Managing Incidental Findings on Thoracic CT: Lung Findings. A White Paper of the ACR Incidental Findings Committee

The ACR Incidental Findings Committee presents recommendations for managing incidentally detected lung findings on thoracic CT. The Chest Subcommittee is composed of thoracic radiologists who endorsed and developed the provided guidance. These recommendations represent a combination of current published evidence and expert opinion and were finalized by informal iterative consensus. The recommendations address commonly encountered incidental findings in the lungs and are not intended to be a comprehensive review of all pulmonary incidental findings. The goal is to improve the quality of care by providing guidance on management of incidentally detected thoracic findings.

Impact of computed tomography window settings on clinical T classifications and prognostic evaluation of patients with subsolid nodules

OBJECTIVES

To investigate the impact of lung window (LW) and mediastinal window (MW) settings on the clinical T classifications and prognostic prediction of patients with subsolid nodules.

METHODS

Seven hundred and nineteen surgically resected subsolid nodules were reviewed, grouping into pure ground-glass nodules (n = 179) or part-solid nodules (n = 540) using LW. Interobserver agreement on nodule classifications was assessed via kappa-value, and predictive performance of the solid portion measurement in LW and MW for pathological invasiveness and malignancy were compared using receiver-operating characteristic analysis. Cox regression was used to identify prognostic factors. Prognostic significance of T classifications based on LW (c[l]T) and MW (c[m]T) was evaluated by Kaplan-Meier method after propensity score matching. The performance of c(m)T for discrimination survival was estimated via the concordance index (C-index), net reclassification improvement and integrated-discrimination improvement.

RESULTS

By adopting MW, 124 part-solid nodules were reclassified as pure ground-glass nodules, and interobserver agreement improved to 0.917 (95% confidence interval 0.888-0.946). The solid portion size under MW more strongly predicted pathological invasiveness (P = 0.030), but did not better predict pathological malignancy. For remaining 416 part-solid nodules, c(l)T and c(m)T were both independent risk factors. c(m)T led to T classifications shifts in 321 nodules (14 upstaged and 307 downstaged) with no significant prognostic difference existing between the shifted c(m)T and matching c(l)T group after propensity score matching. The corrected C-index was improved to 0.695 (0.620-1.000) when adopting c(m)T with no significant difference in net reclassification improvement (P = 0.098) and integrated-discrimination improvement (P = 0.13) analysis.

CONCLUSIONS

As there is no significant benefit provided by MW in evaluating clinical T classification and prognosis, the current usage of LW is appropriate for assessing subsolid nodules.

Verification of the eighth edition of the UICC-TNM classification on surgically resected lung adenocarcinoma: Comparison with previous classification in a local center

BACKGROUND

The UICC 8th TNM classification of lung cancer has been changed dramatically, especially in measuring methods of T-desriptors. Different from squamous- or small-cell carcinomas, in which the solid- and the invasive-diameter mostly agree with each other, the diameter of the radiological solid part and that of pathological invasive part in adenocarcinomas often does not match.

AIM

We aimed to determine radiological and pathological tumor diameters of pulmonary adenocarcinomas with clinicopathological factors and evaluate the validity of the 8th edition in comparison with the 7th edition.

METHODS AND RESULTS

We retrospectively analyzed clinicopathological factors of 429 patients with surgically resected pulmonary adenocarcinomas. The maximum tumor and their solid-part diameters were measured using thin-sectioned computed tomography and compared with pathological tumor and invasive diameters. Overall survival (OS) rate was determined using the Kaplan-Meier method for different subgroups of clinicopathological factors. Akaike's information criteria (AIC) was used as a discriminative measure for the univariate Cox model for the 7th and 8th editions. Multivariate Cox regression analysis was performed to explore independent prognostic factors. Correlation coefficients between radiological and pathological diameters in the 7th and 8th editions were 0.911 and 0.888, respectively, without a significant difference. The major reasons for the difference in the 8th edition were the presence of intratumoral fibrosis and papillary growth pattern. The weighted kappa coefficients in the 8th edition were superior those in the 7th edition for both the T and Stage classifications. In the univariate Cox model, AIC levels were the lowest in the 8th edition. Multivariate analysis revealed that age, lymphovascular invasion, pT(8th), and stage were the most important determinants for OS.

CONCLUSION

The UICC 8th edition is a more discriminative classification than the 7th edition. For subsolid nodules, continuous efforts are necessary to increase the universality of the measurement of solid and invasive diameters.

Utility of PET-CT in non-small cell lung cancer clinical stage IB-IIA according to AJCC 8th edition staging system: an alternative to invasive mediastinal staging?

Objective

Mediastinal nodal staging in lung cancer is essential to determine treatment strategy and prognosis. There are controversies as to whether a mediastinal negative result in PET-CT may spare the invasive staging of the mediastinum. The main endpoint is to evaluate the negative predictive value (NPV) of PET-CT in non-small cell lung cancer (NSCLC) clinical stage IB-IIA without clinical nodal involvement. The secondary endpoint is to evaluate the prevalence of mediastinal and hilar nodal affection in this population.

Methods

We performed an observational descriptive study from January 2010 to January 2020, including 76 patients with clinical stage IB-IIA, who underwent pulmonary resection with systematic nodal sampling (pre-determined lymph node stations based on tumour location) for primary NSCLC. Clinically, nodal involvement was defined as any lymph node greater than 1 cm in the short axis on a CT or with metabolic uptake greater than 2.5 SUV on PET-CT. The prevalence of nodal metastases was recorded.

Results

Fifty six patients had clinical stage IB and 20 had clinical stage IIA. Mean tumour size was 3.74 ± 0.5 cm. Lobectomy was the resection procedure most frequently performed. Of the 76 patients with clinical N0 by PET-CT who underwent surgical resection, 10 (13.1%) were upstaged to pN1 and none were upstaged to pN2. NPV of PET-CT for overall nodal metastasis was 87% (95% CI: 0.79-0.94). NPV of PET-CT for N2 metastasis was 100%.

