Browsing by Subject "Lung cancer"

Purpose: To compare radiomics and deep-learning (DL) methods for predicting NSCLC surgical treatment failure. Methods: A cohort of 83 patients undergoing lobectomy or wedge resection for early-stage NSCLC from our institution was studied. There were 7 local failures and 16 non-local failures (regional and/or distant). Gross tumor volumes (GTV) were contoured on pre-surgery CT datasets after 1mm3 isotropic resolution resampling. For the radiomics analysis, 92 radiomics features were extracted from the GTV and z-score normalizations were performed. The multivariate association between the extracted features and clinical endpoints were investigated using a random forest model following 70%-30% training-test split. For the DL analysis, both 2D and 3D model designs were executed using two different deep neural networks as transfer learning problems: in 2D-based design, 8x8cm2 axial fields-of-view(FOVs) centered within the GTV were adopted for VGG-16 training; in 3D-based design, 8x8x8 cm3 FOVs centered within the GTV were adopted for U-Net’s encoder path training. In both designs, data augmentation (rotation, translation, flip, noise) was included to overcome potential training convergence problems due to the imbalanced dataset, and the same 70%-30% training-test split was used. The performances of the 3 models (Radiomics, 2D-DL, 3D-DL) were tested to predict outcomes including local failure, non-local failure, and disease-free survival. Sensitivity/specificity/accuracy/ROC results were obtained from their 20 trained versions. Results: The radiomics models showed limited performances in all three outcome prediction tasks. The 2D-DL design showed significant improvement compared to the radiomics results in predicting local failure (ROC AUC = 0.546±0.056). The 3D-DL design achieved the best performance for all three outcomes (local failure ROC AUC = 0.768 ± 0.051, non-local failure ROC AUC = 0.683±0.027, disease-free ROC AUC = 0.694±0.042) with statistically significant improvements from radiomics/2D-DL results. Conclusions: 3D-DL execution outperformed the 2D-DL in predicting clinical outcomes after surgery for early-stage NSCLC. By contrast, classic radiomics approach did not achieve satisfactory results.

Tumor angiogenesis is critical to tumor growth and metastasis, yet much is unknown about the role vascular cells play in the tumor microenvironment. A major outstanding challenge associated with studying tumor angiogenesis is that existing preclinical models are limited in their recapitulation of in vivo cellular organization in 3D. This disparity highlights the need for better approaches to study the dynamic interplay of relevant cells and signaling molecules as they are organized in the tumor microenvironment. In this thesis, we combined 3D culture of lung adenocarcinoma cells with adjacent 3D microvascular cell culture in 2-layer cell-adhesive, proteolytically-degradable poly(ethylene glycol) (PEG)-based hydrogels to study tumor angiogenesis and the impacts of neovascularization on tumor cell behavior.

In initial studies, 344SQ cells, a highly metastatic, murine lung adenocarcinoma cell line, were characterized alone in 3D in PEG hydrogels. 344SQ cells formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media that significantly increased with exposure to transforming growth factor beta 1 (TGF-β1), a potent tumor progression-promoting factor. Vascular cells alone in hydrogels formed tubule networks with localized activated TGF-β1. To study cancer cell-vascular cell interactions, the engineered 2-layer tumor angiogenesis model with 344SQ and vascular cell layers was employed. Large, invasive 344SQ clusters developed at the interface between the layers, and were not evident further from the interface or in control hydrogels without vascular cells. A modified model with spatially restricted 344SQ and vascular cell layers confirmed that observed 344SQ cluster morphological changes required close proximity to vascular cells. Additionally, TGF-β1 inhibition blocked endothelial cell-driven 344SQ migration.

Two other lung adenocarcinoma cell lines were also explored in the tumor angiogenesis model: primary tumor-derived metastasis-incompetent, murine 393P cells and primary tumor-derived metastasis-capable human A549 cells. These lung cancer cells also formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media. Epithelial morphogenesis varied for the primary tumor-derived cell lines compared to 344SQ cells, with far less epithelial organization present in A549 spheroids. Additionally, 344SQ cells secreted the highest concentration of two of the three angiogenic growth factors assessed. This finding correlated to 344SQ exhibiting the most pronounced morphological response in the tumor angiogenesis model compared to the 393P and A549 cell lines.

