Effect of computed tomography window settings and reconstruction plane on 8th edition T-stage classification in patients with lung adenocarcinoma manifesting as a subsolid nodule

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.

Comparison of radiological and pathological tumor sizes in resected non-small cell lung cancer

OBJECTIVES

In non-small cell lung cancer (NSCLC), T factor plays an important role in determining staging. The present study aimed to determine the validity of preoperative evaluation of clinical T (cT) factor by comparing radiological and pathological tumor sizes.

METHODS

Data for 1,799 patients with primary NSCLC who underwent curative surgery were investigated. The concordance between cT and pathological T (pT) factors was analyzed. Furthermore, we compared groups with an increase or decrease of ≥ 20% and groups with an increase or decrease of < 20% in the size change between preoperative radiological and pathological diameters.

RESULTS

The mean sizes of the radiological solid components and the pathological invasive tumors were 1.90 cm and 1.99 cm, respectively, correlation degree = 0.782. The group with increased pathological invasive tumor size (≥ 20%) compared with the radiologic solid component was significantly more likely female, consolidation tumor ratio (CTR) ≤ 0.5, and within cT1. Multivariate logistic analysis identified CTR < 1, cT ≤ T1, and adenocarcinoma as independent risk factors for increased pT factor.

CONCLUSION

The radiological invasive area of tumors with cT1, CTR < 1, or adenocarcinoma on preoperative CT may be underestimated compared with pathological invasive diameter.

Lung Cancer Staging: Imaging and Potential Pitfalls

Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.

Differentiation Between Heterogeneous GGN and Part-Solid Nodule Using 2 D Grayscale Histogram Analysis of Thin-Section CT Image

INTRODUCTION/BACKGROUND

To evaluate cases of surgically resected pulmonary adenocarcinoma (Ad) with heterogenous ground-glass nodules (HGGNs) or part-solid nodules (PSNs) and to clarify the differences between them, and between invasive adenocarcinoma (IVA) and minimally invasive adenocarcinoma (MIA) + adenocarcinoma in situ (AIS) using grayscale histogram analysis of thin-section computed tomography (TSCT).

MATERIALS AND METHODS

241 patients with pulmonary Ad were retrospectively classified into HGGNs and PSNs on TSCT by three thoracic radiologists. Sixty HGGNs were classified into 17 IVAs, 26 MIAs, and 17 AISs. 181 PSNs were classified into 114 IVAs, 55 MIAs, and 12 AISs.

RESULTS

We found significant differences in area (P = 0.0024), relative size of solid component (P <0.0001), circumference (P <0.0001), mean CT value (P <0.0001), standard deviation of the CT value (P <0.0001), maximum CT value (P <0.0001), skewness (P <0.0001), kurtosis (P <0.0001), and entropy (P <0.0001) between HGGNs and PSNs. In HGGNs, we found significant differences in relative size of solid component (P <0.0001), mean CT value (P = 0.0005), standard deviation of CT value (P = 0.0071), maximum CT value (P = 0.0237), and skewness (P = 0.0027) between IVAs and MIA+AIS lesions. In PSNs, we found significant differences in area (P = 0.0029), relative size of solid component (P = 0.0003), circumference (P = 0.0004), mean CT value (P = 0.0011), skewness (P = 0.0009), and entropy (P = 0.0002) between IVAs and the MIA+AIS lesions.

CONCLUSION

Quantitative evaluations using grayscale histogram analysis can clearly distinguish between HGGNs and PSNs, and may be useful for estimating the pathology of such lesions.

Excellent survival of pathological N0 small cell lung cancer patients following surgery

BACKGROUND

Current clinical guidelines recommend surgery only for cT1-2N0M0 small cell lung cancer (SCLC) patients. In light of recent studies, the role of surgery in the treatment of SCLC needs to be reconsidered.

METHODS

We reviewed all SCLC patients who underwent surgery from November 2006 to April 2021. Clinicopathological characteristics were retrospectively collected from medical records. Survival analysis was performed by the Kaplan-Meier method. Independent prognostic factors were evaluated by Cox proportional hazard model.

