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radiomics</title> <meta name="description" content="Search results for: radiomics"> <meta name="keywords" content="radiomics"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="radiomics"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: radiomics</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Comparing Accuracy of Semantic and Radiomics Features in Prognosis of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahya%20Naghipoor">Mahya Naghipoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: Non-small cell lung cancer (NSCLC) is the most common lung cancer type. Epidermal growth factor receptor (EGFR) mutation is the main reason which causes NSCLC. Computed tomography (CT) is used for diagnosis and prognosis of lung cancers because of low price and little invasion. Semantic analyses of qualitative CT features are based on visual evaluation by radiologist. However, the naked eye ability may not assess all image features. On the other hand, radiomics provides the opportunity of quantitative analyses for CT images features. The aim of this review study was comparing accuracy of semantic and radiomics features in prognosis of EGFR mutation in NSCLC. Methods: For this purpose, the keywords including: non-small cell lung cancer, epidermal growth factor receptor mutation, semantic, radiomics, feature, receiver operating characteristics curve (ROC) and area under curve (AUC) were searched in PubMed and Google Scholar. Totally 29 papers were reviewed and the AUC of ROC analyses for semantic and radiomics features were compared. Results: The results showed that the reported AUC amounts for semantic features (ground glass opacity, shape, margins, lesion density and presence or absence of air bronchogram, emphysema and pleural effusion) were %41-%79. For radiomics features (kurtosis, skewness, entropy, texture, standard deviation (SD) and wavelet) the AUC values were found %50-%86. Conclusions: In conclusion, the accuracy of radiomics analysis is a little higher than semantic in prognosis of EGFR mutation in NSCLC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer" title="lung cancer">lung cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20tomography" title=" computer tomography"> computer tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=mutation" title=" mutation "> mutation </a> </p> <a href="https://publications.waset.org/abstracts/124165/comparing-accuracy-of-semantic-and-radiomics-features-in-prognosis-of-epidermal-growth-factor-receptor-mutation-in-non-small-cell-lung-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124165.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Impact of Variability in Delineation on PET Radiomics Features in Lung Tumors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20Falahatpour">Mahsa Falahatpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: This study aims to explore how inter-observer variability in manual tumor segmentation impacts the reliability of radiomic features in non–small cell lung cancer (NSCLC). Methods: The study included twenty-three NSCLC tumors. Each patient had three tumor segmentations (VOL1, VOL2, VOL3) contoured on PET/CT scans by three radiation oncologists. Dice coefficients (DCS) were used to measure the segmentation variability. Radiomic features were extracted with 3D-slicer software, consisting of 66 features: first-order (n=15), second-order (GLCM, GLDM, GLRLM, and GLSZM) (n=33). The inter-observer variability of radiomic features was assessed using the intraclass correlation coefficient (ICC). An ICC > 0.8 indicates good stability. Results: The mean DSC of VOL1, VOL2, and VOL3 was 0.80 ± 0.04, 0.85 ± 0.03, and 0.76 ± 0.06, respectively. 92% of all extracted radiomic features were found to be stable (ICC > 0.8). The GLCM texture features had the highest stability (96%), followed by GLRLM features (90%) and GLSZM features (87%). The DSC was found to be highly correlated with the stability of radiomic features. Conclusion: The variability in inter-observer segmentation significantly impacts radiomics analysis, leading to a reduction in the number of appropriate radiomic features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PET%2FCT" title="PET/CT">PET/CT</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a>, <a href="https://publications.waset.org/abstracts/search?q=radiotherapy" title=" radiotherapy"> radiotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=NSCLC" title=" NSCLC"> NSCLC</a> </p> <a href="https://publications.waset.org/abstracts/186981/impact-of-variability-in-delineation-on-pet-radiomics-features-in-lung-tumors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186981.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">45</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> A Radiomics Approach to Predict the Evolution of Prostate Imaging Reporting and Data System Score 3/5 Prostate Areas in Multiparametric Magnetic Resonance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natascha%20C.%20D%27Amico">Natascha C. D'Amico</a>, <a href="https://publications.waset.org/abstracts/search?q=Enzo%20Grossi"> Enzo Grossi</a>, <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Valbusa"> Giovanni Valbusa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ala%20Malasevschi"> Ala Malasevschi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gianpiero%20Cardone"> Gianpiero Cardone</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Papa"> Sergio Papa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=MR%20prostate" title=" MR prostate"> MR prostate</a>, <a href="https://publications.waset.org/abstracts/search?q=PI-Rads%203" title=" PI-Rads 3"> PI-Rads 3</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a> </p> <a href="https://publications.waset.org/abstracts/84292/a-radiomics-approach-to-predict-the-evolution-of-prostate-imaging-reporting-and-data-system-score-35-prostate-areas-in-multiparametric-magnetic-resonance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84292.