Overall, this dissertation demonstrates the development of a novel 3D tumor angiogenesis model that was used to study vascular cell-cancer cell interactions in lung adenocarcinoma cell lines with varying metastatic capacities. Findings in this thesis have helped to elucidate the role of vascular cells in tumor progression and have identified differences in cancer cell behavior in vitro that correlate to metastatic capacity, thus highlighting the usefulness of this model platform for future discovery of novel tumor angiogenesis and tumor progression-promoting targets.

Cancer is a major threat to human health and results in 1 in 6 deaths globally. Despite an extraordinary amount of effort and money spent, eradication or control of advanced disease has not yet been achieved. Understanding cancer from an evolutionary point of view may provide new insight to more effective control and treatment of the disease. Cancer as a disease of dynamic, stochastic somatic genomic evolution was first described by Nowell in 1976, and since then researchers have identified clonal expansions and genetic heterogeneity within many different types of neoplasms. The advancement in sequencing technology, especially single-cell sequencing, has open up new frontier by bringing the study of genomes to the cellular level. Phylogenetic analysis, which is a powerful tool inferring evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics, has recently been applied to cancer studies and start to show promises in deciphering cancer evolution. However, new challenges have also arisen in experimental design, methodology and interpretation regarding to phylogeny of cancer cells. The overarching theme of this dissertation is to bring phylogenetic analysis to the context of cancer evolution. By using in silico simulations, I show the advantages and disadvantages of different sampling designs for phylogenetic analysis. Although bulk sequencing can hardly recover the topology of phylogenetic trees, I then developed a new method to infer sub-clone spatial distribution utilizing phased haplotypes from bulk sequencing. And lastly, I demonstrate the usage of phylogenetic analysis in breast cancer with multi-regional bulk sequencing and lung cancer with single cell sequencing.

We present a new x-ray imaging technique, Correlation Imaging (CI), for improved breast and lung cancer detection. In CI, multiple low-dose radiographic images are acquired along a limited angular arc. Information from unreconstructed angular projections is directly combined to reduce the effect of overlying anatomy - the largest bottleneck in diagnosing cancer with projection imaging. In addition, CI avoids reconstruction artifacts that otherwise limit the performance of tomosynthesis. This work involved assessing the feasibility of the CI technique, its optimization, and its implementation for breast and chest imaging.

First a theoretical model was developed to determine the diagnostic information content of projection images using a mathematical observer. The model was benchmarked for a specific application in assessing the impact of reduced dose in mammography. Using this model, a multi-factorial task-based framework was developed to optimize the image acquisition of CI using existing low-dose clinical data. The framework was further validated using a CADe processor. Performance of CI was evaluated on mastectomy specimens at clinically relevant doses and further compared to tomosynthesis. Finally, leveraging on the expected improvement in breast imaging, a new hardware capable of CI acquisition for chest imaging was designed, prototyped, evaluated, and experimentally validated.

The theoretical model successfully predicted diagnostic performance on mammographic backgrounds, indicating a possible reduction in mammography dose by as much as 50% without adversely affecting lesion detection. Application of this model on low-dose clinical data showed that peak CI performance may be obtained with 15-17 projections. CAD results confirmed similar trends. Mastectomy specimen results at higher dose revealed that the performance of optimized breast CI may exceed that of mammography and tomosynthesis by 18% and 8%, respectively. Furthermore, for both CI and tomosynthesis, highest dose setting and maximum angular span with an angular separation of 2.75o was found to be optimum, indicating a threshold in the number of projections per angular span for optimum performance.

Finally, for the CI chest imaging system, the positional errors were found to be within 1% and motion blur to have minimal impact on the system MTF. The clinical images had excellent diagnostic quality for potentially improved lung cancer detection. The system was found to be robust and scalable to enable advanced applications for chest radiography, including novel tomosynthesis trajectories and stereoscopic imaging.

