Dr. Omar Daher Profile

Dr. Omar Daher

at Western Univ

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Publications (2)

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SPIE Journal Paper | 8 November 2022

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

Jaryd Christie, Omar Daher, Mohamed Abdelrazek, Perrin Romine, Richard Malthaner, Mehdi Qiabi, Rahul Nayak, Sandy Napel, Viswam S. Nair, Sarah Mattonen

JMI, Vol. 9, Issue 06, 066001, (November 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.6.066001

KEYWORDS: Tumors, Image segmentation, Computed tomography, Lung cancer, Positron emission tomography, Bone, Data modeling, Tumor growth modeling, Cancer, Lung

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PurposeWe developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC).ApproachWe retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort (n = 101), validated in the testing cohort (n = 34) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan–Meier analysis.ResultsThe model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 (p < 0.005) and 0.60 (p = 0.008), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training (p < 0.005) and the testing (p = 0.03) cohorts.ConclusionsOur radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.

Proceedings Article | 4 April 2022 Poster + Paper

A semi-automatic threshold-based segmentation algorithm for lung cancer delineation

Jaryd Christie, Omar Daher, Hannah van Dongen, Rory Gilliland, Mohamed Abdelrazek, Sarah Mattonen

Proceedings Volume 12036, 120361T (2022) https://doi.org/10.1117/12.2611501

KEYWORDS: Image segmentation, Tumors, Computed tomography, Feature extraction, Algorithm development, Lung cancer, Lung, Medical imaging

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Radiomic studies utilize AI and quantitative features from medical images to create models that can predict patient outcomes. An integral step in these radiomic studies is the delineation of the regions of interest where the features are extracted. Manual segmentation is labor intensive and time-consuming for large studies. Semi-automatic segmentation tools have been used in recent radiomic studies to achieve more reproducible segmentations and robust radiomics features. However, for the segmentation of lung tumors on CT images, tools in the literature are difficult to find publicly and require extensive user interaction. Therefore, we aimed to build a semi-automatic segmentation tool which was intuitive, fast, and required minimal user interaction. We used one dataset to develop the segmentation algorithm on (n=49), and another to test its performance (n=144). All 144 tumors were segmented on the CT images using the semiautomatic tool by three separate users. A gold standard tumor delineation was determined by a trained radiologist. The segmentation robustness was assessed using the Dice, mean absolute boundary distance (MAD) and volume difference (VD). A total of 408 radiomic features were extracted and feature robustness was determined using an intra-class correlation coefficient (ICC) greater than 0.8. The developed tool achieved an average Dice of 0.90, MAD of 0.62 mm and a VD of 0.97 ml between the three users. A total of 181 (76%) of the extracted features displayed excellent reliability. This tool has the potential to augment the reliability of radiomic studies by making segmentations and feature sets more reproducible.

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