Microwave Ablation versus Stereotactic Body Radiotherapy for Stage I Non–Small Cell Lung Cancer: A Cost-Effectiveness Analysis

PurposeTo assess the cost effectiveness of microwave ablation (MWA) and stereotactic body radiotherapy (SBRT) for patients with inoperable stage I non–small cell lung cancer (NSCLC).Materials and MethodsA literature search was performed in MEDLINE with broad search clusters. A decision-analytic model was constructed over a 5-year period. The model incorporated treatment-related complications and long-term recurrence. All clinical parameters were derived from the literature with preference to long-term prospective trials. A healthcare payers’ perspective was adopted. Outcomes were measured in quality-adjusted life years (QALYs) extracted from prior studies and U.S. dollars from Medicare reimbursements and prior studies. Base case calculations, probabilistic sensitivity analysis with 10,000 Monte Carlo simulations, and multiple 1- and 2-way sensitivity analyses were performed.ResultsMWA yielded a health benefit of 2.31 QALYs at a cost of $195,331, whereas SBRT yielded a health benefit of 2.33 QALYs at a cost of $225,271. The incremental cost-effectiveness ratio was $1,480,597/QALY, indicating that MWA is the more cost-effective strategy. The conclusion remains unchanged in probabilistic sensitivity analysis with MWA being the optimal cost strategy in 99.84% simulations. One-way sensitivity analyses revealed that MWA remains cost effective when its annual recurrence risk is <18.4% averaged over 5 years, when the SBRT annual recurrence risk is >1.44% averaged over 5 years, or when MWA is at least $7,500 cheaper than SBRT.ConclusionsMWA appears to be more cost effective than SBRT for patients with inoperable stage I NSCLC.     

Photodynamic therapy during second surgery for recurrent gliomas improves survival

BackgroundGlioma accounts for most central nervous system tumors, and the degree of invasion and malignancy are higher in the recurrent glioma. Photodynamic therapy (PDT) is an effective strategy in glioma. This study aimed to explore the risk factors for re-recurrence after a second glioma surgery and the effects of PDT on re-recurrence.MethodsThis was a retrospective study in the Second Affiliated Hospital of Harbin Medical University in China, and 43 patients that received the secondary surgery for recurrent glioma were included. The Kaplan-Meier test and Cox proportional hazard method were used to analyze.ResultsThe total re-recurrence rate after the second surgery for recurrent glioma was 48.84%. When the age increased by 1, the risk of re-recurrence increased 1.065 times (95% CI 1.000–1.134, P = 0.049). High matrix metalloproteinase (MMP) 2 expression was associated with a significantly higher risk of re-recurrence than low MMP2 expression (HR = 25.550, 95% CI 3.190–204.650, P = 0.002). Pathological grades IV and III were associated with a significantly higher risk of re-recurrence than pathological grade II (HR = 17.121, 95% CI 2.345–124.986, P = 0.005; HR = 2863.470, 95% CI 100.697–81,427.197, P < 0.001). PDT decreased the risk of re-recurrence (HR = 25.550, 95% CI 3.190–204.650, P = 0.002) and increased survival time (HR = 3.611, 95% CI 1.012–12.888, P = 0.048).ConclusionThe age, MMP2 expression, and pathological grade are independent risk factors for re-recurrence after a second surgery for recurrent glioma. PDT during the second surgery decreased the risk of re-recurrence and increased survival time.     

The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015

The 2021 WHO Classification of Thoracic Tumours was published earlier this year, with classification of lung tumors being one of the chapters. The principles remain those of using morphology first, supported by immunohistochemistry, and then molecular techniques. In 2015, there was particular emphasis on using immunohistochemistry to make classification more accurate. In 2021, there is greater emphasis throughout the book on advances in molecular pathology across all tumor types. Major features within this edition are (1) broader emphasis on genetic testing than in the 2015 WHO Classification; (2) a section entirely dedicated to the classification of small diagnostic samples; (3) continued recommendation to document percentages of histologic patterns in invasive nonmucinous adenocarcinomas, with utilization of these features to apply a formal grading system, and using only invasive size for T-factor size determination in part lepidic nonmucinous lung adenocarcinomas as recommended by the eighth edition TNM classification; (4) recognition of spread through airspaces as a histologic feature with prognostic significance; (5) moving lymphoepithelial carcinoma to squamous cell carcinomas; (6) update on evolving concepts in lung neuroendocrine neoplasm classification; (7) recognition of bronchiolar adenoma/ciliated muconodular papillary tumor as a new entity within the adenoma subgroup; (8) recognition of thoracic SMARCA4-deficient undifferentiated tumor; and (9) inclusion of essential and desirable diagnostic criteria for each tumor.     

Pan-cancer proteomic map of 949 human cell lines

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核因子κB受体活化因子配体和肿瘤坏死因子α经炎性牙周膜干细胞外泌体促进破骨细胞分化

目的　慢性牙周炎表现的病理性骨吸收非常常见，牙周膜干细胞（PDLSCs）的外泌体（Exo）对骨吸收的作用和影响尚不明确，本研究分析了该Exo蛋白组分对破骨细胞分化的影响。方法　分别从正畸前拔牙患者和慢性牙周炎患者的牙周膜组织中提取PDLSCs，通过流式细胞术检测表面标记物；通过差速离心分别提取2种细胞的Exo，即Exo-WT和Exo-CP，并通过Western blot、透射电子显微镜（TEM）、蛋白浓度检测、纳米粒径追踪检测Exo特征；通过蛋白质谱检测2种Exo的蛋白组分；通过酶联免疫吸附（ELISA）验证了差异表达蛋白肿瘤坏死因子α（TNF-α）、核因子κB受体活化因子配体（RANKL）、白细胞介素（IL）-1α、转化生长因子β（TGF-β）、骨形态发生蛋白2（BMP-2）表达水平；将10、100、1 000 μg·mL^-1的Exo-CP或Exo-WT加入RAW264.7培养基中并于5 d时通过实时荧光定量聚合酶链反应（RT-qPCR）、Western blot、抗酒石酸酸性磷酸酶（TRAP）染色检测破骨分化相关指标表达情况；采用SPSS 24.0软件对实验数据进行统计学分析。结果　Exo-CP富集的差异表达蛋白主要与破骨细胞分化的TNF信号通路、核转录因子κB（NF-κB）信号通路相关；ELISA实验证实了Exo-CP中高表达TNF-α、RANKL、IL-1α，低表达TGF-β1、BMP-2（P<0.05）；Exo-CP作用于RAW264.7，显著提高了细胞的破骨分化相关基因及蛋白的表达水平，TRAP染色可见分化的破骨细胞，且呈现浓度依赖性，100、1 000 μg·mL^-1浓度Exo-CP对破骨细胞分化的促进作用显著高于10 μg·mL^-1浓度组（P<0.05）。结论　慢性牙周炎的病理性骨吸收可能由炎性PDLSCs所分泌的Exo通过促进破骨细胞分化引起，Exo中主要的作用蛋白可能为RANKL和TNF-α，本研究为慢性牙周炎骨吸收的发病机制提供了新视角。     

An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy

A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T₁, T₂, and contrast enhanced T₁ (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128 × 192 × 192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3 Houndsfield unit (HU) when trained using T1 images, 71.1/186.1 HU for T2, and 82.9/236.4 HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2 cm and for 98% of all spots the difference was less than 1 cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. “real-world data”).