Value of Whole-Tumor Dual-Input Perfusion CT in Predicting the Effect of Multiarterial Infusion Chemotherapy on Advanced Non–Small Cell Lung Cancer

Evaluation of the dual vascular supply patterns in ground-glass nodules with a dynamic volume computed tomography

BACKGROUND

In recent years, the detection rate of ground-glass nodules (GGNs) has been improved dramatically due to the popularization of low-dose computed tomography (CT) screening with high-resolution CT technique. This presents challenges for the characterization and management of the GGNs, which depends on a thorough investigation and sufficient diagnostic knowledge of the GGNs. In most diagnostic studies of the GGNs, morphological manifestations are used to differentiate benignancy and malignancy. In contrast, few studies are dedicated to the assessment of the hemodynamics, i.e., perfusion parameters of the GGNs.

AIM

To assess the dual vascular supply patterns of GGNs on different histopathology and opacities.

METHODS

Forty-seven GGNs from 47 patients were prospectively included and underwent the dynamic volume CT. Histopathologic diagnoses were obtained within two weeks after the CT examination. Blood flow from the bronchial artery [bronchial flow (BF)] and pulmonary artery [pulmonary flow (PF)] as well as the perfusion index (PI) = [PF/(PF + BF)] were obtained using first-pass dual-input CT perfusion analysis and compared respectively between different histopathology and lesion types (pure or mixed GGNs) and correlated with the attenuation values of the lesions using one-way ANOVA, student’s t test and Pearson correlation analysis.

RESULTS

Of the 47 GGNs (mean diameter, 8.17 mm; range, 5.3-12.7 mm), 30 (64%) were carcinoma, 6 (13%) were atypical adenomatous hyperplasia and 11 (23%) were organizing pneumonia. All perfusion parameters (BF, PF and PI) demonstrated no significant difference among the three conditions (all P > 0.05). The PFs were higher than the BFs in all the three conditions (all P < 0.001). Of the 30 GGN carcinomas, 14 showed mixed GGNs and 16 pure GGNs with a higher PI in the latter (P < 0.01). Of the 17 benign GGNs, 4 showed mixed GGNs and 13 pure GGNs with no significant difference of the PI between the GGN types (P = 0.21). A negative correlation (r = -0.76, P < 0.001) was demonstrated between the CT attenuation values and the PIs in the 30 GGN carcinomas.

CONCLUSION

The GGNs are perfused dominantly by the PF regardless of its histopathology while the weight of the BF in the GGN carcinomas increases gradually during the progress of its opacification.

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data

OBJECTIVES

To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI.

MATERIAL AND METHODS

Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations).

RESULTS

Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness.

CONCLUSION

A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.

Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies

Pulmonary perfusion with dynamic contrast‐enhanced (DCE‐) MRI is typically assessed using a single‐input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual‐input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual‐input tracer kinetic models to DCE‐MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single‐input (pulmonary or systemic arterial input) tracer kinetic models for the voxel‐level analysis within the tumor with respect to goodness‐of‐fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE‐MRI prior to radiotherapy. DCE‐MRI data were analyzed using five different single‐ or dual‐input tracer kinetic models: Tofts‐Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability‐surface area product (PS), fractional interstitial volume (v_I), and volume transfer constant (K^Trans) were calculated for both single‐ and dual‐input models. The pulmonary arterial flow fraction (γ), pulmonary arterial blood flow (BF_PA) and systemic arterial blood flow (BF_A) were additionally calculated for only dual‐input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual‐input models yielded lower cAIC values than their corresponding single‐input models. The 2CX model was the best‐fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual‐input 2CX‐model‐derived BF_A was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.

Retinoblastoma vascular perfusion and intra-arterial chemotherapy cycle requirements

IMPORTANCE

Retinoblastoma is a life- and sight-threatening malignancy.

BACKGROUND

To assess the relationship between tumour perfusion and intra-arterial chemotherapy (IAC) requirements to achieve retinoblastoma control.

DESIGN

Retrospective case series at the Ocular Oncology Service of Wills Eye Hospital (Philadelphia, Pennsylvania).

PARTICIPANTS

Fifty-nine eyes of 55 patients.

METHODS

Review of medical and fluorescein angiography (FA) records for retinoblastoma treated with primary or secondary IAC from 2012 to 2017. Vascular supply of the main tumour was evaluated in the pre-treatment FA.

MAIN OUTCOME MEASURES

Tumour fluorescence was classified as partial <67% or complete tumour perfusion >67%. Partially vs completely perfused tumours were compared for IAC cycle requirements.

