Kyoung Doo Song

OBJECTIVE: We compared surgical resection (SR) and radiofrequency ablation (RFA) as first-line treatment in patients with hepatocellular carcinoma (HCC) based on the risk of microvascular invasion (MVI). BACKGROUND: The best curative treatment modality between SR and RFA in patients with HCC with MVI remains unclear. METHODS: Data from 2 academic cancer center-based cohorts of patients with a single, small (≤3 cm) HCC who underwent SR were used to derive (n = 276) and validate (n = 101) prediction models for MVI using clinical and imaging variables. The MVI prediction model was developed using multivariable logistic regression analysis and externally validated. Early recurrence (<2 years) based on risk stratification between SR (n = 276) and RFA (n = 240) was evaluated via propensity score matching. RESULTS: In the multivariable analysis, alpha-fetoprotein (≥15 ng/mL), protein induced by vitamin K absence-II (≥48 mAU/mL), arterial peritumoral enhancement, and hepatobiliary peritumoral hypointensity on magnetic resonance imaging were associated with MVI. Incorporating these factors, the area under the receiver operating characteristic curve of the predictive model was 0.87 (95% confidence interval: 0.82-0.92) and 0.82 (95% confidence interval: 0.74-0.90) in the derivation and validation cohorts, respectively. SR was associated with a lower rate of early recurrence than RFA based on the risk of MVI after propensity score matching (P < 0.05). CONCLUSIONS: Our model predicted the risk of MVI in patients with a small (≤ 3 cm) HCC with high accuracy. Patients with MVI who had undergone RFA were more vulnerable to recurrence than those who had undergone SR.

Gastric cancer is one of the most common cancers in Korea and the world. Since 2004, this is the 4th gastric cancer guideline published in Korea which is the revised version of previous evidence-based approach in 2018. Current guideline is a collaborative work of the interdisciplinary working group including experts in the field of gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology and guideline development methodology. Total of 33 key questions were updated or proposed after a collaborative review by the working group and 40 statements were developed according to the systematic review using the MEDLINE, Embase, Cochrane Library and KoreaMed database. The level of evidence and the grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation proposition. Evidence level, benefit, harm, and clinical applicability was considered as the significant factors for recommendation. The working group reviewed recommendations and discussed for consensus. In the earlier part, general consideration discusses screening, diagnosis and staging of endoscopy, pathology, radiology, and nuclear medicine. Flowchart is depicted with statements which is supported by meta-analysis and references. Since clinical trial and systematic review was not suitable for postoperative oncologic and nutritional follow-up, working group agreed to conduct a nationwide survey investigating the clinical practice of all tertiary or general hospitals in Korea. The purpose of this survey was to provide baseline information on follow up. Herein we present a multidisciplinary-evidence based gastric cancer guideline.

PURPOSE: To compare radiofrequency (RF) ablation with nonanatomic resection (NAR) as first-line treatment in patients with a single Barcelona Clinic Liver Cancer (BCLC) stage 0 or A hepatocellular carcinoma (HCC) and to evaluate the long-term outcomes of both therapies. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board. The requirement for informed consent was waived. Data were reviewed from 580 patients with HCCs measuring 3 cm or smaller (BCLC stage 0 or A) who underwent ultrasonographically (US) guided percutaneous RF ablation (n = 438) or NAR (n = 142) as a first-line treatment. Local tumor progression, intrahepatic distant recurrence, disease-free survival, and overall survival rates were analyzed by using propensity score matching to compare therapeutic efficacy. In addition, major complications and length of postoperative hospital stay were compared. RESULTS: Before propensity score matching (n = 580), the 5-year cumulative rates of local tumor progression for RF ablation and NAR (20.9% vs 12.7%, respectively; P = .093) and overall survival rates (85.5% vs 90.9%, respectively; P = .194) were comparable, while the 5-year cumulative intrahepatic distant recurrence rates (62.7% vs 36.6%, respectively; P < .001) and disease-free survival rates (31.7% vs 61.1%, respectively; P < .001) in the NAR group were significantly better than those in the RF ablation group. After matching (n = 198), there were no significant differences in therapeutic outcomes between the RF ablation and NAR groups, including 5-year cumulative intrahepatic distant recurrence (47.0% vs 40.2%, respectively; P = .240) and disease-free survival rates (48.9% vs 54.4%, respectively; P = .201). RF ablation was superior to NAR for major complication rates and length of postoperative hospital stay (P < .001). CONCLUSION: In patients with one BCLC stage 0 or A (≤ 3 cm) HCC who received RF ablation or NAR as first-line treatment, there were no significant differences in long-term therapeutic outcomes; however, RF ablation was associated with fewer major complications and a shorter hospital stay after treatment.

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists' understanding.