Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017

Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

PURPOSE

To develop a prediction model based on ¹⁸F-fludeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) for solid pulmonary nodules (SPNs) with high malignant probability.

PATIENTS AND METHODS

We retrospectively reviewed the records of CT-undetermined SPNs, which were further evaluated by PET/CT between January 2008 and December 2015. A total of 312 cases were included as a training set and 159 as a validation set. Logistic regression was applied to determine independent predictors, and a mathematical model was deduced. The area under the receiver operating characteristic curve (AUC) was compared to other models. Model fitness was assessed based on the American College of Chest Physicians guidelines.

RESULTS

There were 215 (68.9%) and 127 (79.9%) malignant lesions in the training and validation sets, respectively. Eight independent predictors were identified: age [odds ratio (OR) = 1.030], male gender (OR = 0.268), smoking history (OR = 2.719), lesion diameter (OR = 1.067), spiculation (OR = 2.530), lobulation (OR = 2.614), cavity (OR = 2.847), and standardized maximum uptake value of SPNs (OR = 1.229). Our AUCs (training set, 0.858; validation set, 0.809) was better than those of previous models (Mayo: 0.685, P = .0061; Peking University People's Hospital: 0.646, P = .0180; Herder: 0.708, P = .0203; Zhejiang University: 0.757, P = .0699). The C index of the nomogram was 0.858. Our model reduced the diagnosis of indeterminate nodules (26.4% vs. 79.2%, 53.5%, 39.6%, and 34.0%, respectively) while improved sensitivity (81.3% vs. 16.4%, 49.2%, 62.5%, and 68.0%, respectively) and accuracy (65.4% vs. 16.4%, 39.6%, 52.8%, and 58.5%, respectively).

CONCLUSION

Our model could permit accurate diagnoses and may be recommended to identify malignant SPNs with high malignant probability, as our data pertain to a very high-prevalence cohort only.

Visceral Pleural Invasion in Pulmonary Adenocarcinoma: Differences in CT Patterns between Solid and Subsolid Cancers

PURPOSE

To analyze the incidence and CT patterns of visceral pleural invasion (VPI) in adenocarcinomas on the basis of their CT presentation as solid or subsolid nodules.

MATERIALS AND METHODS

A total of 286 adenocarcinomas in direct contact with a pleural surface, resected at an institution between 2005 and 2016, were included in this retrospective, institutional review board-approved study. CT size and longest contact length with a pleural surface were measured and their ratios computed. Pleural deviation, pleural thickening, spiculations, different pleural tag types, pleural effusion, and the CT appearance of transgression into an adjacent lobe or infiltration of surrounding tissue were evaluated. Fisher exact tests and simple and multiple logistic regression models were used.

RESULTS

Of the 286 nodules, 179 of 286 (62.6%) were solid and 107 of 286 (37.4%) were subsolid. VPI was present in 49 of 286 (17.1%) nodules and was significantly more frequent in solid (44 of 179; 24.6%) than in subsolid nodules (five of 107; 4.7%; P < .001). In solid nodules, multiple regression analysis showed an association of higher contact length-to-size ratio (adjusted odds ratio [OR], 1.02; P = .007) and the presence of multiple pleural tag types (adjusted OR, 5.88; P = .002) with VPI. In subsolid nodules, longer pleural contact length of the solid nodular component (adjusted OR, 1.27; P = .017) and the CT appearance of transgression or infiltration (adjusted OR, 10.75; P = .037) were associated with VPI.

CONCLUSION

During preoperative evaluation of adenocarcinomas for the likelihood of VPI, whether a tumor manifests as a solid or a subsolid nodule is important to consider because the incidence of VPI is significantly higher in solid than in subsolid nodules. In addition, this study showed that the CT patterns associated with VPI differ between solid and subsolid nodules.© RSNA, 2019Supplemental material is available for this article.See also the commentary by Elicker in this issue.

Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study

Background

To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs.

Methods

CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDI_vol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP).

Results

Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP.

Conclusions

Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDI_vol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDI_vol: 0.10 mGy).

Placement of markers to assist minimally invasive resection of peripheral lung lesions

With development of lung cancer screening programs and increased utilization of radiographic imaging there is significantly higher detection of smaller lung nodules and subsolid lesions. These nodules could be malignant and pose a diagnostic challenge. Video-assisted thoracoscopic surgery and robotic-assisted thoracoscopic surgery (RATS) represent minimally invasive methods for tissue sampling. Intraoperative identification of these lesions maybe difficult, requiring marking prior to surgery. We review different techniques for the placement of markers to assist in the resection of peripheral lung lesions (PLL).

Ambulatory Safety Nets to Reduce Missed and Delayed Diagnoses of Cancer

BACKGROUND

An ambulatory safety net (ASN) is an innovative organizational intervention for addressing patient safety related to missed and delayed diagnoses of abnormal test results. ASNs consist of a set of tools, reports and registries, and associated work flows to create a high-reliability system for abnormal test result management.

METHODS

Two ASNs implemented at an academic medical center are described, one focusing on colon cancer and the other on lung cancer. Data from electronic registries and chart reviews were used to evaluate the effectiveness of the ASNs, which were defined as follows: colon cancer-the proportion of patients who were scheduled for or completed a colonoscopy following safety net team outreach to the patient; lung cancer-the proportion of patients for whom the safety net was able to identify and implement appropriate follow-up, as defined by scheduled or completed chest CT.

RESULTS

The effectiveness of the colon cancer ASN was 44.0%, and the effectiveness of the lung cancer ASN was 56.9%. The ASNs led to the development of registries to address patient safety, fostered collaboration among interdisciplinary teams of clinicians and administrative staff, and created new work flows for patient outreach and tracking.

CONCLUSION

Two ASNs were successfully implemented at an academic medical center to address missed and delayed recognition of abnormal test results related to colon cancer and lung cancer. The ASNs are providing a framework for development of additional safety nets in the organization.

