Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence

An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5-1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0-I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma

OBJECTIVES

To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

METHODS

A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors.

RESULTS

The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors.

CONCLUSION

DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors.

CLINICAL RELEVANCE STATEMENT

Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma.

KEY POINTS

• Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study

AIM

To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs).

MATERIALS AND METHODS

This study was conducted from January 2015 to October 2021 at three centres: 355 patients with 355 PSN lung adenocarcinomas who underwent surgical resection were included and classified into the training (n=222) and validation (n=133) cohorts. PSN segmentation on CT images was performed automatically with a commercial deep-learning algorithm, and CT texture features were extracted. The least absolute shrinkage and selection operator was used for feature selection and transformed into a DL-TA score. The combined nomogram that incorporated the DL-TA score and identified clinical-radiological features was developed for the prediction of pathological invasiveness of the PSNs and validated in terms of discrimination and calibration.

RESULTS

The present study generated a combined nomogram for predicting the invasiveness of PSNs that included age, consolidation-to-tumour ratio, smoking status, and DL-TA score, with a C-index of 0.851 (95% confidence interval: 0.826-0.877) for the training cohort and 0.854 (95% confidence interval: 0.817-0.891) for the validation cohort, indicating good discrimination. Furthermore, the model had a Brier score of 0.153 for the training cohort and 0.135 for the validation cohort, indicating good calibration.

CONCLUSION

The developed combined nomogram consisting of the DL-TA score and clinical-radiological features and has the potential to predict the individual risk for the invasiveness of stage IA PSN lung adenocarcinomas.

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Contextualizing the Role of Volumetric Analysis in Pulmonary Nodule Assessment: AJR Expert Panel Narrative Review

Pulmonary nodules are managed on the basis of their size and morphologic characteristics. Radiologists are familiar with assessing nodule size by measuring diameter using manually deployed electronic calipers. Size may also be assessed with 3D volumetric measurements (referred to as volumetry) obtained with software. Nodule size and growth are more accurately assessed with volumetry than on the basis of diameter, and the evidence supporting clinical use of volumetry has expanded, driven by its use in lung cancer screening nodule management algorithms in Europe. The application of volumetry has the potential to reduce recommendations for imaging follow-up of indeterminate solid nodules without impacting cancer detection. Although changes in scanning conditions and volumetry software packages can lead to variation in volumetry results, ongoing technical advances have improved the reliability of calculated volumes. Volumetry is now the primary method for determining size of solid nodules in the European lung cancer screening position statement and British Thoracic Society recommendations. The purposes of this article are to review technical aspects, advantages, and limitations of volumetry and, by considering specific scenarios, to contextualize the use of volumetry with respect to its importance in morphologic evaluation, its role in predicting malignancy in risk models, and its practical impact on nodule management. Implementation challenges and areas requiring further evidence are also highlighted.

A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules

Purpose

This study aims to explore the value of the delta-radiomics (DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSNs).

Methods

A total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analyzed from January 2017 to December 2021. All patients underwent diagnostic noncontrast-enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) algorithm based on the training cohort. The conventional radiomics model based on NCECT was also constructed following the same process for comparison purposes. The prediction performance was assessed using area under the ROC curve (AUC). To provide an easy-to-use tool, a radiomics-based integrated nomogram was constructed and evaluated by integrated discrimination increment (IDI), calibration curves, decision curve analysis (DCA), and clinical impact plot.

Results

The DelRADx signature, which consisted of nine robust selected features, showed significant differences between the AIS/MIA group and IAC group (p &lt; 0.05) in both training and validation sets. The DelRADx signature showed a significantly higher AUC (0.902) compared to the conventional radiomics model based on NCECT (AUC = 0.856) in the validation set. The IDI was significant at 0.0769 for the integrated nomogram compared with the DelRADx signature. The calibration curve of the integrated nomogram demonstrated favorable agreement both in the training set and validation set with a mean absolute error of 0.001 and 0.019, respectively. Decision curve analysis and clinical impact plot indicated that if the threshold probability was within 90%, the integrated nomogram showed a high clinical application value.

Conclusion

The DelRADx method has the potential to assist doctors in predicting the invasiveness for patients with PSNs. The integrated nomogram incorporating the DelRADx signature with the radiographic features could facilitate the performance and serve as an alternative way for determining management.

Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review

Simple Summary

The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers.

Abstract

Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.

Prognostic impact of artificial intelligence-based volumetric quantification of the solid part of the tumor in clinical stage 0-I adenocarcinoma

INTRODUCTION

The size of the solid part of a tumor, as measured using thin-section computed tomography, can help predict disease prognosis in patients with early-stage lung cancer. Although three-dimensional volumetric analysis may be more useful than two-dimensional evaluation, measuring the solid part of some lesions is difficult using this methods. We developed an artificial intelligence-based analysis software that can distinguish the solid and non-solid parts (ground-grass opacity). This software calculates the solid part volume in a totally automated and reproducible manner. The predictive performance of the artificial intelligence software was evaluated in terms of survival or recurrence-free survival.

METHODS

We analyzed the high-resolution computed tomography images of the primary lesion in 772 consecutive patients with clinical stage 0-I adenocarcinoma. We performed automated measurement of the solid part volume using an artificial intelligence-based algorithm in collaboration with FUJIFILM Corporation. The solid part size, the solid part volume based on traditional three-dimensional volumetric analysis, and the solid part volume based on artificial intelligence were compared.

RESULTS

Higher areas under the curve related to the solid part volume were provided by the artificial intelligence-based method (0.752) than by the solid part size (0.722) and traditional three-dimensional volumetric analysis-based method (0.723). Multivariate analysis demonstrated that the solid part volume based on artificial intelligence was independently correlated with overall survival (P = 0.019) and recurrence-free survival (P < 0.001).

CONCLUSION

The solid part volume measured by artificial intelligence was superior to conventional methods in predicting the prognosis of clinical stage 0-I adenocarcinoma.

[Research Progress in 3D-reconstruction Based Imaging Analysis in Partial Solid Pulmonary Nodule]

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features

OBJECTIVE

To evaluate the inter-observer consistency for subsolid pulmonary nodule radiomic features.

MATERIALS AND METHODS

Subsolid nodules were selected by reviewing radiology reports of CT examinations performed December 1, 2015 to April 1, 2016. Patients with CTs at two time points were included in this study. There were 55 patients with subsolid nodules, of whom 14 had two nodules. Of 69 subsolid nodules, 66 were persistent at the second time point, yielding 135 lesions for segmentation. Two thoracic radiologists and an imaging fellow segmented the lesions using a semi-automated volumetry algorithm (Syngo.via Vb20, Siemens). Coefficient of variation (CV) was used to assess consistency of 91 quantitative measures extracted from the subsolid nodule segmentations, including first and higher order texture features. The accuracy of segmentation was visually graded by an experienced thoracic radiologist. Influencing factors on radiomic feature consistency and segmentation accuracy were assessed using generalized estimating equation analyses and the Exact Mann-Whitney test.

RESULTS

Mean patient age was 71 (38-93 years), with 39 women and 16 men. Mean nodule volume was 1.39mL, range .03-48.2mL, for 135 nodules. Several radiomic features showed high inter-reader consistency (CV<5%), including entropy, uniformity, sphericity, and spherical disproportion. Descriptors such as surface area and energy had low consistency across inter-reader segmentations (CV>10%). Nodule percent solid component and attenuation influenced inter-reader variability of some radiomic features. The presence of contrast did not significantly affect the consistency of subsolid nodule radiomic features. Near perfect segmentation, within 5% of actual nodule size, was achieved in 68% of segmentations, and very good segmentation, within 25% of actual nodule size, in 94%. Morphologic features including nodule margin and shape (each p <0.01), and presence of air bronchograms (p = 0.004), bubble lucencies (p = 0.02) and broad pleural contact (p < 0.01) significantly affected the probability of near perfect segmentation. Stroke angle (p = 0.001) and length (p < 0.001) also significantly influenced probability of near perfect segmentation.

CONCLUSIONS

The inter-observer consistency of radiomic features for subsolid pulmonary nodules varies, with high consistency for several features, including sphericity, spherical disproportion, and first and higher order entropy, and normalized non-uniformity. Nodule morphology influences the consistency of subsolid nodule radiomic features, and the accuracy of subsolid nodule segmentation.

