Distinct CT imaging features of new liver metastases from primary genitourinary cancers

To apply natural language processing (NLP) to a large volume of structured radiology reports in the investigation of CT imaging features of new liver metastases from primary genitourinary cancers. In this retrospective study, a previously reported NLP model was applied to consecutive structured CT reports from 2016 to 2022 to predict those patients with primary genitourinary cancer who developed liver metastasis. Pathology or imaging follow-up served as the reference standard for validating NLP predictions. Subsequently, diagnostic CTs of the identified patients were qualitatively assessed by two radiologists, whereby several imaging features of new liver metastasis were assessed. Proportions of the assessed imaging features were compared between primary genitourinary cancers using the Chi-square or Fisher’s exact test. In 112 patients (mean age = 72 years; 83 males), the majority of new liver metastases were hypovascular (73.2