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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. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔRAB= (RB-RA)/RA) and delta volumes (ΔVAB= (VB-VA)/VA) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs1) and delta radiomics signatures (ΔRs31+ ΔRs21+ ΔRs32). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV31+ ΔV21+ ΔV32), and clinical variable (smoking status, BMI) models (train test split (2:1))., Results: In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test)., Conclusions: Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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. Method . The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Results. DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. Conclusion . DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments., (Creative Commons Attribution license.)

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. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDI vol : 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.

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. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Institute of Radiology.)

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. Matching was done based on RT technique (protons vs photons), age, sex, and cardiovascular comorbidity. For each patient, the whole heart and 10 cardiovascular substructures on the RT planning computerized tomography scan were manually contoured. Dosimetric comparisons were made between those who did and did not experience cardiac events and between the proton and photon groups., Results: There was no significant difference in heart or any cardiovascular substructure dose between those patients who experienced post-treatment cardiac events and those who did not ( P > .05 for all). The mean heart dose in the patients receiving proton therapy was significantly lower than the mean heart dose in the patients receiving photon therapy ( P  = .032). The left ventricle, right ventricle, and the left anterior descending artery also had significantly lower doses (by multiple measures) when treated with protons ( P  = .0004, P < .0001, and P  = .0002, respectively)., Conclusions: Proton therapy may have a significant effect on decreasing dose to individual cardiovascular substructures compared with photon therapy. There was no significant difference in heart dose or dose to any cardiovascular substructure between patients who did and did not experience post-treatment cardiac events. Further research should be done to assess the association between cardiovascular substructure dose and post-treatment cardiac events.

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., Design, Setting, and Participants: This retrospective cohort study was conducted in 2 tertiary care academic cancer centers (University of Pennsylvania Hospital Abramson Cancer Center and Memorial Sloan Kettering Cancer Center). All patients with DMPM treated between January 1, 2015, and September 1, 2019, were retrospectively identified and followed until January 1, 2021. Statistical analysis was performed between September 2021 and February 2022., Exposures: Pembrolizumab (200 mg or 2 mg/kg every 21 days)., Main Outcomes and Measures: Median progression-free survival (PFS) and median overall survival (OS) were assessed using Kaplan-Meier estimates. The best overall response was determined using RECIST (Response Evaluation Criteria in Solid Tumors) criteria, version 1.1. The association of disease characteristics with partial response was evaluated using the Fisher exact test., Results: This study included 24 patients with DMPM who received pembrolizumab monotherapy. Patients had a median age of 62 years (IQR, 52.4-70.6 years); 14 (58.3%) were women, 18 (75.0%) had epithelioid histology, and most (19 [79.2%]) were White. A total of 23 patients (95.8%) received systemic chemotherapy prior to pembrolizumab, and the median number of lines of prior therapy was 2 (range, 0-6 lines). Of the 17 patients who underwent programmed death ligand 1 (PD-L1) testing, 6 (35.3%) had positive tumor PD-L1 expression (range, 1.0%-80.0%). Of the 19 evaluable patients, 4 (21.0%) had a partial response (overall response rate, 21.1% [95% CI, 6.1%-46.6%]), 10 (52.6%) had stable disease, and 5 (26.3%) had progressive disease (5 of 24 patients [20.8%] were lost to follow-up). There was no association between a partial response and the presence of a BAP1 alteration, PD-L1 positivity, or nonepithelioid histology. With a median follow-up of 29.2 (95% CI, 19.3 to not available [NA]) months, the median PFS was 4.9 (95% CI, 2.8-13.3) months and the median OS was 20.9 (95% CI, 10.0 to NA) months from pembrolizumab initiation. Three patients (12.5%) experienced PFS of more than 2 years. Among patients with nonepithelioid vs epithelioid histology, there was a numeric advantage in median PFS (11.5 [95% CI, 2.8 to NA] vs 4.0 [95% CI, 2.8-8.8] months) and median OS (31.8 [95% CI, 8.3 to NA] vs 17.5 [95% CI, 10.0 to NA] months); however, this did not reach statistical significance., Conclusions and Relevance: The results of this retrospective dual-center cohort study of patients with DMPM suggest that pembrolizumab had clinical activity regardless of PD-L1 status or histology, although patients with nonepithelioid histology may have experienced additional clinical benefit. The partial response rate of 21.0% and median OS of 20.9 months in this cohort with 75.0% epithelioid histology warrants further investigation to identify those most likely to respond to immunotherapy.

