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

Comparing quantitative imaging biomarker alliance volumetric CT classifications with RECIST response categories

Purpose

To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Solid Tumors (RECIST) categories.

Materials and Methods

Target lesions in lung, liver, and lymph nodes were randomly chosen from patients in 10 historical clinical trials for various cancers, ensuring a balanced representation of lesion types, diameter ranges described in the QIBA Profile, and variations in change magnitudes. Three radiologists independently segmented these lesions at baseline and follow-up scans using 2 software tools. Two types of predefined disagreements were assessed: Type I: substantive disagreement, where the disagreement between QIBA CTvol classifications and RECIST categories could not be attributed to the improved sensitivity of volumetry in detecting changes; and Type II: disagreement potentially arising from the improved sensitivity of volumetry in detecting changes. The proportion of lesions with disagreements between QIBA CTvol and RECIST, as well as the type of disagreements, was reported along with 95% CIs, both overall and within subgroups representing various factors.

Results

A total of 2390 measurements from 478 lesions (158 lungs, 170 livers, 150 lymph nodes) in 281 patients were included. QIBA CTvol agreed with RECIST in 66.6% of interpretations. Of the 33.4% of interpretations with discrepancies, substantive disagreement (Type I) occurred in only 1.5% (95% CI: [0.8%, 2.1%]). Factors such as scanner vendor (P = .584), segmentation tool (P = .331), and lesion type (P = .492) were not significant predictors of disagreement. Significantly more disagreements were observed for larger lesions (≥50 mm, as defined in the QIBA Profile).

Conclusion

We conclude that QIBA CTvol classifications agree with RECIST categories.

Use of Computed Tomography-Based Texture Analysis to Differentiate Benign From Malignant Salivary Gland Lesions

OBJECTIVE

Salivary gland lesions show overlapping morphological findings and types of time/intensity curves. This research aimed to evaluate the role of 2-phase multislice spiral computed tomography (MSCT) texture analysis in differentiating between benign and malignant salivary gland lesions.

METHODS

In this prospective study, MSCT was carried out on 90 patients. Each lesion was segmented on axial computed tomography (CT) images manually, and 33 texture features and morphological CT features were assessed. Logistic regression analysis was used to confirm predictors of malignancy ( P < 0.05 was considered to be statistically significant), followed by receiver operating characteristics analysis to assess the diagnostic performance.

RESULTS

Univariate logistic regression analysis revealed that morphological CT features (shape, size, and invasion of adjacent tissues) and 17 CT texture parameters had significant differences between benign and malignant lesions ( P < 0.05). Multivariate binary logistic regression demonstrated that shape, invasion of adjacent tissues, entropy, and inverse difference moment were independent factors for malignant tumors. The diagnostic accuracy values of multivariate binary logistic models based on morphological parameters, CT texture features, and a combination of both were 87.8%, 90%, and 93.3%, respectively.

CONCLUSIONS

Two-phase MSCT texture analysis was conducive to differentiating between malignant and benign neoplasms in the salivary gland, especially when combined with morphological CT features.

Assessment of Solid Pulmonary Nodules or Masses Using Zero Echo Time MR Lung Imaging: A Prospective Head-to-Head Comparison With CT

Objective

The aim of this study is to determine the potential of zero echo time (ZTE) MR lung imaging in the assessment of solid pulmonary nodules or masses and diagnostic consistency to CT in terms of morphologic characterization.

Methods

Our Institutional Review Board approved this prospective study. Seventy-one patients with solid pulmonary nodules or masses larger than 1 cm in diameter confirmed by chest CT were enrolled and underwent further lung ZTE-MRI scans within 7 days. ZTE-MRI and CT images were compared in terms of image quality and imaging features. Unidimensional diameter and three-dimensional volume measurements on both modalities were manually measured and compared using the Wilcoxon signed-rank test, intraclass correlation coefficient (ICC), Pearson's correlation analysis, and Bland-Altman analysis. Multivariable logistic regression analysis was used to identify the factors associated with significant inter-modality variation of volume.

Results

Fifty-four of 71 (76.1%) patients were diagnosed with lung cancer. Subjective image quality was superior in CT compared with ZTE-MRI (p < 0.001). Inter-modality agreement for the imaging features was moderate for emphysema (kappa = 0.50), substantial for fibrosis (kappa = 0.76), and almost perfect (kappa = 0.88-1.00) for the remaining features. The size measurements including diameter and volume between ZTE-MRI and CT showed no significant difference (p = 0.36 for diameter and 0.60 for volume) and revealed perfect inter-observer (ICC = 0.975-0.980) and inter-modality (ICC = 0.942-0.992) agreements. Multivariable analysis showed that non-smooth margin [odds ratio (OR) = 6.008, p = 0.015] was an independent predictor for the significant inter-modality variation of volume.

Conclusion

ZTE lung imaging is feasible as a part of chest MRI in the assessment and surveillance for solid pulmonary nodules or masses larger than 1 cm, presenting perfect agreement with CT in terms of morphologic characterization.