Non-small-cell lung cancer dimensions: CT–pathological correlation and interobserver variation

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.

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P >.11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P = .98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P = .0005), but significantly superior specificity (P = .02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists.

Radiologic-Pathologic Correlation of Solid Portions on Thin-section CT Images in Lung Adenocarcinoma: A Multicenter Study

BACKGROUND

Measuring the size of invasiveness on computed tomography (CT) for the T descriptor size was deemed important in the 8th edition of the TNM lung cancer classification. We aimed to correlate the maximal dimensions of the solid portions using both lung and mediastinal window settings on CT imaging with the pathologic invasiveness (> 0.5 cm) in lung adenocarcinoma patients.

MATERIALS AND METHODS

The study population consisted of 378 patients with a histologic diagnosis of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IVA)-lepidic, IVA-acinar and/or IVA-papillary, and IVA-micropapillary and/or solid adenocarcinoma. A panel of 15 radiologists was divided into 2 groups (group A, 9 radiologists; and group B, 6 radiologists). The 2 groups independently measured the maximal and perpendicular dimensions of the solid components and entire tumors on the lung and mediastinal window settings. The solid proportion of nodule was calculated by dividing the solid portion size (lung and mediastinal window settings) by the nodule size (lung window setting). The maximal dimensions of the invasive focus were measured on the corresponding pathologic specimens by 2 pathologists.

RESULTS

The solid proportion was larger in the following descending order: IVA-micropapillary and/or solid, IVA-acinar and/or papillary, IVA-lepidic, MIA, and AIS. For both groups A and B, a solid portion > 0.8 cm in the lung window setting or > 0.6 cm in the mediastinal window setting on CT was a significant indicator of pathologic invasiveness > 0.5 cm (P < .001; receiver operating characteristic analysis using Youden's index).

CONCLUSION

A solid portion > 0.8 cm on the lung window setting or solid portion > 0.6 cm on the mediastinal window setting on CT predicts for histopathologic invasiveness to differentiate IVA from MIA and AIS.

Size Measurement and T-staging of Lung Adenocarcinomas Manifesting as Solid Nodules ≤30 mm on CT: Radiology-Pathology Correlation

RATIONALE AND OBJECTIVES

This study aimed to compare long-axis diameter to average computed tomography (CT) diameter measurements of lung adenocarcinomas manifesting as solid lung nodules ≤30 mm on CT, as referenced to pathologic measurements, and to determine the impact of the two CT measurement approaches on tumor (T)-staging of nodules.

MATERIALS AND METHODS

This institutional review board-approved study included all 274 radiologic solid adenocarcinomas resected at our institution over 10 years. Two observers measured long- and short-axis diameters on pre-resection chest CT in lung and mediastinal windows. T-stages were determined. CT measurements and T-stages were compared to pathology measurements and T-stages using Wilcoxon signed rank test and McNemar test. Inter- and intraobserver variability was determined with intraclass correlation coefficients (ICC) and Bland-Altman plots.

RESULTS

For lung and mediastinal windows, nodule size was significantly larger using long-axis diameter rather than average diameter (16.93 vs. 14.92 mm, P <.001; and 14.02 vs. 12.17 mm, P <.001, respectively). The correlation of CT with pathologic measurements was stronger with long-axis than with average diameter (ICC 0.808 vs. 0.730; and 0.731 vs. 0.621, respectively). Lung window measurements correlated stronger with pathology than mediastinal window measurements. CT T-stages differed from pathology T-stages in more than 20% of nodules (P <.001). Inter- and intraobserver variability was small with long-axis and average diameter (ICC range 0.96-0.991, and 0.970-0.993, respectively), but long-axis diameter showed wider scatter on Bland-Altman plots.

CONCLUSIONS

Long-axis CT diameter is preferable for T-staging because it better reflects the pathology T-stage. Average CT diameter might be used for longitudinal nodule follow-up because it shows less measurement variability and is more conservative in size assessment.