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, 18 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. In addition, modality-specific technical considerations and future directions are discussed. A bulleted summary of all technical recommendations is provided., (Copyright © 2022. Published by Elsevier Inc.)

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., can dramatically affect quantitative measures, including radiomics. We recently developed a method (PixelPrint) for 3D-printing lifelike computed tomography (CT) lung phantoms, paving the way for future diagnostic imaging standardization. PixelPrint generates phantoms with accurate attenuation profiles and textures by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. The present study introduces a library of 3D printed lung phantoms covering a wide range of lung diseases, including usual interstitial pneumonia with advanced fibrosis, chronic hypersensitivity pneumonitis, secondary tuberculosis, cystic fibrosis, Kaposi sarcoma, and pulmonary edema. CT images of the patient-based phantom are qualitatively comparable to original CT images, both in texture, resolution and contrast levels allowing for clear visualization of even subtle imaging abnormalities. The variety of cases chosen for printing include both benign and malignant pathology causing a variety of alveolar and advanced interstitial abnormalities, both clearly visualized on the phantoms. A comparison of regions of interest revealed differences in attenuation below 6 HU. Identical features on the patient and the phantom have a high degree of geometrical correlation, with differences smaller than the intrinsic spatial resolution of the scans. Using PixelPrint, it is possible to generate CT phantoms that accurately represent different pulmonary diseases and their characteristic imaging features.

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. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03-0.29, using a 1-5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint's production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms. Classification: CT lung phantoms, reader study., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset-Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset-Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution-c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing-c-score: 0.54, large pixel spacing-c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario-harmonize by voxel spacing parameters: IO dataset; thin slice-c-score: 0.62, thick slice-c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making., (© 2022. The Author(s).)

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. The aim of our study was to assess the outcomes of patients receiving neoadjuvant chemotherapy, compared to those receiving adjuvant or no perioperative chemotherapy., Patients and Methods: We conducted a single-center retrospective cohort study of treatment-naïve, non-papillary DMPM patients seen at our institution between 1/1/2009 and 9/1/2019. We explored the effect of type of systemic therapy on clinical outcomes and estimated median overall survival (mOS) using Kaplan-Meier curves. Hazard ratios (HR) calculated by Cox proportional hazard model were used to estimate effect of the exposures on overall survival., Results: 47 patients were identified with DMPM (median age at diagnosis 61.2 years, 76.6% epithelioid histology, 74.5% white race, 55.3% known asbestos exposure). CRS was performed in 53.2% of patients (25/47); 76.0% of surgical patients received HIPEC (19/25). The majority received systemic chemotherapy (37/47, 78.7%); among patients receiving both CRS and chemotherapy, neoadjuvant chemotherapy was more common than adjuvant chemotherapy (12 neoadjuvant, 8 adjuvant). Overall mOS was 84.1 months. Among neoadjuvant patients, 10/12 underwent surgery, and 2 were lost to follow-up; the majority (9/10) had clinically stable or improved disease during the pre-operative period. There were numerical more issues with chemotherapy with the adjuvant patients (4/8: 2 switches in platinum agent, 2 patients stopped therapy) than with the neoadjuvant patients (2/10: 1 switch in platinum agent, 1 delay due to peri-procedural symptoms). Neoadjuvant chemotherapy was not associated with worse mOS compared to adjuvant chemotherapy (mOS NR vs 95.1 mo, HR 0.89, 95% CI 0.18-4.5, p = 0.89)., Conclusions: When used preferentially, the use of neoadjuvant chemotherapy in DMPM patients was not associated with worse outcomes compared to adjuvant chemotherapy. It was well-tolerated and did not prevent surgical intervention., Competing Interests: Melina E. Marmarelis reports researching funding from Eli Lilly (Inst), Trizell (Inst), AstraZeneca (Inst); consulting role with Novocure, Boehringer Ingelheim; stock in Gilead Sciences, Portola Pharmaceuticals, Merck, Bluebird Bio, Johnson & Johnson, Pfizer; and previous medical writing support from Novartis. Christine A. Ciunci reports research funding from Celgene (Inst), Merck (Inst), Bristol-Myers Squibb (Inst), MacroGenics (Inst); and honoraria from Imedex. Corey J. Langer reports research funding from Merck (Inst), Advantagene (Inst), Clovis Oncology (Inst), Celgene (Inst), Inovio Pharmaceuticals (Inst), ARIAD (Inst), GlaxoSmithKline (Inst), Genentech/Roche (Inst), Stem CentRx (Inst), Lilly, Trizell (Inst); honoraria from Bristol-Myers Squibb, Genentech/Roche, Lilly/ImClone, AstraZeneca, Takeda Science Foundation; consulting roles with Genentech/Roche, Lilly/ImClone, Merck, Abbott Biotherapeutics, Bayer/Onyx, Clarient, Clovis Oncology, Celgene, Cancer Support Community, Bristol-Myers Squibb, ARIAD, Takeda, AstraZeneca, Pfizer, Novocure, Gilead Sciences; and other relationships with Lilly, Amgen, Peregrine Pharmaceuticals, Synta. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables., (© 2022. The Author(s).)

