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Clinical diagnosis of hepatocellular carcinoma (HCC) relies heavily on radiological imaging. However, information pertaining to liver cancer treatment such as the proliferation status is lacking. Imaging tumor proliferation can be valuable in patient management. This study investigated 18F-labeled clofarabine ([18F]CFA) targeting deoxycytidine kinase (dCK) for PET imaging of dCK-dependent proliferation in HCC. Since clinical PET scans showed a high liver background uptake of [18F]CFA, the aim of this study was to reduce this liver background uptake. A clinically relevant animal model of spontaneously developed HCC in the woodchucks was used for imaging experiments. Several modifiers were tested and compared with the baseline PET scan: Forodesine, probenecid, and cold clofarabine, all applied before the hot [18F]CFA injection to evaluate the reduction in liver background uptake. Application of forodesine before hot [18F]CFA injection did not reduce the background uptake. Instead, it increased the background by 11.6&ndash, 36.3%. Application of probenecid also increased the liver background uptake by 16.6&ndash, 32.1%. Cold CFA application did reduce the liver background uptake of [18F]CFA, comparing to the baseline scan. Combining cold CFA with [18F]CFA for PET imaging of liver cancers is a promising strategy, worthy of further clinical evaluation.

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were &gt, 80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

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Hepatocellular carcinoma (HCC) is a leading cause of new cancer diagnoses in the United States, with an incidence that is expected to rise. The etiology of HCC is varied and can lead to differences between patients in terms of presentation and natural history. Subsequently, physicians treating these patients need to consider a variety of disease and patient characteristics when they select from the many different treatment options that are available for these patients. At the same time, the treatment landscape for patients with HCC, particularly those with unresectable HCC, has been rapidly evolving as new, evidence-based options become available. The treatment plan for patients with HCC can include surgery, transplant, ablation, transarterial chemoembolization, transarterial radioembolization, radiation therapy, and/or systemic therapies. Implementing these different modalities, where the optimal sequence and/or combination has not been defined, requires coordination between physicians with different specialties, including interventional radiologists, hepatologists, and surgical and medical oncologists. As such, the implementation of a multidisciplinary team is necessary to develop a comprehensive care plan for patients, especially those with unresectable HCC.

Neuroendocrine tumors (NETs) are a group of malignancies that originated from neuroendocrine cells, with the most common sites being lungs and the gastrointestinal tract. Lung NETs comprise 25% of all lung malignancies. Small cell lung cancer is the most common form of lung NETs, and other rare forms include well-differentiated typical carcinoids (TCs) and poorly differentiated atypical carcinoids (ACs). Given the paucity of randomized studies, rational treatment is challenging. Therefore, it is recommended that these decisions be made using a multidisciplinary collaborative approach. Surgery remains the mainstay of treatment, when feasible. Following surgery, various guidelines offer different recommendations in the adjuvant setting. In this paper, we describe the adjuvant management of lung NETs, as recommended by different guidelines, and highlight their differences. In addition to that, we also discuss the management of metastatic lung NETS, including the use of peptide receptor radionucleotide therapy.