Natural Language Processing in Oncology

Yim, Wen-wai; Yetisgen, Meliha; Harris, William Proctor; Kwan, Sharon W.

doi:10.1001/jamaoncol.2016.0213

Cited by 238 publications

(162 citation statements)

References 40 publications

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“…Equally attractive is the technology of natural language processing (NLP), which concerns with the interactions between computers and natural languages and has been considered auspicious in the field of human–computer interaction. In the medical profession, researchers regard NLP as one of the most potential and powerful tools to gather sporadic laboratory and clinical data trapped in enormous electronic records, thus accelerating the application of scientific advance to clinical practice (Yim et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

Yang

Zhang

Cai

et al. 2017

PeerJ

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BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

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Section: Introductionmentioning

confidence: 99%

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

Yang

Zhang

Cai

et al. 2017

PeerJ

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“…20 Another direction includes linkages of data between cancer registries and occupational registries as has been done to improve race and ethnicity data through linkages with Indian Health Services records. 21 …”

Section: | Discussionmentioning

confidence: 99%

Capture and coding of industry and occupation measures: Findings from eight National Program of Cancer Registries states

Freeman

Rees

Johnson³

et al. 2017

American J Industrial Med

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Background Although data on industry and occupation (I&O) are important for understanding cancer risks, obtaining standardized data is challenging. This study describes the capture of specific I&O text and the ability of a web-based tool to translate text into standardized codes. Methods Data on 62 525 cancers cases received from eight National Program of Cancer Registries (NPCR) states were submitted to a web-based coding tool developed by the National Institute for Occupational Safety and Health for translation into standardized I&O codes. We determined the percentage of sufficiently analyzable codes generated by the tool. Results Using the web-based coding tool on data obtained from chart abstraction, the NPCR cancer registries achieved between 48% and 75% autocoding, but only 12–57% sufficiently analyzable codes. Conclusions The ability to explore associations between work-related exposures and cancer is limited by current capture and coding of I&O data. Increased training of providers and registrars, as well as software enhancements, will improve the utility of I&O data.

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“…A number of researchers have investigated NLP methods to automatically recognize and encode the concepts conveyed in medical texts [27,29,31,34–37,48,52,54–60]. Most of those NLP systems have been created as general purpose systems that attempt to extract diseases and findings that are commonly discussed in a medical texts.…”

Section: Discussionmentioning

confidence: 99%

Using automatically extracted information from mammography reports for decision-support

Bozkurt

Gimenez

Burnside

et al. 2016

Journal of Biomedical Informatics

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Objective To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. Materials and methods We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect (“reference standard”) structured inputs to the DSS (“RS-DSS”) vs NLP-derived inputs (“NLP-DSS”) on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. Results The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004 ± 0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. Conclusion The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions.

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Natural Language Processing in Oncology

Cited by 238 publications

References 40 publications

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

Capture and coding of industry and occupation measures: Findings from eight National Program of Cancer Registries states

Using automatically extracted information from mammography reports for decision-support

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