RESULTS

196 SCLC patients undergoing surgical resection were enrolled. The 5-year overall survival for the entire cohort was 49.0% (95% CI: 40.1-58.5%). PN0 patients had significantly superior survival to pN1-2 patients (p < 0.001). The 5-year survival rate of pN0 and pN1-2 patients were 65.5% (95% CI: 54.0-80.8%) and 35.1% (95% CI: 23.3-46.6%), respectively. Multivariate analysis revealed that smoking, older age, and advanced pathological T and N stages were independently associated with poor prognosis. Subgroup analyses demonstrated similar survival among pN0 SCLC patients regardless of pathological T stages (p = 0.416). Furthermore, multivariate analysis showed factors, including age, smoking history, type of surgery, and range of resection, were not independently prognostic factors for the pN0 SCLC patients.

CONCLUSION

Pathological N0 stage SCLC patients have significantly superior survival to pN1-2 patients, regardless of features, including T stage. Thorough preoperative evaluation should be applied to estimate the status of lymph node involvement to achieve better selection of patients who might be candidate for surgery. Studies with larger cohort might help verify the benefit of surgery, especially for T3/4 patients.

Surgically resected lung adenocarcinoma: do heterogeneous GGNs and part-solid nodules on thin-section CT show different prognosis?

PURPOSE

This study aimed to evaluate the clinical courses of patients with surgically resected stage IA pulmonary adenocarcinoma (Ad) who exhibited heterogeneous ground-glass nodules (GGNs) or part-solid nodules on thin-section computed tomography (TSCT) and to clarify the prognostic differences between them.

MATERIALS AND METHODS

The cases of 242 patients with proven pulmonary Ad with heterogeneous GGN or part-solid nodule who underwent surgical resection were retrospectively reviewed. After surgery, they were examined pathologically. Disease-free survival (DFS) and overall survival (OS) were also investigated.

RESULTS

There were no cases of recurrent pulmonary Ad or death from the primary disease in the heterogeneous GGN group. In the part-solid nodule group, recurrent pulmonary Ad and death from the primary disease were observed in 12 and 6 of 181 patients, respectively. Heterogeneous GGNs were associated with significantly longer DFS than part-solid nodules (p = 0.042). While, there was no significant difference in OS between the two groups (p = 0.134). Pathological diagnoses were available for all 242 patients. 181 part-solid nodules were classified into 116 invasive Ads, 54 minimally invasive Ads (MIAs), and 11 Ad in situ (AIS) lesions, and 61 heterogeneous GGNs were classified into 18 invasive Ads, 25 MIAs, and 18 AIS lesions.

CONCLUSION

Heterogeneous GGNs were significantly associated with longer DFS than part-solid nodules. Pathologically, there were significant differences between the heterogeneous GGNs and part-solid nodules.

Tumor size, but not consolidation-to-tumor ratio, is an independent prognostic factor for part-solid clinical T1 non-small cell lung cancer

BACKGROUND

Tumor size and consolidation-to-tumor ratio (CTR) are crucial for non-small cell lung cancer (NSCLC) prognosis. However, the optimal CTR cutoff remains unclear. Whether tumor size and CTR are independent prognostic factors for part-solid NSCLC is under debate. Here, we aimed to evaluate the prognostic impacts of CTR and tumor size on NSCLC, especially on part-solid NSCLC.

METHODS

We reviewed 1366 clinical T1 NSCLC patients who underwent surgical treatment. Log-rank test and Cox regression analyses were adopted for prognostic evaluation. The "surv_cutpoint" function was used to identify the optimal CTR and tumor size cutoff values.

RESULTS

There were 416, 510, and 440 subjects with pure ground-glass opacity (pGGO), part-solid, and pure solid nodules. The 5-year overall survival (disease-free survival) for patients with pGGO, part-solid, and pure solid nodules were 99.5% (99.5%), 97.3% (95.8%), and 90.4% (78.9%), respectively. Multivariate Cox regression analysis indicated that CTR was an independent prognostic factor for the whole patients, and the optimal CTR cutoff was 0.99. However, for part-solid NSCLC, CTR was not independently associated with survival, even if categorized by the optimal cutoffs. The predicted optimal cutoffs of total tumor size and solid component size were 2.4 and 1.4 cm for part-solid NSCLC. Total tumor size (HR = 6.21, 95% CI: 1.58-24.34, p = 0.009) and solid component size (HR = 2.27, 95% CI: 1.04-5.92, p = 0.045) grouped by the cutoffs were significantly associated with part-solid NSCLC prognosis.

