PURPOSE: To update an evidence-based guideline to assist in clinical decision-making for patients with advanced hepatocellular carcinoma (HCC). METHODS: ASCO convened an Expert Panel to update the 2020 guideline on systemic therapy for HCC. The panel updated the systematic review to include randomized controlled trials (RCTs) published through October 2023 and updated recommendations. RESULTS: Ten new RCTs met the inclusion criteria and were added to the evidence base. RECOMMENDATIONS: Atezolizumab + bevacizumab (atezo + bev) or durvalumab + tremelimumab (durva + treme) may be offered first-line for patients with advanced HCC, Child-Pugh class A liver disease, and Eastern Cooperative Oncology Group performance status 0-1. Where there are contraindications to these therapies, sorafenib, lenvatinib, or durvalumab may be offered first-line. Following first-line treatment with atezo + bev, second-line therapy with a tyrosine kinase inhibitor (TKI), ramucirumab (for patients with alpha-fetoprotein [AFP] ≥400 ng/mL), durva + treme, or nivolumab + ipilimumab (nivo + ipi) may be recommended for appropriate candidates. Following first-line therapy with durva + treme, second-line therapy with a TKI is recommended. Following first-line treatment with sorafenib or lenvatinib, second-line therapy options include cabozantinib, regorafenib for patients who previously tolerated sorafenib, ramucirumab (AFP ≥400 ng/mL), nivo + ipi, or durvalumab; atezo + bev or durva + treme may be considered for patients who did not have access to these therapies in the first-line setting, and do not have contraindications. Pembrolizumab or nivolumab are also options for appropriate patients following sorafenib or lenvatinib. Third-line therapy may be considered in Child-Pugh class A patients with good PS, using one of the agents listed previously that has a nonidentical mechanism of action with previously received therapy. A cautious approach to systemic therapy is recommended for patients with Child-Pugh class B advanced HCC. Further guidance on choosing between options is included within the guideline. Additional information is available at www.asco.org/gastrointestinal-cancer-guidelines .

Purpose: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. Experimental Design: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. Results: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). Conclusions: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.

Tumour cells frequently utilize glutamine to meet bioenergetic and biosynthetic demands of rapid cell growth. However, glutamine dependence can be highly variable between in vitro and in vivo settings, based on surrounding microenvironments and complex adaptive responses to glutamine deprivation. Soft tissue sarcomas (STSs) are mesenchymal tumours where cytotoxic chemotherapy remains the primary approach for metastatic or unresectable disease. Therefore, it is critical to identify alternate therapies to improve patient outcomes. Using autochthonous STS murine models and unbiased metabolomics, we demonstrate that glutamine metabolism supports sarcomagenesis. STS subtypes expressing elevated glutaminase (GLS) levels are highly sensitive to glutamine starvation. In contrast to previous studies, treatment of autochthonous tumour-bearing animals with Telaglenastat (CB-839), an orally bioavailable GLS inhibitor, successfully inhibits undifferentiated pleomorphic sarcoma (UPS) tumour growth. We reveal glutamine metabolism as critical for sarcomagenesis, with CB-839 exhibiting potent therapeutic potential.

Transarterial embolization–induced alterations in the tumor immune microenvironment are influenced by underlying liver disease, embolic agent type, and vascular location and result in increased pro... Background Transarterial embolization (TAE) is the most common treatment for hepatocellular carcinoma (HCC); however, there remain limited data describing the influence of TAE on the tumor immune m...

Dysregulated cellular metabolism is a characteristic feature of malignancy that has been exploited for both imaging and targeted therapy. With regard to imaging, deranged glucose metabolism has been leveraged with FDG-PET. Metabolic imaging with FDG, though, only probes the early steps of glycolysis; the complexities of metabolism beyond these early steps in this single pathway are not directly captured. New imaging technologies-both PET with novel radiotracers and magnetic resonance-based methods-provide unique opportunities to investigate other aspects of cellular metabolism and expand the metabolic imaging armamentarium. This review will discuss the underlying biology of metabolic dysregulation in cancer, focusing on glucose, glutamine, and acetate metabolism. Novel imaging strategies will be discussed within this biologic framework, highlighting particular strengths and limitations of each technique. Particular emphasis is placed on the role that combining modalities will play in enabling multi-parametric imaging to fully characterize tumor biology in order to better inform treatment.