Unboxing AI - Radiological Insights Into a Deep Neural Network for Lung Nodule Characterization.
- 作者列表："Venugopal VK","Vaidhya K","Murugavel M","Chunduru A","Mahajan V","Vaidya S","Mahra D","Rangasai A","Mahajan H
RATIONALE AND OBJECTIVES:To explain predictions of a deep residual convolutional network for characterization of lung nodule by analyzing heat maps. MATERIALS AND METHODS:A 20-layer deep residual CNN was trained on 1245 Chest CTs from National Lung Screening Trial (NLST) trial to predict the malignancy risk of a nodule. We used occlusion to systematically block regions of a nodule and map drops in malignancy risk score to generate clinical attribution heatmaps on 103 nodules from Lung Image Database Consortium image collection and Image Database Resource Initiative (LIDC-IDRI) dataset, which were analyzed by a thoracic radiologist. The features were described as heat inside nodule -bright areas inside nodule, peripheral heat continuous/interrupted bright areas along nodule contours, heat in adjacent plane -brightness in scan planes juxtaposed with the nodule, satellite heat - a smaller bright spot in proximity to nodule in the same scan plane, heat map larger than nodule bright areas corresponding to the shape of the nodule seen outside the nodule margins and heat in calcification. RESULTS:These six features were assigned binary values. This feature vector was fedinto a standard J48 decision tree with 10-fold cross-validation, which gave an 85 % weighted classification accuracy with a 77.8% True Positive (TP) rate, 8% False Positive (FP) rate for benign cases and 91.8% TP and 22.2% FP rates for malignant cases. Heat Inside nodule was more frequently observed in nodules classified as malignant whereas peripheral heat, heat in adjacent plane, and satellite heat were more commonly seen in nodules classified as benign. CONCLUSION:We discuss the potential ability of a radiologist to visually parse the deep learning algorithm generated "heat map" to identify features aiding classification.
原理和目的: 通过分析热图，解释用于表征肺结节的深层剩余卷积网络的预测。 材料和方法: 对来自国家肺筛查试验 (NLST) 试验的 1245 个胸部 CTs 进行 20 层深残余 CNN 训练，以预测结节的恶性风险。我们使用闭塞系统地阻断结节区域和恶性肿瘤风险评分中的 map 下降，从肺图像数据库联盟图像收集和图像数据库资源倡议 (LIDC- IDRI) 数据集，由胸部放射科医生进行分析。特征描述为结节内部的热-结节内部的明亮区域，沿结节轮廓的周边热连续/中断的明亮区域，相邻平面中的热-与结节并列的扫描平面中的亮度, 卫星热-在同一扫描平面的结节附近较小的亮点,热图大于结节明亮的区域，与结节边缘外所见的结节形状和钙化中的热相对应。 结果: 这六个特征被分配了二进制值。将该特征向量纳入 10 倍交叉验证的标准 J48 决策树中，得到 85% 的加权分类准确率，真阳性 (TP) 率为 77.8%, 良性病例 8% 假阳性 (FP) 率，恶性病例 91.8% TP 和 22.2% FP 率。结节内热更常见于恶性结节，而周边热、邻近平面热和卫星热更常见于良性结节。 结论: 我们讨论了放射科医生视觉解析深度学习算法生成的 “热图” 以识别辅助分类的特征的潜在能力。
METHODS:BACKGROUND AND PURPOSE:A critical role for sphingosine kinase/sphingosine-1-phosphate (S1P) pathway in the control of airway function has been demonstrated in respiratory diseases. Here, we address S1P contribution in a mouse model of mild chronic obstructive pulmonary disease (COPD). EXPERIMENTAL APPROACH:C57BL/6J mice have been exposed to room air or cigarette smoke up to 11 months and killed at different time points. Functional and molecular studies have been performed. KEY RESULTS:Cigarette smoke caused emphysematous changes throughout the lung parenchyma coupled to a progressive collagen deposition in both peribronchiolar and peribronchial areas. The high and low airways showed an increased reactivity to cholinergic stimulation and α-smooth muscle actin overexpression. Similarly, an increase in airway reactivity and lung resistances following S1P challenge occurred in smoking mice. A high expression of S1P, Sph-K2 , and S1P receptors (S1P2 and S1P3 ) has been detected in the lung of smoking mice. Sphingosine kinases inhibition reversed the increased cholinergic response in airways of smoking mice. CONCLUSIONS AND IMPLICATIONS:S1P signalling up-regulation follows the disease progression in smoking mice and is involved in the development of airway hyperresponsiveness. Our study defines a therapeutic potential for S1P inhibitors in management of airways hyperresponsiveness associated to emphysema in smokers with both asthma and COPD.
METHODS::The interim results from this 90-day multi-dose, inhalation toxicology study with life-time post-exposure observation has shown an important fundamental difference in persistence and pathological response in the lung between brake dust derived from brake-pads manufactured with chrysotile, TiO2 or chrysotile alone in comparison to the amphiboles, crocidolite and amosite asbestos. In the brake dust exposure groups no significant pathological response was observed at any time. Slight macrophage accumulation of particles was noted. Wagner-scores, were from 1 to 2 (1 = air-control group) and were similar to the TiO2 group. Chrysotile being biodegradable, shows a weakening of its matrix and breaking into short fibers & particles that can be cleared by alveolar macrophages and continued dissolution. In the chrysotile exposure groups, particle laden macrophage accumulation was noted leading to a slight interstitial inflammatory response (Wagner-score 1-3). There was no peribronchiolar inflammation and occasional very slight interstitial fibrosis. The histopathology and the confocal analyses clearly differentiate the pathological response from amphibole asbestos, crocidolite and amosite, compared to that from the brake dust and chrysotile. Both crocidolite and amosite induced persistent inflammation, microgranulomas, and fibrosis (Wagner-scores 4), which persisted through the post exposure period. The confocal microscopy of the lung and snap-frozen chestwalls quantified the extensive inflammatory response and collagen development in the lung and on the visceral and parietal surfaces. The interim results reported here, provide a clear basis for differentiating the effects from brake dust exposure from those following amphibole asbestos exposure. The subsequent results through life-time post-exposure will follow.
METHODS::The respiratory tract is lined by a pseudo-stratified epithelium from the nose to terminal bronchioles. This first line of defense of the lung against external stress includes five main cell types: basal, suprabasal, club, goblet and multiciliated cells, as well as rare cells such as ionocytes, neuroendocrine and tuft/brush cells. At homeostasis, this epithelium self-renews at low rate but is able of fast regeneration upon damage. Airway epithelial cell lineages during regeneration have been investigated in the mouse by genetic labeling, mainly after injuring the epithelium with noxious agents. From these approaches, basal cells have been identified as progenitors of club, goblet and multiciliated cells, but also of ionocytes and neuroendocrine cells. Single-cell RNA sequencing, coupled to lineage inference algorithms, has independently allowed the establishment of comprehensive pictures of cell lineage relationships in both mouse and human. In line with genetic tracing experiments in mouse trachea, studies using single-cell RNA sequencing (RNAseq) have shown that basal cells first differentiate into club cells, which in turn mature into goblet cells or differentiate into multiciliated cells. In the human airway epithelium, single-cell RNAseq has identified novel intermediate populations such as deuterosomal cells, 'hybrid' mucous-multiciliated cells and progenitors of rare cells. Novel differentiation dynamics, such as a transition from goblet to multiciliated cells have also been discovered. The future of cell lineage relationships in the respiratory tract now resides in the combination of genetic labeling approaches with single-cell RNAseq to establish, in a definitive manner, the hallmarks of cellular lineages in normal and pathological situations.