Diagnosis and management of peripheral lung nodule

A solitary pulmonary nodule (SPN) is a well-defined radiographic opacity up to 3 cm in diameter that is surrounded by unaltered aerated lung. Frequently, it is an incidental finding on chest radiographs and chest CT scans. Determining the probability of malignancy is the first step in the evaluation of SPN. This can be done by looking at specific risk factors and the rate of radiographic progression. Subsequent management is guided by the type of the nodule. Patients with solid nodules and low pretest probability can be followed radiographically; those with high probability, who are good surgical candidates, can be referred for surgical resection. When the pretest probability is in the intermediate range additional testing such as biopsy should be done. Various modalities are now available to obtain tissue diagnosis. These modalities differ in their yield and complication rate. Patients with SPN should be well informed of each approach's risks and benefits and should be able to make an informed decision regarding the different diagnostic and therapeutic modalities.

Lung Adenocarcinoma Manifesting as Ground-Glass Opacity Nodules 3 cm or Smaller: Evaluation With Combined High-Resolution CT and PET/CT Modality

OBJECTIVE. The purpose of this study is to evaluate high-resolution CT (HRCT) combined with PET/CT for preoperative differentiation of invasive adenocarcinoma (IAC) from preinvasive lesions and minimally invasive adenocarcinoma (MIA) (the combination of which is hereafter referred to as preinvasive-MIA) in lung adenocarcinoma manifesting as ground-glass opacity nodules (GGNs) 3 cm or smaller. MATERIALS AND METHODS. We retrospectively analyzed the data of patients with lung adenocarcinoma with GGNs that were 3 cm or smaller between November 2011 and November 2018. The HRCT and PET/CT parameters for GGNs were compared to differentiate between IAC and preinvasive-MIA. Qualitative and quantitative parameters were analyzed using univariate and multivariate logistic regression models. The diagnostic performance of different parameters was compared using ROC curves and the McNemar test. RESULTS. The study enrolled 89 patients (24 men and 65 women) with lung adenocarcinoma who had a mean (± SD) age of 60.1 ± 8.1 years (range, 36-78 years). The proportions of mixed GGN type, polygonal or irregular shape, lobulated or spiculated edge, and dilated, distorted, or cutoff bronchial sign were higher for IAC GGNs than for preinvasive-MIA GGNs, and the attenuation value of the ground-glass opacity component on CT (CT_GGO), maximum standardized uptake value, and the standardized uptake value (SUV) index (i.e., the ratio of the tumor maximum SUV to the liver mean SUV) for IAC GGNs were also higher (p = 0.001-0.022). Logistic regression analyses showed that the CT_GGO and SUV index were independent predictors for IAC GGNs. The accuracy of CT_GGO in combination with the SUV index for predicting IAC was 81.4% on a per-GGN basis and 85.4% on a per-patient basis. The combined HRCT and PET/CT modality had higher sensitivity and accuracy than did morphologic features, HRCT, and PET/CT measurement parameters alone (p < 0.001). CONCLUSION. The combined HRCT and PET/CT modality is an effective method to preoperatively identify IAC in lung adenocarcinoma manifesting as GGNs 3 cm or smaller.

Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy

PURPOSE

To assess the feasibility of implementing fully automated computer-aided diagnosis (CAD) for detection of pulmonary nodules on CT pulmonary angiography (CTPA) studies in emergency setting.

MATERIALS AND METHODS

CTPA of 48 emergency patients was retrospectively reviewed. Fully automated CAD nodule detection was performed at the scanner and results were automatically submitted to PACS. A third-year radiology resident (RAD1) and a cardiothoracic radiologist with 6 years' experience (RAD2) reviewed the scans independently to detect pulmonary nodules in two different sessions 8 weeks apart: session 1, CAD was reviewed first and then all images were reviewed; session 2, CAD was reviewed last after all images were reviewed. Time spent by RAD to evaluate image sets was measured for each case. Fisher's exact test and t test were used.

RESULTS

There were 17 male and 31 female patients with mean ± SD age of 48.7 ± 16.4 years. Using CAD at the beginning was associated with lower average reading time for both readers. However, difference in reading time did not reach statistical significance for RAD1 (RAD1 94.6 s vs. 102.7 s, P > 0.05; RAD2 61.1 s vs. 76.5 s, P < 0.05). Using CAD at the end significantly increased rate of RAD1 and RAD2 nodule detection by 34% (2.52 vs. 2.12 nodule/scan, P < 0.05) and 27% (2.23 vs. 1.81 nodule/scan, P < 0.05), respectively.

CONCLUSION

Routine utilization of CAD in emergency setting is feasible and can improve detection rate of pulmonary nodules significantly. Different methods of incorporating CAD in detecting pulmonary nodules can improve both the rate of detection and interpretation speed.

[Diagnosis and Treatment of Pulmonary Ground-glass Nodules]

Recent widespread use of high resolution computed tomography (HRCT) for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass nodule (GGN). However, it had been proved that GGN was well associated with lung cancer in previous studies. Therefore, the classification, imaging characteristics, pathological type, follow-up, suggested managements and other clinical concerns of GGN were reviewed in this paper.
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Accuracy of Model-Based Iterative Reconstruction for CT Volumetry of Part-Solid Nodules and Solid Nodules in Comparison with Filtered Back Projection and Hybrid Iterative Reconstruction at Various Dose Settings: An Anthropomorphic Chest Phantom Study

Objective

To investigate the accuracy of model-based iterative reconstruction (MIR) for volume measurement of part-solid nodules (PSNs) and solid nodules (SNs) in comparison with filtered back projection (FBP) or hybrid iterative reconstruction (HIR) at various radiation dose settings.

Materials and Methods

CT scanning was performed for eight different diameters of PSNs and SNs placed in the phantom at five radiation dose levels (120 kVp/100 mAs, 120 kVp/50 mAs, 120 kVp/20 mAs, 120 kVp/10 mAs, and 80 kVp/10 mAs). Each CT scan was reconstructed using FBP, HIR, or MIR with three different image definitions (body routine level 1 [IMR-R1], body soft tissue level 1 [IMR-ST1], and sharp plus level 1 [IMR-SP1]; Philips Healthcare). The SN and PSN volumes including each solid/ground-glass opacity portion were measured semi-automatically, after which absolute percentage measurement errors (APEs) of the measured volumes were calculated. Image noise was calculated to assess the image quality.

