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Montgomery MK, Duan C, Manzuk L, Chang S, Cubias A, Brun S, Giddabasappa A, Jiang ZK. Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography. Transl Oncol 2024; 40:101833. [PMID: 38128467 PMCID: PMC10776660 DOI: 10.1016/j.tranon.2023.101833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/01/2023] [Accepted: 11/14/2023] [Indexed: 12/23/2023] Open
Abstract
Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r2=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r2=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r2=0.98, DSC=0.78) performed within inter-reader variability (r2=0.79, DSC=0.69) and close to intra-reader variability (r2=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.
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Affiliation(s)
| | - Chong Duan
- Early Clinical Development, Pfizer Inc., 1 Portland Street, Cambridge, MA 02139, United States
| | - Lisa Manzuk
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Stephanie Chang
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Aiyana Cubias
- Early Clinical Development, Pfizer Inc., 1 Portland Street, Cambridge, MA 02139, United States
| | - Sonja Brun
- Oncology Research and Development, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Anand Giddabasappa
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Ziyue Karen Jiang
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States.
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Van Court B, Neupert B, Nguyen D, Ross R, Knitz MW, Karam SD. Measurement of mouse head and neck tumors by automated analysis of CBCT images. Sci Rep 2023; 13:12033. [PMID: 37491456 PMCID: PMC10368694 DOI: 10.1038/s41598-023-39159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/20/2023] [Indexed: 07/27/2023] Open
Abstract
Animal experiments are often used to determine effects of drugs and other biological conditions on cancer progression, but poor accuracy and reproducibility of established tumor measurement methods make results unreliable. In orthotopic mouse models of head and neck cancer, tumor volumes approximated from caliper measurements are conventionally used to compare groups, but geometrical challenges make the procedure imprecise. To address this, we developed software to better measure these tumors by automated analysis of cone-beam computed tomography (CBCT) scans. This allows for analyses of tumor shape and growth dynamics that would otherwise be too inaccurate to provide biological insight. Monitoring tumor growth by calipers and imaging in parallel, we find that caliper measurements of small tumors are weakly correlated with actual tumor volume and highly susceptible to experimenter bias. The method presented provides a unique window to sources of error in a foundational aspect of preclinical head and neck cancer research and a valuable tool to mitigate them.
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Affiliation(s)
- Benjamin Van Court
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Brooke Neupert
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Diemmy Nguyen
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Richard Ross
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Michael W Knitz
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Sana D Karam
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA.
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Buccardi M, Ferrini E, Pennati F, Vincenzi E, Ledda RE, Grandi A, Buseghin D, Villetti G, Sverzellati N, Aliverti A, Stellari FF. A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy. Respir Res 2023; 24:126. [PMID: 37161569 PMCID: PMC10170869 DOI: 10.1186/s12931-023-02432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 04/24/2023] [Indexed: 05/11/2023] Open
Abstract
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory μCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.
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Affiliation(s)
- Martina Buccardi
- Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | - Erica Ferrini
- Department of Veterinary Science, University of Parma, Parma, Italy
| | - Francesca Pennati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico Di Milano, Milan, Italy
| | - Elena Vincenzi
- Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
- Camelot Biomedical System S.R.L, Via Al Ponte Reale 2/20, 16124, Genoa, Italy
| | | | - Andrea Grandi
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | - Davide Buseghin
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Gino Villetti
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | | | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico Di Milano, Milan, Italy
| | - Franco Fabio Stellari
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy.
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Zaw Thin M, Moore C, Snoeks T, Kalber T, Downward J, Behrens A. Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice. Nat Protoc 2023; 18:990-1015. [PMID: 36494493 DOI: 10.1038/s41596-022-00769-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/09/2022] [Indexed: 12/14/2022]
Abstract
X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4-8 min, and data analysis typically requires 10-30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents.