CONCLUSIONS

CTR was an independent prognostic factor for the whole NSCLC, but not for the part-solid NSCLC. Tumor size was still meaningful for part-solid NSCLC.

Tumor size in patients with severe pulmonary emphysema might be underestimated on preoperative CT

OBJECTIVES

To evaluate the effect of emphysema on tumor diameter measured on preoperative computed tomography (CT) images versus pathological specimens.

MATERIALS AND METHODS

We investigated patients who underwent primary lung cancer surgery: 55 patients (57 tumors) with severe emphysema and 57 patients (57 tumors) without emphysema. The tumor diameters measured in the postoperative pathological specimens were compared with those measured on the axial CT images and on multiplanar reconstruction (MPR) CT images by two independent radiologists; a subgroup analysis according to tumor size was also performed. A paired or unpaired t test was performed, depending on the tested subjects.

RESULTS

In the emphysema group, the mean axial CT diameter was significantly smaller than the mean pathological diameter (p = 0.025/0.001 for reader 1/2), whereas in the non-emphysema group, the mean axial CT diameter was not significantly different from the pathological one for both readers. The difference between CT axial diameter and pathological diameter (= CT diameter - pathological diameter) was significantly smaller (i.e., had a stronger tendency toward underestimation on radiological measurements) in the emphysema group compared with the non-emphysema group (p = 0.014/0.008 for reader 1/2), and the difference was significantly smaller in tumors sized > 30 mm than tumors sized ≤ 20 mm in both groups.

CONCLUSIONS

Tumor size is significantly smaller on preoperative CT in patients with severe emphysema compared to patients without emphysema, especially in the case of large tumors. MPR measurement using the widest of three dimensions should be used to select T-stage for patients with severe emphysema.

KEY POINTS

• The presence of emphysema affects the accuracy of tumor size measurements on CT. • Compared to patients without emphysema, the tumor size in severe emphysema patients tends to be measured smaller in preoperative CT than the pathological specimen. • This trend is more evident when large tumors are measured on axial CT images alone.

Clinical Non-Small Cell Lung Cancer Staging and Tumor Length Measurement Results From U.S. Cancer Hospitals

RATIONALE AND OBJECTIVES

Examine the accuracy of clinical non-small cell lung cancer staging and tumor length measurements, which are critical to prognosis and treatment planning.

MATERIALS AND METHODS

Compare clinical and pathological staging and lengths using 10,320 2016 National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) and 559 2010-2018 non-SEER single-institute surgically-treated cases, and analyze modifiable causes of disagreement.

RESULTS

The SEER clinical and pathological group-stages agree only 62.3% ± 0.9% over all stage categories. The lymph node N-stage agrees much better at 83.0% ± 1.0%, but the tumor length-location T-stage agrees only 57.7% ± 0.8% with approximately 29% of the cases having a greater pathology than clinical T-stage. Individual T-stage category agreements with respect to the number of pathology cases are Tis, T1a, T1b, T2a, T2b, T3, T4: 89.9% ± 10.0%; 78.7% ± 1.7%; 51.8% ± 1.9%; 46.1% ± 1.3%; 40.5% ± 3.1%; 44.1% ± 2.2%; 56.4% ± 4.7%, respectively. Most of the single-institute results statistically agree with SEER's. Excluding Tis cases, the mean difference in SEER tumor length is ∼1.18 ± 9.26 mm (confidence interval: 0.97-1.39 mm) with pathological lengths being longer than clinical lengths except for small tumors; the two measurements correlate well (Pearson-r >0.87, confidence interval: 0.86-0.87). Reasons for disagreement include the use of family-category descriptors (e.g., T1) instead of their subcategories (e.g., T1a and T1b), which worsens the T-stage agreement by over 15%. Disagreement is also associated with higher tumor grade, larger resected specimens, higher N-stage, patient age, and periodic biases in clinical and pathological tumor size measurements.

CONCLUSIONS

By including preliminary non-small cell lung cancer clinical stage values in their evaluation, diagnostic radiologists can improve the accuracy of staging and standardize tumor-size measurements, which improves patient care.

Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination

Background The solid portion size of lung cancer lesions manifesting as subsolid lesions is key in their management, but the automatic measurement of such lesions by means of a deep learning (DL) algorithm needs evaluation. Purpose To evaluate the performance of a commercially available DL algorithm for automatic measurement of the solid portion of surgically proven lung adenocarcinomas manifesting as subsolid lesions. Materials and Methods Surgically proven lung adenocarcinomas manifesting as subsolid lesions on CT images between January 2018 and December 2018 were retrospectively included. Five radiologists independently measured the maximal axial diameter of the solid portion of lesions. The DL algorithm automatically segmented and measured the maximal axial diameter of the solid portion. Reader measurements, software measurements, and invasive component size at pathologic examination were compared by using intraclass correlation coefficient (ICC) and Bland-Altman plots. Results A total of 448 patients (mean age, 63 years ± 10 [standard deviation]; 264 women) with 448 lesions were evaluated (invasive component size, 3-65 mm). The measurement agreements between each radiologist and the DL algorithm were very good (ICC range, 0.82-0.89). When a radiologist was replaced with the DL algorithm, the ICCs ranged from 0.87 to 0.90, with an ICC of 0.90 among five radiologists. The mean difference between the DL algorithm and each radiologist ranged from -3.7 to 1.5 mm. The widest 95% limit of agreement between the DL algorithm and each radiologist (-15.7 to 8.3 mm) was wider than pairwise comparisons of radiologists (-7.7 to 13.0 mm). The agreement between the DL algorithm and invasive component size at pathologic evaluation was good, with an ICC of 0.67. Measurements by the DL algorithm (mean difference, -6.0 mm) and radiologists (mean difference, -7.5 to -2.3 mm) both underestimated invasive component size. Conclusion Automatic measurements of solid portions of lung cancer manifesting as subsolid lesions by the deep learning algorithm were comparable with manual measurements and showed good agreement with invasive component size at pathologic evaluation. © RSNA, 2021 Online supplemental material is available for this article.

Clinical T category for lung cancer staging: A pragmatic approach for real-world practice

BACKGROUND

To determine which components should be measured and which window settings are appropriate for computerized tomography (CT) size measurements of lung adenocarcinoma (ADC) and to explore interobserver agreement and accuracy according to the eighth edition of TNM staging.

METHODS

A total of 165 patients with surgically resected lung ADC earlier than stage 3A were included in this study. One radiologist and two pulmonologists independently measured the total and solid sizes of components of tumors on different window settings and assessed solidity. CT measurements were compared with pathologic size measurements.

RESULTS

In categorizing solidity, 25% of the cases showed discordant results among observers. Measuring the total size of a lung adenocarcinoma predicted pathologic invasive components to a degree similar to measuring the solid component. Lung windows were more accurate (intraclass correlation [ICC] = 0.65-0.81) than mediastinal windows (ICC = 0.20-0.72) at predicting pathologic invasive components, especially in a part-solid nodule. Interobserver agreements for measurement of solid components were good with little significant difference (lung windows, ICC = 0.89; mediastinal windows, ICC = 0.91). A high level of interobserver agreement was seen between the radiologist and pulmonologists and between residents (from the division of pulmonology and critical care) versus a fellow (from the division of pulmonology and critical care) on different windows.

CONCLUSIONS

A considerable percentage (25%) of discrepancies was encountered in categorizing the solidity of lesions, which may decrease the accuracy of measurements. Lung window settings may be superior to mediastinal windows for measuring lung ADCs, with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Lung window settings are better for evaluating part-solid lung adenocarcinoma (ADC), with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. The considerable percentage (25%) of discrepancies in categorizing solidity of the lesions may also have decreased the accuracy of measurements.

WHAT THIS STUDY ADDS

For accurate measurement and categorization of lung ADC, robust quantitative analysis is needed rather than a simple visual assessment.

CT Evaluation for Clinical Lung Cancer Staging: Do Multiplanar Measurements Better Reflect Pathologic T-Stage than Axial Measurements?

Objective

To retrospectively investigate whether tumor size assessment on multiplanar reconstruction (MPR) CT images better reflects pathologic T-stage than evaluation on axial images and evaluate the additional value of measurement in three-dimensional (3D) space.