Results

Across all nodules and dose settings, the APEs were significantly lower in MIR than in FBP and HIR (all p < 0.01). The APEs of the smallest inner solid portion of the PSNs (3 mm) and SNs (3 mm) were the lowest when MIR (IMR-R1 and IMR-ST1) was used for reconstruction for all radiation dose settings. (IMR-R1 and IMR-ST1 at 120 kVp/100 mAs, 1.06 ± 1.36 and 8.75 ± 3.96, p < 0.001; at 120 kVp/50 mAs, 1.95 ± 1.56 and 5.61 ± 0.85, p = 0.002; at 120 kVp/20 mAs, 2.88 ± 3.68 and 5.75 ± 1.95, p = 0.001; at 120 kVp/10 mAs, 5.57 ± 6.26 and 6.32 ± 2.91, p = 0.091; at 80 kVp/10 mAs, 5.84 ± 1.96 and 6.90 ± 3.31, p = 0.632). Image noise was significantly lower in MIR than in FBP and HIR for all radiation dose settings (120 kVp/100 mAs, 3.22 ± 0.66; 120 kVp/50 mAs, 4.19 ± 1.37; 120 kVp/20 mAs, 5.49 ± 1.16; 120 kVp/10 mAs, 6.88 ± 1.91; 80 kVp/10 mAs, 12.49 ± 6.14; all p < 0.001).

Conclusion

MIR was the most accurate algorithm for volume measurements of both PSNs and SNs in comparison with FBP and HIR at low-dose as well as standard-dose settings. Specifically, MIR was effective in the volume measurement of the smallest PSNs and SNs.

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge

RATIONALE AND OBJECTIVES

To evaluate a new approach to establish compliance of segmentation tools with the computed tomography volumetry profile of the Quantitative Imaging Biomarker Alliance (QIBA); and determine the statistical exchangeability between real and simulated lesions through an international challenge.

MATERIALS AND METHODS

The study used an anthropomorphic phantom with 16 embedded physical lesions and 30 patient cases from the Reference Image Database to Evaluate Therapy Response with pathologically confirmed malignancies. Hybrid datasets were generated by virtually inserting simulated lesions corresponding to physical lesions into the phantom datasets using one projection-domain-based method (Method 1), two image-domain insertion methods (Methods 2 and 3), and simulated lesions corresponding to real lesions into the Reference Image Database to Evaluate Therapy Response dataset (using Method 2). The volumes of the real and simulated lesions were compared based on bias (measured mean volume differences between physical and virtually inserted lesions in phantoms as quantified by segmentation algorithms), repeatability, reproducibility, equivalence (phantom phase), and overall QIBA compliance (phantom and clinical phase).

RESULTS

For phantom phase, three of eight groups were fully QIBA compliant, and one was marginally compliant. For compliant groups, the estimated biases were -1.8 ± 1.4%, -2.5 ± 1.1%, -3 ± 1%, -1.8 ± 1.5% (±95% confidence interval). No virtual insertion method showed statistical equivalence to physical insertion in bias equivalence testing using Schuirmann's two one-sided test (±5% equivalence margin). Differences in repeatability and reproducibility across physical and simulated lesions were largely comparable (0.1%-16% and 7%-18% differences, respectively). For clinical phase, 7 of 16 groups were QIBA compliant.

CONCLUSION

Hybrid datasets yielded conclusions similar to real computed tomography datasets where phantom QIBA compliant was also compliant for hybrid datasets. Some groups deemed compliant for simulated methods, not for physical lesion measurements. The magnitude of this difference was small (<5.4%). While technical performance is not equivalent, they correlate, such that, volumetrically simulated lesions could potentially serve as practical proxies.

Interobserver size measurement variability in part-solid lung adenocarcinoma using pre-operative computed tomography

Background

In the current lung cancer tumor-node-metastasis classification, solid tumor size is used for tumor diameter measurement as the dense component. However, measuring solid tumor size is sometimes difficult and inter-observer variability may increase, particularly in part-solid nodules with ground-glass opacity (GGO). This study aimed to investigate inter-observer size measurement variability in lung adenocarcinoma.

Methods

Of 47 patients with part-solid lung adenocarcinoma who had undergone surgery at our department from January to December 2016, five surgeons and one radiologist undertook unidimensional solid and total size tumor measurements using pre-operative axial computed tomography images, and we assessed inter-observer size measurement variability. Variability was then subclassified into five groups, according to computer tomography-identified tumor morphological characteristics, namely: (I) minimally invasive; (II) peribronchovascular; (III) spiculation/atelectasis; (IV) adjacent to cystic lesion, and; (V) diffuse consolidation and GGO.

Results

The mean inter-observer variability was 9.7 mm (solid size) and 7.7 mm (total size). Analysis of the maximum and minimum measurement size values for each patient undertaken showed that the most experienced surgeon and the radiologist measured the minimum size more frequently. To correct for differences in mean tumor diameter in each group, a comparison was made using a coefficient of variation (CV) calculated as the ratio of the standard deviation to the mean. Group I characteristics showed the largest coefficient value for variation in solid size measurement.

Conclusions

Inter-observer measurement variability for solid size was larger than for total size in lung adenocarcinoma. Large variability in group I indicated the difficulty of size measurement for low-grade malignant potential nodules such as adenocarcinoma in situ, minimally invasive adenocarcinoma, and early-stage invasive adenocarcinoma. The possibility of unavoidable size measurement variability should be recognized when deciding on surgical procedures for these diseases.