Accuracy and Reproducibility of a Software Prototype for Semi-Automated Computer-Aided Volumetry of the solid and subsolid Components of part-solid Pulmonary Nodules

PURPOSE

 To test the accuracy and reproducibility of a software prototype for semi-automated computer-aided volumetry (CAV) of part-solid pulmonary nodules (PSN) with separate segmentation of the solid part.

MATERIALS AND METHODS

 66 PSNs were retrospectively identified in 34 thin-slice unenhanced chest CTs of 19 patients. CAV was performed by two medical students. Manual volumetry (MV) was carried out by two radiology residents. The reference standard was determined by an experienced radiologist in consensus with one of the residents. Visual assessment of CAV accuracy was performed. Measurement variability between CAV/MV and the reference standard as a measure of accuracy, CAV inter- and intra-rater variability as well as CAV intrascan variability between two recontruction kernels was determined via the Bland-Altman method and intraclass correlation coefficients (ICC).

RESULTS

 Subjectively assessed accuracy of CAV/MV was 77 %/79 %-80 % for the solid part and 67 %/73 %-76 % for the entire nodule. Measurement variability between CAV and the reference standard ranged from -151-117 % for the solid part and -106-54 % for the entire nodule. Interrater variability was -16-16 % for the solid part (ICC 0.998) and -102-65 % for the entire nodule (ICC 0.880). Intra-rater variability was -70-49 % for the solid part (ICC 0.992) and -111-31 % for the entire nodule (ICC 0.929). Intrascan variability between the smooth and the sharp reconstruction kernel was -45-39 % for the solid part and -21-46 % for the entire nodule.

CONCLUSION

 Although the software prototype delivered satisfactory results when segmentation is evaluated subjectively, quantitative statistical analysis revealed room for improvement especially regarding the segmentation accuracy of the solid part and the reproducibility of measurements of the nodule's subsolid margins.

KEY POINTS

  · Assessed visually CAV delivers similar accuracy compared to manual volumetry. · Accuracy of CAV was higher for the entire nodule. · Reproducibility was better for the solid part. · Variability between the kernels was higher for the solid part.

Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning

Background

To develop and validate a deep learning-based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs).

Materials and Methods

This study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis.

Results

A total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885-0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers' performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877-0.939), sensitivity of 87.4%, and specificity of 80.8%.

Conclusion

The deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.

Clinical Value and Pathologic Basis of Cystic Airspace Within Subsolid Nodules Confirmed as Lung Adenocarcinomas by Surgery

PURPOSE

To investigate the clinical value and pathologic basis of cystic airspace within lung adenocarcinomas manifesting as subsolid nodules.

PATIENTS AND METHODS

A retrospective study was conducted on a total of 541 surgically confirmed lung adenocarcinomas manifesting as subsolid nodules in computed tomography images, including 87 cases with cystic airspace and 454 cases without cystic airspace. The pathologic characteristics of the cases with and without cystic airspace were compared. The investigation of the pathologic structure of cystic airspace was attempted on the postoperative paraffin sections.

RESULTS

There was a significant difference in the containing of cystic airspace between preinvasive and invasive adenocarcinomas (10.5 vs 26.6%; P < .001). Multivariate analysis indicated that cystic airspace is an independent predictor of invasive adenocarcinomas (odds ratio, 3.220; 95% confidence interval, 1.822-5.687). Nodules containing multiple cystic airspaces are more likely to be invasive adenocarcinomas than nodules with a single cystic airspace (47.1 vs 72.2%; P < .05). On paraffin sections, the walls of the cystic airspace seemed to be mainly composed of atypical hyperplasia and/or tumor cells on the surface and the remaining smooth muscle cells and stroma below, which is similar to the structure of bronchi.

CONCLUSIONS

Cystic airspace may be a reliable predictor of invasive adenocarcinomas, the classification method based on the number of cystic airspaces might be suitable for the computed tomography-based typing of cystic airspace within subsolid nodules. Cystic airspace may derive from the destroyed and enlarged bronchi owing to the growth or infiltration of atypical hyperplasia and/or tumor cells.

Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT

RATIONALE AND OBJECTIVES

To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules.

MATERIALS AND METHODS

We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests.