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. Statistical analysis was performed using descriptive statistics, logistic regression, Pearson correlation coefficient, and unweighted κ coefficient for interobserver agreement. Sensitivity and specificity of GGO for lymphatic PB were calculated., Results: Lymphatic imaging was performed in 44 patients (median age, 52 years ± 21 [IQR]; 23 women): 35 with lymphatic PB and nine with nonlymphatic PB. GGO was more frequently observed in patients with lymphatic PB than in those with nonlymphatic PB (91% [32 of 35] vs 33% [three of nine]; P < .001). Univariate logistic regression confirmed this result by showing that GGO was a significant predictor of lymphatic PB (odds ratio, 21 (95% CI: 3.8, 159.7). The model areas under the receiver operating characteristic curve (AUCs) of GGO unadjusted and adjusted for demographics were 0.79 and 0.86, respectively. The location of GGO correlated with lymphatic imaging and bronchoscopic findings. Overall sensitivity and specificity of GGO for lymphatic PB were 91% (32 of 35; 95% CI: 76, 98) and 67% (six of nine; 95% CI: 30, 93), respectively., Conclusion: Patients with lymphatic PB predominantly had multifocal GGO with or without a "crazy paving" pattern; identification of GGO should prompt lymphatic workup in this frequently misdiagnosed condition. Keywords: Lymphography, Lymphatic, CT, Tracheobronchial Tree, Thorax© RSNA, 2022See also commentary by Kligerman and White in this issue., Competing Interests: Disclosures of conflicts of interest: C.O. No relevant relationships. M.I. No relevant relationships. L.R. No relevant relationships. S.K. National Institutes of Health grant, unrelated to this article; National Institute of Biomedical Imaging and Bioengineering grant, unrelated to this article; imaging consultant to Trizell, unrelated to this article; consulting fees from Trizell as imaging consultant on a mesothelioma clinical trial; institutional support for travel from Penn; member of Radiology: Cardiothoracic Imaging editorial board. Q.C. No relevant relationships. M.H. No relevant relationships. M.G.A. No relevant relationships., (© 2022 by the Radiological Society of North America, Inc.)

Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patient-based lung phantoms that accurately represent 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-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-of-interest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.

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 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.

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. Cox proportional hazard (CPH) models and Kaplan-Meier (KM) curves of overall survival (OS) prediction for each dataset were also generated. SD and CC for segmentations demonstrated high similarities, yielding, SD: 0.79 and CC: 0.92 (BY-SK), SD: 0.81 and CC: 0.83 (LS-SK), and SD: 0.84 and CC: 0.91 (MH-SK) in average for both databases, respectively. OS through the maximal CPH model for the two datasets yielded c-statistics of 0.7 (95% CI) and 0.69 (95% CI), while adding radiomic and clinical variables (sex, stage/morphological status, and histology) together. KM curves also showed significant discrimination between high- and low-risk patients ( p -value < 0.005). This supports that readers' level of training and clinical experience may not significantly influence the ability to extract accurate radiomic features for NSCLC on CT. This potentially allows flexibility in the training required to produce robust prognostic imaging biomarkers for potential clinical translation.

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 in situ 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 in situ longitudinally in the range of post-operative settings, in order to promote awareness of this entity when interpreting post-operative imaging. We present this as a pictorial review focusing primarily but not exclusively on the chest noting that the thoracic imaging appearance of Surgicel ® is less well reported in the published literature. An understanding of this entity may help to minimize erroneous diagnosis of a postoperative complication leading to unnecessary interventions., (© 2021 The Authors. Published by the British Institute of Radiology.)