Solid Indeterminate Pulmonary Nodules of Less Than 300 mm3: Application of Different Volume Doubling Time Cut-offs in Clinical Practice

In the British Thoracic Society guidelines for incidental pulmonary nodules, volumetric analysis has become the recommended method for growth assessment in solid indeterminate pulmonary nodules (SIPNs) <300 mm³. In these guidelines, two different volume doubling time (VDT) cut-offs, 400 and 600 days, were proposed to differentiate benign from malignant nodules. The present study aims to evaluate the performance of these VDT cut-offs in a group of SIPNs <300 mm³ which were incidentally detected in a routine clinical setting. During a 7-year period, we retrospectively selected 60 patients with a single SIPN <300 mm³. For each SIPN, the volume and VDT were calculated using semiautomatic software throughout the follow-up period, and the performance of the 400- and 600-day VDT cut-offs was compared. In the selected sample, there were 38 benign and 22 malignant nodules. In this group of nodules, the sensitivity, negative predictive value and accuracy of the 600-day VDT cut-off were higher than those of the 400-day VDT cut-off. Therefore, in the management of SIPNs <300 mm³ which were incidentally detected in a clinical setting, the 600-day VDT cut-off was better at differentiating benign from malignant nodules than the 400-day VDT cut-off, by reducing the number of false negatives.

[A Preliminary Study to Evaluate the Efficacy and Safety of A Optimized Computed Tomography-guided Pulmonary Nodule Microcoil Localization Technique]

背景与目的

肺内小结节微弹簧圈穿刺定位是微创手术切除肺内小结节常用的术前定位方法，然而该方法仍有操作复杂、轻微并发症多等不足之处，我们将原有方法进行了优化。本研究旨在探讨优化后的预充式计算机断层扫描（computed tomography, CT）引导下肺内小结节微弹簧圈穿刺定位法在临床中的应用价值。

方法

对2018年9月-2019年1月间31例患者的35枚肺结节，于术前采用预充式CT引导下肺内小结节微弹簧圈穿刺定位，然后施行胸腔镜下（video-assisted thoracoscopic surgery, VATS）手术。统计分析定位操作相关数据、成功率、并发症、病理结果等。

结果

定位成功率97.1%，VATS切除成功率100%。CT定位时间平均10.1 min（5 min-31 min），微创切除病灶所需时间平均38.2 min（10 min-100 min）。术中发现微弹簧圈脱位回缩至胸壁内1例，通过充气膨肺状态下自胸壁穿刺点刺入穿刺针，成功定位肺内结节并予以切除。3例患者定位后发生微量气胸，但无需闭式引流处理。3例患者出现肺内血肿。35枚肺结节术后病理结果为：高分化腺癌15例，原位癌7例，微浸润腺癌5例，非典型腺瘤样增生4例，肺内淋巴结增生、炎性结节各2例。

结论

采用预充式微弹簧圈定位肺内小结节简便、安全、有效，值得推广。

Attenuation and Morphologic Characteristics Distinguishing a Ground-Glass Nodule Measuring 5-10 mm in Diameter as Invasive Lung Adenocarcinoma on Thin-Slice CT

OBJECTIVE. The purpose of this study is to comprehensively investigate the role of multiple features seen on thin-section CT (TSCT) in the differential diagnosis of ground-glass nodules (GGNs) measuring 5-10 mm in diameter as invasive adenocarcinoma (IAC). MATERIALS AND METHODS. The TSCT features of 313 surgically diagnosed GGNs from 288 patients were retrospectively reviewed. A logistic regression model was applied, and the AUC values for the model and the size and attenuation of the lesions were compared using ROC curve analysis. RESULTS. A total of 247 lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) (hereafter referred to as the AIS-MIA group) and 66 invasive adenocarcinomas (IACs) were identified. Compared with the AIS-MIA group, the IAC groups were significantly larger in size and had higher attenuation values, a higher frequency of mixed GGNs (all p < 0.001), bubblelike appearance, spiculation, pleural indentation, different locations, and a lower frequency of clear tumor-lung interface (all p < 0.05). The logistic model included size and attenuation (both p < 0.001; odds ratio [OR], 1.872 and 1.009, respectively) as well as tumor-lung interface (p = 0.001; OR, 0.242), bubblelike appearance (p < 0.05; OR, 2.205), and type of nodule. The AUC value for the logistic model was 0.847 (sensitivity, 80.3%; specificity, 81.0%) and was significantly higher than that for size or attenuation (both p < 0.01). CONCLUSION. Radiologic features could help in the differential diagnosis of a GGN that was 5-10 mm in diameter as IAC versus AIS or MIA. GGNs larger than 8.12 mm and with attenuation greater than -449.52 HU were more likely to be IAC.

Translation of Quantitative Imaging Biomarkers into Clinical Chest CT

Quantitative imaging has been proposed as the next frontier in radiology as part of an effort to improve patient care through precision medicine. In 2007, the Radiological Society of North America launched the Quantitative Imaging Biomarkers Alliance (QIBA), an initiative aimed at improving the value and practicality of quantitative imaging biomarkers by reducing variability across devices, sites, patients, and time. Chest CT occupies a strategic position in this initiative because it is one of the most frequently used imaging modalities, anatomically encompassing the leading causes of mortality worldwide. To date, QIBA has worked on profiles focused on the accurate, reproducible, and meaningful use of volumetric measurements of lung lesions in chest CT. However, other quantitative methods are on the verge of translation from research grounds into clinical practice, including (a) assessment of parenchymal and airway changes in patients with chronic obstructive pulmonary disease, (b) analysis of perfusion with dual-energy CT biomarkers, and (c) opportunistic screening for coronary atherosclerosis and low bone mass by using chest CT examinations performed for other indications. The rationale for and the key facts related to the application of these quantitative imaging biomarkers in cardiothoracic chest CT are presented. ^©RSNA, 2019 See discussion on this article by Buckler (pp 977-980).

Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules

Pulmonary nodules are frequently detected radiological abnormalities in lung cancer screening. Nodules of the highest- and lowest-risk for cancer are often easily diagnosed by a trained radiologist there is still a high rate of indeterminate pulmonary nodules (IPN) of unknown risk. Here, we test the hypothesis that computer extracted quantitative features (“radiomics”) can provide improved risk-assessment in the diagnostic setting. Nodules were segmented in 3D and 219 quantitative features are extracted from these volumes. Using these features novel malignancy risk predictors are formed with various stratifications based on size, shape and texture feature categories. We used images and data from the National Lung Screening Trial (NLST), curated a subset of 479 participants (244 for training and 235 for testing) that included incident lung cancers and nodule-positive controls. After removing redundant and non-reproducible features, optimal linear classifiers with area under the receiver operator characteristics (AUROC) curves were used with an exhaustive search approach to find a discriminant set of image features, which were validated in an independent test dataset. We identified several strong predictive models, using size and shape features the highest AUROC was 0.80. Using non-size based features the highest AUROC was 0.85. Combining features from all the categories, the highest AUROC were 0.83.

Comparison of 18F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy

AIM

To assess the ability of artificial neural networks (ANNs) to predict the likelihood of malignancy of pure ground-glass opacities (GGOs), using observations from computed tomography (CT) and 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) images and relevant clinical information.

MATERIALS AND METHODS

One hundred and twenty-five cases of pure GGOs described in a previous article were used to train and evaluate the performance of an ANN to predict the likelihood of malignancy in each of the GGOs. Eighty-five cases selected randomly were used for training the network and the remaining 40 cases for testing. The ANN was constructed from the image data and basic clinical information. The predictions of the ANN were compared with blinded expert estimates of the likelihood of malignancy.

RESULTS

The ANN showed excellent predictive value in estimating the likelihood of malignancy (AUC = 0.98±0.02). Employing the optimal cut-off point from the receiver operating characteristic (ROC) curve, the ANN correctly identified 11/11 malignant lesions (sensitivity 100%) and 27/29 benign lesions (specificity 93.1%). The expert readers found 23 lesions indeterminate and correctly identified 17 lesions as benign.

CONCLUSION

ANNs have potential to improve diagnostic certainty in the classification of pure GGOs, based upon their CT appearance, intensity of FDG uptake, and relevant clinical information, and may therefore, be useful to help direct clinical and imaging follow-up.

Individualized Strategies for Intraoperative Localization of Non-palpable Pulmonary Nodules in a Hybrid Operating Room

Background: Precise preoperative localization of small pulmonary nodules is a key prerequisite to their successful excision. With the advent of hybrid operating rooms (HORs), a patient-tailored approach encompassing simultaneous localization and removal of small pulmonary nodules has become feasible. In this study, we describe our individualized image-guided video assisted thoracoscopic surgery (iVATS) strategies implemented within a HOR environment. Specifically, localization was performed through different marking approaches (single- vs. double-marker) and access routes [percutaneous technique with Dyna-computed tomography (DynaCT) imaging vs. electromagnetic navigation bronchoscopy (ENB)]. Methods: Between April 2017 and November 2018, a total of 159 consecutive patients (harboring 174 pulmonary nodules) were treated with iVATS. The marking approach and access route were individually tailored according to lesion localization and its distance from the pleural surface. The efficacy and safety of our iVATS technique were determined through a retrospective review of clinical charts. Results: All of the localization procedures were performed in a HOR by a single team of thoracic surgeons. The mean tumor size on preoperative CT was 8.28 mm (95% confidence interval [CI]: 7.6-8.96 mm), whereas their mean distance from the pleural surface was 9.44 mm (95% CI: 8.11-10.77 mm). Of the 174 tumors, 150 were localized through a percutaneous DynaCT-guided approach (single-marker: 139, dual-marker: 11), whereas localization in the remaining 24 was accomplished via the ENB-guided approach (single-marker: 4; dual-marker: 20). The mean localization time was 17.78 min (95% CI:16.17-19.39 min). The overall localization success rate was 95.9%. We failed to localize a total of seven nodules either because of technical complications (pneumothorax, n = 3; microcoil dislodgement; n = 1) or machine failure (n = 3). No operative deaths were observed, and the mean length of postoperative stay was 3.65 days (95% CI: 3.19-4.11 days). Conclusions: The use of tailored marking approaches and access routes allowed us to individualize the iVATS procedure for small pulmonary nodules, ultimately promoting a more patient-centered workflow.

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

Background

To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging.

Materials and methods

The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software.

Results

Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001).

Conclusions

Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT

PURPOSE

Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.

METHODS

Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign).

RESULTS

The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures.

CONCLUSIONS

Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Postoperative Imaging After Lobectomy: Predicting the Displacement and Change in Orientation of Nonresected Lung Nodules

OBJECTIVES

The objective of this study was to determine the effect of a lobectomy to the location and orientation of nonresected lung nodule and its corresponding airway.

METHODS

We reviewed preoperative and postoperative computed tomography of patients who underwent lobectomies and have a separate nonresected nodule in the ipsilateral lung. Displacement of the nonresected nodule and angulation of its corresponding segmental bronchus were measured.

RESULTS

Fifty nodules from 40 patients (30 females, 10 male; mean ± SD age, 67 ± 7 years) were assessed. Nodules are displaced clockwise after right upper, right middle, and left lower lobectomies and counterclockwise after right lower and left upper lobectomies. Displacement of the remaining nodules was greater in the craniocaudal plane, followed by anteroposterior and transverses planes (mean, 3.7, 2.5, and 1.9 cm, respectively).

CONCLUSIONS

Remaining ipsilateral nodules and their associated segmental airways are displaced in a predictable fashion after lobectomy. This may help in the assessment of follow-up imaging.

Risk factors associated with an increase in the size of ground-glass lung nodules on chest computed tomography

BACKGROUND

The detection rate of ground-glass nodules (GGNs) in the lung has increased with the increased use of low-dose computed tomography (CT) of the chest for cancer screening; however, limited data is available on the natural history, follow-up, and treatment of GGNs. The aim of this study was to identify factors associated with an increase in the size of GGNs.