Optimal tumor shrinkage predicts long-term outcome in advanced nonsmall cell lung cancer (NSCLC) treated with target therapy: Result from 3 clinical trials of advanced NSCLC by 1 institution

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are used as standard therapies for advanced nonsmall cell lung cancer (NSCLC) patients with EGFR mutation positive. Because these targeted therapies could cause tumor necrosis and shrinkage, the purpose of the study is to search for a value of optimal tumor shrinkage as an appropriate indicator of outcome for advanced NSCLC.A total of 88 NSCLC enrollees of 3 clinical trials (IRESSA registration clinical trial, TRUST study and ZD6474 study), who received Gefitinib (250 mg, QD), Erlotinib (150 mg, QD), and ZD6474 (100 mg, QD), respectively, during December 2003 and October 2007, were retrospectively analyzed. The response evaluation criteria in solid tumors (RECIST) were used to identify responders, who had complete response (CR) or partial responses (PR) and nonresponders who had stable disease (SD) or progressive disease (PD). Receiver operating characteristics (ROC) analysis was used to find the optimal tumor shrinkage as an indicator for tumor therapeutic outcome. Univariate and multivariate Cox regression analyses were performed to compare the progression-free survival (PFS) and overall survival (OS) between responders and nonresponders stratified based on radiologic criteria.Among the 88 NSCLC patients, 26 were responders and 62 were nonresponders based on RECIST 1.0. ROC indicated that 8.32% tumor diameter shrinkage in the sum of the longest tumor diameter (SLD) was the cutoff point of tumor shrinkage outcomes, resulting in 46 responders (≤8.32%) and 42 nonresponders (≥8.32%). Univariate and multivariate Cox regression analyses indicated that (1) the responders (≤8.32%) and nonresponders (≥ -8.32%) were significantly different in median PFS (13.40 vs 1.17 months, P < 0.001) and OS (19.80 vs 7.90 months, P < 0.001) and (2) -8.32% in SLD could be used as the optimal threshold for PFS (hazard ratio [HR], 8.11, 95% CI, 3.75 to 17.51, P < 0.001) and OS (HR, 2.36, 95% CI, 1.41 to 3.96, P = 0.001).However, 8.32% tumor diameter shrinkage is validated as a reliable outcome predictor of advanced NSCLC patients receiving EGFR-TKIs therapies and may provide a practical measure to guide therapeutic decisions.

The importance of surrounding tissues and window settings for contouring of moving targets

AIM

The aim of the study was to assess the importance of surrounding tissues for the delineation of moving targets in tissue-specific phantoms and to find optimal settings for lung, soft tissue, and liver tumors.

MATERIALS AND METHODS

Tumor movement was simulated by a water-filled table tennis ball (target volume, TV). Three phantoms were created: corkboards to simulate lung tissue (lung phantom, LunPh), animal fat as fatty soft tissue (fatty tissue phantom, FatPh), and water enhanced with contrast medium as the liver tissue (liver phantom, LivPh). Slow planning three-dimensional compute tomography images (3D-CTs) were acquired with and without phantom movements. One-dimensional tumor movement (1D), three-dimensional tumor movement (3D), as well as a real patient's tumor trajectories were simulated. The TV was contoured using two lung window settings, two soft-tissue window settings, and one liver window setting. The volumes were compared to mathematical calculated values.

RESULTS

TVs were underestimated in all phantoms due to movement. The use of soft-tissue windows in the LivPh led to a significant underestimation of the TV (70.8% of calculated TV). When common window settings [LunPh + 200 HU/-1,000 HU (upper window/lower window threshold); FatPh: + 240 HU/-120 HU; LivPh: + 175 HU/+ 50 HU] were used, the contoured TVs were: LivPh, 84.0%; LunPh, 93.2%, and FatPh, 92.8%. The lower window threshold had a significant impact on the size of the delineated TV, whereas changes of the upper threshold led only to small differences.

CONCLUSION

The decisive factor for window settings is the lower window threshold (for adequate TV delineation in the lung and fatty-soft tissue it should be lower than density values of surrounding tissue). The use of a liver window should be considered.

Correlation between the size of the solid component on thin-section CT and the invasive component on pathology in small lung adenocarcinomas manifesting as ground-glass nodules

INTRODUCTION

We aimed to evaluate the correlation between the size of the solid component on thin-section computed tomography (CT) and invasive component on pathology in small lung adenocarcinomas manifesting as subsolid nodules.

