Background: Since lobaplatin (LBP) has been approved to treat metastatic breast cancer in China, this study aimed to evaluate the safety and efficacy of LBP-based chemotherapy in clinical practice. Methods: This trial was a prospective, open-label, multicenter phase IV clinical trial that enrolled patients with unresectable locally advanced or recurrent/metastatic breast cancer from 34 sites between July 2013 and March 2017. Patients were treated with LBP monotherapy or in combination for four to six cycles. The primary endpoint was safety. Secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). Results: A total of 1179 patients were analyzed; 59 (5.0%) were treated with LBP alone, 134 (11.4%) with LBP plus paclitaxel, 263 (22.3%) with LBP plus docetaxel, 237 (20.1%) with LBP plus gemcitabine, 403 (34.2%) with LBP plus vinorelbine, and 83 (7.0%) with other LBP-based regimens. The overall incidence of adverse events (AEs) was 95.2%, and 57.9% of patients had grade >3 AEs. The most common grade >3 AEs were neutropenia (43.9%), leukopenia (39.4%), anemia (17.8%), and thrombopenia (17.7%). LBP monotherapy showed the lowest incidence of grade >3 AEs (39.0%), followed by LBP plus docetaxel (52.9%), LBP plus paclitaxel (59.0%), LBP plus vinorelbine (62.5%), and LBP plus gemcitabine (62.9%). The ORR and DCR were 36.8 and 77.0%, respectively. The median PFS was 5.5 months (95% confidence interval: 5.2–5.9). Conclusion: LBP-based chemotherapy shows favorable efficacy in patients with advanced breast cancer, with manageable safety profile. Trial registration: This trial was registered with ChiCTR.org.cn, ChiCTR-ONC-13003471.

We aimed to develop and validate a pyradiomics model for preoperative prediction of initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). To this end, computed tomography (CT) images were acquired from multi-centers. Numerous pyradiomics features were extracted and machine learning approach was used to build a model for predicting initial response of TACE treatment. The predictive accuracy, overall survival (OS), and progression-free survival (PFS) were analyzed. Gene Set Enrichment Analysis (GSEA) was further used to explore signaling pathways in The Cancer Genome Atlas (TCGA)-HCC cohort. Overall, 24 of the 1,209 pyradiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. The pyradiomics signature showed high predictive accuracy across the discovery set (AUC: 0.917, 95% confidence interval [CI]: 86.93-96.39), validation set 1 (AUC: 0.902, 95% CI: 84.81-95.59), and validation set 2 (AUC: 0.911; 95% CI: 83.26-98.98). Based on the classification of pyradiomics model, we found that a group with high values base on pyramidomics score showed good PFS and OS (both P<0.001) and was negatively correlated with glycolysis pathway. The proposed pyradiomics signature could accurately predict initial treatment response and prognosis, which may be helpful for clinicians to better screen patients who are likely to benefit from TACE.