Long-term outcomes of rotating gamma knife for vestibular schwannoma: A 4-year prospective longitudinal study of 89 consecutive patients in Vietnam.

BACKGROUND: Microsurgical total removal of vestibular schwannoma (VS) is the definitive treatment but has a high incidence of postoperative neurological deficits. Rotating Gamma Knife (RGK) is a preferred option for a small tumor. This study aims to evaluate long-term neurological outcomes of RGK for VS. METHODS: This prospective longitudinal study was conducted at the Nuclear Medicine and Oncology Center, Bach Mai Hospital, Hanoi, Vietnam. Eighty-nine consecutive patients were enrolled from October 2011 to October 2015 and followed up to June 2017. RGK was indicated for VS measuring <2.2 cm, while RGK for tumors measuring 2.2-3 cm was considered in patients with severe comorbidities, high-risk surgery, and who denied surgery. Concurrently, VS consisted of newly diagnosed, postoperative residual, and recurrent tumors. Patients with neurofibromatosis type 2 were excluded from the study. Primary outcomes were radiological tumor control rate, vestibulocochlear functions, facial and trigeminal nerve preservation. Stereotactic radiosurgery was performed by the Rotating Gamma System Gamma ART 6000. RESULTS: The tumors were measured 20.7 ± 5.6 mm at pre treatment and 17.6 ± 4.1 mm at 3-year post treatment. The mean radiation dose was 13.5 ± 0.9 Gy. Mean follow-up was 40.6 ± 13.3 months. The radiological tumor control rate was achieved 95.5% at 5-year post treatment. The hearing and vestibular functions were preserved in 70.3% and 68.9%, respectively. The facial and trigeminal nerve preservation rates were 94.4% and 73.3%, respectively. CONCLUSION: RGK is an effective and safe treatment for VS measuring ≤3 cm with no significant complications during long-term follow-up.

Autologous adipose-derived stem cells therapy in COPD treatment: a case report.

Here, we describe the clinical course of a patient with chronic obstructive pulmonary disease treated with autologous adipose-derived stem cell therapy. In September 2019, our patient was admitted to Bach Mai Hospital. His post-bronchodilator forced expiratory volume in 1 sec (FEV1) was 21% and FEV1/forced vital capacity (FVC) was 40%. He had suffered from two exacerbations of chronic obstructive pulmonary disease (COPD) in the previous year. He received treatment with autologous stem cells from adipose tissue. Follow-up indicated that autologous stem cells from adipose tissue was a safe treatment and improved the patient's dyspnoea and quality of life.

Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion.

PURPOSE: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. METHODS: The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio ( PSNR ), structural similarity ( SSIM ), and compression ratio ( CR ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. RESULTS: The results show that the method can significantly improve CR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. CONCLUSIONS: We thus conclude that the method has a high potential to be applied in teleintervention applications.

Willingness to pay for a quality-adjusted life year among advanced non-small cell lung cancer patients in Viet Nam, 2018.

To examine the willingness to pay (WTP) for a quality-adjusted life year (QALY) gained among advanced non-small cell lung cancer (NSCLC) patients in Viet Nam and to analyze the factors affecting an individual's WTP.A cross-sectional, contingent valuation study was conducted among 400 NSCLC patients across 6 national hospitals in Viet Nam. Self-reported information was recorded from patients regarding their socio-demographic status, EQ-5D (EuroQol-5 dimensions) utility, EQ-5D vas, and WTP for 1 QALY gained. To explore the factors related to the WTP, Gamma Generalized Linear Model and multiple logistic regression tools were applied to analyze data.The overall mean and median of WTP/QALY among the NSCLC patients were USD $11,301 and USD $8002, respectively. Strong association was recorded between WTP/QALY amount and the patient's education, economic status, comorbidity status, and health utility.Government and policymakers should consider providing financial supports to disadvantaged groups to improve their access to life saving cancer treatment.

Ultra-deep massively parallel sequencing with unique molecular identifier tagging achieves comparable performance to droplet digital PCR for detection and quantification of circulating tumor DNA from lung cancer patients.

The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of circulating tumor DNA (ctDNA) in plasma, known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a protocol for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Paired plasma and tumor tissue samples were used to evaluate mutation profiles detected by ultra-deep MPS, which showed 87.5% concordance. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72-0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90-0.98). Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.