A pilot study of volumetric and density tumor analysis of ACC patients treated with vorinostat in a phase II clinical trial.

Rationale and objectives: Adenoid cystic carcinoma (ACC) is a rare salivary gland cancer. The vast majority of clinical trials evaluating systemic therapy efficacy in solid tumors use the Response Evaluation Criteria in Solid Tumors (RECIST) to measure response that is limited to 2 dimensional only evaluations, not taking volume or density into account. The indolent behavior ACC represents a challenge toward an appropriate evaluation of therapy response. Objectives: 1) To describe and contrast volumetric and density changes at each time-point, including changes noted from baseline to best response, to currently used 2 dimensional-only criteria (RECIST) and 2) To report the coefficient of variation in volume measurement among three reviewers on a subset of ACC patients. Materials and methods: We retrospectively assessed a cohort of 18 prospectively treated patients with ACC in a phase 2 trial with vorinostat using a volumetric (viable tumor volume, VTV) and density criteria. Three independent and blinded observers segmented target lesions across a sample of randomly selected computed tomography (CT) exams to examine inter-observer variation. Results: We found that the average coefficient of variation among observers for all target lesions was 16.1%, with lung lesions displaying a smaller variation at 14.0% (p-value >0.17). We describe examples of decrease in volume and density in several lesions despite stable disease by RECIST. Conclusion: This pilot study demonstrates that two-dimensional criteria such as RECIST may not be the best criteria to assess response to therapy, especially with evolving tools within picture archiving and communication system (PACS) that can assess volumetric size, density and texture, however, this should be prospectively studied.

Annotations of Lung Abnormalities in Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases.

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People's Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV (comma-separated values) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs. Dataset: https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.

Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

GIMAP6 regulates autophagy, immune competence, and inflammation in mice and humans.

Inborn errors of immunity (IEIs) unveil regulatory pathways of human immunity. We describe a new IEI caused by mutations in the GTPase of the immune-associated protein 6 (GIMAP6) gene in patients with infections, lymphoproliferation, autoimmunity, and multiorgan vasculitis. Patients and Gimap6-/- mice show defects in autophagy, redox regulation, and polyunsaturated fatty acid (PUFA)-containing lipids. We find that GIMAP6 complexes with GABARAPL2 and GIMAP7 to regulate GTPase activity. Also, GIMAP6 is induced by IFN-γ and plays a critical role in antibacterial immunity. Finally, we observed that Gimap6-/- mice died prematurely from microangiopathic glomerulosclerosis most likely due to GIMAP6 deficiency in kidney endothelial cells.

Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers.

Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays: CXR) using convolutional neural networks (CNNs) has demonstrated superior performance due to the emergence of publicly available, large-scale datasets with expert annotations and availability of scalable computational resources. However, these studies use only the frontal CXR projections, i.e., the posterior-anterior (PA), and the anterior-posterior (AP) views for analysis and decision-making. Lateral CXRs which are heretofore not studied help detect clinically suspected pulmonary TB, particularly in children. Further, Vision Transformers (ViTs) with built-in self-attention mechanisms have recently emerged as a viable alternative to the traditional CNNs. Although ViTs demonstrated notable performance in several medical image analysis tasks, potential limitations exist in terms of performance and computational efficiency, between the CNN and ViT models, necessitating a comprehensive analysis to select appropriate models for the problem under study. This study aims to detect TB-consistent findings in lateral CXRs by constructing an ensemble of the CNN and ViT models. Several models are trained on lateral CXR data extracted from two large public collections to transfer modality-specific knowledge and fine-tune them for detecting findings consistent with TB. We observed that the weighted averaging ensemble of the predictions of CNN and ViT models using the optimal weights computed with the Sequential Least-Squares Quadratic Programming method delivered significantly superior performance (MCC: 0.8136, 95% confidence intervals (CI): 0.7394, 0.8878, p < 0.05) compared to the individual models and other ensembles. We also interpreted the decisions of CNN and ViT models using class-selective relevance maps and attention maps, respectively, and combined them to highlight the discriminative image regions contributing to the final output. We observed that (i) the model accuracy is not related to disease region of interest (ROI) localization and (ii) the bitwise-AND of the heatmaps of the top-2-performing models delivered significantly superior ROI localization performance in terms of mean average precision [mAP@(0.1 0.6) = 0.1820, 95% CI: 0.0771,0.2869, p < 0.05], compared to other individual models and ensembles. The code is available at https://github.com/sivaramakrishnan-rajaraman/Ensemble-of-CNN-and-ViT-for-TB-detection-in-lateral-CXR.

