Journal
EUROPEAN RADIOLOGY
Volume 29, Issue 12, Pages 6741-6749Publisher
SPRINGER
DOI: 10.1007/s00330-019-06265-x
Keywords
AI (artificial intelligence); Neural network models; Fluorodeoxyglucose F18; PET-CT scan; Cervical cancer
Funding
- Ministry of Health and Welfare, Taiwan [MOHW107-TDU-B-212-123004]
- China Medical University Hospital [DMR-107-192, CRS-106-036, CRS106-039, CRS106-040, CRS106-041]
- Asia University [DMR-106-150]
- Academia Sinica Stroke Biosignature Project [BM10701010021]
- MOST Clinical Trial Consortium for Stroke [MOST 107-2321-B-039-004-]
- Tseng-Lien Lin Foundation, Taichung, Taiwan
- Katsuzo and Kiyo Aoshima Memorial Funds, Japan
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Background We designed a deep learning model for assessing F-18-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer. Methods All 142 patients with cervical cancer underwent F-18-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result. ResultsIn total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively. Conclusion This is the first study to use deep learning model for assessing F-18-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. Key Points This is the first study to use deep learning model for assessing F-18-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. All 142 patients with cervical cancer underwent F-18-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.
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