Publications by authors named "Roshkovan L"

Purpose: Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers.

Materials And Methods: Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points.

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Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance.

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. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR.

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Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing.

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Objective: Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.

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Imaging is often a first-line method for diagnostics and treatment. Radiological workflows increasingly mine medical images for quantifiable features. Variability in device/vendor, acquisition protocol, data processing, etc.

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Purpose: Radiation therapy (RT) plays a critical role in treating locally advanced non-small cell lung cancer but has been associated with deleterious cardiac effects. We hypothesized that RT dose to certain cardiovascular substructures may be higher among those who experience post-chemoradiation (CRT) cardiac events, and that dose to specific substructures-the great vessels, atria, ventricles, and left anterior descending coronary artery-may be lower with proton- versus photon-based RT.

Methods And Materials: In this retrospective review, we selected 26 patients who experienced cardiac events after CRT for locally advanced non-small cell lung cancer and matched them to 26 patients who did not experience cardiac events after CRT.

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In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis.

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Importance: Diffuse malignant peritoneal mesothelioma (DMPM) represents a rare and clinically distinct entity among malignant mesotheliomas. Pembrolizumab has activity in diffuse pleural mesothelioma but limited data are available for DMPM; thus, DMPM-specific outcome data are needed.

Objective: To evaluate outcomes after the initiation of pembrolizumab monotherapy in the treatment of adults with DMPM.

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Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and reproducible assessment of disease extent at staging and follow-up imaging. By optimizing and harmonizing technical approaches to imaging MPM, the best quality imaging can be achieved for individual patient care, clinical trials, and imaging research. This consensus statement represents agreement on harmonized, standard practices for routine multimodality imaging of MPM, including radiography, computed tomography, F-2-deoxy-D-glucose positron emission tomography, and magnetic resonance imaging, by an international panel of experts in the field of pleural imaging assembled by the International Mesothelioma Interest Group.

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Article Synopsis
  • The study examines how variations in image parameters affect the reliability of prognostic models using radiomic biomarkers for cancer patients.
  • Using two datasets (Breast I-SPY1 and NSCLC IO), the researchers compare models based on raw features versus those adjusted for image heterogeneity, finding improved prognostic scores in the latter.
  • Results show that models from heterogeneity-mitigated features yield higher c-statistic scores than those from raw features, indicating better performance for patient prognosis.
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Background: Diffuse malignant peritoneal mesothelioma (DMPM) is a rare variant of malignant mesothelioma, representing 10-15% of malignant mesothelioma cases. The preferred therapeutic approach is cytoreductive surgery (CRS) accompanied by hyperthermic intraperitoneal chemotherapy (HIPEC); the role of systemic chemotherapy is not well established. While some limited retrospective studies report worse outcomes with neoadjuvant chemotherapy, our institution has favored the use of neoadjuvant chemotherapy for symptom relief and surgical optimization.

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We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans.

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Article Synopsis
  • Phantoms are crucial for testing and verifying CT performance, especially realistic lung phantoms that mimic patient conditions for better hardware and software development.
  • The study introduces PixelPrint, a 3D-printing method that turns patient images into lung phantoms with accurate density and texture, matching the features of actual lung scans.
  • Evaluation of PixelPrint showed that the printed phantoms closely matched real patient scans in terms of texture and geometric accuracy, making them a useful tool for optimizing CT protocols and enhancing research.
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Purpose: To distinguish CT patterns of lymphatic and nonlymphatic causes of plastic bronchitis (PB) through comparison with lymphatic imaging.

Materials And Methods: In this retrospective study, chest CT images acquired prior to lymphatic workup were assessed in 44 patients with PB from January 2014 to August 2020. The location and extent of ground-glass opacity (GGO) was compared with symptoms and lymphatic imaging.

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We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.

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Purpose: Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures.

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This study tackles interobserver variability with respect to specialty training in manual segmentation of non-small cell lung cancer (NSCLC). Four readers included for segmentation are: a data scientist (BY), a medical student (LS), a radiology trainee (MH), and a specialty-trained radiologist (SK) for a total of 293 patients from two publicly available databases. Sørensen-Dice (SD) coefficients and low rank Pearson correlation coefficients (CC) of 429 radiomics were calculated to assess interobserver variability.

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Absorbable hemostatic agents such as Surgicel are hemostatic materials composed of an oxidized cellulose polymer used to control post-surgical bleeding and cause coagulation. This material is sometimes purposefully left where it slowly degrades over time and can produce an imaging appearance that mimics serious post-operative complications such as gangrenous infections and anastomotic leaks as well as potentially mimicking disease recurrence in later stages. In this article, we review the multimodality imaging appearance of this material longitudinally in the range of post-operative settings, in order to promote awareness of this entity when interpreting post-operative imaging.

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We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image.

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A 53-year-old man presented to the ED at a time of low severe acute respiratory syndrome coronavirus 2, also known as coronavirus disease 2019 (COVID-19), prevalence and reported 2 weeks of progressive shortness of breath, dry cough, headache, myalgias, diarrhea, and recurrent low-grade fevers to 39°C for 1 week with several days of recorded peripheral capillary oxygen saturation of 80% to 90% (room air) on home pulse oximeter. Five days earlier, he had visited an urgent care center where a routine respiratory viral panel was reportedly negative. A COVID-19 reverse transcriptase polymerase chain reaction test result was pending at the time of ED visit.

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Objectives: Soluble mesothelin-related protein (SMRP) and fibulin-3 serum levels may serve as diagnostic and prognostic biomarkers of malignant pleural mesothelioma (MPM). Here, we evaluate these markers for correlation to tumor volume, prognosis and response assessment in a clinical trial of immunogene therapy in combination with chemotherapy.

Materials And Methods: Serial serum levels of SMRP and fibulin-3 were measured in adult patients with biopsy-proven MPM enrolled in two prospective clinical trials.

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