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. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant ( p < 0.001) overlap difference for spleen (Dice IML /Dice Manual = 0.91/0.87), breast tumors (Dice IML /Dice Manual = 0.84/0.82), and lung nodules (Dice IML /Dice Manual = 0.78/0.83). For intra-rater consistency, a significant ( p = 0.003) difference was found for spleen (Dice IML /Dice Manual = 0.91/0.89). For inter-rater consistency, significant ( p < 0.045) differences were found for spleen (Dice IML /Dice Manual = 0.91/0.87), breast (Dice IML /Dice Manual = 0.86/0.81), lung (Dice IML /Dice Manual = 0.85/0.89), the non-enhancing (Dice IML /Dice Manual = 0.79/0.67) and the enhancing (Dice IML /Dice Manual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Background: Radiologic assessment of malignant pleural mesothelioma (MPM) on computed tomography (CT) imaging can be limited by similar attenuations of MPM and adjacent tissues. This can result in inaccuracies in defining the presence and extent of pleural tumor burden. We hypothesized that increasing the time delay for pleural enhancement will optimize discrimination between MPM and noncancerous tissues on CT. Here we conduct a prospective observational study to determine the optimal time delay for imaging MPM on CT., Patients and Methods: Adult MPM patients (n = 15) were enrolled in this prospective exploratory imaging trial. Patients with < 1 cm MPM thickness, prior pleurectomy, pleurodesis, pleural radiotherapy, or antiangiogenic therapy were excluded. All patients underwent a dynamically-enhanced CT with multiple time delays (0 - 10 minutes) after intravenous contrast administration. Tumor tissue attenuation was measured at each phase of enhancement. A qualitative assessment of tumor enhancement kinetics was also performed. The optimal phase of enhancement based on qualitative lesion conspicuity and quantitative tumor enhancement was then compared., Results: MPM tumor enhancement was quantitatively and qualitatively increased at time delays beyond the conventional time delay for thoracic CT imaging (40-60 seconds). Patient tumor enhancement kinetics, displayed as the fraction of maximal tumor tissue attenuation as a function of time, revealed an optimal time delay of 230 to 300 seconds after intravenous contrast administration. There was an association between degree of tumor enhancement and subjective lesion conspicuity., Conclusion: Optimal MPM contrast enhancement occurs at a later phase than typically acquired with conventional thoracic CT imaging., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

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. Pre-therapy computed tomography (CT) measurements of tumor burden were calculated and correlated with pre-therapy serum SMRP and fibulin-3 levels in these two trials. Serological data were also correlated with radiological assessment of response using Modified RECIST criteria over the first 6 months of intrapleural delivery of adenovirus-IFN alpha (Ad.IFN-α) combined with chemotherapy., Results: A cohort of 58 patients who enrolled in either a photodynamic therapy trial or immunotherapy clinical trial had available imaging and SMRP serological data for analysis of whom 45 patients had serological fibulin-3 data. The cohort mean total tumor volume was 387 cm 3 (STD 561 cm 3 ). Serum SMRP was detectable in 57 of 58 patients (mean 3.8 nM, STD 6.0). Serum fibulin-3 was detected in 44 of 45 patients (mean 23 ng/mL, STD 14). At pre-therapy baseline in these two trials, there was a strong correlation between tumor volume and serum SMRP levels (r = 0.61, p < 0.001), and a moderate correlation between tumor volume and serum fibulin-3 levels (r = 0.36, p = 0.014). Twenty-eight patients in the immunotherapy trial had longitudinal serologic and radiographic data. Fold-changes in SMRP and fibulin-3 did not show significant correlations with modified RECIST measurements., Conclusions: Although our data show correlations of SMRP and fibulin-3 with initial tumor volumes as measured by CT scanning, the use of SMRP and fibulin-3 as serological biomarkers in the immunotherapy trial were not useful in following tumor response longitudinally., (Copyright © 2021. Published by Elsevier B.V.)

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems., (© RSNA, 2021 See also the editorial by Bai and Thomasian in this issue.)

