Treatment planning based on water density image generated using dual‐energy computed tomography for pancreatic cancer with contrast‐enhancing agent: Phantom and clinical study

Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique

The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70 keV (VMI70) and 140 keV (VMI140) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI70 and VMI140 measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT120 (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT140 (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.

Dual-energy computed tomography-based iodine concentration as a predictor of histopathological response to preoperative chemoradiotherapy for pancreatic cancer

To explore predictors of the histopathological response to preoperative chemoradiotherapy (CRT) in patients with pancreatic cancer (PC) using dual-energy computed tomography-reconstructed images. This retrospective study divided 40 patients who had undergone preoperative CRT (50-60 Gy in 25 fractions) followed by surgical resection into two groups: the response group (Grades II, III and IV, evaluated from surgical specimens) and the nonresponse group (Grades Ia and Ib). The computed tomography number [in Hounsfield units (HUs)] and iodine concentration (IC) were measured at the locations of the aorta, PC and pancreatic parenchyma (PP) in the contrast-enhanced 4D dual-energy computed tomography images. Logistic regression analysis was performed to identify predictors of histopathological response. Univariate analysis did not reveal a significant relation between any parameter and patient characteristics or dosimetric parameters of the treatment plan. The HU and IC values in PP and the differences in HU and IC between the PP and PC (ΔHU and ΔIC, respectively) were significant predictors for distinguishing the response (n = 24) and nonresponse (n = 16) groups (P < 0.05). The IC in PP and ΔIC had a higher area under curve values [0.797 (95% confidence interval, 0.659-0.935) and 0.789 (0.650-0.928), respectively] than HU in PP and ΔHU [0.734 (0.580-0.889) and 0.721 (0.562-0.881), respectively]. The IC value could potentially be used for predicting the histopathological response in patients who have undergone preoperative CRT.

Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT

PURPOSE

Low-energy virtual monochromatic images (VMIs) derived from dual-energy computed tomography (DECT) systems improve lesion conspicuity of head and neck cancer over single-energy CT (SECT). However, DECT systems are installed in a limited number of facilities; thus, only a few facilities benefit from VMIs. In this work, we present a deep learning (DL) architecture suitable for generating pseudo low-energy VMIs of head and neck cancers for facilities that employ SECT imaging.

METHODS

We retrospectively analyzed 115 patients with head and neck cancers who underwent contrast enhanced DECT. VMIs at 70 and 50 keV were used as the input and ground truth (GT), respectively. We divided them into two datasets: for DL (104 patients) and for inference with SECT (11 patients). We compared four DL architectures: U-Net, DenseNet-based, and two ResNet-based models. Pseudo VMIs at 50 keV (pVMI_50keV) were compared with the GT in terms of the mean absolute error (MAE) of Hounsfield unit (HU) values, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). The HU values for tumors, vessels, parotid glands, muscle, fat, and bone were evaluated. pVMI_50keV were generated from actual SECT images and the HU values were evaluated.

RESULTS

U-Net produced the lowest MAE (13.32 ± 2.20 HU) and highest PSNR (47.03 ± 2.33 dB) and SSIM (0.9965 ± 0.0009), with statistically significant differences (P < 0.001). The HU evaluation showed good agreement between the GT and U-Net. U-Net produced the smallest absolute HU difference for the tumor, at < 5.0 HU.

CONCLUSION

Quantitative comparisons of physical parameters demonstrated that the proposed U-Net could generate high accuracy pVMI_50keV in a shorter time compared with the established DL architectures. Although further evaluation on diagnostic accuracy is required, our method can help obtain low-energy VMI from SECT images without DECT systems.

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT

Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT.

In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography

PURPOSE

To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality.

MATERIALS AND METHODS

Fifty patients with pancreatic cancer underwent a dual-energy CT simulation and VMIs at 77 and 60 keV were reconstructed. A 2D deep densely connected convolutional neural network was modeled to learn the relationship between the VMIs at 77 (input) and 60 keV (ground-truth). Subsequently, VMIs were generated for 20 patients from SECT images using the trained deep learning model.