RESULTS

There were 59 eyes of 55 patients with pre-treatment FA managed with IAC. Partially perfused tumours (n = 20, 34%) required fewer IAC infusions than completely perfused tumours (n = 39, 66%) (2.5 vs 3.7 infusions, P = .02), even after adjustment for confounding factors (tumour diameter, thickness and drug scheme, adjusted P = .04). Tumour perfusion correlated with number of IAC cycles required for tumour control (r = 0.46, P < .001). For primary IAC (n = 18, 31%), tumour perfusion was not associated with number of IAC cycles (P = .63). For secondary IAC (n = 41, 69%), partially perfused tumours (n = 15, 37%) required fewer IAC infusions than completely perfused tumours (n = 26, 63%) (2.1 vs 3.7 infusions, P < .01).

CONCLUSIONS AND RELEVANCE

FA demonstrating partial retinoblastoma tumour perfusion is associated with fewer IAC cycle requirements for secondary but not primary IAC. FA might be useful in judging anticipated treatment cycles of retinoblastoma managed with primary or secondary IAC.

CT perfusion imaging of lung cancer: benefit of motion correction for blood flow estimates

PURPOSE

CT perfusion (CTP) imaging assessment of treatment response in advanced lung cancer can be compromised by respiratory motion. Our purpose was to determine whether an original motion correction method could improve the reproducibility of such measurements.

MATERIALS AND METHODS

The institutional review board approved this prospective study. Twenty-one adult patients with non-resectable non-small-cell lung cancer provided written informed consent to undergo CTP imaging. A motion correction method that consisted of manually outlining the tumor margins and then applying a rigid manual landmark registration algorithm followed by the non-rigid diffeomorphic demons algorithm was applied. The non-motion-corrected and motion-corrected images were analyzed with dual blood supply perfusion analysis software. Two observers performed the analysis twice, and the intra- and inter-observer variability of each method was assessed with Bland-Altman statistics.

RESULTS

The 95% limits of agreement of intra-observer reproducibility for observer 1 improved from -84.4%, 65.3% before motion correction to -33.8%, 30.3% after motion correction (r = 0.86 and 0.97, before and after motion correction, p < 0.0001 for both) and for observer 2 from -151%, 96% to -49 %, 36 % (r = 0.87 and 0.95, p < 0.0001 for both). The 95% limits of agreement of inter-observer reproducibility improved from -168%, 154% to -17%, 25%.

CONCLUSION

The use of a motion correction method significantly improves the reproducibility of CTP estimates of tumor blood flow in lung cancer.

KEY POINTS

• Tumor blood flow estimates in advanced lung cancer show significant variability. • Motion correction improves the reproducibility of CT blood flow estimates in advanced lung cancer. • Reproducibility of blood flow measurements is critical to characterize lung tumor biology and the success of treatment in lung cancer.

Intra-observer and inter-observer agreements for the measurement of dual-input whole tumor computed tomography perfusion in patients with lung cancer: Influences of the size and inner-air density of tumors

Background

This study was conducted to assess intra‐observer and inter‐observer agreements for the measurement of dual‐input whole tumor computed tomography perfusion (DCTP) in patients with lung cancer.

Methods

A total of 88 patients who had undergone DCTP, which had proved a diagnosis of primary lung cancer, were divided into two groups: (i) nodules (diameter ≤3 cm) and masses (diameter >3 cm) by size, and (ii) tumors with and without air density. Pulmonary flow, bronchial flow, and pulmonary index were measured in each group. Intra‐observer and inter‐observer agreements for measurement were assessed using intraclass correlation coefficient, within‐subject coefficient of variation, and Bland–Altman analysis.

Results

In all lung cancers, the reproducibility coefficient for intra‐observer agreement (range 26.1–38.3%) was superior to inter‐observer agreement (range 38.1–81.2%). Further analysis revealed lower agreements for nodules compared to masses. Additionally, inner‐air density reduced both agreements for lung cancer.

Conclusion

The intra‐observer agreement for measuring lung cancer DCTP was satisfied, while the inter‐observer agreement was limited. The effects of tumoral size and inner‐air density to agreements, especially between two observers, should be emphasized. In future, an automatic computer‐aided segment of perfusion value of the tumor should be developed.

Assessing Tumor Response to Treatment in Patients with Lung Cancer Using Dynamic Contrast-Enhanced CT

The aim of this study was to provide an overview of the literature available on dynamic contrast-enhanced computed tomography (DCE-CT) as a tool to evaluate treatment response in patients with lung cancer. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning treatment response in patients with lung cancer assessed with DCE-CT were included. To assess the validity of each study we implemented Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). The initial search yielded 651 publications, and 16 articles were included in this study. The articles were divided into groups of treatment. In studies where patients were treated with systemic chemotherapy with or without anti-angiogenic drugs, four out of the seven studies found a significant decrease in permeability after treatment. Four out of five studies that measured blood flow post anti-angiogenic treatments found that blood flow was significantly decreased. DCE-CT may be a useful tool in assessing treatment response in patients with lung cancer. It seems that particularly permeability and blood flow are important perfusion values for predicting treatment outcome. However, the heterogeneity in scan protocols, scan parameters, and time between scans makes it difficult to compare the included studies.

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer

Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.