The small GTPases HRAS, NRAS and KRAS are mutated in approximately one-third of all human cancers, rendering the proteins constitutively active and oncogenic. Lung cancer is the leading cause of cancer deaths worldwide, and more than 20% of human lung cancers harbor mutations in RAS, with 98% of those occurring in the KRAS isoform. While there have been many advances in the understanding of KRAS–driven lung tumorigenesis, it remains a therapeutic challenge. To further this understanding and assess novel approaches for treatment, I have investigated two aspects of Kras–driven tumorigenesis in the lung:

(I) Despite nearly identical protein sequences, the three RAS proto-oncogenes exhibit divergent codon usage. Of the three isoforms, KRAS contains the most rare codons resulting in lower levels of KRAS protein expression relative to HRAS and NRAS. To determine the consequences of rare codon bias during de novo tumorigenesis, we created a knock-in Krasex3op mouse in which synonymous mutations in exon 3 converted codons from rare to common. These mice had reduced tumor burden and fewer oncogenic mutations in the Krasex3op allele following carcinogen exposure. The reduction in tumorigenesis appeared to be a product of rare codons affecting both the oncogenic and non–oncogenic alleles. Converting rare codons to common codons yielded a more potent oncogenic allele that promoted growth arrest and enhanced tumor suppression by the non-oncogenic allele. Thus, rare codons play an integral role in Kras tumorigenesis.

(II) Lung cancer patients exhale higher levels of NO and iNOS-/- mice are resistant to chemically induced lung tumorigenesis. I hypothesize that NO promotes Kras–driven lung adenocarcinoma, and NOS inhibition may decrease Kras–driven lung tumorigenesis. To test this hypothesis, I assessed efficacy of the NOS inhibitor L–NAME in a genetically engineered mouse model of Kras-driven lung adenocarcinoma. Adenoviral Cre recombinase was delivered into the lungs intranasally, resulting in expression of oncogenic KrasG12D and dominant-negative Trp53R172H in lung epithelial cells. L–NAME treatment was provided in the water and continued until survival endpoints. In this model, L–NAME treatment decreased tumor growth and prolonged survival. These data establish a potential clinical role for NOS inhibition in lung cancer treatment.

The developments of highly conformal and precise radiation therapy techniques promote the necessity of more accurate treatment target localization and tracking. On-board imaging techniques, especially the x-ray based techniques, have found a great popularity nowadays for on-board target localization and tracking. With an objective to improve the accuracy of on-board imaging for lung cancer patients, the dissertation work focuses on the investigations of using limited-angle on-board x-ray projections for image guidance. The limited-angle acquisition enables scan time and imaging dose reduction and improves the mechanical clearance of imaging.

First of all, the dissertation developed a phase-matched digital tomosynthesis (DTS) technique using limited-angle (<=30 deg) projections for lung tumor localization. This technique acquires the same traditional motion-blurred on-board DTS image as the 3D-DTS technique, but uses the planning 4D computed tomography (CT) to synthesize a phase-matched reference DTS to register with the on-board DTS for tumor localization. Of the 324 different scenarios simulated using the extended cardiac torso (XCAT) digital phantom, the phase-matched DTS technique localizes the 3D target position with an localization error of 1.07 mm (± 0.57 mm) (average ± standard deviation (S.D.)). Similarly, for the total 60 scenarios evaluated using the computerized imaging reference system (CIRS) 008A physical phantom, the phase-matched DTS technique localizes the 3D target position with an average localization error of 1.24 mm (± 0.87 mm). In addition to the phantom studies, preliminary clinical cases were also studied using imaging data from three lung cancer patients. Using the localization results of 4D cone beam computed tomography (CBCT) as `gold-standard', the phase-matched DTS techniques localized the tumor to an average localization error of 1.5 mm (± 0.5 mm).

The phantom and patient study results show that the phase-matched DTS technique substantially improved the accuracy of moving lung target localization, as compared to the 3D-DTS technique. The phase-matched DTS technique can provide accurate lung target localizations like 4D-DTS, but with much reduced imaging dose and scan time. The phase-matched DTS technique is also found more robust, being minimally affected by variations of respiratory cycle lengths, fractions of respiration cycle contained within the DTS scan and the scan directions, which potentially enables quasi-instantaneous (within a sub-breathing cycle) moving target verification during radiation therapy, preferably arc therapy.

Though the phase-matched DTS technique can provide accurate target localization under normal scenarios, its accuracy is limited when the patient on-board breathing experiences large variations in motion amplitudes. In addition, the limited-angle based acquisition leads to severe structural distortions in DTS images reconstructed by the current clinical gold-standard Feldkamp-Davis-Kress (FDK) reconstruction algorithm, which prohibit accurate target deformation tracking, delineation and dose calculation.