Materials and Methods

From 1661 patients who had undergone surgical resection for primary lung cancer between June 2013 and November 2016, 210 patients (145 men; mean age, 64.4 years) were randomly selected and 30 were assigned to each pathologic T-stage. Two readers independently measured the maximal lesion diameters on MPR CT. The longest diameters on 3D were obtained using volume segmentation. T-stages determined on CT images were compared with pathologic T-stages (overall and subgroup—Group 1, T1a/b; Group 2, T1c or higher), with differences in accuracy evaluated using McNemar's test. Agreement between readers was evaluated with intraclass correlation coefficients (ICC).

Results

The diagnostic accuracy of MPR measurements for determining T-stage was significantly higher than that of axial measurement alone for both reader 1 (74.3% [156/210] vs. 63.8% [134/210]; p = 0.001) and reader 2 (68.1% [143/210] vs. 61.9% [130/210]; p = 0.049). In the subgroup analysis, diagnostic accuracy with MPR diameter was significantly higher than that with axial diameter in only Group 2 (p < 0.05). Inter-reader agreements for the ICCs on axial and MPR measurements were 0.98 and 0.98. The longest diameter on 3D images showed a significantly lower performance than MPR, with an accuracy of 54.8% (115/210) (p < 0.05).

Conclusion

Size measurement on MPR CT better reflected the pathological T-stage, specifically for T1c or higher stage lung cancer. Measurements in a 3D plane showed no added value.

Efficacy of measuring the invasive diameter of lung adenocarcinoma using mediastinal window settings: A retrospective study

The recently published 8th edition of the tumor node and metastasis Classification of Lung Cancer proposes using the maximum dimension of the solid component of a ground glass nodule (GGN) for the T categorization. However, few studies have investigated the collection of this information when using mediastinal window settings. In this study, we evaluated tumor measurement data obtained from computed tomography (CT) scans when using mediastinal window settings.This study included 202 selected patients with persistent, partly solid GGNs detected on thin-slice CT after surgical treatment between 2004 and 2013. We compared the differences in tumor diameters measured by 2 different radiologists using a repeated-measures analysis of variance. We divided the patients into 2 groups based on the clinical T stage (T1a+T1b vs T1c) and estimated the probability of overall survival (OS) and disease-free survival (DFS) using Kaplan-Meier curves.The study included 94 male and 108 female patients. The inter-reviewer differences between tumor diameters were significantly smaller when the consolidation to maximum tumor diameter ratio was ≤0.5. The 2 clinical groups classified by clinical T stage differed significantly with respect to DFS when using the mediastinal window settings. However, no significant differences in OS or DFS were observed when using the lung window setting.Our study yielded 2 major findings. First, the diameters of GGNs could be measured more accurately using the mediastinal window setting. Second, measurements obtained using the mediastinal window setting more clearly depicted the effect of clinical T stage on DFS.

CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm²; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm²), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

External validation of the eighth edition of the TNM classification for lung cancer in 3,611 surgically treated patients at a single institution

BACKGROUND

A new revision of the tumor, node, metastasis (TNM) classification for lung cancer has been proposed by the International Association for the Study of Lung Cancer (IASLC), but external validation for it is required. This study aimed to evaluate stage groupings in the 8th edition of the TNM classification in an independent Chinese cohort.

METHODS

We retrospectively analyzed 3,611 patients who were diagnosed as stage I to IV non-small cell lung cancer (NSCLC) and who received surgical treatment at our institute in China between October 2009 and August 2017. Long-rank tests were used to compare survival between two adjacent stage groups.

RESULTS

Based on the 8th edition of the TNM classification, differences between every 2 adjacent stage groups were found to be significant except between Ia1 and Ia2 (P=0.062), and between IIIc and IVa (P=0.063). Significant differences were found between every 2 adjacent categories stratified by the T and N descriptors. Additionally, significant differences were found between M0 and M1a (P<0.001), while no significant difference was observed between M1a and M1b (P=0.092).

CONCLUSIONS

Our study provides an external validation of the stage groupings in the 8th edition of the TNM staging system in surgically treated Chinese patients with NSCLC.

New T1 classification

The IASLC staging and Prognostic Factor Committee proposed new changes to the descriptors for the 8th edition of the Tumour Node Metastasis Staging for Lung Cancer. The T1 descriptor changes include (1) T1 tumours are subclassified into T1a (< 1 cm), T1b (> 1 to < 2 cm), T1c (> 2 to < 3 cm). The corresponding changes are introduced to the overall staging: T1aN0M0 = Stage IA1; T1bN0M0 = Stage IA2; T1cN0M0 = Stage IA3. (2) The introduction of the pathological entities Adenocarcinoma-In-Situ (AIS), Minimally Invasive Adenocarcinoma, and Lepidic Predominant Adenocarcinoma. The corresponding changes on the T descriptor are as follows: Adenocarcinoma-in situ is coded as Tis (AIS); Minimally Invasive Adenocarcinoma is coded as T1a(mi). In this review, the basis for these changes will be described, and the implications on clinical practice will be discussed.

