ABSTRACT
Hepatocellular carcinoma (HCC) is a major public health burden in Hong Kong, and chronic hepatitis B is the most common HCC etiology in our region. With the high case load, extensive local expertise on HCC has been accumulated. This article summarized local guidelines and real-life practice on HCC management in Hong Kong. For HCC surveillance, liver ultrasound and serum alpha-fetoprotein for periodic screening is recommended in viral hepatitis or cirrhotic patients, and this is adhered to in clinical practice. HCC diagnosis is not covered in local guidelines, yet our practice is in-line with regional guidelines, where diagnosis is usually achieved by cross-sectional imaging and without the need for histology. Our guidelines recommend using the Hong Kong Liver Cancer Staging for pre-treatment staging, yet we routinely use other widely-adopted systems such as the Barcelona Clinic Liver Cancer Staging and the Tumor-Node-Metastasis Staging as well. Our local guidelines have provided clear treatment algorithms for the whole range of HCC therapies, including resection, ablation, transplant, transarterial chemoembolization, transarterial radioembolization, stereotactic body radiation therapy, targeted therapy, and immunotherapy. Real-life treatment choices are largely in line with the guidelines, although treatment protocols are individualized, and availability of specific therapies can vary between centers. Overall, HCC guidelines in Hong Kong are tailored based on local expertise and our unique patient population. The guidelines are up-to-date and provide practical pathways to assist our routine practice. Regular updates of local guidelines are warranted to account for the rapidly evolving paradigm of HCC management.
ABSTRACT
Purpose@#Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the ablative treatment of thoracic tumors. This study aims to identify predictors of treatment responses in patients affected by early stage non-small cell lung cancer (NSCLC) or pulmonary oligo-metastases treated with SBRT and to develop an accurate machine learning model to predict radiological response to SBRT. @*Materials and Methods@#Computed tomography (CT) images of 85 tumors (stage I–II NSCLC and pulmonary oligo-metastases) from 69 patients treated with SBRT were analyzed. Gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) or partial response (PR) were defined as responders. One hundred ten radiomic features were extracted using PyRadiomics module based on the GTV. The association of features with response to SBRT was evaluated. A model using support vector machine (SVM) was then trained to predict response based solely on the extracted radiomics features. Receiver operating characteristic curves were constructed to evaluate model performance of the identified radiomic predictors. @*Results@#Sixty-nine patients receiving thoracic SBRT from 2008 to 2018 were retrospectively enrolled. Skewness and root mean squared were identified as radiomic predictors of response to SBRT. The SVM machine learning model developed had an accuracy of 74.8%. The area under curves for CR, PR, and non-responder prediction were 0.86 (95% confidence interval [CI], 0.794–0.921), 0.946 (95% CI, 0.873–0.978), and 0.857 (95% CI, 0.789–0.915), respectively. @*Conclusion@#Radiomic analysis of pre-treatment CT scan is a promising tool that can predict tumor response to SBRT.