Conclusion

PET-CT might be an alternative to invasive mediastinal staging in patients with NSCLC clinical stage IB-IIA who are surgical candidates. Further prospective multi-institutional studies are necessary to verify the external validity of our study.

Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management

Pulmonary nodules are the most common incidental finding in the chest, particularly on computed tomographs that include a portion or all of the chest, and may be encountered more frequently with increasing utilization of cross-sectional imaging. Established guidelines address the reporting and management of incidental pulmonary nodules, both solid and subsolid, synthesizing nodule and patient features to distinguish benign nodules from those of potential clinical consequence. Standard nodule assessment is essential for the accurate reporting of nodule size, attenuation, and morphology, all features with varying risk implications and thus management recommendations.

General Review on the Current Management of Incidental Findings on Cross-Sectional Imaging: What Guidelines to Use, How to Follow Them, and Management and Medical-Legal Considerations

"Incidentalomas" are a common part of daily practice for radiologists, and knowledge of appropriate management guidelines is important in ensuring that no potentially clinically relevant findings are missed or are lost to follow-up in asymptomatic patients. Incidental findings of the brain, spine, thyroid, lungs, breasts, liver, adrenals, spleen, pancreas, kidneys, bowel, and ovaries are discussed, including where to find guidelines for management recommendations, how to follow them, and medical-legal considerations.

Subsolid Lung Adenocarcinomas: Radiological, Clinical and Pathological Features and Outcomes

Lung adenocarcinomas manifesting as subsolid nodules usually have a favorable prognosis. This study aimed to have a comprehensive investigation of the radiological and clinicopathologic features and oncological outcomes of subsolid nodules. Between March 2010 and December 2015, 865 patients with surgically resected clinical IA subsolid lung adenocarcinoma were retrospectively reviewed. Patients were classified into the pure ground-glass nodules (GGN) (pGGN [n = 358], without solid component on lung and mediastinal windows), heterogeneous GGN (hGGN [n = 65], only with solid components on lung window), and real part-solid nodule (rPSN [n = 442], with solid component on both lung and mediastinal windows) groups. The clinicopathological features and survival time of the three groups were compared between groups. There was a significant increase in median tumor size (P < 0.001), solid component size measured at lung window (LW-SCS) (P < 0.001), and the proportion of invasive adenocarcinoma subtypes (P < 0.001) from pGGNs to hGGNs to rPSNs. After adjustment for LW-SCS, adenocarcinomas with predominant lepidic patterns were still more common in hGGNs than in rPSNs (P = 0.009). Patients with rPSNs had a significantly worse recurrence-free survival (RFS) than those with pGGNs and hGGNs (5-year: 91.9% versus 100% versus 100%, P < 0.001). Multivariate Cox analyses revealed that gender (both P < 0.05) and clinical T category (based on lung window [LW-cT] [P = 0.002] or mediastinal window [MW-cT] [P < 0.001]) were independent prognostic factors of RFS in the rPSN group. HGGNs represented as an intermediate subtype between pGGNs and rPSNs. Both pGGNs and hGGNs had excellent outcomes, while rPSNs exhibited a worse prognosis than them. Clinical T category and gender had prognostic implications for rPSNs.

A phantom study to optimise the automatic tube current modulation for chest CT in COVID-19

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic. The expert organisations recommend more cautious use of thoracic computed tomography (CT), opting for low-dose protocols. We aimed at determining a threshold value of automatic tube current modulation noise index below which there is a chance to miss an onset of ground-glass opacities (GGO) in COVID-19. A team of radiologists and medical physicists performed 25 phantom CT studies using different automatic tube current modulation settings (^SUREExposure3D technology). We then conducted a retrospective evaluation of the chest CT images from 22 patients with COVID-19 and calculated the density difference between the GGO and unaffected tissue. Finally, the results were matched to the phantom study results to determine the minimum noise index threshold value. The minimum density difference at the onset of COVID-19 was 252 HU (p < 0.001). This was found to correspond to the ^SUREExposure 3D noise index of 36. We established the noise index threshold of 36 for the Canon scanner without iterative reconstructions, allowing for a decrease in the dose-length product by 80%. The proposed protocol needs to be validated in a prospective study.

The Value of PETRA in Pulmonary Nodules of <3 cm Among Patients With Lung Cancer

Objective

This study aimed to evaluate the visibility of different subgroups of lung nodules of <3 cm using the pointwise encoding time reduction with radial acquisition (PETRA) sequence on 3T magnetic resonance imaging (MRI) in comparison with that obtained using low-dose computed tomography (LDCT).

Methods

The appropriate detection rate was calculated for each of the different subgroups of lung nodules of <3 cm. The mean diameter of each detected nodule was determined. The detection rates and diameters of the lung nodules detected by MRI with the PETRA sequence were compared with those detected by computed tomography (CT). The sensitivity of detection for the different subgroups of pulmonary nodules was determined based on the location, size, type of nodules and morphologic characteristics. Agreement of nodule characteristics between CT and MRI were assessed by intraclass correlation coefficient (ICC) and Kappa test.

Results

The CT scans detected 256 lung nodules, comprising 99 solid nodules (SNs) and 157 subsolid nodules with a mean nodule diameter of 8.3 mm. For the SNs, the MRI detected 30/47 nodules of <6 mm in diameter and 52/52 nodules of ≥6 mm in diameter. For the subsolid nodules, the MRI detected 30/51 nodules of <6 mm in diameter and 102/106 nodules of ≥6 mm in diameter. The PETRA sequence returned a high detection rate (84%). The detection rates of SN, ground glass nodules, and PSN were 82%, 72%, and 94%, respectively. For nodules with a diameter of >6 mm, the sensitivity of the PETRA sequence reached 97%, with a higher rate for nodules located in the upper lung fields than those in the middle and lower lung fields. Strong agreement was found between the CT and PETRA results (correlation coefficients = 0.97).

Conclusion

The PETRA technique had high sensitivity for different type of nodule detection and enabled accurate assessment of their diameter and morphologic characteristics. It may be an effective alternative to CT as a tool for screening and follow up pulmonary nodules.