RESULTS

This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (D_max), the diameter of the solid component (D_solid) and the enhanced monochromatic CT value at 40 keV-190 keV (CT_{40 keV-190 keV}) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, D_max, and CT_{60 keV} values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, D_max, and CT_{60 keV} was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05).

CONCLUSION

The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.

Iodine-related attenuation in contrast-enhanced dual-energy computed tomography in small-sized solid-type lung cancers is associated with the postoperative prognosis

Background

To investigate the correlation between iodine-related attenuation in contrast-enhanced dual-energy computed tomography (DE-CT) and the postoperative prognosis of surgically resected solid-type small-sized lung cancers.

Methods

We retrospectively reviewed the DE-CT findings and postoperative course of solid-type lung cancers ≤3 cm in diameter. After injection of iodinated contrast media, arterial phases were scanned using 140-kVp and 80-kVp tube voltages. Three-dimensional iodine-related attenuation (3D-IRA) of primary tumors at the arterial phase was computed using the “lung nodule” application software. The corrected 3D-IRA normalized to the patient’s body weight and contrast medium concentration was then calculated.

Results

A total of 120 resected solid-type lung cancers ≤3 cm in diameter were selected for analysis (82 males and 38 females; mean age, 67 years). During the observation period (median, 47 months), 32 patients showed postoperative recurrence. Recurrent tumors had significantly lower 3D-IRA and corrected 3D-IRA at early phase compared to non-recurrent tumors (p = 0.046 and p = 0.027, respectively). The area under the receiver operating characteristic curve for postoperative recurrence was 0.624 for the corrected 3D-IRA at early phase (p = 0.025), and the cutoff value was 5.88. Kaplan–Meier curves for disease-free survival indicated that patients showing tumors with 3D-IRA > 5.88 had a significantly better prognosis than those with tumors showing 3D-IRA < 5.88 (p = 0.017).

Conclusions

The 3D-IRA of small-sized solid-type lung cancers on contrast-enhanced DE-CT was significantly associated with postoperative prognosis, and low 3D-IRA tumors showed a higher TNM stage and a significantly poorer prognosis.

The Potential of Computed Tomography Volumetry for the Surgical Treatment in Bilateral Macronodular Adrenal Hyperplasia: A Case Report

Although adrenal resection is a major option to control hypercortisolemia in patients with bilateral macronodular adrenal hyperplasia, a predictive method for postoperative cortisol production has not been established. A 53-year-old man with ulcerative colitis was referred to our hospital for bilateral multiple adrenal nodules and hypertension. Physical and endocrinological examination revealed inappropriate cortisol production and suppressed secretion of adrenocorticotropic hormone with no typical signs of Cushing's syndrome. Imaging analysis revealed bilateral adrenal nodular enlargement, the nodules of which had the radiological features of adrenocortical adenomas without inter-nodular heterogeneity. In addition, computed tomography volumetry demonstrated that the left adrenal gland (70 mL) accounts for three quarters of the total adrenal volume (93 mL). The patient was diagnosed as subclinical Cushing's syndrome due to bilateral macronodular adrenal hyperplasia, and subsequently underwent a left laparoscopic adrenalectomy with the estimation of 75% decrease in the cortisol level based on the adrenal volume. The surgical treatment ultimately resulted in control of the cortisol level within the normal range, which was compatible to our preoperative prediction. However, regardless of the sufficient cortisol level, ulcerative colitis was exacerbated after the surgery, which needed a systemic therapy for remission. This case indicates successful surgical control of hypercortisolemia based on computed tomography volumetry in bilateral macronodular adrenal hyperplasia, as well as the perioperative exacerbation risk for inflammatory diseases in Cushing's syndrome. We report the potential utility of computed tomography volumetry as a quantitative method with retrospective evaluation of our historical cases.

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking. Purpose To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models. Materials and Methods This retrospective multicenter study (https://ClinicalTrials.gov, NCT03872362) included 291 patients (median age, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training (n = 229) and test (n = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric, and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features. Results The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; P = .007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; P = .03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; P = .008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; P = .01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; P = .56). Conclusion Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models. Online supplemental material is available for this article. See also the editorial by Nishino in this issue. Published under a CC BY 4.0 license.

