We aimed to investigate the accuracy of each imaging feature of LI-RADS treatment response (LR-TR) viable category for diagnosing tumor viability of locoregional therapy (LRT)-treated HCC. Studies evaluating the per feature accuracy of the LR-TR viable category on dynamic contrast-enhanced CT or MRI were identified in databases. A bivariate random-effects model was used to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of LR-TR viable features. Ten studies assessing the accuracies of LR-TR viable features (1153 treated observations in 971 patients) were included. The pooled sensitivities and specificities for diagnosing viable HCC were 81% (95% confidence interval [CI], 63–92%) and 95% (95% CI, 88–98%) for nodular, mass-like, or irregular thick tissue (NMLIT) with arterial phase hyperenhancement (APHE), 55% (95% CI, 34–75%) and 96% (95% CI, 94–98%) for NMLIT with washout appearance, and 21% (95% CI, 6–53%) and 98% (95% CI, 92–100%) for NMLIT with enhancement similar to pretreatment, respectively. Of these features, APHE showed the highest pooled DOR (81 [95% CI, 25–261]), followed by washout appearance (32 [95% CI, 13–82]) and enhancement similar to pretreatment (14 [95% CI, 5–39]). In conclusion, APHE provided the highest sensitivity and DOR for diagnosing viable HCC following LRT, while enhancement similar to pretreatment showed suboptimal performance.

Osteolytic lesions can be seen in both multiple myeloma (MM), and osteolytic bone metastasis on computed tomography (CT) scans. We sought to assess the feasibility of a CT-based radiomics model to distinguish MM from metastasis. This study retrospectively included patients with pre-treatment thoracic or abdominal contrast-enhanced CT from institution 1 (training set: 175 patients with 425 lesions) and institution 2 (external test set: 50 patients with 85 lesions). After segmenting osteolytic lesions on CT images, 1218 radiomics features were extracted. A random forest (RF) classifier was used to build the radiomics model with 10-fold cross-validation. Three radiologists distinguished MM from metastasis using a five-point scale, both with and without the assistance of RF model results. Diagnostic performance was evaluated using the area under the curve (AUC). The AUC of the RF model was 0.807 and 0.762 for the training and test set, respectively. The AUC of the RF model and the radiologists (0.653–0.778) was not significantly different for the test set (p ≥ 0.179). The AUC of all radiologists was significantly increased (0.833–0.900) when they were assisted by RF model results (p < 0.001). In conclusion, the CT-based radiomics model can differentiate MM from osteolytic bone metastasis and improve radiologists’ diagnostic performance.