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Affiliation(s)
- May Zaw Thin
- Cancer Stem Cell Laboratory, Institute of Cancer Research, London, UK. .,Adult Stem Cell Laboratory, The Francis Crick Institute, London, UK.
| | - Christopher Moore
- Oncogene Biology Laboratory, The Francis Crick Institute, London, UK
| | - Thomas Snoeks
- Imaging Research Facility, The Francis Crick Institute, London, UK
| | - Tammy Kalber
- Centre for Advanced Biomedical Imaging (CABI), University College London, London, UK
| | - Julian Downward
- Oncogene Biology Laboratory, The Francis Crick Institute, London, UK. .,Lung Cancer Group, Division of Molecular Pathology, Institute of Cancer Research, London, UK.
| | - Axel Behrens
- Cancer Stem Cell Laboratory, Institute of Cancer Research, London, UK.,Adult Stem Cell Laboratory, The Francis Crick Institute, London, UK.,Department of Surgery and Cancer, Imperial College London, London, UK.,Cancer Research UK Convergence Science Centre, Imperial College London, London, UK
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5
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Ferl GZ, Barck KH, Patil J, Jemaa S, Malamut EJ, Lima A, Long JE, Cheng JH, Junttila MR, Carano RA. Automated segmentation of lungs and lung tumors in mouse micro-CT scans. iScience 2022; 25:105712. [PMID: 36582483 PMCID: PMC9792881 DOI: 10.1016/j.isci.2022.105712] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.
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Affiliation(s)
- Gregory Z. Ferl
- Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA,Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA,Corresponding author
| | - Kai H. Barck
- Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA,Corresponding author
| | - Jasmine Patil
- Genetic Science Group, Thermo Fisher Scientific, South San Francisco, CA 94080, USA
| | - Skander Jemaa
- Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
| | - Evelyn J. Malamut
- Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA
| | - Anthony Lima
- Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA
| | - Jason E. Long
- ORIC Pharmaceuticals, South San Francisco, CA 94080, USA
| | - Jason H. Cheng
- Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA
| | | | - Richard A.D. Carano
- Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
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Winn CB, Hwang SK, Morin J, Bluette CT, Manickam B, Jiang ZK, Giddabasappa A, Liu CN, Matthews K. Automated monitoring of respiratory rate as a novel humane endpoint: A refinement in mouse metastatic lung cancer models. PLoS One 2021; 16:e0257694. [PMID: 34543354 PMCID: PMC8452061 DOI: 10.1371/journal.pone.0257694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/07/2021] [Indexed: 11/19/2022] Open
Abstract
In oncology research, while xenograft tumor models are easily visualized and humane endpoints can be clearly defined, metastatic tumor models are often based on more subjective clinical observations as endpoints. This study aimed at identifying objective non-invasive criteria for predicting imminent distress and mortality in metastatic lung tumor-bearing mice. BALB/c and C57BL/6 mice were inoculated with CT26 or B16F10 cells, respectively. The mice were housed in Vium smart cages to continuously monitor and stream respiratory rate and locomotion for up to 28 days until scheduled euthanasia or humane endpoint criteria were met. Body weight and body temperature were measured during the study. On days 11, 14, 17 and 28, lungs of subsets of animals were microCT imaged in vivo to assess lung metastasis progression and then euthanized for lung microscopic evaluations. Beginning at day 21, most tumor-bearing animals developed increased respiratory rates followed by decreased locomotion 1-2 days later, compared with the baseline values. Increases in respiratory rate did not correlate to surface tumor nodule counts or lung weight. Body weight measurement did not show significant changes from days 14-28 in either tumor-bearing or control animals. We propose that increases in respiratory rate (1.3-1.5 X) can be used to provide an objective benchmark to signal the need for increased clinical observations or euthanasia. Adoption of this novel humane endpoint criterion would allow investigators time to collect tissue samples prior to spontaneous morbidity or death and significantly reduce the distress of mice in the terminal stages of these metastatic lung tumor models.
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Affiliation(s)
- Caroline B. Winn
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, Cambridge, Massachusetts, United States of America
| | - Seo-Kyoung Hwang
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, Groton, Connecticut, United States of America
| | - Jeffrey Morin
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, Cambridge, Massachusetts, United States of America
| | - Crystal T. Bluette
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, Cambridge, Massachusetts, United States of America
| | - Balasubramanian Manickam
- Global Pathology and Investigative Toxicology, Pfizer Worldwide Research, Development & Medical, Groton, Connecticut, United States of America
| | - Ziyue K. Jiang
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, San Diego, California, United States of America
| | - Anand Giddabasappa
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, San Diego, California, United States of America
| | - Chang-Ning Liu
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, Groton, Connecticut, United States of America
| | - Kristin Matthews
- Comparative Medicine, Pfizer Worldwide Research, Development & Medical, San Diego, California, United States of America
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