Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma

IntroductionLung adenocarcinomas (ADCs) with a micropapillary pattern have been reported to have a poor prognosis. However, few studies have reported on the imaging-based identification of a micropapillary component, and all of them have been subjective studies dealing with qualitative computed tomography variables. We aimed to explore imaging phenotyping using a radiomics approach for predicting a micropapillary pattern within lung ADC.MethodsWe enrolled 339 patients who underwent complete resection for lung ADC. Histologic subtypes and grades of the ADC were classified. The amount of micropapillary component was determined. Clinical features and conventional imaging variables such as tumor disappearance rate and maximum standardized uptake value on positron emission tomography were assessed. Quantitative computed tomography analysis was performed on the basis of histogram, size and shape, Gray level co-occurrence matrix–based features, and intensity variance and size zone variance–based features.ResultsHigher tumor stage (OR = 3.270, 95% confidence interval [CI]: 1.483–7.212), intermediate grade (OR = 2.977, 95% CI: 1.066–8.316), lower value of the minimum of the whole pixel value (OR = 0.725, 95% CI: 0.527–0.98800), and lower value of the variance of the positive pixel value (OR = 0.961, 95% CI: 0.927–0.997) were identified as being predictive of a micropapillary component within lung ADC. On the other hand, maximum standardized uptake value and tumor disappearance rate were not significantly different in groups with a micropapillary pattern constituting at least 5% or less than 5% of the entire tumor.ConclusionA radiomics approach can be used to interrogate an entire tumor in a noninvasive manner. Combining imaging parameters with clinical features can provide added diagnostic value to identify the presence of a micropapillary component and thus, can influence proper treatment planning.     

基于深度学习的超声影像组学在乳腺癌中的研究进展

基于深度学习的影像组学(deep learning radiomics,DLR)通过不同构架从医学图像中提取深层特征,并将提取出的深层特征进一步分析,辅助临床决策。相比传统影像组学,DLR能够自动地提取深层特征,不依赖于医师人工标注,进一步提高其在肿瘤诊断及预测预后中的准确性和可靠性。超声检查是乳腺癌早期诊断的主要方式。全文分析近几年基于超声的DLR在乳腺肿物良恶性的鉴别诊断、乳腺癌分子分型的预测、腋窝淋巴结状态评估、新辅助化疗疗效评估中的研究现状。     

33例纵隔型周围型肺癌CT特点

[目的]探讨纵隔型周围型肺癌的CT影像特点。[方法]收集分析33例纵隔型周围型肺癌CT表现。[结果]肿瘤侵犯前纵隔为主21例，侵犯中纵隔为主3例，侵犯后纵隔为主9例。瘤体最大径〉3cm30例，≤3cm3例。瘤肺交界面模糊27例，清晰6例。纵隔大血管受压变形30例。[结论]纵隔型周围型肺癌有一定的影像学特征，CT具有一定的诊断价值。     

Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm

AimsDespite the promising results achieved by radiomics prognostic models for various clinical applications, multiple challenges still need to be addressed. The two main limitations of radiomics prognostic models include information limitation owing to single imaging modalities and the selection of optimum machine learning and feature selection methods for the considered modality and clinical outcome. In this work, we applied several feature selection and machine learning methods to single-modality positron emission tomography (PET) and computed tomography (CT) and multimodality PET/CT fusion to identify the best combinations for different radiomics modalities towards overall survival prediction in non-small cell lung cancer patients.Materials and methodsA PET/CT dataset from The Cancer Imaging Archive, including subjects from two independent institutions (87 and 95 patients), was used in this study. Each cohort was used once as training and once as a test, followed by averaging of the results. ComBat harmonisation was used to address the centre effect. In our proposed radiomics framework, apart from single-modality PET and CT models, multimodality radiomics models were developed using multilevel (feature and image levels) fusion. Two different methods were considered for the feature-level strategy, including concatenating PET and CT features into a single feature set and alternatively averaging them. For image-level fusion, we used three different fusion methods, namely wavelet fusion, guided filtering-based fusion and latent low-rank representation fusion. In the proposed prognostic modelling framework, combinations of four feature selection and seven machine learning methods were applied to all radiomics modalities (two single and five multimodalities), machine learning hyper-parameters were optimised and finally the models were evaluated in the test cohort with 1000 repetitions via bootstrapping. Feature selection and machine learning methods were selected as popular techniques in the literature, supported by open source software in the public domain and their ability to cope with continuous time-to-event survival data. Multifactor ANOVA was used to carry out variability analysis and the proportion of total variance explained by radiomics modality, feature selection and machine learning methods was calculated by a bias-corrected effect size estimate known as ω².ResultsOptimum feature selection and machine learning methods differed owing to the applied radiomics modality. However, minimum depth (MD) as feature selection and Lasso and Elastic-Net regularized generalized linear model (glmnet) as machine learning method had the highest average results. Results from the ANOVA test indicated that the variability that each factor (radiomics modality, feature selection and machine learning methods) introduces to the performance of models is case specific, i.e. variances differ regarding different radiomics modalities and fusion strategies. Overall, the greatest proportion of variance was explained by machine learning, except for models in feature-level fusion strategy.ConclusionThe identification of optimal feature selection and machine learning methods is a crucial step in developing sound and accurate radiomics risk models. Furthermore, optimum methods are case specific, differing due to the radiomics modality and fusion strategy used.     