To solve the above issues, the dissertation further developed a prior knowledge based image estimation technique to fundamentally change the landscape of limited-angle based imaging. The developed motion modeling and free-form deformation (MM-FD) method estimates high quality on-board 4D-CBCT images through applying deformation field maps to existing prior planning 4D-CT images. The deformation field maps are solved using two steps: first, a principal component analysis based motion model is built using the planning 4D-CT (motion modeling). The deformation field map is constructed as an optimized linear combination of the extracted motion modes. Second, with the coarse deformation field maps obtained from motion modeling, a further fine-tuning process called free-form deformation is applied to further correct the residual errors from motion modeling. Using the XCAT phantom, a lung patient with a 30 mm diameter tumor was simulated to have various anatomical and respirational variations from the planning 4D-CT to on-board 4D-CBCTs, including respiration amplitude variations, tumor size variations, tumor average position variations, and phase shift between tumor and body respiratory cycles. The tumors were contoured in both the estimated and the `ground-truth' on-board 4D-CBCTs for comparison. 3D volume percentage error (VPE) and center-of-mass error (COME) were calculated to evaluate the estimation accuracy of the MM-FD technique. For all simulated patient scenarios, the average (± S.D.) VPE / COME of the tumor in the prior image without image estimation was 136.11% (± 42.76%) / 15.5 mm (± 3.9 mm). Using orthogonal-view 30 deg scan angle, the average VPE/COME of the tumors in the MM-FD estimated on-board images was substantially reduced to 5.22% (± 2.12%) / 0.5 mm (± 0.4 mm).

In addition to XCAT simulation, CIRS phantom measurements and actual patient studies were also performed. For these clinical studies, we used the normalized cross-correlation (NCC) as a new similarity metric and developed an updated MMFD-NCC method, to improve the robustness of the image estimation technique to the intensity mismatches between CT and CBCT imaging systems. Using 4D-CBCT reconstructed from fully-sampled on-board projections as `gold-standard', for the CIRS phantom study, the average (± S.D.) VPE / COME of the tumor in the prior image and the tumors in the MMFD-NCC estimated images was 257.1% (± 60.2%) / 10.1 mm (± 4.5 mm) and 7.7% (± 1.2%) / 1.2 mm (± 0.2mm), respectively. For three patient cases, the average (± S.D.) VPE / COME of tumors in the prior images and tumors in the MMFD-NCC estimated images was 55.6% (± 45.9%) / 3.8 mm (± 1.9 mm) and 9.6% (± 6.1%) / 1.1 mm (± 0.5 mm), respectively. With the combined benefits of motion modeling and free-form deformation, the MMFD-NCC method has achieved highly accurate image estimation under different scenarios.

Another potential benefit of on-board 4D-CBCT imaging is the on-board dose calculation and verification. Since the MMFD-NCC estimates the on-board 4D-CBCT through deforming prior 4D-CT images, the 4D-CBCT inherently has the same image quality and Hounsfield unit (HU) accuracy as 4D-CT and therefore can potentially improve the accuracy of on-board dose verification. Both XCAT and CIRS phantom studies were performed for the dosimetric study. Various inter-fractional variations featuring patient motion pattern change, tumor size change and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the on-board CBCTs estimated by MMFD-NCC (MMFD-NCC doses) were compared to the doses calculated on the `gold-standard' on-board images (gold-standard doses). The absolute deviations of minimum dose (DDmin), maximum dose (DDmax), mean dose (DDmean) and prescription dose coverage (DV100%) of the planning target volume (PTV) were evaluated. In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MMFD-NCC in the CIRS phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films.

The MMFD-NCC doses matched very well with the gold-standard doses. For the XCAT phantom study, the average (± S.D.) DDmin, DDmax, DDmean and DV100% (values normalized by the prescription dose or the total PTV volume) between the MMFD-NCC PTV doses and the gold-standard PTV doses were 0.3% (± 0.2%), 0.9% (± 0.6%), 0.6% (± 0.4%) and 1.0% (± 0.8%), respectively. Similarly, for the CIRS phantom study, the corresponding values between the MMFD-NCC PTV doses and the gold-standard PTV doses were 0.4% (± 0.8%), 0.8% (± 1.0%), 0.5% (± 0.4%) and 0.8% (± 0.8%), respectively. For the 4D dose accumulation study, the average (± S.D.) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.0% (± 2.4%). The average gamma index (3%/3mm) between the accumulated doses and the radiochromic film measured doses was 96.1%. The MMFD-NCC estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy under different scenarios. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy.

