Abstract:
:Identification of nodal metastasis and tumor extranodal extension (ENE) is crucial for head and neck cancer management, but currently only can be diagnosed via postoperative pathology. Pretreatment, radiographic identification of ENE, in particular, has proven extremely difficult for clinicians, but would be greatly influential in guiding patient management. Here, we show that a deep learning convolutional neural network can be trained to identify nodal metastasis and ENE with excellent performance that surpasses what human clinicians have historically achieved. We trained a 3-dimensional convolutional neural network using a dataset of 2,875 CT-segmented lymph node samples with correlating pathology labels, cross-validated and fine-tuned on 124 samples, and conducted testing on a blinded test set of 131 samples. On the blinded test set, the model predicted ENE and nodal metastasis each with area under the receiver operating characteristic curve (AUC) of 0.91 (95%CI: 0.85-0.97). The model has the potential for use as a clinical decision-making tool to help guide head and neck cancer patient management.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Kann BH,Aneja S,Loganadane GV,Kelly JR,Smith SM,Decker RH,Yu JB,Park HS,Yarbrough WG,Malhotra A,Burtness BA,Husain ZAdoi
10.1038/s41598-018-32441-ysubject
Has Abstractpub_date
2018-09-19 00:00:00pages
14036issue
1issn
2045-2322pii
10.1038/s41598-018-32441-yjournal_volume
8pub_type
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