Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
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DOI：
10.1371/journal.pone.0252950
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Giddabasappa A
中科院分区：
文献类型：
--
作者：
Montgomery MK;David J;Zhang H;Ram S;Deng S;Premkumar V;Manzuk L;Jiang ZK;Giddabasappa A
与大多数癌症不同，自20世纪70年代初以来，肺癌的生存率没有多大改善，生存率仍然很低。基因工程小鼠肿瘤模型具有高度的翻译相关性，因为我们可以产生在肺癌患者中观察到的组织特异性突变。由于这些肿瘤不能通过传统方法检测和量化，我们使用显微计算机断层扫描成像进行纵向评估，并测量对治疗的反应。传统上，我们通过手动分割来分析肺癌的microCT图像。手动分割非常耗时，并且对分析员内部和分析员之间的差异非常敏感。为了克服手动分割的局限性，我们着手开发一种全自动的替代方案，即小鼠肺自动分割工具（MLAST）。MLAST定位胸部感兴趣区域，将肺野阈值化并分类为三个组织类别：软组织、中间组织和肺。肿瘤负荷的增加通过肺体积的减少以及软组织和中间组织数量的同时增加来测量。MLAST分割针对三种方法进行了验证：手动评分、手动分割和组织学。在使用Kras/Lkb 1非小细胞肺癌模型的疗效试验中应用了MLAST，并证明了在定量药物治疗后的肿瘤生长抑制方面具有足够的精确度和灵敏度。MLAST的实施大大加速了microCT数据分析，允许更大的研究规模和中期研究读数。这项研究说明了如何在临床前成像中使用大型数据集的自动图像分析工具，以提供高通量和定量结果。
Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.
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