However, a major limitation of the estimated 4D-CBCTs above is that they can only capture inter-fractional patient variations as they were acquired prior to each treatment. The intra-treatment patient variations cannot be captured, which can also affect the treatment accuracy. In light of this issue, an aggregated kilo-voltage (kV) and mega-voltage (MV) imaging scheme was developed to enable intra-treatment imaging. Through using the simultaneously acquired kV and MV projections during the treatment, the MMFD-NCC method enabled 4D-CBCT estimation using combined kV and MV projections.

For all XCAT-simulated patient scenarios, the average (± S.D.) VPE / COME of the tumor in the prior image and tumors in the MMFD-NCC estimated images (using kV + open field MV) was 136.11% (± 42.76%) / 15.5 mm (± 3.9 mm) and 4.5% (± 1.9%) / 0.3 mm (± 0.4 mm), respectively. In contrast, the MMFD-NCC estimation using kV + beam's eye view (BEV) MV projections yielded results of 4.3% (± 1.5%) / 0.3 mm (± 0.3 mm). The kV + BEV MV aggregation can estimate the target as accurately as the kV + open field MV aggregation. The impact of this study is threefold: 1. the kV and MV projections can be acquired at the same time. The imaging time will be cut to half as compared to the cases which use kV projections only. 2. The kV and MV aggregation enables intra-treatment imaging and target tracking, since the MV projections can be the side products of the treatment beams (BEV MV). 3. As the BEV MV projections originate from the treatment beams, there will be no extra MV imaging dose to the patient.

The above introduced 4D-CBCT estimation techniques were all based on limited-angle acquisition. Though limited-angle acquisition enables substantial scan time and dose reduction as compared to the full-angle scan, it is still not real-time and cannot provide `cine' imaging, which refers to the instantaneous imaging with negligible scan time and imaging dose. Cine imaging is important in image guided radiation therapy practice, considering the respirational variations may occur quickly and frequently during the treatment. For instance, the patient may experience a breathing baseline shift after every respiratory cycle. The limited-angle 4D-CBCT approach still requires a scan time of multiple respiratory cycles, which will not be able to capture the baseline shift in a timely manner.

In light of this issue, based on the previously developed MMFD-NCC method, an AI-FD-NCC method was further developed to enable quasi-cine CBCT imaging using extremely limited-angle (<=6 deg) projections. Using pre-treatment 4D-CBCTs acquired just before the treatment as prior information, AI-FD-NCC enforces an additional prior adaptive constraint to estimate high quality `quasi-cine' CBCT images. Two on-board patient scenarios: tumor baseline shift and continuous motion amplitude change were simulated through the XCAT phantom. Using orthogonal-view 6 deg projections, for the baseline shift scenario, the average (± S.D.) VPE / COME of the tumors in the AI-FD-NCC estimated images was 1.3% (± 0.5%) / 0.4 mm (± 0.1 mm). For the amplitude variation scenario, the average (± S.D.) VPE / COME of the tumors in the AI-FD-NCC estimated images was 1.9% (± 1.1%) / 0.5 mm (± 0.2 mm). The impact of this study is three-fold: first, the quasi-cine CBCT technique enables actual real-time volumetric tracking of tumor and normal tissues. Second, the method enables real-time tumor and normal tissues dose calculation and accumulation. Third, the high-quality volumetric images obtained can potentially be used for real-time adaptive radiation therapy.

In summary, the dissertation work uses limited-angle on-board x-ray projections to reconstruct/estimate volumetric images for lung tumor localization, delineation and dose calculation. Limited-angle acquisition reduces imaging dose, scan time and improves imaging mechanical clearance. Using limited-angle projections enables continuous, sub respiratory-cycle tumor localization, as validated in the phase-matched DTS study. The combination of prior information, motion modeling, free-form deformation and limited-angle on-board projections enables high-quality on-board 4D-CBCT estimation, as validated by the MM-FD / MMFD-NCC techniques. The high-quality 4D-CBCT not only can be applied for accurate target localization and delineation, but also can be used for accurate treatment dose verification, as validated in the dosimetric study. Through aggregating the kV and MV projections for image estimation, intra-treatment 4D-CBCT imaging was also proposed and validated for its feasibility. At last, the introduction of more accurate prior information and additional adaptive prior knowledge constraints also enables quasi-cine CBCT imaging using extremely-limited angle projections. The dissertation work contributes to lung on-board imaging in many aspects with various approaches, which can be beneficial to the future lung image guided radiation therapy practice.

