Browsing by Subject "Lung cancer"

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Open Access
Anatomic context-aware segmentation of organs-at-risk in thorax computed tomography scans
(2022-12) Khattak, Haya Shamim Khan
Organ segmentation plays a crucial role in disease diagnosis and radiation therapy planning. Efficient and automated segmentation of the organs-at-risk (OARs) re-quires immediate attention since manual segmentation is a time consuming and costly task that is also prone to inter-observer variability. Automatic segmen-tation of organs-at-risk using deep learning is prone to predicting extraneous regions, especially in apical and basal slices of the organs where the shape is dif-ferent from the center slices. This thesis presents a novel method to incorporate prior knowledge on shape and anatomical context into deep-learning based organ segmentation. This prior knowledge is quantified using distance transforms that capture characteristics of the shape, location, and relation of the organ position with respect to the surrounding organs. In this thesis, the role of various distance transform maps has been explored to show that using distance transform regres-sion, alone or in conjunction with classification, improves the overall performance of the organ segmentation network. These maps can be the distance between each pixel and the center of the organ, or the closest distance between two organs; such as the esophagus and the spine. When used in a single-task regression model, these distance maps improved the segmentation results. Moreover, when used in a multi-task network with classification being the other task, they acted as regularizers for the classification task and yielded improved segmentations. The experiments were conducted on a computed tomography (CT) thorax dataset of 265 patients and the organs of interest are the heart, the esophagus, the lungs, and the spine. The results revealed a significant increase in f-scores and decrease in the Hausdorff distances for the OARs when segmented using the proposed model compared to the baseline network architectures.
Open Access
Development and validation of methods for the diagnosis of lung cancer via serological biomarkers
(2019-02) Akçay, Abbas Güven
Over 10% of all new cancer cases are lung cancer. Moreover, estimates till 2030 indicate that already increasing lung cancer incidences will keep increasing, especially in developing countries like Turkey. Lung cancer, the leading cause of cancer deaths, has two large divisions: Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC). SCLC is the most aggressive subtype of lung cancer. And although, the treatment options and median survival time is more favorable in Limited Disease (LD), high tumor growth rate and metastatic tendency of SCLC even in the early stages, makes the diagnosis troublesome. Similarly, if NSCLC is diagnosed in early stages, surgery option is open and this increases the patient survival rate. However, current methods in screening and diagnosis, such as computed tomography (CT) and positron emission tomography (PET), are all limited by false positivity rates. Additionally, biopsy methods used in histological evaluations are both invasive and prone to false negativity. Therefore, new diagnostic tools which are cheap, accurate and non-invasive are in high demand. Autologous antibodies are abundantly elicited and stably exist in patient sera years before the clinical diagnosis of disease. Several such antibodies were reported by our group and other groups in lung cancer. Therefore, new diagnostic methods incorporating autologous antibodies can be a huge step forward in early diagnosis of lung cancer. Moreover, miRNAs, with their unique hormone like features such as circulation in serum and their regulatory effects in cell, are another good candidate for the early diagnosis of lung cancer. Therefore, in this study I aimed to develop a reliable, robust and automated evaluation method to re-evaluate custom Protein Array (cPA) screenings previously performed in our lab, and to determine the autologous antibodies with highest discriminatory power between SCLC patients & healthy controls. Moreover, I aimed to develop a Quartz Crystal Microbalance with Dissipation (QCM-D) based immunoassay to be incorporated later in the validation of cPA results. Lastly, in a parallel study I aimed to identify and validate novel miRNA biomarkers NSCLC. My results indicate that cPAs can have better sensitivity and specificity than ELISA and that QCM-D can be developed as an alternative to ELISA. miRNAs identified in silico, can also be validated ex