Radiological-pathological correlation of subsolid pulmonary nodules: A single centre retrospective evaluation of the 2011 IASLC adenocarcinoma classification system

INTRODUCTION

The 2011 IASLC classification system proposes guidelines for radiologists and pathologists to classify adenocarcinomas spectrum lesions as preinvasive, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). IA portends the worst clinical prognosis, and the imaging distinction between MIA and IA is controversial.

MATERIALS AND METHODS

Subsolid pulmonary nodules resected by microcoil localization over a three-year period were retrospectively reviewed by three chest radiologists and a pulmonary pathologist. Nodules were classified radiologically based on preoperative computed tomography (CT), with the solid nodule component measured on mediastinal windows applied to high-frequency lung kernel reconstructions, and pathologically according to 2011 IASLC criteria. Radiology interobserver and radiological-pathological variability of nodule classification, and potential reasons for nodule classification discordance were assessed.

RESULTS

Seventy-one subsolid nodules in 67 patients were included. The average size of invasive disease focus at histopathology was 5 mm (standard deviation 5 mm). Radiology interobserver agreement of nodule classification was good (Cohen's Kappa = 0.604, 95 % CI: 0.447 to 0.761). Agreement between consensus radiological interpretation and pathological category was fair (Cohen's Kappa = 0.236, 95 % CI: 0.054-0.421). Radiological and pathological nodule classification were concordant in 52 % (37 of 71) of nodules. The IASLC proposed CT solid component cut-off of 5 mm to distinguish MIA and IA yielded a sensitivity of 59 % and specificity of 80 %. Common reasons for nodule classification discordance included multiple solid components within a nodule on CT, scar and stromal collapse at pathology, and measurement variability.

CONCLUSION

Solid component(s) within persistent part-solid pulmonary nodules raise suspicion for invasive adenocarcinoma. Preoperative imaging classification is frequently discordant from final pathology, reflecting interpretive and technical challenges in radiological and pathological analysis.

CT Volumetry and Basic Texture Analysis as Surrogate Markers in Advanced Non-small-cell Lung Cancer

INTRODUCTION

We evaluated volumetric tumor measurements and computed tomography texture analysis as prognostic indicators in patients with advanced non-small-cell lung cancer when compared with the unidimensional tumor size measurements used in Response Evaluation Criteria in Solid Tumors (RECIST).

PATIENTS AND METHODS

In a retrospective review, computed tomography examinations in 77 patients with advanced non-small-cell lung cancer were evaluated before and after 2 cycles of chemotherapy. Baseline and changes in tumor diameter, volume, and texture were analyzed. Survival was analyzed with Cox regression analysis and Kaplan-Meier survival statistics.

RESULTS

Cox regression analysis demonstrated that only change in tumor volume (exp(B) = 1.006; P = .02) and the initial sum of the largest target lesion diameters predicted survival (exp(B) = 1.013; P = .02). Kaplan-Meier statistics demonstrated that patients with an initial sum of the largest target lesion diameters less than 88 mm had median survival time of 587 days (95% confidence interval [CI], 269-905 days), compared with the survival of those with larger tumor burden of 407 days (95% CI, 235-579 days). Patients in whom tumor volume decreased by more than 29% had a median survival time of 622 days (95% CI, 448-796 days), compared with 305 days for those with less decrease (95% CI, 34-240 days).

CONCLUSION

This study demonstrates that change in lung tumor volume is a better marker of patient survival than change of unidimensional diameter measurements in our cohort. If confirmed in larger studies, this suggests that volumetry might improve clinical decision-making for individual patients and allow for faster assessment of new treatments.

Propensity-Matched Analysis Comparing Survival After Sublobar Resection and Lobectomy for cT1N0 Lung Adenocarcinoma

BACKGROUND

The optimal surgical method for cT1N0 lung adenocarcinoma remains controversial.

OBJECTIVE

The aim of this study was to evaluate the differences in clinical outcomes of sublobar resection and lobectomy for cT1N0 lung adenocarcinoma patients.

METHODS

We included 1035 consecutive patients with cT1N0 lung adenocarcinoma who underwent surgery at our institute from January 2011 to December 2016. The surgical approach, either sublobar resection or lobectomy, was determined at the discretion of each surgeon. A propensity-matched analysis incorporating total tumor diameter, solid component diameter, consolidation-to-tumor (C/T) ratio, and performance status was used to compare the clinical outcomes of the sublobar resection and lobectomy groups.