Background: Radiomics describes the study of converting medical images into high-dimensional quantitative features and following analysis for further decision making and genomics focuses on the understanding genomes of individual organisms and characterizations of different genomes. Radiogenomics is a new emerging method that combines both radiomics and genomics together in clinical studies as well as researches the relation of genetic characteristics and radiomic features. It has the potential as a tool for medical treatment assessment in the future. In this study, we used machine learning methods to build two models for treatment assessment: 1) the output is p53 mutation, and the inputs are radiomic features; 2) the output is patient overall survival, and the inputs are radiomic features and p53 mutation. The modelling process was divided into feature selection and classification. Machine learning is a popular area of artificial intelligence that can make machines “learn by itself”. Machine learning algorithm learns from datasets called “training data”, and generates a prediction model from its learning process. The prediction model can then be used to make predictions and decisions from other datasets.

Purpose: 1) To investigate the correlation between p53 mutation and radiomic features in lung cancer, and to detect p53 mutation from radiomic features using different machine learning methods, 2) To investigate the correlation between genomic (p53 mutation), radiomic, radiogenomic features and overall patient survival in lung cancer using machine learning methods.

Material and Methods: The study used 24 patients with advanced lung cancers who had received radiotherapy and chemotherapy. CT was used as medical imaging modality in radiomics study. A radiomics study was then performed which involved three parts: Pre-treatment (Pre-Tx) Radiomics, Post-treatment (Post-Tx) Radiomics, and Delta Radiomics. The pre-Tx radiomic features were calculated from treatment planning CT images, the post-Tx radiomic features were calculated from the follow-up CT images after the radiotherapy, and the delta radiomic features were calculated as the change of radiomic features between cancer treatment. 19 of 24 patients had both pre-Tx and post-Tx CT images. Totally 61 representative radiomic features were extracted from CT images, including Intensity features, Grey Level Co-occurrence Matrix features, Grey Level Run Length Matrix Features, Grey Level Size Zone Matrix features, Neighborhood Grey Level Difference Matrix features, and Morphological features. Feature selection was implemented to avoid feature redundancy. Spearman Correlation analysis and Lasso regression were used for feature selection for p53 mutation detection. Cox regression and lasso regression were used for feature selection for patient survival prediction. Then, several common machine learning based classification methods were used for modelling of p53 mutation detection and patient survival prediction, including linear discriminative analysis, quadratic discriminative analysis, Naïve Bayes, Linear Support Vector Machine, Kernel Support Vector Machine, Bootstrap Aggregating (Bagging), Logistic Regression, and Lasso generalized linear regression. Radiomic models were used for p53 mutation detection in tumor. Radiogenomic models based on combined radiomic features and p53 mutation were used for patient overall survival prediction. To avoid bias, the leave-one-out cross validation method was used for both feature selection and classification. Receiver Operator Characteristic (ROC) Curves were used as an evaluation method for the model, and Area Under Curve (AUC) values were compared for different classification methods.

Results: For p53 mutation detection, the highest AUC of pre-Tx radiomics (24 patients), pre-Tx radiomics (19 patients), and post-Tx radiomics (19 patients) was 0.6993, 0.5606, and 0.6591. For patient survival prediction, the highest AUC of pre-Tx radiomics (24 patients), pre-Tx radiomics (19 patients), post-Tx radiomics (19 patients), and delta radiomics (19 patients) was 0.7045, 0.7125, 0.6063, and 0.8000, and the highest AUC of pre-Tx radiogenomics (24 patients), pre-Tx radiogenomics (19 patients), post-Tx radiogenomics (19 patients), and delta radiogenomics (19 patients) was 0.7500, 0.7375, 0.5857, and 0.9143.

Conclusion: From limited dataset, it might be feasible to detect p53 mutation by both pre-Tx and post-Tx radiomics. Lasso and LSVM has shown the best performance in classification.

