781–7.
doi: 10.1093/jamia/ocw176
pubmed: 28339690
pmcid: 6080677
Cancer Statistics. Cancer Information Service, National Cancer Center, Japan (National Cancer Registry, Ministry of Health, Labour and Welfare). https://www.mhlw.go.jp/content/10900000/000942181.pdf . Accessed 23 Mar 2023
Hara H, Sakai Y, Kawamoto T, Fukase N, Kawakami Y, Takemori T, et al. Surgical outcomes of metastatic bone tumors in the extremities (Surgical outcomes of bone metastases). J Bone Oncol. 2021;27:100352.
doi: 10.1016/j.jbo.2021.100352
pubmed: 33850700
pmcid: 8039818
Ulas A, Bilici A, Durnali A, Tokluoglu S, Akinci S, Silay K, et al. Risk factors for skeletal-related events (SREs) and factors affecting SRE-free survival for nonsmall cell lung cancer patients with bone metastases. Tumour Biol. 2016;37(1):1131–40.
doi: 10.1007/s13277-015-3907-z
pubmed: 26276360
Callen JL, Westbrook JI, Georgiou A, Li J. Failure to follow-up test results for ambulatory patients: a systematic review. J Gen Intern Med. 2012;27(10):1334–48.
doi: 10.1007/s11606-011-1949-5
pubmed: 22183961
Poon EG, Gandhi TK, Sequist TD, Murff HJ, Karson AS, Bates DW. I wish I had seen this test result earlier: dissatisfaction with test result management systems in primary care. Arch Intern Med. 2004;164(20):2223–8.
doi: 10.1001/archinte.164.20.2223
pubmed: 15534158
Singh H, Sethi S, Raber M, Petersen LA. Errors in cancer diagnosis: current understanding and future directions. J Clin Oncol. 2007;25(31):5009–18.
doi: 10.1200/JCO.2007.13.2142
pubmed: 17971601
Do RK, Lupton K, Causa Andrieu PI, Luthra A, Taya M, Batch K, et al. Patterns of metastatic disease in patients with cancer derived from natural language processing of structured CT radiology reports over a 10-year period. Radiology. 2021;301(1):115–22.
doi: 10.1148/radiol.2021210043
pubmed: 34342503
Kehl KL, Elmarakeby H, Nishino M, Van Allen EM, Lepisto EM, Hassett MJ, et al. Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports. JAMA Oncol. 2019;5(10):1421–9.
doi: 10.1001/jamaoncol.2019.1800
pubmed: 31343664
pmcid: 6659158
MeCab: Yet Another Part-of-Speech and Morphological Analyzer. https://taku910.github.io/mecab/ . Accessed 12 May 2022
MANBYO Dictonary. Large-scale disease name dictionary for tabulating and analyzing diesase names acutually used in clinical settings. https://sociocom.jp/~data/2018-manbyo/index.html . Accessed 12 May 2022
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, et al. Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc. 2020;27(3):457–70.
doi: 10.1093/jamia/ocz200
pubmed: 31794016
Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: lstm cells and network architectures. Neural Comput. 2019;31(7):1235–70.
doi: 10.1162/neco_a_01199
pubmed: 31113301
Han S, Oh JS, Lee JJ. Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer. Eur J Nucl Med Mol Imaging. 2022;49(2):585–95.
doi: 10.1007/s00259-021-05481-2
pubmed: 34363089
Zhao Z, Pi Y, Jiang L, Xiang Y, Wei J, Yang P, et al. Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci Rep. 2020;10(1):17046.
doi: 10.1038/s41598-020-74135-4
pubmed: 33046779
pmcid: 7550561
Macedo F, Ladeira K, Pinho F, Saraiva N, Bonito N, Pinto L, et al. Bone metastases: an overview. Oncol Rev. 2017;11(1):321.
pubmed: 28584570
pmcid: 5444408
Jang M, Kim M, Bae SJ, Lee SH, Koh JM, Kim N. Opportunistic osteoporosis screening using chest radiographs with deep learning: development and external validation with a cohort dataset. J Bone Miner Res. 2022;37(2):369–77.
doi: 10.1002/jbmr.4477
pubmed: 34812546
Rizk B, Brat H, Zille P, Guillin R, Pouchy C, Adam C, et al. Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation. Phys Med. 2021;83:64–71.
doi: 10.1016/j.ejmp.2021.02.010
pubmed: 33714850
Nguyen Q-N, Chun SG, Chow E, Komaki R, Liao Z, Zacharia R, et al. Single-fraction stereotactic vs conventional multifraction radiotherapy for pain relief in patients with predominantly nonspine bone metastases. JAMA Oncol. 2019;5(6):665.
doi: 10.1001/jamaoncol.2019.0192
Palma DA, Olson R, Harrow S, Gaede S, Louie AV, Haasbeek C, et al. Stereotactic ablative radiotherapy for the comprehensive treatment of oligometastatic cancers: long-term results of the sabr-comet phase ii randomized trial. J Clin Oncol. 2020;38(25):2830–8.
doi: 10.1200/JCO.20.00818
pubmed: 32484754
pmcid: 7460150
Kawazoe Y, Shibata D, Shinohara E, Aramaki E, Ohe K. A clinical specific bert developed using a huge Japanese clinical text corpus. PLoS ONE. 2021;16(11):0259763.
doi: 10.1371/journal.pone.0259763