Lung Nodules in Melanoma Patients: Morphologic Criteria to Differentiate Non-Metastatic and Metastatic Lesions

Lung nodules are frequent findings in chest computed tomography (CT) in patients with metastatic melanoma. In this study, we assessed the frequency and compared morphologic differences of metastases and benign nodules. We retrospectively evaluated 85 patients with melanoma (AJCC stage III or IV). Inclusion criteria were ≤20 lung nodules and follow-up using CT ≥183 days after baseline. Lung nodules were evaluated for size and morphology. Nodules with significant growth, nodule regression in line with RECIST assessment or histologic confirmation were judged to be metastases. A total of 438 lung nodules were evaluated, of which 68% were metastases. At least one metastasis was found in 78% of patients. A 10 mm diameter cut-off (used for RECIST) showed a specificity of 95% and a sensitivity of 20% for diagnosing metastases. Central location (n = 122) was more common in metastatic nodules (p = 0.009). Subsolid morphology (n = 53) was more frequent (p < 0.001), and calcifications (n = 13) were solely found in non-metastatic lung nodules (p < 0.001). Our data show that lung nodules are prevalent in about two-thirds of melanoma patients (AJCC stage III/IV) and the majority are metastases. Even though we found a few morphologic indicators for metastatic or non-metastatic lung nodules, morphology has limited value to predict the presence of lung metastases.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).

Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.

Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.

Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

Reliability of Coronary Artery Calcium Severity Assessment on Non-Electrocardiogram-Gated CT: A Meta-Analysis

OBJECTIVE

The purpose of this meta-analysis was to investigate the pooled agreements of the coronary artery calcium (CAC) severities assessed by electrocardiogram (ECG)-gated and non-ECG-gated CT and evaluate the impact of the scan parameters.

MATERIALS AND METHODS

PubMed, EMBASE, and the Cochrane library were systematically searched. A modified Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to evaluate the quality of the studies. Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and the correlation coefficient of the CAC scores or the weighted kappa for the categorization of the CAC severities detected by the two modalities. The heterogeneity among the studies was also assessed. Subgroup analyses were performed based on factors that could affect the measurement of the CAC score and severity: slice thickness, reconstruction kernel, and radiation dose for non-ECG-gated CT.

RESULTS

A total of 4000 patients from 16 studies were included. The pooled bias was 62.60, 95% LOA were -36.19 to 161.40, and the pooled correlation coefficient was 0.94 (95% confidence interval [CI] = 0.89-0.97) for the CAC score. The pooled weighted kappa of the CAC severity was 0.85 (95% CI = 0.79-0.91). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, the agreement between the CAC categorizations was better when the two CT examinations had reconstructions based on the same slice thickness and kernel.

CONCLUSION

The pooled agreement of the CAC severities assessed by the ECG-gated and non-ECG-gated CT was excellent; however, it was significantly affected by scan parameters, such as slice thickness and the reconstruction kernel.

Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography

Background

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) helps early lung cancer detection, commonly presenting as small pulmonary nodules. Artificial intelligence (AI)-based vessel suppression (AI-VS) and automatic detection (AI-AD) algorithm can improve detection of subsolid nodules (SSNs) on LDCT. We assessed the impact of AI-VS and AI-AD in detection and classification of SSNs [ground-glass nodules (GGNs) and part-solid nodules (PSNs)], on LDCT performed for LCS.

Methods

Following regulatory approval, 123 LDCT examinations with sub-solid pulmonary nodules (average diameter ≥6 mm) were processed to generate three image series for each examination-unprocessed, AI-VS, and AI-AD series with annotated lung nodules. Two thoracic radiologists in consensus formed the standard of reference (SOR) for this study. Two other thoracic radiologists (R1 and R2; 5 and 10 years of experience in thoracic CT image interpretation) independently assessed the unprocessed images alone, then together with AI-VS series, and finally with AI-AD for detecting all ≥6 mm GGN and PSN. We performed receiver operator characteristics (ROC) and Cohen's Kappa analyses for statistical analyses.

Results

On unprocessed images, R1 and R2 detected 232/310 nodules (R1: 114 GGN, 118 PSN) and 255/310 nodules (R2: 122 GGN, 133 PSN), respectively (P>0.05). On AI-VS images, they detected 249/310 nodules (119 GGN, 130 PSN) and 277/310 nodules (128 GGN, 149 PSN), respectively (P≥0.12). When compared to the SOR, accuracy (AUC) for detection of PSN on the AI-VS images (AUC 0.80-0.81) was greater than on the unprocessed images (AUC 0.70-0.76). AI-VS images enabled detection of solid components in five nodules deemed as GGN on the unprocessed images. Accuracy of AI-AD was lower than both the radiologists (AUC 0.60-0.72).

Conclusions

AI-VS improved the detection and classification of SSN into GGN and PSN on LDCT of the chest for the two radiologist (R1 and R2) readers.

Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs

OBJECTIVES

To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs.

METHODS

A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement.

RESULTS

An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation.

CONCLUSION

This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening.

KEY POINTS

• Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.

Structural and functional radiomics for lung cancer

INTRODUCTION

Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.

METHODS

Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.

CONCLUSION

The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Lung Nodule Segmentation with a Region-Based Fast Marching Method

When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust segmentation of nodules becomes a challenging problem. A segmentation algorithm based on the fast marching method is proposed that separates the image into regions with similar features, which are then merged by combining regions growing with k-means. An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data, respectively. The objective experimental results show that the proposed technique can accurately segment nodules, especially in solid cases, given the mean Dice scores of 0.933 and 0.901 for round and irregular nodules. For non-solid and cavitary nodules the performance dropped—0.799 and 0.614 mean Dice scores, respectively. The proposed method was compared to active contour models and to two modern deep learning networks. It reached better overall accuracy than active contour models, having comparable results to DBResNet but lesser accuracy than 3D-UNet. The results show promise for the proposed method in computer-aided diagnosis applications.