Rethinking Clinical Trial Radiology Workflows and Student Training: Integrated Virtual Student Shadowing Experience, Education, and Evaluation.

There is consistent demand for clinical exposure from students interested in radiology; however, the COVID-19 pandemic resulted in fewer available options and limited student access to radiology departments. Additionally, there is increased demand for radiologists to manage more complex quantification in reports on patients enrolled in clinical trials. We present an online educational curriculum that addresses both of these gaps by virtually immersing students (radiology preprocessors, or RPs) into radiologists' workflows where they identify and measure target lesions in advance of radiologists, streamlining report quantification. RPs switched to remote work at the beginning of the COVID-19 pandemic in our National Institutes of Health (NIH). We accommodated them by transitioning our curriculum on cross-sectional anatomy and advanced PACS tools to a publicly available online curriculum. We describe collaborations between multiple academic research centers and industry through contributions of academic content to this curriculum. Further, we describe how we objectively assess educational effectiveness with cross-sectional anatomical quizzes and decreasing RP miss rates as they gain experience. Our RP curriculum generated significant interest evidenced by a dozen academic and research institutes providing online presentations including radiology modality basics and quantification in clinical trials. We report a decrease in RP miss rate percentage, including one virtual RP over a period of 1 year. Results reflect training effectiveness through decreased discrepancies with radiologist reports and improved tumor identification over time. We present our RP curriculum and multicenter experience as a pilot experience in a clinical trial research setting. Students are able to obtain useful clinical radiology experience in a virtual learning environment by immersing themselves into a clinical radiologist's workflow. At the same time, they help radiologists improve patient care with more valuable quantitative reports, previously shown to improve radiologist efficiency. Students identify and measure lesions in clinical trials before radiologists, and then review their reports for self-evaluation based on included measurements from the radiologists. We consider our virtual approach as a supplement to student education while providing a model for how artificial intelligence will improve patient care with more consistent quantification while improving radiologist efficiency.

Gamification in Radiology Training Module Developed During the Society for Imaging Informatics in Medicine Annual Meeting Hackathon.

The purpose of this manuscript is to report our experience in the 2021 SIIM Virtual Hackathon, where we developed a proof-of-concept of a radiology training module with elements of gamification. In the 50 h allotted in the hackathon, we proposed an idea, connected with colleagues from five different countries, and completed an operational proof-of-concept, which was demonstrated live at the hackathon showcase, competing with eight other teams. Our prototype involved participants annotating publicly available chest radiographs of patients with tuberculosis. We showed how we could give experience points to trainees based on annotation precision compared to ground truth radiologists' annotation, ranked in a live leaderboard. We believe that gamification elements could provide an engaging solution for radiology education. Our project was awarded first place out of eight participating hackathon teams.

Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios.

Data-driven approaches have great potential to shape future practices in radiology. The most straightforward strategy to obtain clinically accurate models is to use large, well-curated and annotated datasets. However, patient privacy constraints, tedious annotation processes, and the limited availability of radiologists pose challenges to building such datasets. This review details model training strategies in scenarios with limited data, insufficiently labeled data, and/or limited expert resources. This review discusses strategies to enlarge the data sample, decrease the time burden of manual supervised labeling, adjust the neural network architecture to improve model performance, apply semisupervised approaches, and leverage efficiencies from pretrained models. Keywords: Computer-aided Detection/Diagnosis, Transfer Learning, Limited Annotated Data, Augmentation, Synthetic Data, Semisupervised Learning, Federated Learning, Few-Shot Learning, Class Imbalance.

Fatal autoimmune pneumonitis requiring bilobectomy and omental flap repair in a patient with autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED).