RESULTS

Sublobar resection and lobectomy were performed for 604 (58.4%; wedge resection/segmentectomy: 470/134) and 431 (41.6%) patients, respectively. Patients in the sublobar resection group had smaller total tumor diameters, smaller solid component diameters, lower C/T ratios, and better performance status. More lymph nodes were dissected in the lobectomy group. Patients in the sublobar resection group had better perioperative outcomes. A multivariable analysis revealed that the solid component diameter and serum carcinoembryonic antigen level are independent risk factors for tumor recurrence. After propensity matching, 284 paired patients in each group were included. No differences in overall survival (OS; p = 0.424) or disease-free survival (DFS; p = 0.296) were noted between the two matched groups.

CONCLUSIONS

Sublobar resection is not inferior to lobectomy regarding both DFS and OS for cT1N0 lung adenocarcinoma patients. Sublobar resection may be a feasible surgical method for cT1N0 lung adenocarcinoma.

Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Thin-section computed tomography (TSCT) imaging biomarkers are uncertain to distinguish progressive adenocarcinoma from benign lesions in pGGNs. The purpose of this study was to evaluate the usefulness of TSCT characteristics for differentiating among transient (TRA) lesions, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) presenting as pure ground-glass nodules (pGGNs). Between January 2016 and January 2018, 255 pGGNs, including 64 TRA, 22 AAH, 37 AIS, 108 MIA and 24 IAC cases, were reviewed on TSCT images. Differences in TSCT characteristics were compared among these five subtypes of pGGNs. Logistic analysis was performed to identify significant factors for predicting MIA and IAC. Progressive pGGNs were more likely to be round or oval in shape, with clear margins, air bronchograms, vascular and pleural changes, creep growth, and bubble-like lucency than were non-progressive pGGNs. The optimal cut-off values of the maximum diameter for differentiating non-progressive from progressive pGGNs and IAC from non-IAC were 6.5 mm and 11.5 mm, respectively. For the prediction of IAC vs. non-IAC and non-progressive vs. progressive adenocarcinoma, the areas under the receiver operating characteristics curves were 0.865 and 0.783 for maximum diameter and 0.784 and 0.722 for maximum CT attenuation, respectively. The optimal cut-off values of maximum CT attenuation were -532 HU and -574 HU for differentiating non-progressive from progressive pGGNs and IAC from non-IAC, respectively. Maximum diameter, maximum attenuation and morphological characteristics could help distinguish TRA lesions from MIA and IAC but not from AAH. So, CT morphologic characteristics, diameter and attenuation parameters are useful for differentiating among pGGNs of different subtypes.

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.

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.

Postoperative recurrence of clinical early-stage non-small cell lung cancers: a comparison between solid and subsolid nodules

Background

For subsolid non-small cell lung cancers (NSCLCs), solid size (SS), which is the maximal diameter of the solid component, correlates more accurately with tumor prognosis than the total size, which is the maximal diameter of the entire tumor, including ground-glass opacity. We reviewed the propriety of the TNM staging based on the SS for early-stage NSCLCs.

Methods

We retrospectively reviewed the preoperative radiological reports, clinical records, and pathological reports of NSCLC cases in our hospital between 2010 and 2013, and clinical stage (c-Stage) 0 and I tumors were selected. Disease-free survival (DFS), based on survival analysis, was used to assess the tumor characteristics that predicted the prognosis.

Results

A total of 247 NSCLC diagnoses in 231 patients (88 women and 143 men; age, 67 ± 7 years) were included in our cohort. They were classified into solid (n = 131) and subsolid (n = 116) nodules. The DFS curves indicated that prognosis was significantly worse in the following order: c-Stage 0, c-Stage IA, and c-Stage IB tumors (p = 0.016). Patients with solid nodules showed a significantly worse prognosis than patients with subsolid nodules (p < 0.001). A multivariate Cox proportional hazards model showed that the significant predictive factors for DFS were c-Stage (hazard ratio, 1.600; p = 0.020) and solid nodules (hazard ratio, 3.077; p = 0.031).