Surveillance of small, solid pulmonary nodules at digital chest tomosynthesis: data from a cohort of the pilot Swedish CArdioPulmonary bioImage Study (SCAPIS)

Background

Digital tomosynthesis (DTS) might be a low-dose/low-cost alternative to computed tomography (CT).

Purpose

To investigate DTS relative to CT for surveillance of incidental, solid pulmonary nodules.

Material and Methods

Recruited from a population study, 106 participants with indeterminate solid pulmonary nodules on CT underwent surveillance with concurrently performed CT and DTS. Nodule size on DTS was assessed by manual diameter measurements and semi-automatic nodule segmentations were independently performed on CT. Measurement agreement was analyzed according to Bland–Altman with 95% limits of agreement (LoA). Detection of nodule volume change > 25% by DTS in comparison to CT was evaluated with receiver operating characteristics (ROC).

Results

A total of 81 nodules (76%) were assessed as measurable on DTS by two independent observers. Inter- and intra-observer LoA regarding change in average diameter were ± 2 mm. Calculation of relative volume change on DTS resulted in wide inter- and intra-observer LoA in the order of ± 100% and ± 50%. Comparing relative volume change between DTS and CT resulted in LoA of –58% to 67%. The area under the ROC curve regarding the ability of DTS to detect volumetric changes > 25% on CT was 0.58 (95% confidence interval [CI] = 0.40–0.76) and 0.50 (95% CI = 0.35–0.66) for the two observers.

Conclusion

The results of the present study show that measurement variability limits the agreement between DTS and CT regarding nodule size change for small solid nodules.

Solid Components in the Mediastinal Window of Computed Tomography Define a Distinct Subtype of Subsolid Nodules in Clinical Stage I Lung Cancers

BACKGROUND

We aimed to validate the clinicopathologic characteristics and prognostic value of the presence of solid components in the mediastinal window of computed tomography scan in clinical stage I pulmonary subsolid nodules (SSNs).

METHODS

We retrospectively evaluated patients with pulmonary SSNs resected between 2011 and 2016. We classified SSNs into heterogeneous ground-glass nodules (HGGNs) (solid component detected only in lung window) and part-solid nodules (PSNs) (solid component detected both in lung/mediastinal windows).

RESULTS

A total of 487 patients (216 PSNs) were included. PSNs were associated with higher frequencies of micropapillary or solid pathologic patterns (18.1% vs. 3.3%; P < .001), epidermal growth factor receptor gene mutation (39.4% vs. 32.8%), and other types of gene mutations (2.3% vs. 1.1%; P = .043). Logistic regression analysis revealed that male sex (odds ratio [OR], 2.58; 95% confidence interval [CI], 1.20-5.57; P = .016) and higher consolidation tumor ratio (CTR) (OR, 110.04; 95% CI, 8.56-1414.39; P < .001) remained independent for invasive adenocarcinomas with poor differentiation. Receiver operating characteristic analyses revealed that solid component size in the mediastinal window (area under the curve [AUC], 0.731; 95% CI, 0.653-0.808; P < .0001) showed a better predictive ability to poor differentiation compared with solid component size in the lung window and CTR. The 5-year recurrence-free survival (RFS) rate of PSNs was worse than that of HGGNs (94.6% vs. 99.1%; P = .019). Multivariate Cox regression revealed that positive lymph node status (hazard ratio, 22.99; 95% CI, 4.52-116.86; P < .001) indicated worse RFS for PSNs.

CONCLUSION

SSNs with solid components in mediastinal window demonstrated clinicopathologic and prognostic features different from those without in clinical stage I lung cancer. Solid components in mediastinal window was a strong predictor of poor differentiation.

Intergroupe francophone de cancérologie thoracique, Société de pneumologie de langue française, and Société d'imagerie thoracique statement paper on lung cancer screening

Following the American National Lung Screening Trial results in 2011 a consortium of French experts met to edit a statement. Recent results of other randomized trials gave the opportunity for our group to meet again in order to edit updated guidelines. After literature review, we provide here a new update on lung cancer screening in France. Notably, in accordance with all international guidelines, the experts renew their recommendation in favor of individual screening for lung cancer in France as per the conditions laid out in this document. In addition, the experts recommend the very rapid organization and funding of prospective studies, which, if conclusive, will enable the deployment of lung cancer screening organized at the national level.

Feasibility of lung imaging with a large field-of-view spectral photon-counting CT system

PURPOSE

The purpose of this study was to characterize the technical capabilities and feasibility of a large field-of-view clinical spectral photon-counting computed tomography (SPCCT) prototype for high-resolution (HR) lung imaging.

MATERIALS AND METHODS

Measurement of modulation transfer function (MTF) and acquisition of a line pairs phantom were performed. An anthropomorphic lung nodule phantom was scanned with standard (120kVp, 62mAs), low (120kVp, 11mAs), and ultra-low (80kVp, 3mAs) radiation doses. A human volunteer underwent standard (120kVp, 63mAs) and low (120kVp, 11mAs) dose scans after approval by the ethics committee. HR images were reconstructed with 1024 matrix, 300mm field of view and 0.25mm slice thickness using a filtered-back projection (FBP) and two levels of iterative reconstruction (iDose 5 and 9). The conspicuity and sharpness of various lung structures (distal airways, vessels, fissures and proximal bronchial wall), image noise, and overall image quality were independently analyzed by three radiologists and compared to a previous HR lung CT examination of the same volunteer performed with a conventional CT equipped with energy integrating detectors (120kVp, 10mAs, FBP).

RESULTS

Ten percent MTF was measured at 22.3lp/cm with a cut-off at 31lp/cm. Up to 28lp/cm were depicted. While mixed and solid nodules were easily depicted on standard and low-dose phantom images, higher iDose levels and slice thicknesses (1mm) were needed to visualize ground-glass components on ultra-low-dose images. Standard dose SPCCT images of in vivo lung structures were of greater conspicuity and sharpness, with greater overall image quality, and similar image noise (despite a flux reduction of 23%) to conventional CT images. Low-dose SPCCT images were of greater or similar conspicuity and sharpness, similar overall image quality, and lower but acceptable image noise (despite a flux reduction of 89%).