METHODS

A total of 338 patients (mean ages, 59.8 years; males, 35.5%) with 689 nodules who underwent chest CT at our institute between June 2004 and February 2014 were included in this study. The cut-off date of follow-up was August 2018. We analyzed the size, solidity, number, and margins of the nodules compared with their appearance on previous chest CT images. The Cox proportional hazard model was used to identify risk factors associated with nodule growth.

RESULTS

The median follow-up period was 21.8 months. Of the 338 patients, 38.5% had a history of malignancy, including lung cancer (8.9%). Among the 689 nodules, the median size of the lesions was 6.0 mm (IQR, 5-8 mm), and the proportion of nodules with size ≥10 mm and multiplicity was 17.1% and 66.3%, respectively. Compared to the nodules without an increase in size, the 79 nodules with an increase in size during the follow-up period were initially larger (growth group, 7.0 mm vs. non-growth group, 6.0 mm; P = 0.027), more likely to have a size ≥10 mm (26.6% vs. 15.9%; P = 0.018), and had less frequent multiplicity (54.4% vs. 67.9%, P = 0.028). In the multivariate analysis, nodule size ≥10 mm (hazard ratio [HR], 2.044; P = 0.005), a patient history of lung cancer (HR: 2.190, P = 0.006), and solitary nodule (HR: 2.499, P < 0.001) were independent risk factors for nodule growth.

CONCLUSION

Careful follow-up of GGNs is warranted in patients with a history of malignancy, a large , or a solitary nodule.

Downstream Costs Associated with Incidental Pulmonary Nodules Detected on CT

RATIONALE AND OBJECTIVES

To explore downstream costs associated with incidental pulmonary nodules detected on CT.

MATERIALS AND METHODS

The cohort comprised 200 patients with an incidental pulmonary nodule on chest CT. Downstream events (chest CT, PET/CT, office visits, percutaneous biopsy, and wedge resection) were identified from the electronic medical record. The 2017 Fleischner Society Guidelines were used to classify radiologists' recommendations and ordering physician management for the nodules. Downstream costs for nodule management were estimated from national Medicare rates, and average costs were determined.

RESULTS

Average downstream cost per nodule was $393. Costs were greater when ordering physicians over-managed relative to radiologist recommendations ($940) vs. when adherent ($637) or under-managing ($166) relative to radiologists recommendations. Costs were also greater when ordering physicians over-managed relative to Fleischner Society guidelines ($860) vs. when under-managing ($208) or adherent ($292) to guidelines. Costs did not vary significantly based on whether or not radiologists recommended follow-up imaging ($167-$397), nor whether radiologists were adherent or under- or over-recommended relative to Fleischner Society guidelines ($313-$444). Costs were also higher in older patients, patients with a smoking history, and larger nodules. Five nodules underwent wedge resection and diagnosed as malignancies. No patient demonstrated recurrence or metastasis. Average cost per diagnosed malignancy was $3090.

CONCLUSION

Downstream costs for incidental pulmonary nodules are highly variable and particularly high when ordering physicians over-manage relative to radiologist recommendations and Fleischner Society guidelines. To reduce unnecessary utilization and cost from over-management, radiologists may need to assume a greater role in partnering with ordering physicians to ensure appropriate, guideline-adherent, and follow-up testing.

Early-Stage NSCLC: Advances in Thoracic Oncology 2018

2018 was a banner year for all thoracic oncology, but especially for early-stage NSCLC. Three seminal events occurred in the approximately 18 months from mid-2017 to the end of 2018: in June 2017 at the American Society of Clinical Oncology Annual Meeting a small, relatively unheralded study from Max Diehn's group at Stanford University reported on the use of a novel "cancer personalized profiling by deep sequencing" circulating tumor-DNA technology to identify minimal residual disease in patients after curative-intent radiation or surgery for NSCLC; in April 2018 at the American Association for Cancer Research Annual Meeting, Drew Pardoll presented a small pilot study of 21 patients who had received two doses of preoperative nivolumab; in September 2018, at the 19th World Conference on Lung Cancer, Harry J. De Koning presented the long-awaited results of the Dutch-Belgian Lung Cancer Screening Trial (NELSON). These three seminal studies, along with others which are reviewed in this paper, promise to accelerate our progress towards a world in which lung cancer is identified early, more patients undergo curative-intent treatment that achieves the promised cure, and those at risk for failure after treatment are identified early, when the cancer remains most vulnerable. The day is around the corner when lung cancer is defanged and no longer the worldwide terror it currently is. We herein present an overview of the most recent body of work that moves us inexorably towards that day.

National Survey of Hospitalists' Experiences with Incidental Pulmonary Nodules

Incidental pulmonary nodules (IPNs) are common and often require follow-up. The Fleischner Society guidelines were created to support IPN management. We developed a 14-item survey to examine hospitalists' exposure to and management of IPNs. The survey targeted attendees of the 2016 Society of Hospital Medicine (SHM) annual conference. We recruited 174 attendees. In total, 82% were identified as hospitalist physicians and 7% as advanced practice providers; 63% practiced for >5 years and 62% supervised trainees. All reported seeing ≥1 IPN case in the past six months, with 39% seeing three to five cases and 39% seeing six or more cases. Notwithstanding, 42% were unfamiliar with the Fleischner Society guidelines. When determining the IPN follow-up, 83% used radiology report recommendations, 64% consulted national or international guidelines, and 34% contacted radiologists; 34% agreed that determining the follow-up was challenging; only 15% reported availability of automated tracking systems. In conclusion, despite frequent IPN exposure, hospitalists are frequently unaware of the Fleischner Society guidelines and rely on radiologists' recommendations.

A comparison of ultra-high-resolution CT target scan versus conventional CT target reconstruction in the evaluation of ground-glass-nodule-like lung adenocarcinoma

Background

The aim of this study was to determine whether the clinical value of scanned computed tomography (CT) images is higher when using ultra-high-resolution CT (U-HRCT) target scanning than conventional CT target reconstruction scanning in the evaluation of ground-glass-nodule (GGN)-like lung adenocarcinoma.