vivo. Previously, Protein Arrays (PAs) and cPAs were screened using 49 SCLC patient’s and 50 healthy serums in our laboratory, incorporating visual and manual evaluations. Sensitivity and specificity values were calculated for individual autologous-antibodies and a number of autologous-antibody panels. Moreover, validations of cPA results were carried via ELISA. However, large discrepancies between cPA and ELISA results, as well as inconsistencies among ELISA results urged me to consider re-evaluation of cPA results with a more robust way, and to focus on developing a method superior to ELISA in autologous-antibody evaluations. Therefore, I incorporated AIDA to generate numeric values out of cPA screening images and filtered low quality data with optimized cut-off values. Several Receiver Operating Characteristic (ROC) curves were plotted using evaluated data. Improved results were evident by the increased Area Under Curve (AUC) values in both individual and combined ROC curves. Moreover, I developed a QCM based immunosensor for detection of anti-SOX2 antibody to be incorporated later in validation of cPA results. Binding interaction between anti-SOX2 antibody and SOX2 protein was modelled using 1:1 Langmuir Isothermal Binding and standard curves generated in QCM. In a parallel study, I also investigated miRNAs significantly upregulated in NSCLC when compared to high risk controls. For that purpose, miRNA expression datasets were gathered from GEO. Selected 2 datasets with the same sample type were analyzed for common significantly upregulated miRNAs among these two datasets. Significantly upregulated miRNAs were subjected to logistic regression analysis with LASSO regularization (error metrics: AUC and MSE) to select best panel of miRNAs that can distinguish NSCLC patients from healthy controls in given datasets. Moreover, selected miRNAs were analyzed with qRT-PCR to validate the panel. I was able to re-evaluate cPA results by eliminating low quality data from numeric values generated via AIDA software from cPA images. I identified a panel of 4 autologous antibodies (FKBP8 – P53 – SOX2 – POLB) which resulted in 60% sensitivity at 100% specificity in discrimination of SCLC from controls. ROC of this autologous antibody panel had an AUC of 95.04%. Given panel surpassed diagnostic power of the only commercially available diagnostic kit of the same kind; EarlyCDT-Lung. Moreover, proof of concept for measurements of anti-protein antibodies were carried successfully in QCM, using anti-SOX2 antibody-SOX2 protein pair in PBS buffer as an example for it. Early results of anti-SOX2 mAb QCM indicate a linear assay range comparable to ELISA. Langmuir Isothermal Binding model revealed a strong interaction between antibody and protein in our QCM anti-SOX2 measurement experiments. Lastly, I was able to select 5 miRNAs using logistic regression and LASSO regularization that can best discriminate between NSCLC patients and high risk controls. However, validation experiments using qRT-PCR needs to be repeated as low Ct values and prominent hemolysis in serum samples prevented drawing meaningful conclusions.
Open Access
Nanocarbon-assisted biosensor for diagnosis of exhaled biomarkers of lung cancer: DFT approach
(Sami Publishing Company, 2021-03) Mirzaei, M.; Gülseren, Oğuz; Rafienia, M.; Zare, A.
Density functional theory (DFT) calculations were performed to investigate a nanocarbon-assisted biosensor for diagnosis of exhaled biomarkers of lung cancer. To this aim, an oxidized model of C20 fullerene (OC) was chosen as the surface for adsorbing each of five remarkable volatile organic compounds (VOC) biomarkers including hydrogen cyanide, methanol, methyl cyanide, isoprene, and 1-propanol designated by B1-B5. Geometries of the models were first optimized to achieve the minimum energy structures to be involved in further optimization of B@OC bi-molecular complexes. The relaxation of B counterparts at the surface of OC provided insightful information for capability of the investigated system for possible diagnosis of such biomarkers. In this case, B1 was placed at the highest rank of adsorption to make the strongest B1@OC complex among others whereas the weakest complex was seen for B4@OC complex. The achievement was very much important for differential detection of each of VOC biomarkers by the investigated OC nanocarbon. Moreover, the recorded infrared spectra indicated that the complexes could be very well recognized in complex forms and also among other complexes. As a final remark, such proposed nanocarbon-assisted biosensor could work for diagnosis of remarkable VOC biomarkers of lung cancer.