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Convolutional Neural Networks versus Radiomic Analysis for Classification of Breast Mammogram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehwish%20Asghar">Mehwish Asghar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20%28BC%29" title="breast cancer (BC)">breast cancer (BC)</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20%28ML%29" title=" machine learning (ML)"> machine learning (ML)</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20%28CNN%29" title=" convolutional neural network (CNN)"> convolutional neural network (CNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=radionics" title=" radionics"> radionics</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/143691/convolutional-neural-networks-versus-radiomic-analysis-for-classification-of-breast-mammogram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143691.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">225</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Identification of Clinical Characteristics from Persistent Homology Applied to Tumor Imaging </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eashwar%20V.%20Somasundaram">Eashwar V. Somasundaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Raoul%20R.%20Wadhwa"> Raoul R. Wadhwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacob%20G.%20Scott"> Jacob G. Scott</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20biology" title="cancer biology">cancer biology</a>, <a href="https://publications.waset.org/abstracts/search?q=oncology" title=" oncology"> oncology</a>, <a href="https://publications.waset.org/abstracts/search?q=persistent%20homology" title=" persistent homology"> persistent homology</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a>, <a href="https://publications.waset.org/abstracts/search?q=topological%20data%20analysis" title=" topological data analysis"> topological data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20imaging" title=" tumor imaging"> tumor imaging</a> </p> <a href="https://publications.waset.org/abstracts/125882/identification-of-clinical-characteristics-from-persistent-homology-applied-to-tumor-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125882.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Radiomics: Approach to Enable Early Diagnosis of Non-Specific Breast Nodules in Contrast-Enhanced Magnetic Resonance Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20D%27Amico">N. D'Amico</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Grossi"> E. Grossi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Colombo"> B. Colombo</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Rigiroli"> F. Rigiroli</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Buscema"> M. Buscema</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Fazzini"> D. Fazzini</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Cornalba"> G. Cornalba</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Papa"> S. Papa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast" title="breast">breast</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a> </p> <a href="https://publications.waset.org/abstracts/84293/radiomics-approach-to-enable-early-diagnosis-of-non-specific-breast-nodules-in-contrast-enhanced-magnetic-resonance-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84293.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Integrating Natural Language Processing (NLP) and Machine Learning in Lung Cancer Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrnaz%20Mostafavi">Mehrnaz Mostafavi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer%20diagnosis" title="lung cancer diagnosis">lung cancer diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=structured-query-language%20%28SQL%29" title=" structured-query-language (SQL)"> structured-query-language (SQL)</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29" title=" natural language processing (NLP)"> natural language processing (NLP)</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=CT%20scans" title=" CT scans"> CT scans</a> </p> <a href="https://publications.waset.org/abstracts/181856/integrating-natural-language-processing-nlp-and-machine-learning-in-lung-cancer-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Image Segmentation with Deep Learning of Prostate Cancer Bone Metastases on Computed Tomography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20M.%20Rich">Joseph M. Rich</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinay%20A.%20Duddalwar"> Vinay A. Duddalwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Assad%20A.%20Oberai"> Assad A. Oberai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=medicine" title=" medicine"> medicine</a>, <a href="https://publications.waset.org/abstracts/search?q=nnUNet" title=" nnUNet"> nnUNet</a>, <a href="https://publications.waset.org/abstracts/search?q=prostate%20carcinoma" title=" prostate carcinoma"> prostate carcinoma</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a> </p> <a href="https://publications.waset.org/abstracts/162601/image-segmentation-with-deep-learning-of-prostate-cancer-bone-metastases-on-computed-tomography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162601.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> 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