CONCLUSIONS

A large field-of-view SPCCT prototype demonstrates HR technical capabilities and high image quality for high resolution lung CT in human.

Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization

The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.

Latest CT technologies in lung cancer screening: protocols and radiation dose reduction

The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in post-processing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols.

Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Evaluation of solid portions in non-small cell lung cancer-the solid part is not always measurable for clinical T factor

BACKGROUND

Solid component size on thin-section computed tomography is used for T-staging according to the eighth edition of the Tumor Node Metastasis classification of lung cancer. However, the feasibility of using the solid component to measure clinical T-factor remains controversial.

METHODS

We evaluated the feasibility of measuring the solid component in 859 tumours, which were suspected cases of primary lung cancers, requiring surgical resection regardless of the procedure or clinical stage. After excluding 126 pure ground-glass opacity tumours and 450 solid tumours, 283 part-solid tumours were analysed to determine the frequency of cases where the measurement of the solid portion was difficult along with the associated cause. Pathological invasiveness was also evaluated.

RESULTS

The solid portion of 10 lesions in 283 part-solid nodules was difficult to measure due to an underlying lung disease (emphysema and pneumonitis). The solid portion of 62 lesions (21.9%) without emphysema and pneumonitis was difficult to measure due to imaging features of the tumours. Among the 62 patients, five had no malignancy and one with a tumour size of 33 mm had nodal metastasis. There were 56 lesions with a tumour size of ≤30 mm, wherein nodal metastases, vascular and/or lymphatic invasions were not observed.

CONCLUSION

For one-fifth of the part-solid tumours, measurement of the solid component was difficult. Moreover, these lesions had low invasiveness, especially in T1. The measurement of the solid portion and the classification of T1 in 1-cm increments may be complex.

PET/MRI assessment of lung nodules in primary abdominal malignancies: sensitivity and outcome analysis

PURPOSE

To evaluate PET/MR lung nodule detection compared to PET/CT or CT, to determine growth of nodules missed by PET/MR, and to investigate the impact of missed nodules on clinical management in primary abdominal malignancies.

METHODS

This retrospective IRB-approved study included [18F]-FDG PET/MR in 126 patients. All had standard of care chest imaging (SCI) with diagnostic chest CT or PET/CT within 6 weeks of PET/MR that served as standard of reference. Two radiologists assessed lung nodules (size, location, consistency, position, and [18F]-FDG avidity) on SCI and PET/MR. A side-by-side analysis of nodules on SCI and PET/MR was performed. The nodules missed on PET/MR were assessed on follow-up SCI to ascertain their growth (≥ 2 mm); their impact on management was also investigated.

RESULTS

A total of 505 nodules (mean 4 mm, range 1-23 mm) were detected by SCI in 89/126 patients (66M:60F, mean age 60 years). PET/MR detected 61 nodules for a sensitivity of 28.1% for patient and 12.1% for nodule, with higher sensitivity for > 7 mm nodules (< 30% and > 70% respectively, p < 0.05). 75/337 (22.3%) of the nodules missed on PET/MR (follow-up mean 736 days) demonstrated growth. In patients positive for nodules at SCI and negative at PET/MR, missed nodules did not influence patients' management.

CONCLUSIONS

Sensitivity of lung nodule detection on PET/MR is affected by nodule size and is lower than SCI. 22.3% of missed nodules increased on follow-up likely representing metastases. Although this did not impact clinical management in study group with primary abdominal malignancy, largely composed of extra-thoracic advanced stage cancers, with possible different implications in patients without extra-thoracic spread.

Metastasis Initiation Precedes Detection of Primary Cancer-Analysis of Metastasis Growth in vivo in a Colorectal Cancer Test Case

Most cases of deaths from colorectal cancer (CRC) result from metastases, which are often still undetectable at disease detection time. Even so, in many cases, shedding is assumed to have taken place before that time. The dynamics of metastasis formation and growth are not well-established. This work aims to explore CRC lung metastasis growth rate and dynamics. We analyzed a test case of a metastatic CRC patient with four lung metastases, with data of four serial computed tomography (CT) scans measuring metastasis sizes while untreated. We fitted three mathematical growth models—exponential, logistic, and Gompertzian—to the CT measurements. For each metastasis, a best-fitted model was determined, tumor doubling time (TDT) was assessed, and metastasis inception time was extrapolated. Three of the metastases showed exponential growth, while the fourth showed logistic restraint of the growth. TDT was around 93 days. Predicted metastasis inception time was at least 4–5 years before the primary tumor diagnosis date, though they did not reach detectable sizes until at least 1 year after primary tumor resection. Our results support the exponential growth approximation for most of the metastases, at least for the clinically observed time period. Our analysis shows that metastases can be initiated before the primary tumor is detectable and implies that surgeries accelerate metastasis growth.

Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916-0.964) and validation set (AUC, 0.946; 95% CI, 0.907-0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume

Purpose

Materials and Methods

Results

Conclusion

Evaluate the performance of four artificial intelligence-aided diagnostic systems in identifying and measuring four types of pulmonary nodules

PURPOSE

This study aims to evaluate the performance of four artificial intelligence-aided diagnostic systems in identifying and measuring four types of pulmonary nodules.

METHODS

Four types of nodules were implanted in a commercial lung phantom. The phantom was scanned with multislice spiral computed tomography, after which four systems (A, B, C, D) were used to identify the nodules and measure their volumes.

RESULTS

The relative volume error (RVE) of system A was the lowest for all nodules, except for small ground glass nodules (SGGNs). System C had the smallest RVE for SGGNs, -0.13 (-0.56, 0.00). In the Bland-Altman test, only systems A and C passed the consistency test, P = 0.40. In terms of precision, the miss rate (MR) of system C was 0.00% for small solid nodules (SSNs), ground glass nodules (GGNs), and solid nodules (SNs) but 4.17% for SGGNs. The comparable system D MRs for SGGNs, SSNs, and GGNs were 71.30%, 25.93%, and 47.22%, respectively, the highest among all the systems. Receiver operating characteristic curve analysis indicated that system A had the best performance in recognizing SSNs and GGNs, with areas under the curve of 0.91 and 0.68. System C had the best performance for SGGNs (AUC = 0.91).