RESULTS

The contrast-to-noise ratio was significantly improved (p < 0.001) in the generated VMIs (4.1 ± 1.8) compared to the SECT images (2.8 ± 1.1). The mean overall image quality (4.1 ± 0.6) and tumor enhancement (3.6 ± 0.6) in the generated VMIs assessed on a five-point scale were significantly higher (p < 0.001) than that in the SECT images (3.2 ± 0.4 and 2.8 ± 0.4 for overall image quality and tumor enhancement, respectively).

CONCLUSIONS

The quality of the SECT image was significantly improved both objectively and subjectively using the proposed deep learning model for pancreatic tumors in radiotherapy.

Dual-energy computed tomography image-based volumetric-modulated arc therapy planning for reducing the effect of contrast-enhanced agent on dose distributions

To compare the effect of a contrast-enhanced (CE) agent on volumetric-modulated arc therapy plans based on four types of images-virtual monochromatic images (VMIs) captured at 70 and 140 keV (namely VMI₇₀ and VMI₁₄₀, respectively), water density image (WDI), and virtual non-contrast image (VNC) generated using a dual-energy computed tomography (DECT) system. A tissue characterization phantom and a multi-energy phantom were scanned, and VMI₇₀, VMI₁₄₀, WDI, and VNC were retrospectively reconstructed. For each image, a lookup table (LUT) was created. For 13 patients with nasopharyngeal cancer, non-CE and CE scans were performed, and volumetric-modulated arc therapy plans were generated on the basis of non-CE VMI₇₀. Subsequently, the doses were re-calculated using the four types of DECT images and their corresponding LUTs. The maximum differences in the physical density estimation were 21.3, 5.2, -3.9, and 0.5% for VMI₇₀, VMI₁₄₀, WDI, and VNC, respectively. Compared with VMI₇₀, the WDI approach significantly reduced (p < 0.05) the dosimetric difference due to the CE agent for the planning target volume (PTV) (D_50%), whereas the difference was significantly increased for D_1%. Except for PTV (D_1%), the differences were significantly lower (p < 0.05) in the treatment plans based on VMI₁₄₀ and VNC than that based on VMI₇₀. For the VNC, the mean difference was less than 0.2% for all dosimetric parameters for the PTV. For patients with NPC, treatment plans based on the VNC derived from CE scan showed the best agreement with those based on the non-CE VMI₇₀. Ideally, the effect of CE agent on dose distribution does not appear in treatment planning procedures.

Stereotactic body radiation therapy planning for liver tumors using functional images from dual-energy computed tomography

PURPOSE

This study aimed to generate a functional image of the liver using dual-energy computed tomography (DECT) and a functional-image-based stereotactic body radiation therapy plan to minimize the dose to the volume of the functional liver (V_fl).

MATERIAL AND METHODS

A normalized iodine density (NID) map was generated for fifteen patients with liver tumors. The volume of liver with an NID < 0.46 was defined as V_fl, and the ratio between V_fl and the total volume of the liver (FLR) was calculated. The relationship between the FLR and Fibrosis-4 (FIB-4) was assessed. For patients with 15% < FLR < 85%, functional volumetric modulated-arc therapy plans (F-VMAT) were retrospectively generated to preserve V_fl, and compared to the clinical plans (C-VMAT).

RESULTS

FLR showed a significantly strong correlation with FIB-4 (r = -0.71, p < 0.01). For ten generated F-VMAT plans, the dosimetric parameters of D_99%, D_50%, D_1% and the conformity index were comparable to those of the C-VMAT (p > 0.05). For V_fl, F-VMAT plans achieved lower V_5Gy (122.4 ± 31.7 vs 181.1 ± 57.3 cc), V_10Gy (44.4 ± 22.2 vs 98.2 ± 33.3 cc), V_15Gy (22.6 ± 20.3 vs 49.8 ± 33.7 cc), V_20Gy (11.6 ± 14.1 vs 24.9 ± 25.1 cc), and D_mean (3.9 ± 2.3 vs 5.8 ± 3.0 Gy) values than the C-VMAT plans (p < 0.01).

CONCLUSIONS

The functional image derived from DECT was successfully used, allowing for a reduction in the dose to the V_fl without compromising target coverage.