For predicting the overall patient survival, different features were selected. This may be related to the limited data available for the study. It may also be related to the different characteristics of pre-Tx, post-Tx and delta radiomics. Intensity and texture features showed high frequency being selected for pre-Tx and delta features, and morphological features showed high frequency for post-Tx radiomics. However, we also found that the combination of delta radiomics and p53 mutation showed a better patient survival prediction than pre-Tx, post-Tx, delta radiomics and p53 mutation alone. The reason might be related to the difference of tumor reaction to radiation due to p53 mutation. KSVM and Bagging showed highest performance compared with other classification methods.

Keyword: Radiogenomics, radiomics, delta radiomics, genomics, p53, lung cancer, radiotherapy.

The type III TGF-β receptor (TβRIII) is a TGF-β co-receptor that presents ligand to the type II TGF-β receptor to initiate signaling. TβRIII also undergoes ectodomain shedding to release a soluble form (sTβRIII) that can bind ligand, sequestering it away from cell surface receptors. We have previously identified a TβRIII extracellular mutant that has enhanced ectodomain shedding (“super shedding (SS)” – TβRIII-SS). Here we utilize TβRIII-SS to study the balance of cell surface and soluble TβRIII in the context of lung cancer. We demonstrate that expressing TβRIII-SS in lung cancer cell models induces epithelial-mesenchymal transition (EMT) and that these TβRIII-SS (EMT) cells are less migratory, invasive and adhesive and more resistance to gemcitabine. Moreover, TβRIII-SS (EMT) cells exhibit decreased tumorigenicity but increased tumor growth in vitro and in vivo. These studies suggest that the balance of cell surface and soluble TβRIII may regulate a dichotomous role for TβRIII during cancer progression.

We have also demonstrated that cathepsin G and neutrophil elastase, proteases stored in the azurophil granules of neutrophils, can induce cleavage of cell surface TβRIII though no functional sTβRIII was detected. Similarly, activated neutrophils could induce TβRIII shedding on cancer cells. Interestingly, neutrophils generate sTβRIII that is resistant to shedding. These studies suggest that neutrophils may modulate the balance of cell surface and soluble TβRIII in the tumor microenvironment.

Cellular senescence is a unique cell fate characterized by stable cell cycle arrest and the extensive production and secretion of various cytokines, chemokines, proteases, and growth factors, a phenomenon known as the senescence-associated secretory phenotype (SASP). Although secreted factors are known to have important biological effects on both senescent and non-senescent cells in the contexts of normal aging and disease, the precise molecular mechanisms responsible for generating a SASP in response to senescent stimuli have remained largely obscure. To identify the major initiator, we used an unbiased profiling strategy and discovered a multi-ligand scavenger receptor CD36 is rapidly upregulated in multiple cell types in response to replicative, oncogenic and chemical senescent stimuli. Moreover, ectopic CD36 expression in dividing mammalian cells is sufficient to initiate the production of a large subset of known components of the SASP via activation of the canonical Src-NFκB pathway, resulting in the subsequent onset of a full senescent state. The CD36-mediated secretome is further shown to be ligand-dependent, as fibroblast cultures lacking the CD36 ligand amyloid beta (Aβ) are unresponsive to CD36 upregulation but can be driven to senesce by the addition of exogenous ligand. Finally, loss-of-function experiments revealed a strict requirement for CD36 in secretory molecule production during conventional senescence reprogramming. These results uncover the Aβ-CD36-NFκB signaling axis as an important regulator of the senescent cell fate via induction of the SASP.

To further explore the possible implication of Aβ-CD36-NFκB-SASP signaling, we found that the CD36 expression is significantly down-regulated in the context of lung malignant tissues, specifically in cancer cells. Subsequent explorations revealed CD36 as a strong tumor suppressor by secreting pro-inflammatory cytokines and recruiting cytotoxic T. For the CD36 ligand - Aβ, we observed a major accumulation in the tumor region which might serve as the tumor-suppressing signaling initiation cue once CD36 is introduced. The findings indicate a possible tumor suppressive signaling lead by Aβ-CD36.

Taken together, we discovered a novel signaling of Aβ-CD36-NFκB in regulating SASP during the process of lung aging and the progression of lung malignancy.