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

Background

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

Methods

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

Results

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

Conclusions

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

Effect of CT window settings on size measurements of the solid component in subsolid nodules: evaluation of prediction efficacy of the degree of pathological malignancy in lung adenocarcinoma

OBJECTIVE

To investigate the predictive value of size measurements of the solid components in pulmonary subsolid nodules with different CT window settings and to evaluate the degree of pathological malignancy in lung adenocarcinoma.  Methods: The preoperative chest CT images and pathological data of 125 patients were retrospectively evaluated. The analysis included 127 surgically resected lung adenocarcinomas that manifested as subsolid nodules. All subsolid nodules were divided into two groups: 69 in group A, including 22 adenocarcinomas in situ (AIS) and 47 minimally invasive adenocarcinomas (MIA); 58 in group B that included invasive pulmonary adenocarcinomas (IPA). The size of the solid component in the pulmonary subsolid nodules were calculated in one dimensional, two dimensional and three dimensional views using lung and mediastinal windows that were recorded as 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW and 3D-SCMW, respectively. Furthermore, the volume of solid component with a threshold of -300HU was measured using lung window (3D-SCT). All the quantitative features were evaluated by the Mann-Whitney U test. Multivariate analysis was used to identify the significant predictor of the degree of pathological malignancy.  Results: The 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW, 3D-SCMW and 3D-SCT views of group B were significantly larger than those of group A (p < 0.001). The multivariate logistic regression analysis indicated that 3D-SCT (OR = 1.018, 95%CI: 1.005 ~ 1.03, p <0.05＝was the independent predictive factor. The larger SCT was significantly associated with IPAs.  Conclusion: 3D-SCT of subsolid nodules during preoperative CT can be used to predict the degree of pathological malignancy in lung adenocarcinoma, which may provide a more objective and convenient selection criterion for clinical application.  Advances in knowledge:  Applying threshold of -300 HU with lung window setting would be better than other window setting for the evaluation of solid component in subsolid nodules. Computer-aided volumetry of the solid component in subsolid nodules can more accurately predict the degree of pathological malignancy than the other dimensional measurements.

Diameter of the Solid Component in Subsolid Nodules on Low-Dose Unenhanced Chest Computed Tomography: Measurement Accuracy for the Prediction of Invasive Component in Lung Adenocarcinoma

Objective

To determine if measurement of the diameter of the solid component in subsolid nodules (SSNs) on low-dose unenhanced chest computed tomography (CT) is as accurate as on standard-dose enhanced CT in prediction of pathological size of invasive component of lung adenocarcinoma.

Materials and Methods

From February 2012 to October 2015, 114 SSNs were identified in 105 patients that underwent low-dose unenhanced and standard-dose enhanced CT pre-operatively. Three radiologists independently measured the largest diameter of the solid component. Intraclass correlation coefficients (ICCs) were used to assess inter-reader agreement. We estimated measurement differences between the size of solid component and that of invasive component. We measured diagnostic accuracy of the prediction of invasive adenocarcinoma using a size criterion of a solid component ≥ 6 mm, and compared them using a generalized linear mixed model.

Results

Inter-reader agreement was excellent (ICC, 0.84.0.89). The mean ± standard deviation of absolute measurement differences between the solid component and invasive component was 4 ± 4 mm in low-dose unenhanced CT and 5 ± 4 mm in standard-dose enhanced CT. Diagnostic accuracy was 81.3% (95% confidence interval, 76.7.85.3%) in low-dose unenhanced CT and 76.6% (71.8.81.0%) in standard-dose enhanced CT, with no statistically significant difference (p = 0.130).

Conclusion

Measurement of the diameter of the solid component of SSNs on low-dose unenhanced chest CT was as accurate as on standard-dose enhanced CT for predicting the invasive component. Thus, low-dose unenhanced CT may be used safely in the evaluation of patients with SSNs.