We present a severe case of progressive autoimmune pneumonitis requiring surgical intervention in a patient with the monogenic syndrome, autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED). APECED is caused by loss-of-function mutations in the autoimmune regulator (AIRE) gene, which lead to impaired central immune tolerance and autoimmune organ destruction including pneumonitis, an underrecognized, life-threatening complication. When clinicians evaluate patients with pneumonitis, recurrent mucosal candidiasis, and autoimmunity, APECED should be considered in the differential. Additionally, in patients with established APECED, a chest computed tomography is preferred to identify pneumonitis early on and to promptly initiate lymphocyte-directed immunomodulatory treatment, which can prevent irreversible lung destruction.

Collaborative Writing Projects: Set Yourself up for Success.

ABSTRACT: This article will review critical components for the successful completion of a multi-institution, multiauthor collaborative paper. Best practices for the creation and publication of a collaborative paper will be addressed.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as "interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients." This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Pulmonary Manifestations of GATA2 Deficiency.

BACKGROUND: GATA2 deficiency is a genetic disorder of hematopoiesis, lymphatics, and immunity caused by autosomal dominant or sporadic mutations in GATA2. The disease has a broad phenotype encompassing immunodeficiency, myelodysplasia, leukemia, and vascular or lymphatic dysfunction as well as prominent pulmonary manifestations. RESEARCH QUESTION: What are the pulmonary manifestations of GATA2 deficiency? STUDY DESIGN AND METHODS: A retrospective review was conducted of clinical medical records, diagnostic imaging, pulmonary pathologic specimens, and tests of pulmonary function. RESULTS: Of 124 patients (95 probands and 29 ascertained), the lung was affected in 56%. In addition to chronic infections, pulmonary alveolar proteinosis (11 probands) and pulmonary arterial hypertension (nine probands) were present. Thoracic CT imaging found small nodules in 54% (54 probands and 12 relatives), reticular infiltrates in 40% (45 probands and four relatives), paraseptal emphysema in 25% (30 probands and one relative), ground-glass opacities in 35% (41 probands and two relatives), consolidation in 21% (23 probands and two relatives), and a typical crazy-paving pattern in 7% (eight probands and no relatives). Nontuberculous mycobacteria were the most frequent organisms associated with chronic infection. Allogeneic hematopoietic stem cell transplantation successfully reversed myelodysplasia and immune deficiency and also improved pulmonary hypertension and pulmonary alveolar proteinosis in most patients. INTERPRETATION: GATA2 deficiency has prominent pulmonary manifestations. These clinical observations confirm the essential role of hematopoietic cells in many aspects of pulmonary function, including infections, alveolar proteinosis, and pulmonary hypertension, many of which precede the formal diagnosis, and many of which respond to stem cell transplantation.

CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1.

Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.

Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations.

Deep learning (DL) has drawn tremendous attention for object localization and recognition in both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand-crafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those pretrained on stock photography images. This character helps improve model adaptation, generalization, and class-specific region of interest (ROI) localization. In this study, we train chest X-ray (CXR) modality-specific U-Nets and other state-of-the-art U-Net models for semantic segmentation of tuberculosis (TB)-consistent findings. Automated segmentation of such manifestations could help radiologists reduce errors and supplement decision-making while improving patient care and productivity. Our approach uses the publicly available TBX11K CXR dataset with weak TB annotations, typically provided as bounding boxes, to train a set of U-Net models. Next, we improve the results by augmenting the training data with weak localization, postprocessed into an ROI mask, from a DL classifier trained to classify CXRs as showing normal lungs or suspected TB manifestations. Test data are individually derived from the TBX11K CXR training distribution and other cross-institutional collections, including the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution and cross-institutional collections (p < 0.05). We believe that this is the first study to i) use CXR modality-specific U-Nets for semantic segmentation of TB-consistent ROIs and ii) evaluate the segmentation performance while augmenting the training data with weak TB-consistent localizations.

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays.

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying CXRs as normal, showing bacterial pneumonia, or COVID-19-viral abnormalities. The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.