METHODS

Fifty-nine subsolid nodules in 58 patients were evaluated. The maximum diameters of subsolid nodules and the solid component on CT were measured by two radiologists in three-dimensional (3D) and two-dimensional (2D) planes using in-house software. In addition, the maximum diameters of the tumor and invasive component were measured on pathology by two pathologists. CT measurements were compared with pathologic measurements.

RESULTS

There was a strong correlation between the size of the solid component on CT and invasive component on pathology, as well as the size of subsolid nodules and the tumor size (r = 0.82-0.87 for 3D measurement, 0.72-0.88 for 2D measurement; p < 0.0001). The size of subsolid nodules in 3D and 2D measurements was significantly larger than tumor size (p < 0.0001). In regard to measurement of the solid component, 3D measurements tended to be larger than the size of the invasive component whereas 2D measurement tended to be similar to the size of the invasive component. By applying a size criteria of solid component that was 3 mm or lesser in maximum diameter, preinvasive and minimally invasive adenocarcinoma was predicted with a specificity of 100% (28 of 28).

CONCLUSION

We found a significant correlation between the size of the solid component on thin-section CT and the invasive component on pathology.

Inter- and intrascanner variability of pulmonary nodule volumetry on low-dose 64-row CT: an anthropomorphic phantom study

OBJECTIVE

To assess inter- and intrascanner variability in volumetry of solid pulmonary nodules in an anthropomorphic thoracic phantom using low-dose CT.

METHODS

Five spherical solid artificial nodules [diameters 3, 5, 8, 10 and 12 mm; CT density +100 Hounsfield units (HU)] were randomly placed inside an anthropomorphic thoracic phantom in different combinations. The phantom was examined on two 64-row multidetector CT (64-MDCT) systems (CT-A and CT-B) from different vendors with a low-dose protocol. Each CT examination was performed three times. The CT examinations were evaluated twice by independent blinded observers. Nodule volume was semi-automatically measured by dedicated software. Interscanner variability was evaluated by Bland-Altman analysis and expressed as 95% confidence interval (CI) of relative differences. Intrascanner variability was expressed as 95% CI of relative variation from the mean.

RESULTS

No significant difference in CT-derived volume was found between CT-A and CT-B, except for the 3-mm nodules (p<0.05). The 95% CI of interscanner variability was within ±41.6%, ±18.2% and ±4.9% for 3, 5 and ≥8 mm nodules, respectively. The 95% CI of intrascanner variability was within ±28.6%, ±13.4% and ±2.6% for 3, 5 and ≥8 mm nodules, respectively.

CONCLUSION

Different 64-MDCT scanners in low-dose settings yield good agreement in volumetry of artificial pulmonary nodules between 5 mm and 12 mm in diameter. Inter- and intrascanner variability decreases at a larger nodule size to a maximum of 4.9% for ≥8 mm nodules.

ADVANCES IN KNOWLEDGE

The commonly accepted cut-off of 25% to determine nodule growth has the potential to be reduced for ≥8 mm nodules. This offers the possibility of reducing the interval for repeated CT scans in lung cancer screenings.

Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: a comparison with threshold-based approaches, CT and surgical specimens

PURPOSE

The aim of this study was to validate a gradient-based segmentation method for GTV delineation on FDG-PET in NSCLC through surgical specimen, in comparison with threshold-based approaches and CT.

MATERIALS AND METHODS

Ten patients with stage I-II NSCLC were prospectively enrolled. Before lobectomy, all patients underwent contrast enhanced CT and gated FDG-PET. Next, the surgical specimen was removed, inflated with gelatin, frozen and sliced. The digitized slices were used to reconstruct the 3D macroscopic specimen. GTVs were manually delineated on the macroscopic specimen and on CT images. GTVs were automatically segmented on PET images using a gradient-based method, a source to background ratio method and fixed threshold values at 40% and 50% of SUV(max). All images were finally registered. Analyses of raw volumes and logarithmic differences between GTVs and GTV(macro) were performed on all patients and on a subgroup excluding the poorly defined tumors. A matching analysis between the different GTVs was also conducted using Dice's similarity index.