Volumetric modulated arc therapy treatment planning based on virtual monochromatic images for head and neck cancer: effect of the contrast-enhanced agent on dose distribution

Virtual monochromatic images (VMIs) at a lower energy level can improve image quality but the computed tomography (CT) number of iodine contained in the contrast‐enhanced agent is dramatically increased. We assessed the effect of the use of contrast‐enhanced agent on the dose distributions in volumetric modulated arc therapy (VMAT) planning for head and neck cancer (HNC). Based on the VMIs at 40 keV (VMI_40keV), 60 keV(VMI_60keV), and 77 keV (VMI_77keV) of a tissue characterization phantom, lookup tables (LUTs) were created. VMAT plans were generated for 15 HNC patients based on contrast‐enhanced‐ (CE‐) VMIs at 40‐, 60‐, and 77 keV using the corresponding LUTs, and the doses were recalculated based on the noncontrast‐enhanced‐ (nCE‐) VMIs. For all structures, the difference in CT numbers owing to the contrast‐enhanced agent was prominent as the energy level of the VMI decreased, and the mean differences in CT number between CE‐ and nCE‐VMI was the largest for the clinical target volume (CTV) (125.3, 55.9, and 33.1 HU for VMI_40keV, VMI_60keV, and VMI_77keV, respectively). The mean difference of the dosimetric parameters (D_99%, D_50%, D_1%, D_mean, and D_0.1cc) for CTV and OARs was <1% in the treatment plans based on all VMIs. The maximum difference was observed for CTV in VMI_40keV (2.4%), VMI_60keV (1.9%), and VMI_77keV (1.5%) plans. The effect of the contrast‐enhanced agent was larger in the VMAT plans based on the VMI at a lower energy level for HNC patients. This effect is not desirable in a treatment planning procedure.

Clinical impact of different exosomes' protein expression in pancreatic ductal carcinoma patients treated with standard first line palliative chemotherapy

INTRODUCTION

Pancreatic ductal adenocarcinoma is associated to dismal prognosis despite the use of palliative chemotherapy, partly due to the lack of knowledge of biological processes underlying disease progression. Exosomes have been identified as biomarkers sources in different cancer types. Aim of the study was to analyse the contents of circulating exosomes in patients with pancreatic cancer who received palliative chemotherapy.

PATIENTS AND METHODS

Patients were submitted to blood sample collection before chemotherapy (T0) and after 3 months (T3). We quantified by an ELISA-based technique specific proteins of cancer-derived exosomes (CD44,CD44v6,EpCAM,CD9,CD81,Tspan8,Integrin α6,Integrin β4,CD24,CXCR4). We correlated the baseline levels of these factors and changes between T3 and T0 and survival outcomes. Survival analyses were performed by Kaplan-Meier method. Correlation was assessed by log-rank test and level of statistical significance was set at 0.05. Multivariate analysis was performed by logistic regression analysis.

RESULTS

Nineteen patients were enrolled. EpCAM T0 levels and increased EpCAM levels from T0 to T3 were those mostly associated with differences in survival. Patients having higher EpCAM had median progression free survival (PFS) of 3.18vs7.31 months (HR:2.82,95%CI:1.03-7.73,p = 0.01). Overall survival (OS) was shorter for patients having higher EpCAM (5.83vs16.45 months,HR:6.16,95%CI:1.93-19.58,p = 0.0001) and also response rates (RR) were worse (20%vs87%,p = 0.015). EpCAM increase during treatment was associated with better median PFS (2.88vs7.31 months,HR:0.24,95%CI:0.04-1.22,p = 0.003). OS was also better (8.75vs11.04 months, HR:0.77,95%CI:0.21-2.73,p = 0.66) and RR were 60%vs20% (p = 0.28). Among clinical factors that might determine changes on PFS and OS, only ECOG PS was associated to significantly worse PFS and OS (p = 0.0137and<0.001 respectively).Multivariate analysis confirmed EpCAM T0 levels and EpCAM T0/T3 changes as independent prognostic factors for PFS.

CONCLUSIONS

Pancreatic cancer patients exosomes express EpCAM, whose levels change during treatment. This represents a useful prognostic factor and also suggests that future treatment modalities who target EpCAM should be tested in pancreatic cancer patients selected by exosome EpCAM expression.