Growth Pattern Analysis of Murine Lung Neoplasms by Advanced Semi-Automated Quantification of Micro-CT Images

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses

Search of low-contrast liver lesions in abdominal CT: the importance of scrolling behavior.

Purpose: Visual search using volumetric images is becoming the standard in medical imaging. However, we do not fully understand how eye movement strategies mediate diagnostic performance. A recent study on computed tomography (CT) images showed that the search strategies of radiologists could be classified based on saccade amplitudes and cross-quadrant eye movements [eye movement index (EMI)] into two categories: drillers and scanners. Approach: We investigate how the number of times a radiologist scrolls in a given direction during analysis of the images (number of courses) could add a supplementary variable to use to characterize search strategies. We used a set of 15 normal liver CT images in which we inserted 1 to 5 hypodense metastases of two different signal contrast amplitudes. Twenty radiologists were asked to search for the metastases while their eye-gaze was recorded by an eye-tracker device (EyeLink1000, SR Research Ltd., Mississauga, Ontario, Canada). Results: We found that categorizing radiologists based on the number of courses (rather than EMI) could better predict differences in decision times, percentage of image covered, and search error rates. Radiologists with a larger number of courses covered more volume in more time, found more metastases, and made fewer search errors than those with a lower number of courses. Our results suggest that the traditional definition of drillers and scanners could be expanded to include scrolling behavior. Drillers could be defined as scrolling back and forth through the image stack, each time exploring a different area on each image (low EMI and high number of courses). Scanners could be defined as scrolling progressively through the stack of images and focusing on different areas within each image slice (high EMI and low number of courses). Conclusions: Together, our results further enhance the understanding of how radiologists investigate three-dimensional volumes and may improve how to teach effective reading strategies to radiology residents

EPITHELIAL TO MESENCHYMAL TRANSITION AS A PREDICTOR OF RESPONSE TO POLO-LIKE KINASE 1 INHIBITION-INDUCED APOPTOSIS IN NON-SMALL CELL LUNG CARCINOMA

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Outcomes are poor for patients with recurrent, advanced or metastatic NSCLC. Polo-like kinase 1 (PLK1), involved in the regulation of mitotic processes and the response to DNA damage, is overexpressed in NSCLC. Inhibiting PLK1 may be an effective treatment for NSCLC patients as it is involved in the mechanisms of resistance to several chemotherapy drugs. PLK1 inhibition or knock-down has various effects in cancer cells, including mitotic arrest, apoptosis, and senescence. Predictive biomarkers have not been identified to select those patients who are likely to respond to PLK1 inhibitors although a small subset of NSCLC patients respond well to single agent therapy. Our lab found that mesenchymal NSCLC cell lines were more sensitive to PLK1 inhibitors than the epithelial cell lines in vitro. The induction of an epithelial phenotype using miR-200 expression increased resistance to PLK1 inhibition, whereas the induction of a mesenchymal phenotype using ZEB1 expression or TGF-β increased PLK1 inhibition–induced apoptosis. To elucidate the mechanisms of resistance to PLK1 inhibition, our lab compared gene and protein expression in sensitive and resistant NSCLC cell lines and we identified β-Catenin, SMAD4 and PDK1 to be differentially regulated between epithelial and mesenchymal NSCLC cell lines after PLK1 inhibition. We tested the role of β-Catenin, SMAD4 and PDK1 in PLK1 inhibition induced apoptosis in NSCLC. Here, we demonstrate that mesenchymal NSCLC tumors are more sensitive to PLK1 inhibition compared to epithelial NSCLC in vivo in patient derived-xenograft (PDX) models as well as orthotopic mouse models in which the EMT properties are manipulable by modulating the miR200/ZEB1 axis. To facilitate analysis of these in vivo studies, we developed a novel semi-automated method of metastatic lung tumor burden calculation from computed tomography images by the calculation of the mass of the thoracic cavity. This method takes into account the aggregate tumor metastases in the thoracic cavity which significantly accounts for tumor burden in lung adenocarcinoma and provides details about the dynamic processes that occur in vivo over time

Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

Focal Spot, Summer/Fall 2007

https://digitalcommons.wustl.edu/focal_spot_archives/1106/thumbnail.jp

Early detection of pre-malignant lesions in a KRASG12D-driven mouse lung cancer model by monitoring circulating free DNA.

