INTRODUCTION: In the National Lung Screening Trial (NLST) all cases with a 4-mm nodule (micronodule) and no other findings were classified as a negative study. The prevalence and malignant potential of micronodules in the NLST is evaluated to understand if this classification was appropriate. METHODS AND MATERIALS: In the NLST a total of 53,452 participants were enrolled with 26,722 undergoing low-dose computed tomography (CT) screening. To determine whether a micronodule developed into a lung cancer, a list from the NLST database of those participants who developed lung cancer and had a micronodule recorded was selected. The CT images of this subset were reviewed by experienced, fellowship-trained thoracic radiologists (R.F.M., C.C., P.M.B., and D.R.A.), all of whom participated as readers in the NLST.

In a landmark analysis, investigators of the Multicentric Italian Lung Detection (MILD) trial have confirmed 10-year mortality reductions with lung cancer screening using low-dose helical CT (LDCT). These data complement the reduced lung cancer-specific mortality reported in the National Lung Screening Trial and reinforce the rationale for broad implementation of LDCT screening in high-risk populations.

PURPOSE: Reducing dose level to achieve ALARA is an important task in diagnostic and therapeutic applications of computed tomography (CT) imaging. Effective image quality enhancement strategies are crucial to compensate for the degradation caused by dose reduction. In the past few years, deep learning approaches have demonstrated promising denoising performance on natural/synthetic images. This study tailors a neural network model for (ultra-)low-dose CT denoising, and assesses its performance in enhancing CT image quality and emphysema quantification. METHODS: The noise statistics in low-dose CT images has its unique characteristics and differs from that used in general denoising models. In this study, we first simulate the paired ultra-low-dose and targeted high-quality image of reference, with a well-validated pipeline. These paired images are used to train a denoising convolutional neural network (DnCNN) with residual mapping. The performance of the DnCNN tailored to CT denoising (DnCNN-CT) is assessed over various dose reduction levels, with respect to both image quality and emphysema scoring quantification. The possible over-smoothing behavior of DnCNN and its impact on different subcohort of patients are also investigated.

While deep learning methods have demonstrated performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by end users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level semantic features; and 2) a high-level prediction of nodule malignancy. The low-level outputs reflect diagnostic features often reported by radiologists and serve to explain how the model interprets the images in an expert-interpretable manner. The information from these low-level outputs, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level output. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to using a 3D CNN alone.

Lung ablation is ever more recognized since its initial report and use almost two decades ago. With technological advancements in thermal modalities, particularly microwave ablation and cryoablation, better identification of the cohort of patients who best benefit from ablation, and understanding the role of imaging after ablation, image-guided thermal ablation for primary and secondary pulmonary malignancies is increasingly recognized and accepted as a cogent form of local therapy.

Interventional oncology and management of thoracic malignancies with ablative techniques are becoming ever more recognized therapeutic options. With increased understanding, development, and utility of the ablative techniques, the indications are expanding and efficacy improving. Lung cancer was among the first indications for lung ablation and remains most challenging with multiple therapeutic options. For inoperable patients, the current literature demonstrates equivalent survivals between ablation, sublobar resection, and stereotactic body radiation. Oligometastatic disease remains the most common indication for lung ablation and is gaining acceptance among the oncology community, as lung ablation provides limited patient downtime, repeatability, and minimal to no loss of respiratory function. Other indications for ablation are being explored, including recurrent mesothelioma, drop metastasis from thymoma, and limited pleural metastasis, with excellent local control of tumor and limited complications. Follow-up after ablation is essential to detect early complications, observe the natural evolution of the ablation zone, and detect recurrence. Standardized imaging follow-up allows for these goals to be achieved and provides a framework for oncology practice. In this article, the role of ablation in the management of thoracic neoplasms and postablation imaging features are reviewed. The radiologists, in particular, thoracic radiologists should be able to identify candidates who can benefit from ablation familiarize themselves with postablation imaging features, and recognize the evolution of the postablation zone and hence detect early recurrence.

INTRODUCTION: We performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating. MATERIALS AND METHODS: We assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams et al, we identified 7879 non-calcified nodules discovered at the baseline low-dose CT screen with 2 years of follow-up. We characterised differences between Pan-Canadian Early Detection of Lung Cancer Study and NLST cohorts. We calculated the slope on the prognostic index and the intercept coefficient by fitting the original Brock model to NLST. We also assessed the impact of model recalibration and the addition of new covariates such as body mass index, smoking status, pack-years and asbestos.

Nuclear protein in testis (NUT) midline carcinoma (NMC) is a rare and aggressive subset of poorly differentiated squamous cell carcinoma that is defined by t(15,19) and typically presents in the midline structures of the head, neck, and mediastinum. We report two cases of NMC that presented uniquely with malignant pleural and pericardial effusions including one with cardiac tamponade at presentation. The first case is of a 25-year-old male patient who presented with progressive dyspnea associated with palpitations and dizziness on standing, found to have large bilateral pleural effusions. The second case is of a previously healthy 29-year-old male patient who presented with progressive dyspnea, cough with expectoration, and a large right lower neck mass of 3 months onset, and a large left pleural effusion and left lung infiltrate on imaging studies. Both cases showed malignant cells on cytology suggestive of poorly differentiated carcinoma. Subsequent histopathological and immunochemistry studies were consistent with the diagnosis of NMC. Both patients had a rapid decline in status and suffered comorbidities secondary to their carcinoma, inevitably leading to their death. It is important to consider NUT midline carcinomas can present in a variety of clinical scenarios, and it is important to consider in the differential diagnoses when evaluating malignant effusion cytology. Utilization of ancillary testing with a broad immunostain profile including NUT studies, as well as fluorescent in-situ hydridization (FISH) studies are helpful and necessary in making the appropriate diagnosis.