Methods

A total of 91 consecutive patients with isolated GGN-like lung adenocarcinoma were included in this study from April 2017 to June 2018. U-HRCT and conventional CT scans were conducted in all enrolled patients. Two experienced thoracic radiologists independently assessed image quality and made diagnoses. Based on the pathological results, the accuracies of U-HRCT target scanning and conventional CT target reconstruction for detecting morphological features on CT, including spiculation of GGNs, bronchial vascular bundles, solid components in the nodules, burr, vacuole, air bronchial signs, and fissure distortion, were calculated. All statistical analyses were performed using SPSS 17.0 software. Enumeration data were tested using the Chi-square test. A P value of <0.05 was considered statistically significant.

Results

When both techniques were compared with the pathological findings, the detection rate for CT images obtained using U-HRCT target scanning and conventional CT target reconstruction with regard to the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules were 78% vs. 61.5%, 72.5% vs. 54.9%, 65.9% vs. 49.5%, respectively. The presence of the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules in U-HRCT target scanning was significantly higher than that in conventional CT target reconstruction (all P<0.05). However, no significant difference was observed between the two techniques with regard to the burr, vacuole, air bronchial signs, and fissure distortion (all P>0.05).

Conclusions

When viewing GGNs, the detection rate was higher for U-HRCT target scanning than for conventional CT target reconstruction, and this improvement significantly enhanced the diagnostic accuracy of early lung adenocarcinoma.

Clinical significance of perifissural nodules in the oncologic population

PURPOSE

To evaluate for stability of perifissural nodules (PFNs) in a dedicated oncologic population.

METHODS

A retrospective review of 500 computed tomography (CT) chests from oncologic patients at our tertiary care cancer center with at least a three year follow up yielded 76 patients with PFNs. Patients with metastases on baseline CT chest were excluded (n = 14) as the presence of a PFN would not be clinically relevant, thus our final patient cohort was 62 patients with a total of 112 PFNs. PFN features, clinical features, and ancillary information was recorded from the CT and the electronic medical record for all patients. The two patient cohorts-stable or decreased PFN vs. increased PFN-were then compared.

RESULTS

112 PFNs were examined in 62 patients with a median follow up interval of 5.7 years. Of 62 patients, 59 (95.2%, 95% CI: 86.5, 99.0) had decreased/stable PFNs on follow up scan (median follow up 5.6 years) and 3 (4.8%, 95% CI: 1.0, 13.5%) had enlarged PFNs (median follow up 6.3 years). None of the PFN features, clinical features, nor ancillary information from the CT proved to be statistically significant.

CONCLUSIONS

Despite the lack of statistically significant distinguishing features to predict growth, our results are reassuring, since the majority of PFNs in our oncology patients were decreased or unchanged in size which is comparable to previously published data on PFNs in non-oncologic patients. Thus, we can similarly presume these nodules are most likely benign and can provide reassurance to our oncologic colleagues and our patients. Larger studies are warranted to further evaluate PFNs in the oncologic population which also examines the nodules by cancer type.

Natural Language Processing for Identification of Incidental Pulmonary Nodules in Radiology Reports

PURPOSE

To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations.

METHODS AND MATERIALS

We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017.

RESULTS

The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43).

CONCLUSION

NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.

Migration of a hookwire used as a video-assisted thoracoscopic surgery marker into the splenic artery

We present a case in which a hookwire that was used as a video-assisted thoracoscopic (VATS) surgery marker migrated into the splenic artery. The patient was a 70-year-old man with an 18-mm ground glass nodule (GGN) in the right S2. As the GGN was not located in the peripheral part of the lung, a percutaneous hookwire was placed as a marker under CT-guided just before the surgery. We performed VATS right S2 segmentectomy to remove the GGN and the marker; however, we could not locate the marker in the specimen. Histopathological examination revealed adenocarcinoma, TisN0M0, stage 0. CT findings after surgery showed that the marker had migrated into the splenic artery. We followed up the patient, and CT examination conducted 1, 3 and 6 months after the surgery showed no further migration and no damage of the splenic artery. We report the complication of percutaneous hookwire migration into a blood vessel.

Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study

PURPOSE

To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules.

MATERIALS AND METHODS

A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUC_CV) was calculated together with its 95% confidence interval.

RESULTS

The AUC_CV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis.

CONCLUSIONS

The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients.

KEY POINTS

• Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.

Invasive Pulmonary Adenocarcinomas Versus Preinvasive Lesions Appearing as Pure Ground-Glass Nodules: Differentiation Using Enhanced Dual-Source Dual-Energy CT

OBJECTIVE. The objective of our study was to investigate the potentials of enhanced dual-source dual-energy CT (DECT) and three-planar measurements for differentiating invasive pulmonary adenocarcinomas (IPAs) from preinvasive lesions appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS. Thirty-nine patients with 53 pGGNs who underwent enhanced dual-source DECT were included in this retrospective study. All pGGNs were pathologically confirmed and categorized into two groups: preinvasive lesions or IPAs. The traditional CT features of the pGGNs were evaluated on unenhanced images. Quantitative parameters were measured on iodine-enhanced images of dual-source DECT in three planes, and both intra- and interobserver reproducibility analyses were performed to assess the measurement reproducibility of quantitative parameters. To identify significant factors for differentiating IPAs from preinvasive lesions, we performed logistic regression analysis and ROC curve analysis. RESULTS. For traditional CT features, only lesion size and unenhanced CT attenuation value showed significant differences between preinvasive lesions and IPAs (p < 0.05). Preinvasive lesions and IPAs exhibited significant differences in attenuation on virtual images, so-called "virtual HU" or "VHU," and the modified normalized iodine concentration (NIC) (p < 0.05), and both intra- and interobserver agreement for the quantitative measurements were excellent. Multivariate logistic regression analysis revealed that larger lesion size (adjusted odds ratio [OR], 3.65) and higher modified NIC (adjusted OR, 19.01) were significant differentiators of IPAs from preinvasive lesions (p < 0.05). ROC curve analysis revealed that modified NIC showed excellent performance (AUC, 0.924) and significantly higher performance than lesion size (AUC, 0.711) for differentiating IPAs from preinvasive lesions. CONCLUSION. In pGGNs, a lesion with a modified NIC value of more than 0.29 can be a very specific discriminator of IPAs from preinvasive lesions, and IPAs can be accurately and reliably differentiated from preinvasive lesions using enhanced dual-source DECT and three-planar measurements.