Lung cancer is the leading cause of cancer-related death. Two-thirds of cases are diagnosed at an advanced stage that is refractory to curative treatment. Therefore, strategies for the early detection of lung cancer are urgently sought. Total circulating free DNA (cfDNA) and tumour-derived circulating tumour DNA (ctDNA) are emerging as important biomarkers within a 'liquid biopsy' for monitoring human disease progression and response to therapy. Owing to the late clinical diagnosis of lung adenocarcinoma, the potential for cfDNA and ctDNA as early detection biomarkers remains unexplored. Here, using a Cre-regulated genetically engineered mouse model of lung adenocarcinoma development, driven by KrasG12D (the KrasLSL-G12D mouse), we serially tracked the release of cfDNA/ctDNA and compared this with tumour burden as determined by micro-computed tomography (CT). To monitor ctDNA, a droplet digital PCR assay was developed to permit discrimination of the KrasLox-G12D allele from the KrasLSL-G12D and KrasWT alleles. We show that micro-CT correlates with endpoint histology and is able to detect pre-malignant tumours with a combined volume larger than 7 mm3 Changes in cfDNA/ctDNA levels correlate with micro-CT measurements in longitudinal sampling and are able to monitor the emergence of lesions before the adenoma-adenocarcinoma transition. Potentially, this work has implications for the early detection of human lung adenocarcinoma using ctDNA/cfDNA profiling.A video abstract for this article is available at https://youtu.be/Ku8xJJyGs3UThis article has an associated First Person interview with the joint first authors of the paper.Medical Research Counci

Intelligent computing applications to assist perceptual training in medical imaging

The research presented in this thesis represents a body of work which addresses issues in medical imaging, primarily as it applies to breast cancer screening and laparoscopic surgery. The concern here is how computer based methods can aid medical practitioners in these tasks. Thus, research is presented which develops both new techniques of analysing radiologists performance data and also new approaches of examining surgeons visual behaviour when they are undertaking laparoscopic training. Initially a new chest X-Ray self-assessment application is described which has been developed to assess and improve radiologists performance in detecting lung cancer. Then, in breast cancer screening, a method of identifying potential poor performance outliers at an early stage in a national self-assessment scheme is demonstrated. Additionally, a method is presented to optimize whether a radiologist, in using this scheme, has correctly localised and identified an abnormality or made an error. One issue in appropriately measuring radiological performance in breast screening is that both the size of clinical monitors used and the difficulty in linking the medical image to the observer s line of sight hinders suitable eye tracking. Consequently, a new method is presented which links these two items. Laparoscopic surgeons have similar issues to radiologists in interpreting a medical display but with the added complications of hand-eye co-ordination. Work is presented which examines whether visual search feedback of surgeons operations can be useful training aids

Loss of TIP30 Accelerates Pancreatic Cancer Progression and Metastasis

Indiana University-Purdue University Indianapolis (IUPUI)Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of cancer-related death in the United States, and is characterized by key driver mutations (e.g. KRAS, TP53, CDKN2A, and SMAD4), elevated expression of growth factors such as TGF-βs and the EGF receptor (EGFR), a markedly desmoplastic stroma, and a propensity to develop multi-organ metastases and chemoresistance. Consistent with its aggressive nature, the 5-year survival rate for PDAC is 8-9%, which demonstrates an urgent need to develop novel therapies. High expression levels of microRNA-10b (miR-10b) in PDAC tissues are associated with decreased patient survival and earlier appearance of metastatic disease following neoadjuvant chemoradiotherapy. miR-10b downregulates the expression of transcription coactivator Tat-Interacting Protein 30 (TIP30) by targeting its 3’UTR. TIP30 has multiple reported functions. TIP30 suppresses tumor formation and metastasis, forms a complex that regulates EGFR trafficking and degradation, and transcriptionally upregulates pro-apoptotic genes. Alterations in TIP30 have been reported in multiple human cancers, including pancreatic cancer. We hypothesized that Tip30-deficiency accelerates PDAC progression and metastasis in a murine model of PDAC. To test this hypothesis, we crossed mice with oncogenic Kras (KC) localized to the pancreas epithelium, with Tip30-deficient mice (K30C). We compared PDAC histopathology between Tip30-heterozygous (K30+/-C) and Tip30-null (K30-/-C) mice. Tip30-heterozygosity accelerated PDAC-lesion-associated pancreatic cancer cell (PCC) pulmonary seeding. By contrast, total loss of Tip30 enhanced PCC micrometastatic seeding to the liver and hepatic metastasis. K30+/-C mice also presented with an early, increased penetrance of lung lesions and lung adenocarcinoma; and PCCs isolated from K30+/-C pancreata exhibited increased EGFR protein levels. These findings suggest that TIP30 deficiency can have a dose-dependent effect on organotropic metastasis and EGFR levels in PCCs. Future studies will delineate the molecular consequences of TIP30 loss in PDAC and contribute to a broader understanding of pancreatic cancer metastasis.2020-08-0