Radiotherapy is commonly used to treat lung cancer. However, radiation induced damage to lung tissue is a major limiting factor to its use. To minimize normal tissue lung toxicity from conformal radiotherapy treatment planning, we investigated the use of Perfluoropropane(PFP)-enhanced MR imaging to assess and guide the sparing of functioning lung. Fluorine Enhanced MRI using Perfluoropropane(PFP) is a dynamic multi-breath steady state technique enabling quantitative and qualitative assessments of lung function(1).

Imaging data was obtained from studies previously acquired in the Duke Image Analysis Laboratory. All studies were approved by the Duke IRB. The data was de-identified for this project, which was also approved by the Duke IRB. Subjects performed several breath-holds at total lung capacity(TLC) interspersed with multiple tidal breaths(TB) of Perfluoropropane(PFP)/oxygen mixture. Additive wash-in intensity images were created through the summation of the wash-in phase breath-holds. Additionally, model based fitting was utilized to create parametric images of lung function(1).

Varian Eclipse treatment planning software was used for putative treatment planning. For each subject two plans were made, a standard plan, with no regional functional lung information considered other than current standard models. Another was created using functional information to spare functional lung while maintaining dose to the target lesion. Plans were optimized to a prescription dose of 60 Gy to the target over the course of 30 fractions.

A decrease in dose to functioning lung was observed when utilizing this functional information compared to the standard plan for all five subjects. PFP-enhanced MR imaging is a feasible method to assess ventilatory lung function and we have shown how this can be incorporated into treatment planning to potentially decrease the dose to normal tissue.

Purpose: The goal of this study is to investigate the necessity of adaptive radiation therapy (ART) for lung cancer patients treated with intensity modulated radiation therapy (IMRT) by quantifying the change in the tumor volume and its associate impacts on the target, lungs and esophagus.

Materials and Methods: Fifteen patients enrolled on an IRB-approved lung dose escalation phase I study were treated with IMRT (58-72 Gy, 2Gy/fraction), along with concurrent cisplatin and etoposide. Contrasted CT scans were acquired prior to RT and in the 2nd and 5th weeks of treatment. Tumor, lung and esophagus volumes were segmented on all CT datasets. The clinical target volumes were enlarged by 3 - 5 mm for planning target volume (PTV) expansions. The original plan (generated on pre-RT CT set) was recomputed on the subsequent CT sets and doses were accumulated by deformable registration to approximate the actual delivery. Five patients with the largest tumor shrinkage were selected and their original plans were re-optimized on the 2nd and 5th week CT sets. The plans on the 3 CT sets were summed to simulate ART. Comparisons were made between the original plan, approximated actual treatment and ART plan. Comparison metrics included QUANTEC dose parameters (lungs: V5, V20, and mean dose; esophagus: V35, V50, V70), equivalent uniform dose (EUD), maximum dose to the highest 1% of volume, and target volume covered by the prescription dose. Dosimetric and volumetric changes were tested for significance (Wilcoxon signed-rank test).

Results: Compared to the original plan, the approximated actual delivery had significantly increased lung dose and volume metrics: V5 = 8.10%, V20 = 4.08% (p < 0.05), and EUD (5.42%, p < 0.05). Tumor shrinkage-induced esophageal and lung volume motion outside the originally segmented volume was significant, ranging from 67.2%- 185%, and 16%-49.7% of the original volume (p < 0.05), respectively. The correlation between the original GTV volume and esophageal EUD increase was significant (ρ = 0.83, p < 0.005). Elevated esophagus EUD and spinal cord maximum dose were observed in most patients, with averages of 7.19% and 4.39% (p > 0.05), respectively. PTV/GTV volumes receiving 100% of prescription dose decreased (week 2/5 PTV = -10.0%/-6.88%, week 2/5 GTV = -6.7%/-4.1%), along with slightly increased dose to the highest 1% of volume. Compared to the approximated actual delivery, ART plans overall were superior in lowering dose to the lungs (V5=-4.42% (p=0.3125), V20=-7.52% (p=0.625)), esophagus (V35=-25.98% (p=0.3125), EUD =-13.18% (p=0.1094)), and spinal cord (Dmax=-15.82% (p=0.0625)).

Conclusions: RT-induced esophageal volume displacement and increased lung dose-volume metrics during treatment are significant. Adaptive plan re-optimization may be warranted in cases with larger tumors, where sizeable changes are expected during radiotherapy.