CONCLUSION

Among four types nodules, SGGNs are the most difficult to recognize, indicating the need to improve higher accuracy and precision of artificial systems. System A most accurately measured nodule volume. System C was most precise in recognizing all four types of nodules, especially SGGN.

Late presentation of lung adenocarcinoma in a stable solitary pulmonary nodule: A case presentation and review of the literature

A 67-year-old patient has been followed by our pulmonary clinic for Chronic obstructive pulmonary disease (COPD) and a stable pulmonary nodule. Solitary pulmonary nodule (SPN) was detected on the lung cancer screening by low dose computed tomography (CT) scan of the chest. It remained stable on repeat CT scan at 6, 12 and 24-months interval. Yearly lung cancer low dose CT scans of the chest showed stability of the SPN for 12 years. A mechanical fall necessitating trauma workup unveiled increase in size of the nodule from 4 mm to 11 mm within one year of the previous screening CT chest. Biopsy and Histopathology confirmed the diagnosis of lung adenocarcinoma. The patient then underwent right upper lobectomy followed by chemoradiation therapy. Current guidelines do not recommend follow up for a solitary pulmonary nodules less than 6 mm nodule if it remains stable for 12-24 months. Our case report of the late presentation of lung adenocarcinoma in a stable solitary pulmonary nodule suggests the need to exercise increased caution in the management of incidental pulmonary nodules.

Pearls and pitfalls in lung cancer staging

Lung cancer is the third most common cancer in the UK and is the leading cause of death. Radiology plays a central role in the diagnostic work-up of patients with suspected and known lung cancer. Tumour assessment includes both local staging, as well as distant staging. Local staging objectives include the assessment of technical resectability with regard to the evaluation of tumour size and invasion of surrounding structures. Distant staging objectives aim to identify distant metastasis in lymphatic and extra lymphatic tissues. CT, positron emission tomography/CT, MRI, and ultrasound are routinely used imaging techniques for staging in patients with lung cancer. In this review, we will consider the pitfalls of these examinations that radiologists potentially face during the work-up of patients with lung cancer.

Approach to the Subsolid Nodule

Most focal persistent ground glass nodules (GGNs) do not progress over 10 years. Research suggests that GGNs that do not progress, those that do, and solid lung cancers are fundamentally different diseases, although histologically they seem similar. Surveillance of GGNs to identify those that gradually progress is safe and does not risk losing a window. GGNs with 5 mm solid component or less than 10 mm consolidation (mediastinal and lung windows, respectively, on thin slice CT) are highly curable with resection. The optimal type of resection is unclear; sublobar resection is reasonable but an adequate margin is critically important.

Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening

Lung cancer remains the leading cause of cancer death in the United States. Screening with low-dose computed tomography (LDCT) has been proven to aid in early detection of lung cancer and reduce disease specific mortality. In 2014, the American College of Radiology (ACR) released version 1.0 of the Lung CT Screening Reporting and Data System (Lung-RADS) as a quality tool to standardize the reporting of lung cancer screening LDCT. In 2019, 5 years after the implementation of Lung-RADS version 1.0 the ACR released the updated Lung-RADS version 1.1 which incorporates initial experience with lung cancer screening. In this review, we outline the implications of the changes and additions in Lung-RADS version 1.1 and examine relevant literature for many of the updates. We also highlight several challenges and opportunities as Lung-RADS version 1.1 is implemented in lung cancer screening programs.

Performance of radiomics models for survival prediction in non-small-cell lung cancer: influence of CT slice thickness

OBJECTIVES

To investigate whether CT slice thickness influences the performance of radiomics prognostic models in non-small-cell lung cancer (NSCLC) patients.

METHODS

CT images including 1-, 3-, and 5-mm slice thicknesses acquired from 311 patients who underwent surgical resection for NSCLC between May 2014 and December 2015 were evaluated. Tumor segmentation was performed on CT of each slice thickness and total 94 radiomics features (shape, tumor intensity, and texture) were extracted. The study population was temporally split into development (n = 185) and validation sets (n = 126) for prediction of disease-free survival (DFS). Three radiomics models were built from three different slice thickness datasets (Rad-1, Rad-3, and Rad-5), respectively. Model performance was assessed and compared in three slice thickness datasets and mixed slice thickness dataset using C-indices.

RESULTS

In corresponding slice thickness datasets, the C-indices of Rad-1, Rad-3, and Rad-5 for prediction of DFS were 0.68, 0.70, and 0.68 in the development set, and 0.73, 0.73, and 0.76 in the validation set (p = 0.40-0.89 and 0.27-0.90, respectively). Performance of the models was not significantly changed when they were applied to different slice thicknesses data in the validation set (C-index, 0.73-0.76, 0.72-0.73, 0.75-0.76; p = 0.07-0.92). In the mixed slice thickness dataset, performances of the models were similar to or slightly lower than their performances in the corresponding slice thickness datasets (C-index, 0.72-0.75 vs. 0.73-0.76) in the validation set.

CONCLUSIONS

The performance of radiomics models for predicting DFS in NSCLC patients was not significantly affected by CT slice thickness.

KEY POINTS

• Three radiomics models based on 1-, 3-, and 5-mm CT datasets showed C-indices for predicting disease-free survival of 0.68-0.70 in the development set and 0.73-0.76 in the validation set, without statistical differences (p = 0.27-0.90). • Application of the radiomics models to different slice thickness datasets showed no significant differences in performance between the values in the prediction of disease-free survival (p = 0.07-0.99). • Three radiomics models based on 1-, 3-, and 5-mm CT datasets performed well in mixed slice thickness datasets, showing similar or slightly lower performances.

