Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography
Mary Katherine Montgomery1, John David1, Haikuo Zhang2
1Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America.
Abstract:
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.
Insights
A new automated tool, the Mouse Lung Automated Segmentation Tool (MLAST), significantly speeds up the analysis of lung cancer in mice. This innovation aids in developing more effective lung cancer therapies.
Area of Science:
- Preclinical Cancer Research
- Medical Imaging Analysis
- Computational Biology
Background:
- Lung cancer survival rates have stagnated since the 1970s, necessitating improved research tools.
- Genetically engineered mouse models offer high translational relevance for studying human lung cancer mutations.
- Traditional methods for quantifying lung tumors in mice are insufficient for longitudinal studies and therapy response assessment.
Purpose of the Study:
- To develop a fully-automated alternative to manual segmentation for analyzing micro-computed tomography (microCT) images of mouse lung tumors.
- To create the Mouse Lung Automated Segmentation Tool (MLAST) for precise and efficient quantification of tumor burden.
- To validate MLAST's performance against established methods and assess its utility in preclinical drug efficacy trials.
Main Methods:
- Development of the Mouse Lung Automated Segmentation Tool (MLAST) for automated thoracic region identification and lung field segmentation.
- MLAST categorizes lung tissue into soft tissue, intermediate, and lung categories to quantify tumor burden.
- Validation of MLAST against manual scoring, manual segmentation, and histology; application in a Kras/Lkb1 non-small cell lung cancer efficacy study.
Main Results:
- MLAST accurately quantifies tumor burden by measuring decreased lung volume and increased soft/intermediate tissue.
- Validation confirmed MLAST's precision and sensitivity compared to manual methods and histology.
- Application in an efficacy trial demonstrated MLAST's ability to precisely quantify tumor growth inhibition following drug treatment.
Conclusions:
- MLAST provides a fully-automated, efficient, and validated method for analyzing microCT lung cancer data in mice.
- The tool accelerates data analysis, enabling larger study sizes and mid-study therapeutic readouts.
- Automated image analysis tools like MLAST are crucial for high-throughput, quantitative results in preclinical imaging research.
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