Long-Term Follow-Up of Ground-Glass Nodules After 5 Years of Stability

INTRODUCTION

Small ground-glass nodules (GGNs) or those with an indeterminate risk on low-dose computed tomography (LDCT) of the chest are recommended at 5-year follow-up, but the rationale for follow-up beyond 5 years is unclear.

METHODS

An observational study was conducted to investigate the natural course of GGNs that had been stable for 5 years by LDCT over 10 years. All eligible GGNs were detected during regular health checkups. Baseline characteristics were compared between GGNs with and without growth. Risk factors for GGN growth were evaluated.

RESULTS

A total of 208 GGNs were detected in 160 participants. GGN growth was identified in 27 (13.0%) GGNs during a follow-up of 136 months on LDCT scans. In approximately 95% of these GGNs, the initial size was less than 6 mm, with 3.2 mm of growth over 8.5 years. Biopsies were performed in 3 of 27 GGNs, revealing adenocarcinoma. In 8 of 27 cases, GGN growth preceded the development of a new solid component. In a multivariate analysis, bubble lucency (p = 0.001), a history of cancer other than lung cancer (p = 0.036), and development of a new solid component (p < 0.001) were significant risk factors for GGN growth.

CONCLUSIONS

GGNs should not be ignored, even when smaller than 6 mm and stable for 5 years, especially when a new solid component appears during follow-up.

Incidental pulmonary nodules: characterization and management

This update covers the management of solitary or multiple pulmonary nodules detected incidentally in imaging studies done for other reasons. It describes the most appropriate computed tomography technique for the evaluation of these nodules, how they are classified, and how the different types of nodules are measured. It also reviews the patient-related and nodule-related criteria for determining the risk of malignancy. It discusses the recommendations in the guidelines recently published by the Fleischner Society for the management and follow-up of each type of nodules according to its size and risk of malignancy.

Lung Cancer

Lung cancer is the world's leading cause of cancer death. Screening for lung cancer by low-dose computed tomography improves mortality. Various modalities exist for diagnosis and staging. Treatment is determined by subtype and stage of cancer; there are several personalized therapies that did not exist just a few years ago. Caring for the patient with lung cancer is a complex task. This review provides a broad outline of this disease, helping clinicians identify such patients and familiarizing them with lung cancer care options, so they are better equipped to guide their patients along this challenging journey.

Update on screening for lung cancer

As the leading cause of cancer related death world wide, lung cancer is responsible for an enormous amount of suffering and disability. Detection of disease when it is surgically curable is associated with far greater odds of cure, and therefore it is a disease for which mass screening of high-risk populations has significant potential benefit. Starting in 2011, with the publication of the National Lung Screening Trial from United States (U.S.), mass screening programs have emerged throughout the U.S., as well as in other countries. More recently, large European screening trials have confirmed the potential mortality benefit of lung cancer screening. This invited non-systematic review paper covers the trial that data justify mass-screening, for lung cancer and proposes strategies for maximizing benefits and minimizing harms in the context of a mass public health lung cancer screening program.

Integration of Chest CT CAD into the Clinical Workflow and Impact on Radiologist Efficiency

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.

RESULTS

CAD assistance reduced reading times by 7%-44%, relative to the conventional manual method, for the three radiologists from opening of the case to signing of the final report.

CONCLUSION

This study provides an investigation of the impact of CAD and measurement on chest CTs within a clinical reporting workflow. Prepopulation of a report with automated nodule and aorta measurements yielded substantial time savings relative to manual measurement and entry.

Identification of Nonaggressive Pulmonary Nodules Using an Optimized Scoring System

Supplemental Digital Content is available in the text.

Purpose:

The purpose of this study was to define the optimal scoring method for identifying benign intrapulmonary lymph nodes.

Materials and Methods:

Subjects for this study were selected from the COPDGene study, a large multicenter longitudinal observational cohort study. A retrospective case-control analysis was performed using identified nodules on a subset of 377 patients who demonstrated 765 pulmonary nodules on their baseline computed tomography (CT) study. Nodule characteristics of 636 benign nodules (which resolved or showed <20% growth rate at 5 y follow-up) were compared with 51 nodules that occurred in the same lobe as a reported malignancy. Two radiologists scored each pulmonary nodule on the basis of intrapulmonary lymph node characteristics. A simple scoring strategy weighing all characteristics equally was compared with an optimized scoring strategy that weighed characteristics on the basis of their relative importance in identifying benign pulmonary nodules.

Results:

A total of 479 of 636 benign pulmonary nodules had the majority of lymph node characteristics, whereas only 1 subpleural nodule with the majority of lymph node characteristics appeared to be malignant. Only 279 of 479 (58%) of benign pulmonary nodules with the majority of lymph node characteristics were intrafissural or subpleural. The optimized scoring strategy showed improved performance compared with the simple scoring strategy with average area under the curve of 0.80 versus 0.55. Optimized cutoff scores showed negative likelihood values for both readers of <0.2. A simulation showed a potential reduction in CT utilization of up to 36% for Fleischner criteria and up to 5% for LUNG-RADS.

Conclusions:

Nodules with the majority of lymph node characteristics, regardless of location, are likely benign, and weighing certain lymph node characteristics greater than others can improve overall performance. Given the potential to reduce CT utilization, lymph node characteristics should be considered when recommending appropriate follow-up.