Risk Factors for The Growth of Residual Nodule in Surgical Patients with Adenocarcinoma Presenting as Multifocal Ground-glass Nodules

PURPOSE

We aim to investigate the risk factors influencing the growth of residual nodule (RN) in surgical patients with adenocarcinoma presenting as multifocal ground-glass nodules (GGNs).

METHOD

From January 2014 to June 2018, we enrolled 238 patients with multiple GGNs in a retrospective review. Patients were categorized into growth group 63 (26.5%), and non-growth group 175 (73.5%). The median follow-up time was 28.2 months (range, 6.3-73.0 months). To obtain the time of RN growth and find the risk factors for growth, data such as age, gender, history of smoking, history of malignancy, type of surgery, pathology and radiological characteristics were analyzed to use Kaplan-Meier method with the log-rank test and Cox regression analysis.

RESULTS

The median growth time of RN was 56.0 months (95% CI, 45.0-67.0 months) in all 238 patients. Roundness (HR 4.62, 95% CI 2.20-9.68), part-solid nodule (CTR ≥ 50%) (HR 4.39, 95% CI 2.29-8.45), vascular convergence sign (HR 2.32, 95% CI 1.36-3.96) of RN, and age (HR 1.04, 95% CI 1.01-1.07) were independent predictors of further nodule growth. However, radiological characteristics and pathology of domain tumour (DT) cannot be used as indicators to predict RN growth.

CONCLUSIONS

RN showed an indolent growth pattern in surgical patients with multifocal GGNs. RN with a higher roundness, presence of vascular convergence sign, more solid component, and in the elder was likely to grow. However, the growth of RN showed no association with the radiological features and pathology of DT.

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as "compatible pairs" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.

Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives

In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).

Benefits of the multiplanar and volumetric analyses of pancreatic cancer using computed tomography

Although pancreatic cancer tumors are irregularly shaped in terms of their three-dimensional (3D) structure, when T staging by imaging results, generally only the axial plane is used to measure the largest tumor diameter. We investigated the size of pancreatic cancer tumors using multi-plane and 3D reconstructed computed tomography (CT) images and investigated their clinical usefulness. Patients who underwent surgery for pancreatic adenocarcinoma were included. We measured the largest diameter of each pancreatic tumor in the axial, coronal, and sagittal planes of CT images. In addition, maximal diameter and cancer volume were measured from 3D images that were constructed using a semi-automated software system. Final data were compared with pathologic examination and the effect of each value on prognosis was analyzed. A total of 183 patients were analyzed. The maximal diameters measured on the axial, coronal, and sagittal planes were 2.9 ± 1.1, 3.2 ± 0.9, and 3.2 ± 1.0 cm, respectively, which were significantly smaller than pathologic results (3.4 ± 1.4 cm, all p<0.05 by paired t-test). The longest diameter among them (3.4 ± 1.1 cm) was nearly similar to the pathologic diameter. Cancer volume measured on 3D images demonstrated a higher area under the receptor operating characteristic curve [0.714, (95% confidence interval: 0.640–0.788)] for predicting early death compared to any unidimensional CT diameters measured. The longest pancreatic tumor diameter measured on multiplanar CT images was most accurate when compared to its corresponding pathologic diameter. Tumor volume had a stronger correlation with overall survival than tumor diameter.

Shades of Gray: Subsolid Nodule Considerations and Management

Subsolid nodules are common on chest CT imaging and may be either benign or malignant. Their varied features and broad differential diagnoses present management challenges. Although subsolid nodules often represent lung adenocarcinomas, other possibilities are common and influence management. Practice guidelines exist for subsolid nodule management for both incidentally and screening-detected nodules, incorporating patient and nodule characteristics. This review highlights the similarities and differences among these algorithms, with the intent of providing a resource for comparison and aid in choosing management options.

Lung cancer screening intervals based on cancer risk

OBJECTIVES

As low-dose CT screening is gaining acceptance, focus is on increasing the efficiency of screening. One major consideration is to reduce the total number of annual rounds by increasing the interval between screening rounds. It has been suggested that longer intervals could be used for individuals who are at lower risk of lung cancer. In this study, we explored whether eligible participants in a program of LDCT screening who are at lower risk of lung cancer have less aggressive cancers than those at higher risk.

METHODS

We retrospectively identified 118 participants in I-ELCAP database between 1992-2019 who had been screened using HIPAA-compliant protocols and had solid lung cancers diagnosed on an annual round of screening, 7-18 months after the prior round. Volume doubling time (VDT) for each cancer was calculated. Estimated risk of developing lung cancer was calculated using PLCO_M2012 model. The strength of the relationship between VDT and individual PLCO_M2012 scores was assessed by Pearson(r) and Spearman (ρ) correlation coefficients.

RESULTS

VDTs were significantly different by cell-type (p < 0.0001); median VDT for small cell was 34.0 days, followed by other cell-types (61.8 days), squamous-cell (73.3 days), and adenocarcinoma (135.7 days). The median VDT for the 78 (66.1 %) Stage I lung cancers was significantly longer than the 40 Stage II + lung cancers (101.4 days vs. 45.5 days, p < 0.0001). None of the established lung cancer risk indicators (age, pack-years of smoking, or PLCO_M2012 scores) were significant predictors of VDT or lung cancer stage.

CONCLUSION

No significant relationship was demonstrated between risk of developing lung cancer (measured by risk models, age or smoking history) and lung cancer aggressiveness (measured by VDT, cell-type and Stage). This suggests that there is no evidence for determining intervals between repeat screenings using risk-based characteristics. It does not, however, exclude the possibility that future models may establish such a relationship.

Growth Assessment of Pulmonary Adenocarcinomas Manifesting as Subsolid Nodules on CT: Comparison of Diameter-Based and Volume Measurements

RATIONALE AND OBJECTIVES

To analyze the performances of diameter-based measurements, either using diameters, or by calculating diameter-based volumes, as compared to volume measurements in assessing growth of pulmonary adenocarcinomas manifesting as subsolid nodules on CT.