经皮冷冻猪肺的影像学和病理学研究

背景与目的 肺癌已成为最常见死因的恶性肿瘤之一,对不能手术切除的肺癌,冷冻是一种安全可选择的消融治疗手段,但肺为含气组织,与冷冻肝脏、胰腺等实体器官不同,在理论上冷冻范围很难超过肿瘤边缘.本研究旨在通过正常猪肺模型实验了解不同冷冻-复温循环对肺部组织坏死范围的影响并探讨经皮冷冻肺治疗的技术方案.方法 采用6只平均体重为23 kg的正常西藏小型猪作为模型.在CT引导下选择猪肺上叶1点和下叶2点作为靶点,使用直径为1.7 mm的冷冻探针分别插入肺叶各靶点做经皮穿刺冷冻.左肺行冷冻10 min、复温5 min共2个周期的冷冻-复温循环;右肺先行冷冻5 min、复温5 min的2个冷冻-复温循环,然后行冷冻10 min、复温5 min的第3个冷冻一复温循环.左右肺的实验条件和实验方法均相同.实验中,观察CT影像下冰球的形态学变化.分别取冷冻后4h、3 d和7 d的猪肺标本,观察其大体形态及其在光镜下的组织学变化.结果 猪肺冷冻过程中随着时间的延长和循环次数的增加,冰球逐渐增大;无论2个或3个冷冻-复温循环,所产生的冷冻范围("假定坏死区")在大体标本上均超过CT上冷冻过程中显示的冰球大小;冷冻后随着时间延长,组织学坏死区逐步增大,3天及以后,假定坏死区即为组织学坏死区.结论 经皮冷冻肺可以达到有效破坏靶组织的目的;在技术上,肺冷冻以3个冷冻-复温循环为佳;冷冻范围不强求冷冻"1 cm安全边缘".上述研究结果对于简化冷冻治疗过程及减少并发症具有临床价值..     

肺结核合并肺癌的临床特点及影像学检查在其诊断中的应用价值

目的 探讨肺结核合并肺癌的临床特点,并通过分析研究影像学检查在该疾病诊断中的应用价值.方法 选取60例肺结核合并肺癌的患者作为观察组,同时选取同期收治的单纯肺结核患者60例为对照组,分析2组患者在临床特征、实验室检查和CT影像学检查中的差异.结果 肺结核结合肺癌患者的吸烟史、胸痛、声音嘶哑和消瘦的比例均明显高于单纯肺结核组(P＜0.05);同时,其CEA、CA125及非小细胞肺癌抗原的指标值也显著高于单纯肺结核组.2组患者的各项影像学特征均有统计学差异(P＜0.05).结论 患者的吸烟史、胸痛、声音嘶哑和消瘦等临床特征,以及CEA、CA125和非小细胞肺癌抗原3种血清癌抗原在诊断肺结核合并肺癌中有参考价值;CT影像学检查在诊断该疾病中有较高的应用价值,值得在临床推广应用.     

Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning

Objective: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images.  The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC). Methods: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy. Results: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively. Conclusion: Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models’ accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.     

Prognostic Value of Computed Tomography and/or 18F-Fluorodeoxyglucose Positron Emission Tomography Radiomics Features in Locally Advanced Non-small Cell Lung Cancer

IntroductionWe investigated whether adding computed tomography (CT) and/or ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET radiomics features to conventional prognostic factors (CPFs) improves prognostic value in locally advanced non-small cell lung cancer (NSCLC).Materials and MethodsWe retrospectively identified 39 cases with stage III NSCLC who received chemoradiotherapy and underwent planning CT and staging ¹⁸F-FDG PET scans. Seven CPFs were recorded. Feature selection was performed on 48 CT and 49 PET extracted radiomics features. A penalized multivariate Cox proportional hazards model was used to generate models for overall survival based on CPFs alone, CPFs with CT features, CPFs with PET features, and CPFs with CT and PET features. Linear predictors generated and categorized into 2 risk groups for which Kaplan-Meier survival curves were calculated. A log-rank test was performed to quantify the discrimination between the groups and calculated the Harrell's C-index to quantify the discriminatory power. A likelihood ratio test was performed to determine whether adding CT and/or PET features to CPFs improved model performance.ResultsAll 4 models significantly discriminated between the 2 risk groups. The discriminatory power was significantly increased when CPFs were combined with PET features (C-index 0.82; likelihood ratio test P < .01) or with both CT and PET features (0.83; P < .01) compared with CPFs alone (0.68). There was no significant improvement when CPFs were combined with CT features (0.68).ConclusionAdding PET radiomics features to CPFs yielded a significant improvement in the prognostic value in locally advanced NSCLC; adding CT features did not.     