INTRODUCTION: We performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating. MATERIALS AND METHODS: We assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams et al, we identified 7879 non-calcified nodules discovered at the baseline low-dose CT screen with 2 years of follow-up. We characterised differences between Pan-Canadian Early Detection of Lung Cancer Study and NLST cohorts. We calculated the slope on the prognostic index and the intercept coefficient by fitting the original Brock model to NLST. We also assessed the impact of model recalibration and the addition of new covariates such as body mass index, smoking status, pack-years and asbestos.

RATIONALE AND OBJECTIVES: The purpose of this paper is to describe the integration of a commercial chest CT computer-aided detection (CAD) system into the clinical radiology reporting workflow and perform an initial investigation of its impact on radiologist efficiency. It seeks to complement research into CAD sensitivity and specificity of stand-alone systems, by focusing on report generation time when the CAD is integrated into the clinical workflow. MATERIALS AND METHODS: A commercial chest CT CAD software that provides automated detection and measurement of lung nodules, ascending and descending aorta, and pleural effusion was integrated with a commercial radiology report dictation application. The CAD system automatically prepopulated a radiology report template, thus offering the potential for increased efficiency. The integrated system was evaluated using 40 scans from a publicly available lung nodule database. Each scan was read using two methods: (1) without CAD analytics, i.e., manually populated report with measurements using electronic calipers, and (2) with CAD analytics to prepopulate the report for reader review and editing. Three radiologists participated as readers in this study.

PURPOSE: With recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research. METHODS: The pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.

Aluminum oxide based optically stimulated luminescent dosimeters (OSLD) have been recognized as a useful dosimeter for measuring CT dose, particularly for patient dose measurements. Despite the increasing use of this dosimeter, appropriate dosimeter calibration techniques have not been established in the literature; while the manufacturer offers a calibration procedure, it is known to have relatively large uncertainties. The purpose of this work was to evaluate two clinical approaches for calibrating these dosimeters for CT applications, and to determine the uncertainty associated with measurements using these techniques. Three unique calibration procedures were used to calculate dose for a range of CT conditions using a commercially available OSLD and reader. The three calibration procedures included calibration (a) using the vendor-provided method, (b) relative to a 120 kVp CT spectrum in air, and (c) relative to a megavoltage beam (implemented with 60 Co). The dose measured using each of these approaches was compared to dose measured using a calibrated farmer-type ion chamber. Finally, the uncertainty in the dose measured using each approach was determined. For the CT and megavoltage calibration methods, the dose measured using the OSLD nanoDot was within 5% of the dose measured using an ion chamber for a wide range of different CT scan parameters (80-140 kVp, and with measurements at a range of positions). When calibrated using the vendor-recommended protocol, the OSLD measured doses were on average 15.5% lower than ion chamber doses. Two clinical calibration techniques have been evaluated and are presented in this work as alternatives to the vendor-provided calibration approach. These techniques provide high precision for OSLD-based measurements in a CT environment.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with great variability in disease severity and rate of progression. The need for a reliable, sensitive, and objective biomarker to track disease progression and response to therapy remains a great challenge in IPF clinical trials. Over the past decade, quantitative computed tomography (QCT) has emerged as an area of intensive research to address this need. We have gathered a group of pulmonologists, radiologists and scientists with expertise in this area to define the current status and future promise of this imaging technique in the evaluation and management of IPF. In this Pulmonary Perspective, we review the development and validation of six computer-based QCT methods and offer insight into the optimal use of an imaging-based biomarker as a tool for prognostication, prediction of response to therapy, and potential surrogate endpoint in future therapeutic trials.

PURPOSE: It is important to enhance image quality for low-dose CT acquisitions to push the ALARA boundary. Current state-of-the-art block-matching three-dimensional (BM3D) denoising scheme assumes white Gaussian noise (WGN) model. This study proposes a novel filtering module to be incorporated into the BM3D framework for ultra-low-dose CT denoising, by accounting for its specific power spectral properties. METHODS: In the current BM3D algorithm, the Wiener filtering is applied in the transform domain to a post-thresholding signal for enhanced denoising. However, unlike most natural/synthetic images, low-dose CTs do not obey the ideal Gaussian noise model. Based on the specific noise properties of ultra-low-dose CT, we derive the optimal transform-domain coefficients of Wiener filter based on the minimum mean-square-error (MMSE) criterion, taking the noise spectrum and the signal/noise cross spectrum into consideration. In the absence of ground-truth signal, the hard-thresholding denoising module in the previous stage is used as a plug-in estimator. We evaluate the denoising performance on thoracic CT image datasets containing paired full-dose and ultra-low-dose images simulated by a well-validated clinical engine (or pipeline). We also assess its clinical implication by applying the denoising methods to the emphysema quantification task. Our modified BM3D method is compared with the current one, using peak signal-to-noise ratio (PSNR) and emphysema scoring results as evaluation metrics.