MATERIALS AND METHODS

In this IRB-approved, retrospective study, 74 pulmonary adenocarcinomas presenting as subsolid nodules and resected in 69 patients (21 men, 48 women, mean age 70 ± 9 years) were included. Three CTs were available for each patient. Nodule size on each CT was assessed with diameter measurements, calculated volume based on diameter measurements, and measured volume. Nodule growth was defined as an increase of measured volume ≥25% between two sequential CTs. Sensitivity, specificity, accuracy, positive and negative predictive values of diameter-based measurements for growth assessment were calculated. Nodule characteristics were compared with nonparametric tests and analysis of variance.

RESULTS

There were fewer growing nodules during CT1-CT2 interval (n = 22, 30%) than during CT2-CT3 interval (n = 33, 45%, p =.060). Specificity and negative predictive value of diameter-based measurements for growth assessment ranged respectively from 52 to 77% and 81 to 83% between CT1 and CT2, and from 66 to 76% and 79 to 90% between CT2 and CT3. Nongrowing nodules tended to be larger, regardless how size was measured, and some of these differences in size were statistically significant (p =.002 to .046).

CONCLUSION

For pulmonary adenocarcinomas presenting as subsolid nodules on CT, diameter-based assessment of nodule volume is reasonably accurate at confirming a lack of nodule growth but may overestimate actual growth, as compared to growth assessment based on measured volume.

Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data

BACKGROUND

To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation.

METHODS

The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once (YOLO) v3, as a deep learning approach. This method is featured by two distinct properties: (I) an automatic multi-scale feature extractor for nodule feature screening, and (II) a feature-based bounding box generator for nodule localization and diameter estimation. Two independent studies were performed to train and evaluate this CAD method. One study comprised of a computer simulation that utilized computer-based ground truth. In this study, 300 CT scans were simulated by Cardiac-torso (XCAT) digital phantom. Spherical nodules of various sizes (i.e., 3-10 mm in diameter) were randomly implanted within the lung region of the simulated images-the second study utilized human-based ground truth in patients. The CAD method was developed by CT scans sourced from the LIDC-IDRI database. CT scans with slice thickness above 2.5 mm were excluded, leaving 888 CT images for analysis. A 10-fold cross-validation procedure was implemented in both studies to evaluate network hyper-parameterization and generalization. The overall accuracy of the CAD method was evaluated by the detection sensitivities, in response to average false positives (FPs) per image. In the patient study, the detection accuracy was further compared against 9 recently published CAD studies using free-receiver response operating characteristic (FROC) curve analysis. Localization and diameter estimation accuracies were quantified by the mean and standard error between the predicted value and ground truth.

RESULTS

The average results among the 10 cross-validation folds in both studies demonstrated the CAD method achieved high detection accuracy. The sensitivity was 99.3% (FPs =1), and improved to 100% (FPs =4) in the simulation study. The corresponding sensitivities were 90.0% and 95.4% in the patient study, displaying superiority over several conventional and CNN-based lung nodule CAD methods in the FROC curve analysis. Nodule localization and diameter estimation errors were less than 1 mm in both studies. The developed CAD method achieved high computational efficiency: it yields nodule-specific quantitative values (i.e., number, existence confidence, central coordinates, and diameter) within 0.1 s for 2D CT slice inputs.

CONCLUSIONS

The reported results suggest that the developed lung pulmonary nodule CAD method possesses high accuracies of nodule localization and diameter estimation. The high computational efficiency enables its potential clinical application in the future.

Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?

OBJECTIVE

The purpose of this study was to determine how well radiologists could visually detect a change in lung nodule size on the basis of visual image perception alone.

SUBJECTS AND METHODS

Under IRB approval, 109 standard chest CT image series were anonymized and exported from PACS. Nine hundred forty virtual lung nodule pairs (six baseline diameters, six relative volume differences, two nodule types-solid and ground glass-and 14 repeats) were digitally inserted into the chest CT image series (same location, different sizes between the pair). These digitally altered CT image pairs were shown to nine radiologists who were tasked to visually determine which image contained the larger nodule using a two-alternative forced-choice perception experimental design. These data were statistically analyzed using a generalized linear mixed effects model to determine how accurately the radiologists were able to correctly identify the larger nodule.

RESULTS

Nominal baseline nodule diameter, relative volume difference, and nodule type were found to be statistically significant factors (p < 0.001) in influencing the radiologists' accuracy. For solid (ground-glass) nodules, the baseline diameter needed to be at least 6.3 mm (13.2 mm) to be able to visually detect a 25% change in volume with 95 ± 1.4% accuracy. Accuracy was lowest for the nodules with the smallest baseline diameters and smallest relative volume differences. Additionally, accuracy was lower for ground-glass nodules compared to solid nodules.

CONCLUSIONS

Factors that impacted visual size assessment were baseline nodule diameter, relative volume difference, and solid versus non-solid nodule type, with larger and more solid lesions offering a more precise assessment of change.

KEY POINTS

• For solid nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 6.3-mm baseline diameter. • For ground-glass nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 13.2-mm baseline diameter. • Accuracy in detecting a change in nodule size began to stabilize around 90-100% for nodules with larger baseline diameters (> 8 mm for solid nodules, > 12 mm for ground-glass nodules) and larger relative volume differences (>15% for solid nodules, > 25% for ground-glass nodules).

68Ga-FAPI PET/CT Improves Therapeutic Strategy by Detecting a Second Primary Malignancy in a Patient With Rectal Cancer

A 71-year-old man with pathologically confirmed rectal cancer underwent F-FDG PET/CT before radical operation, which showed multiple nodules with low uptake in bilateral pleura and 1 solitary pulmonary nodule with slight uptake in left lung. The subpleural nodule was diagnosed as benign lesion through the biopsy. Ga-labeled fibroblast-activation-protein inhibitor PET/CT was performed for further evaluation, which showed low uptake in bilateral subpleural nodules but focally increased uptake in the nodule of left lung. This nodule was found to be a primary lung adenocarcinoma by the CT-guided biopsy. A diagnosis of rectum and lung double primary malignancies was finally made.