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus that has rapidly escalated into a global pandemic leading to an urgent medical effort to better characterize this disease biologically, clinically, and by imaging. In this review, we present the current approach to imaging of COVID-19 pneumonia. We focus on the appropriate use of thoracic imaging modalities to guide clinical management. We also describe radiologic findings that are considered typical, atypical, and generally not compatible with COVID-19. Furthermore, we review imaging examples of COVID-19 imaging mimics, such as organizing pneumonia, eosinophilic pneumonia, and other viral infections.

Competing Interests: Disclosures of Conflicts of Interest: B.Y. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is working as a scientist in a group at the University of Pennsylvania that applies for funding for radiomic-based study for NSCLC and immunotherapy. Other relationships: disclosed no relevant relationships. S.I.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is imaging consultant for Trizell for a clinical trial of immunotherapy in malignant pleural mesothelioma; institution has grant funding for radiomics in NSCL undergoing immunotherapy from Precision Center of Precision Medicine. Other relationships: disclosed no relevant relationships. L.R. disclosed no relevant relationships.

Introduction: The lymphangitic carcinomatosis (LC) pattern of metastatic malignancy is associated with a poor prognosis but is currently not well defined in malignant pleural mesothelioma (MPM). Here, we report the incidence and prognostic significance of the radiographic development of LC in MPM following extended pleurectomy/decortication (EPD)., Methods: Consecutive patients with biopsy-proven MPM undergoing EPD with intraoperative photodynamic therapy (PDT) at our institution from 2008 to 2014 were included in this retrospective study. Patients without available post-surgical clinical or imaging data for direct review were excluded. CT images were reviewed by an experienced, board-certified thoracic radiologist and confirmed by consensus review. Overall survival (OS) and progression-free survival (PFS) were calculated by Kaplan Meier methodology. Hazard ratios were compared with a cox proportional hazard model., Results: 44 patients underwent EPD with PDT during the study period and had available clinical and imaging data. During the follow-up period (median 34 months), 17 patients (39%) developed LC at a median of 10 months after surgery (IQR 5-21 months). 16 of the 17 patients who developed LC (94%) died during the follow-up period, compared to 17 of the 27 who did not develop LC (63%). OS for the LC versus non-LC group was 53% versus 93% at 1 year and 18% versus 67% at 3 years. LC was significantly associated with a lower OS (HR 4.07; 95% confidence interval 1.44-11.48; p = 0.008). PFS for the LC group versus non-LC group was 8 months (IQR 5-9 months) compared to 17 months (IQR 11-24 months) (p < 0.001)., Conclusion: LC is a common form of failure in MPM following EPD and is associated with a poor prognosis. Thus, further studies are warranted to determine if any evidence of preoperative LC should be an absolute contraindication to EPD and may warrant an EPP or no surgery at all., (Copyright © 2019. Published by Elsevier B.V.)

Purpose of Review: CT angiography has become the gold standard for evaluation of suspected pulmonary embolism; however, continuous evolution in radiology has led to new imaging approaches that offer improved options for detection and characterization of pulmonary embolism while exposing patients to lower contrast and radiation dose. The purpose of this review is to summarize state of the art imaging approaches for the evaluation of pulmonary embolism, focusing on technical innovations in this field., Recent Findings: The introduction of dual-energy CT has resulted in the ability to add functional and prognostic information beyond the morphologic assessment of the pulmonary arteries and potentially offer improved image quality without additional radiation burden. New approaches and strategies in CT scanning have resulted in decreased radiation exposure as well as a significant decrease in contrast material used without decreasing the sensitivity for detection of pulmonary embolism. Continuous developments and improvements in MR angiography techniques offer a valuable and efficient option for certain patient populations without the risk of radiation exposure. Improvements in the technical success rate and reliability of this modality will mean more widespread use in the future. Moving beyond planar ventilation/perfusion (V/Q) scintigraphy, nuclear imaging offers several new approaches, including the use of single photon emission computed tomography (SPECT) and SPECT/CT resulting in superior diagnostic performance and a decrease in nondiagnostic studies, potentially surpassing the diagnostic capabilities of computed tomography pulmonary angiography. Ongoing research in the use of V/Q PET/CT demonstrates superior temporal and spatial resolution and quantitative capabilities compared to SPECT-CT; this modality will likely play an increasing role in the detection and characterization of pulmonary embolism. The field of pulmonary embolism imaging has demonstrated continuous evolution in both development of novel techniques and improvement in current technologies, resulting in better detection, decreased radiation exposure, and enhanced functional information beyond morphologic characterization of the pulmonary vasculature.