Frontal Sinus Displacement of Silicone Implant After Previous Rhinoplasty.

Rhinoplasty, a historic surgical procedure for facial esthetics, has been actively performed in Asia. The use of autologous tissues or artificial materials, such as silicone, Gore-Tex, and Medpore, is common in achieving cosmetic improvements. However, artificial material poses risks of inflammation and foreign body reactions, leading to complications like infection and necessitating material removal and antibiotic treatment. According to previous reports, various clinical aspects appear across inflammation, skin necrosis, and, in severe cases, systemic symptoms caused by implants. In this case study, the goal is to share a rare case of silicone implant migration into the frontal sinus after augmentation rhinoplasty. A 38-year-old female patient who had previously undergone rhinoplasty surgery visited the outpatient clinic complaining of headaches and a deviated nose. On computed tomography, the silicone implant moved upward, penetrating the nasoethmoid bone and invading the frontal sinus. Fortunately, there was no intracranial invasion. The authors planned the implant removal and performed the complete implant removal with capsulectomy. The patient has been undergoing follow-up without any complications after surgery. Augmentation rhinoplasty with implants, while common, carries long-term risks. This case highlights the severity of complications, emphasizing infection and migration into the frontal sinus and, in extreme cases, the brain cavity. Therefore, surgeons must continually refine operation techniques to minimize iatrogenic causes and consider modifying surgical procedures to prevent potential complications.

A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma.

PURPOSE: Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment. EXPERIMENTAL DESIGN: We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C). RESULTS: The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup. CONCLUSIONS: ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.

Pathologic complete response to chemoembolization improves survival outcomes after curative surgery for hepatocellular carcinoma: predictive factors of response.

BACKGROUND: We identified the predictive factors and prognostic significance of transarterial chemoembolization (TACE) for achieving pathologic complete response (pCR) before curative surgery for hepatocellular carcinoma (HCC) in hepatitis B-endemic areas. METHODS: Among 753 HCC patients treated with surgery, 124 patients underwent preoperative TACE before liver resection (LR), and 166 before liver transplantation (LT) between 2005 and 2016. Overall survival (OS) and recurrence-free survival (RFS) were analyzed. Pathologic response (PR) was defined as the mean percentage of necrotic area, and pCR was defined as the absence of viable tumor. RESULTS: A total of 34 (27%) and 38 (23%) patients had pCR before LR and LT, respectively. Alpha-fetoprotein (AFP) < 100 ng/mL and single tumor were significant preoperative predictors of pCR. OS and RFS were significantly improved in patients with pCR or a PR ≥ 90%, but not in patients with PR ≥ 50% after LR and LT. On multivariate analyses, PR ≥ 90% remained an independent predictor of better OS and RFS in LR and LT groups. CONCLUSION: Overall, our data clearly demonstrate that pCR predicts favorable prognosis after curative surgery for HCC, and predictors of pCR are AFP <100 ng/mL and single tumor.