Conclusions

For early-stage NSCLCs, the c-Stage based on the SS in subsolid nodules was useful for predicting postoperative DFS. In addition, whether nodules were solid or subsolid was another independent prognostic factor.

The "solid" component within subsolid nodules: imaging definition, display, and correlation with invasiveness of lung adenocarcinoma, a comparison of CT histograms and subjective evaluation

OBJECTIVE

To validate three proposed definitions of the "solid" component of subsolid nodules, as compared to CT histograms and the use of different window settings, for discriminating the invasiveness of adenocarcinomas in a manner that facilitates routine clinical assessment.

METHODS

We retrospectively analyzed 328 pathologically confirmed lung adenocarcinomas, manifesting as subsolid nodules. Three-dimensional CT histograms were generated by setting 11 CT attenuation intervals from - 400 to 50 HU, at 50 HU intervals, and the voxel percentage within each CT attenuation interval was generated automatically. Three definitions of the "solid" component were proposed, and 10 medium window settings were set to evaluate the "solid" component. The diagnostic performance of the three definitions for identifying invasive adenocarcinoma was compared with that of CT histogram analysis and subjective evaluation with medium window settings.

RESULTS

A parallel diagnosis using five intervals with the largest AUC (AUC ≥ 0.797) demonstrated good differential diagnostic performance, with 78% sensitivity and 73.7% specificity. Definition 2 (visibility in the mediastinum window) yielded higher accuracy (75.6%) than the other two definitions (p < 0.01). A medium window setting of - 50 WL/2 WW gave a larger AUC than the other nine medium window settings as well as definition 2, with 82.5% specificity and 88.5% PPV, which was higher than those of parallel diagnosis with CT histogram and definition 2.

CONCLUSION

Using - 50 WL/2 WW is the optimum approach for evaluating the "solid" component and discriminating invasiveness, superior to using 3D CT histograms and definition 2, and convenient in routine clinical assessment.

KEY POINTS

• - 50 WL/2 WW gave a larger AUC than definition 2. • The specificity of - 50 WL/2 WW was higher than CT histograms. • - 50 WL/2 WW offers the best evaluation of the solid component.

Implication of total tumor size on the prognosis of patients with clinical stage IA lung adenocarcinomas appearing as part-solid nodules: Does only the solid portion size matter?

OBJECTIVES

The aim was to investigate the effect of clinico-radiologic variables, including total tumor (T_total) size and clinical T category, on the prognosis of patients with stage IA (T1N0M0) lung adenocarcinomas appearing as part-solid nodules (PSNs).

METHODS

This institutional review board-approved retrospective study included 506 patients (male:female = 200:306; median age, 62 years) with PSNs of the adenocarcinoma spectrum in clinical stage IA who underwent standard lobectomy at a single tertiary medical center. Prognostic stratification of the patients in terms of disease-free survival was analyzed with variables including age, sex, T_total size, solid portion size, clinical T category, and tumor location using univariate and subsequent multivariate Cox regression analysis. Subgroup analysis was performed to reveal the effect of the T_total size at each clinical T category.

RESULTS

Multivariate Cox regression analysis demonstrated that T_total size*cT1b [interaction term; hazard ratio (HR) = 1.091; 95% confidence interval (CI): 1.015, 1.173; p = 0.019] and cT1c (HR = 68.436; 95% CI: 2.797, 1674.415; p = 0.010) were independent risk factors for the tumor recurrence. When patients with cT1b were dichotomized based on a T_total size cutoff of 3.0 cm, PSNs with T_total > 3.0 cm showed a significantly worse outcome (HR = 3.796; 95% CI: 1.006, 14.317; p = 0.049). No significant difference was observed in the probability of recurrence between cT1b with T_total > 3.0 cm and cT1c (p = 0.915).

CONCLUSIONS

T_total size is a significant prognostic factor in adenocarcinoma patients in cT1b without lymph node or distant metastasis. PSNs in cT1b with T_total > 3.0 cm have a comparable risk of lung cancer recurrence to those in cT1c.

KEY POINTS

• Current T descriptor was a powerful prognostic factor in stage IA adenocarcinomas appearing as part-solid nodules. • Total tumor size further stratified risk of recurrence of adenocarcinomas in cT1b. • Upstaging of tumors in cT1b with total tumor size > 3.0 cm may be more appropriate.