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Background and Objectives@#This study aimed to investigate the protective effect of polydeoxyribonucleotide (PDRN) against skin flap necrosis in a murine skin flap model.Materials and Method Twenty mice with rectangular skin flaps on the dorsum were randomly divided into the PDRN (n=10) and pentobarbital sodium (PBS) (n=10) injection groups. PDRN (8 mg/kg) was subdermally injected at 12 different points immediately after the operation. After 7 days, the flap perfusions were evaluated using a laser speckle contrast imaging (LSCI) system, and specimens were collected for immunohistochemistry analysis. @*Results@#The percentage of survival area relative to the total flap area was significantly higher in the PDRN group (60.87%±7.63%) than in the PBS group (45.23%±10.72%) (p<0.05). The mean LSCI perfusion signal of the distal part of the skin flap in the PBS group was 0.57±0.12, and that in the PDRN group was 0.74±0.13 (p<0.05). The PDRN group had a significantly lower interleukin 1 beta expression than the PBS group and higher vascular endothelial growth factor α expression than the PBS group (p<0.05). @*Conclusion@#These findings suggest that subdermally injected PDRN is more effective in enhancing flap survival during necrosis.
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Purpose@#Adjuvant radiotherapy (RT) has been performed to reduce locoregional failure (LRF) following radical cystectomy for locally advanced bladder cancer; however, its efficacy has not been well established. We analyzed the locoregional recurrence patterns of post-radical cystectomy to identify patients who could benefit from adjuvant RT and determine the optimal target volume. @*Materials and Methods@#We retrospectively reviewed 160 patients with stage ≥ pT3 bladder cancer who were treated with radical cystectomy between January 2006 and December 2015. The impact of pathologic findings, including the stage, lympho-vascular invasion, perineural invasion, margin status, nodal involvement, and the number of nodes removed on failure patterns, was assessed. @*Results@#Median follow-up period was 27.7 months. LRF was observed in 55 patients (34.3%), 12 of whom presented with synchronous local and regional failures as the first failure. The most common failure pattern was distant metastasis (40%). Among LRFs, the most common recurrence site was the cystectomy bed (15.6%). Patients with positive resection margins had a significantly higher recurrence rate compared to those without (28% vs. 10%, p=0.004). The pelvic nodal recurrence rate was < 5% in pN0 patients; the rate of recurrence in the external and common iliac nodes was 12.5% in pN+ patients. The rate of recurrence in the common iliac nodes was significantly higher in pN2–3 patients than in pN0–1 patients (15.2% vs. 4.4%, p=0.04). @*Conclusion@#Pelvic RT could be beneficial especially for those with positive resection margins or nodal involvement after radical cystectomy. Radiation fields should be optimized based on the patient-specific risk factors.
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Purpose@#Adjuvant radiotherapy (RT) has been performed to reduce locoregional failure (LRF) following radical cystectomy for locally advanced bladder cancer; however, its efficacy has not been well established. We analyzed the locoregional recurrence patterns of post-radical cystectomy to identify patients who could benefit from adjuvant RT and determine the optimal target volume. @*Materials and Methods@#We retrospectively reviewed 160 patients with stage ≥ pT3 bladder cancer who were treated with radical cystectomy between January 2006 and December 2015. The impact of pathologic findings, including the stage, lympho-vascular invasion, perineural invasion, margin status, nodal involvement, and the number of nodes removed on failure patterns, was assessed. @*Results@#Median follow-up period was 27.7 months. LRF was observed in 55 patients (34.3%), 12 of whom presented with synchronous local and regional failures as the first failure. The most common failure pattern was distant metastasis (40%). Among LRFs, the most common recurrence site was the cystectomy bed (15.6%). Patients with positive resection margins had a significantly higher recurrence rate compared to those without (28% vs. 10%, p=0.004). The pelvic nodal recurrence rate was < 5% in pN0 patients; the rate of recurrence in the external and common iliac nodes was 12.5% in pN+ patients. The rate of recurrence in the common iliac nodes was significantly higher in pN2–3 patients than in pN0–1 patients (15.2% vs. 4.4%, p=0.04). @*Conclusion@#Pelvic RT could be beneficial especially for those with positive resection margins or nodal involvement after radical cystectomy. Radiation fields should be optimized based on the patient-specific risk factors.
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Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.
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Reprogramming of cellular metabolism is an important, emerging, and universal hallmark of cancer which has received considerable attention during the recent era of cancer research. Cancer cells show characteristic alterations in glucose metabolism in order to fulfill the needs of biosynthesis for tumor proliferation and growth. However, under certain circumstances such as invasion and metastasis, cancer cells are prone to metabolic stress and will require different strategies to meet the high energetic demand from cancer progression. From various metabolic rewiring mechanisms, cancer cells adopt other metabolic pathways with alternative nutrient sources. Therefore, targeting cancer metabolism holds promising but great challenge caused by the metabolic plasticity of cancer cells. This review will discuss characteristic cancer metabolism in detail with special focus on lipid metabolism which is gathering increasingly keen interest, in order to find novel therapeutic approaches to head and neck cancer. By understanding and exploiting the synthesis, oxidation, and storage of fatty acids, we could investigate potential strategies to block cancer proliferation and progression.
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The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.