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungalinfection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Coxproportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materialsand Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employingStudent’s t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly relatedto the presence of deep fungal infection selected as candidates for input into the final artificial neural networkanalysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used toevaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results:The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696),deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99%(194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albuminconcentrations (≤37.18g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67g /L), long time ofhospitalization (≥14days) were apt to deep fungal infection and the ANN model consisted of the seven factors.The AUC of ANN model(0.829±0.019)was higher than that of LR model (0.756±0.021). Conclusions: The artificialneural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, receivedradiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deepfungal infection in lung cancer.     

Evaluation of Angiogenesis Using Micro-Computed Tomography in a Xenograft Mouse Model of Lung Cancer

Quantitative evaluation of lung tumor angiogenesis using immunohistochemical techniques has been limited by difficulties in generating reproducible data. To analyze intrapulmonary tumor angiogenesis, we used high-resolution micro-computed tomography (micro-CT) of lung tumors of mice inoculated with mouse Lewis lung carcinoma (LLC1) or human adenocarcinoma (A549) cell lines. The lung vasculature was filled with the radiopaque silicone rubber, Microfil, through the jugular vein (in vivo application) or pulmonary artery (ex vivo application). In addition, human adenocarcinoma lung tumor-bearing mice treated site-specifically with humanized monoclonal antibody (bevacizumab) against vascular endothelial growth factor. Quantitative analysis of lung tumor microvessels imaged with micro-CT showed that more vessels (mainly small, <0.02 mm²) were filled using the in vivo (5.4%) compared with the ex vivo (2.1%) method. Furthermore, bevacizumab-treated lung tumor-bearing mice showed significantly reduced lung tumor volume and lung tumor angiogenesis compared with untreated mice as assessed by micro-CT. Interestingly, microvascularization of mainly the smaller vessels (<0.02 mm²) was reduced after bevacizumab treatment. This observation with micro-CT was nicely correlated with immunohistochemical measurement of microvessels. Therefore, micro-CT is a novel method for investigating lung tumor angiogenesis, and this might be considered as an additional complementary tool for precise quantification of angiogenesis.     

非小细胞肺癌FDG PET/CT显像与分子指标的相关性研究

背景与目的 正电子发射断层扫描(positron emission tomograph,PET)是体外测定肺实体瘤恶性程度的灵敏和特异的方法.研究行正电子发射断层扫描检查的I期可手术非小细胞肺癌(non-small cell lung cancer,NSCLC)18F-氟脱氧葡萄糖(18F-deoxyglucose,FDG)摄取与各种分子生物学标志物的相关性.方法 回顾性分析23例术前行PET-CT检查的Ⅰ期NSCLC术后病理切片,利用流式细胞仪检测术后标本的S期细胞比例(S-phase fraction,SPF),利用免疫组化分析技术检测术后标本增殖细胞核抗原(proliferating cell nuclear antigen,PCNA)、血管内皮生长因子(vascular endothelial growth factor,VEGF)、葡萄糖转运因子-1(glucose transporter-1,GLUT-1)、Ki-67、p52基因表达与肿瘤的标准摄取值(standard uptake value,SUV)之间的相关性.结果 NSCLC肿瘤SUVmax与病理低分化程度、PCNA、VEGF、p53、Ki-67基因表达、SPF旱相关关系(P＜0.05),与GLUT-1明显相关(P=0.001),与病理类型无相关关系(P＞0.05).结论 Glut-1、PCNA、VEGF、Ki-67、p53基因表达、SPF与NSCLC肿瘤SUVmax具有相关性.     

非小细胞肺癌合并Trousseau综合征以急性脑梗死为表现的临床及影像学特征

背景与目的急性脑梗死是Trousseau综合征(Trousseau syndrome, TS)的一种表现形式,但相对少见,往往容易被临床医师忽视。本研究探讨非小细胞肺癌(non-small cell lung cancer, NSCLC)合并TS以急性脑梗死为表现的临床、实验室检查及影像学特点。方法回顾性收集25例以急性脑梗死为表现的NSCLC合并TS患者的临床资料、实验室检查和影像学资料,进行分析总结。结果 25例患者中男性18例,女性7例,年龄39岁-78岁,其中腺癌22例、鳞癌2例和大细胞癌1例;所有患者均有急性脑梗死的临床症状和体征;血浆D-二聚体明显升高,凝血酶原时间及活化部分凝血活酶时间均有不同程度的缩短;所有患者在头部磁共振成像(magnetic resonance imaging, MRI)[扩散加权成像(diffusion-weighted imaging, DWI)序列]平扫上均表现为累及多个颅内动脉供血区的急性多发性脑梗死灶,头磁共振血管成像(MR angiography, MRA)上梗死灶对应的供血血管管腔未见中重度狭窄。结论 NSCLC合并急性多发性脑梗死是TS的少见表现类型,其特点是累及多个动脉供血区的急性多发性脑梗死灶伴有明显高凝状态;提高对该病的早期认识可以为临床诊疗提供一定的帮助。