Cabozantinib in patients with platinum-refractory metastatic urothelial carcinoma: an open-label, single-centre, phase 2 trial.

BACKGROUND: Cabozantinib is a multikinase inhibitor of MET, VEGFR, AXL, and RET, which also has an effect on the tumour immune microenvironment by decreasing regulatory T cells and myeloid-derived suppressor cells. In this study, we examined the activity of cabozantinib in patients with metastatic platinum-refractory urothelial carcinoma. METHODS: This study was an open-label, single-arm, three-cohort phase 2 trial done at the National Cancer Institute (Bethesda, MD, USA). Eligible patients were 18 years or older, had histologically confirmed urothelial carcinoma or rare genitourinary tract histologies, Karnofsky performance scale index of 60% or higher, and documented disease progression after at least one previous line of platinum-based chemotherapy (platinum-refractory). Cohort one included patients with metastatic urothelial carcinoma with measurable disease as defined by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Two additional cohorts that enrolled in parallel (patients with bone-only urothelial carcinoma metastases and patients with rare histologies of the genitourinary tract) were exploratory. Patients received cabozantinib 60 mg orally once daily in 28-day cycles until disease progression or unacceptable toxicity. The primary endpoint was investigator-assessed objective response rate by RECIST in cohort one. Response was assessed in all patients who met the eligibility criteria and who received at least 8 weeks of therapy. All patients who received at least one dose of cabozantinib were included in the safety analysis. This completed study is registered with ClinicalTrials.gov, NCT01688999. FINDINGS: Between Sept 28, 2012, and Oct, 20, 2015, 68 patients were enrolled on the study (49 in cohort one, six in cohort two, and 13 in cohort three). All patients received at least one dose of cabozantinib. The median follow-up was 61·2 months (IQR 53·8-70·0) for the 57 patients evaluable for response. In the 42 evaluable patients in cohort one, there was one complete response and seven partial responses (objective response rate 19%, 95% CI 9-34). The most common grade 3-4 adverse events were fatigue (six [9%] patients), hypertension (five [7%]), proteinuria (four [6%]), and hypophosphataemia (four [6%]). There were no treatment-related deaths. INTERPRETATION: Cabozantinib has single-agent clinical activity in patients with heavily pretreated, platinum-refractory metastatic urothelial carcinoma with measurable disease and bone metastases and is generally well tolerated. Cabozantinib has innate and adaptive immunomodulatory properties providing a rationale for combining cabozantinib with immunotherapeutic strategies. FUNDING: National Cancer Institute Intramural Program and the Cancer Therapy Evaluation Program.

Web-based Conferencing: Tips, Tricks, and Scenario-based Best Practices for Clinicians During a Pandemic Crisis.

This article will familiarize the reader with useful tools and trouble-shooting tips for web-based conferencing. Radiology-based scenarios for web conferencing are also provided.

INVESTIGATION OF THE INFLUENCE OF THYROID LOCATION ON IODINE-131 S VALUES.

The use of iodine-131 S values based on reference computational phantoms with fixed thyroid model may lead to significant dosimetric errors in patients who may have different thyroid location from the reference phantoms. In the present study, we investigated individual thyroid location variation by examining the computed tomography image sets of 40 adult male and female patients. Subsequently, the thyroid location of the adult male and female mesh-type reference phantoms of the International Commission on Radiological Protection (ICRP) was adjusted to match each the highest, mean and the lowest locations of the thyroid observed in this dataset. The thyroid-adjusted phantoms were implemented into the Geant4 Monte Carlo code to calculate thyroid location-dependent iodine-131 S values (rT â† thyroid) for a total of 30 target regions. The maximum variation among the observed thyroid locations was 39 mm and 36 mm for male and female patients, respectively. The mean thyroid locations of both male and female patients showed a good agreement with the ICRP reference phantoms. The thyroid location-dependent Iodine-131 S values were significantly different from the reference phantoms for most target regions by up to a factor of 3. The use of thyroid location-dependent S values in dose reconstructions should help quantify the dosimetric uncertainty in epidemiologic investigations of patients receiving iodine-131 therapy for hyperthyroidism and thyroid cancer.