Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [ 18 F]-fluorodeoxyglucose positron emission tomography/computed tomography

ConclusionThis is the first study to use deep learning model for assessing18F-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 assessing18F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients.• All 142 patients with cervical cancer underwent18F-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.
Source: European Radiology - Category: Radiology Source Type: research