RESULTS

Considering all patients, both lung and mediastinal windowed CT overestimated the macroscopy, while FDG-PET provided closer values. Among various PET segmentation methods, the gradient-based technique best estimated the true tumor volume. When analysis was restricted to well defined tumors without lung fibrosis or atelectasis, the mediastinal windowed CT accurately assessed the macroscopic specimen. Finally, the matching analysis did not reveal significant difference between the different imaging modalities.

CONCLUSIONS

FDG-PET improved the GTV definition in NSCLC including when the primary tumor was surrounded by modifications of the lung parenchyma. In this context, the gradient-based method outperformed the threshold-based ones in terms of accuracy and robustness. In other cases, the conventional mediastinal windowed CT remained appropriate.

Bidimensional measurements in brain tumors: assessment of interobserver variability

OBJECTIVE

Bidimensional tumor measurements indicating a greater than 25% increase in tumor size are generally accepted as indicating tumor progression. We hypothesized that use of digital images and a homogeneous reader population would have lower interobserver variability than in previous studies.

SUBJECTS AND METHODS

Eight board-certified radiologists measured tumor diameters in three planes in two consecutive MRI examinations of 22 patients with contrast-enhancing high-grade brain tumors. Products of tumor measurements were calculated, and determinations were made about tumor progression (> 25% increase in area). A variance components model was run on diameter products and the ratios of consecutive maximal diameter products. The variance components included patient examination effect, reader effect, and residual effect.

RESULTS

Complete agreement was found among readers in 10 cases (45%), all indicating stable disease. In the other 12 cases, at least one reader considered progressive disease present. The variance components model showed variance due to readers was small, indicating only modest bias among readers. The residual variance component was large (0.038), indicating that repeated measurements on the same image likely are variable even for the same reader. This variability in measurement implies that repeated measurements by the typical reader have an inherent 14% false-positive rate in the diagnosis of progression of tumors that are stable.

CONCLUSION

Our hypothesis was disproved. We found substantial interreader disagreement and indications that the very nature of the measurement method produces a high rate of false-positive readings of stable tumors. These findings should be considered in interpretation of images with this widely accepted criterion for brain tumor progression.

PET CT thresholds for radiotherapy target definition in non-small-cell lung cancer: how close are we to the pathologic findings?

PURPOSE

Optimal target delineation threshold values for positron emission tomography (PET) and computed tomography (CT) radiotherapy planning is controversial. In this present study, different PET CT threshold values were used for target delineation and then compared pathologically.

METHODS AND MATERIALS

A total of 31 non-small-cell lung cancer patients underwent PET CT before surgery. The maximal diameter (MD) of the pathologic primary tumor was obtained. The CT-based gross tumor volumes (GTV(CT)) were delineated for CT window-level thresholds at 1,600 and -300 Hounsfield units (HU) (GTV(CT1)); 1,600 and -400 (GTV(CT2)); 1,600 and -450 HU (GTV(CT3)); 1,600 and -600 HU (GTV(CT4)); 1,200 and -700 HU (GTV(CT5)); 900 and -450 HU (GTV(CT6)); and 700 and -450 HU (GTV(CT7)). The PET-based GTVs (GTV(PET)) were autocontoured at 20% (GTV(20)), 30% (GTV(30)), 40% (GTV(40)), 45% (GTV(45)), 50% (GTV(50)), and 55% (GTV(55)) of the maximal intensity level. The MD of each image-based GTV in three-dimensional orientation was determined. The MD of the GTV(PET) and GTV(CT) were compared with the pathologically determined MD.

RESULTS

The median MD of the GTV(CT) changed from 2.89 (GTV(CT2)) to 4.46 (GTV(CT7)) as the CT thresholds were varied. The correlation coefficient of the GTV(CT) compared with the pathologically determined MD ranged from 0.76 to 0.87. The correlation coefficient of the GTV(CT1) was the best (r=0.87). The median MD of GTV(PET) changed from 5.72 cm to 2.67 cm as the PET thresholds increased. The correlation coefficient of the GTV(PET) compared with the pathologic finding ranged from 0.51 to 0.77. The correlation coefficient of GTV(50) was the best (r=0.77).

CONCLUSION

Compared with the MD of GTV(PET), the MD of GTV(CT) had better correlation with the pathologic MD. The GTV(CT1) and GTV(50) had the best correlation with the pathologic results.