PURPOSE: To develop predictive models for @entity1600 ( @entity1600 ) @entity3 in @entity2969 . MATERIALS AND METHODS: 291 @entity1 with @entity2969 were included, which were divided into @entity10711 and nonseminomatous groups. For screening the risk factors for @entity1600 @entity3 , the @entity5 -related characteristics (including histopathological information and @entity5 markers) alpha fetoprotein and the @entity1600 -related features on CT were compared between metastatic cases and nonmetastatic cases. Two logistic regression models were built for each histological group, one depending on all @entity5 - and @entity1600 -related risk factors (Model 1) and another only on @entity5 -related factors (Model 2). Receivers operating characteristic curves were used to evaluate the predictive abilities of these models. RESULTS: 117 positive nodes/regions were identified in 68 @entity1 , including 51 @entity3 and 17 occult @entity3 . Based on the selected independent risk factors, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of Models 1 and 2 in @entity10711 and nonseminomatous groups were (95.5%, 95.3%, 95.3%, 77.8%, and 99.2%), (63.6%, 83.6%, 80.7%, 40.0%, and 93.0%), (93.5%, 94.7%, 94.3%, 89.6%, and 96.8%), and (89.1%, 44.2%, 58.9%, 43.6%, and 89.4%), respectively. CONCLUSION: Two predictive models for each @entity10711 and nonseminomatous @entity5 were established based on @entity1600 - and @entity5 -related risk factors. In @entity1 with @entity5 and @entity1600 -related risk factors, regular CT surveillance is likely sufficient for predicting @entity1600 status, while in the @entity1 without any @entity5 and @entity1600 -related risk factors a long interval-time CT follow-up should be considered. Additionally, right side @entity5 tend to involve contralateral LNs compared to left side ones. Positive inguinal LNs more frequently occur in @entity1 with a history of groin surgery.

Predictive models for @entity1600 @entity3 in XXXX with @entity2969 .

@entity1 :: ('9606', 'Species') :: ['patients'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'testicular tumor', 'tumors'] @entity1600 :: ('MESH:D014388', 'Disease') :: ['LN', 'lymph node'] @entity10711 :: ('MESH:C537844', 'Disease') :: ['seminomatous'] @entity2969 :: ('MESH:C563236', 'Disease') :: ['testicular germ cell tumors'] @entity3 :: ('MESH:D009362', 'Disease') :: ['metastasis', 'metastases']
@entity1 :: (9606,Species) :: ['patients']
