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Special Issue "Quantitative Imaging in Oncology"

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A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 10049

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Dr. Wonmo Sung

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Guest Editor

Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
Interests: radiology; radiation oncology; medical physics; tumor

Special Issue Information

Dear Colleagues,

The field of quantitative imaging analysis in oncology is rapidly evolving. Quantitative imaging in oncology has reached a critical mass of investigative capacity, ranging from molecular imaging to CT/MR-based imaging to image-guided radiation therapy. Imaging ability to diagnose and evaluate therapy response and forecast symptom development in cancer has advanced significantly. Moreover, advanced machine learning techniques, such as neural networks, are transforming the practice of quantitative imaging in oncology. Thus, this Special Issue is looking for papers, including the development and implementation of quantitative imaging methods, imaging protocols, software, and machine learning solutions for cancer patients.

Dr. Wonmo Sung
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

radiology
radiation therapy
quantitative imaging
tumor

Published Papers (6 papers)

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Editorial

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2 pages, 159 KiB

Open AccessEditorial

Quantitative Imaging in Oncology

Wonmo Sung

Tomography 2022, 8(4), 1676-1677; https://doi.org/10.3390/tomography8040139 - 24 Jun 2022

Cited by 1 | Viewed by 923

Abstract

The Special Issue of Tomography is a collection of articles focused on the quantitative imaging methods in clinical oncology [...] Full article

(This article belongs to the Special Issue Quantitative Imaging in Oncology)

Research

Jump to: Editorial

15 pages, 5480 KiB

Open AccessArticle

Textural Features of Mouse Glioma Models Measured by Dynamic Contrast-Enhanced MR Images with 3D Isotropic Resolution

Karl Kiser

Jin Zhang

and

Sungheon Gene Kim

Tomography 2023, 9(2), 721-735; https://doi.org/10.3390/tomography9020058 - 24 Mar 2023

Viewed by 1123

Abstract

This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images. Full article

(This article belongs to the Special Issue Quantitative Imaging in Oncology)

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11 pages, 1115 KiB

Open AccessFeature PaperArticle

Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion

Wenjuan Xu

Bingjie Zheng

and

Hailiang Li

Tomography 2022, 8(6), 2676-2686; https://doi.org/10.3390/tomography8060223 - 31 Oct 2022

Cited by 1 | Viewed by 1417

Abstract

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast–enhanced magnetic resonance imaging (DCE–MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann–Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_{_ADC}, model_{_IVIM}, model_{_DCE}, model_{_DCE+ADC}, and model_{_DCE+IVIM}). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_{_DCE+IVIM} showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828–0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_{_ADC} (p = 0.014) and model_{_IVIM} (p = 0.033). The model_{_ADC} had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672–0.846) but was not significantly different from model_{_IVIM} (p = 0.168). The united quantitative model with DCE–MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated. Full article

(This article belongs to the Special Issue Quantitative Imaging in Oncology)

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16 pages, 2381 KiB

Open AccessArticle

Chemical Exchange Saturation Transfer for Lactate-Weighted Imaging at 3 T MRI: Comprehensive In Silico, In Vitro, In Situ, and In Vivo Evaluations

Matthias Boschheidgen

and

Tomography 2022, 8(3), 1277-1292; https://doi.org/10.3390/tomography8030106 - 07 May 2022

Cited by 6 | Viewed by 1626

Abstract

Based on in silico, in vitro, in situ, and in vivo evaluations, this study aims to establish and optimize the chemical exchange saturation transfer (CEST) imaging of lactate (Lactate-CEST—LATEST). To this end, we optimized LATEST sequences using Bloch–McConnell simulations for optimal detection of lactate with a clinical 3 T MRI scanner. The optimized sequences were used to image variable lactate concentrations in vitro (using phantom measurements), in situ (using nine human cadaveric lower leg specimens), and in vivo (using four healthy volunteers after exertional exercise) that were then statistically analyzed using the non-parametric Friedman test and Kendall Tau-b rank correlation. Within the simulated Bloch–McConnell equations framework, the magnetization transfer ratio asymmetry (MTR_asym) value was quantified as 0.4% in the lactate-specific range of 0.5–1 ppm, both in vitro and in situ, and served as the imaging surrogate of the lactate level. In situ, significant differences (p < 0.001) and strong correlations (τ = 0.67) were observed between the MTR_asym values and standardized intra-muscular lactate concentrations. In vivo, a temporary increase in the MTR_asym values was detected after exertional exercise. In this bench-to-bedside comprehensive feasibility study, different lactate concentrations were detected using an optimized LATEST imaging protocol in vitro, in situ, and in vivo at 3 T, which prospectively paves the way towards non-invasive quantification and monitoring of lactate levels across a broad spectrum of diseases. Full article

(This article belongs to the Special Issue Quantitative Imaging in Oncology)

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9 pages, 2084 KiB

Open AccessArticle

Quantitative Assessment of Airway Changes in Fibrotic Interstitial Lung Abnormality Patients by Chest CT According to Cumulative Cigarette Smoking

and

Tomography 2022, 8(2), 1024-1032; https://doi.org/10.3390/tomography8020082 - 03 Apr 2022

Cited by 2 | Viewed by 1703

Abstract

Purpose: The aim of this study was to evaluate the role of Pi10 in patients with fibrotic interstitial lung abnormality (fibrotic ILA) in a chest CT, according to cumulative cigarette smoking. Methods: We retrospectively assessed 54 fibrotic ILA patients and 18 healthy non-smokers (control) who underwent non-enhanced CT and pulmonary function tests. We quantitatively analyzed airway changes (the inner luminal area, airway inner parameter, airway wall thickness, Pi10, skewness, and kurtosis) in the chest CT of fibrotic ILA patients, and the fibrotic ILA patients were categorized into groups based on pack-years: light, moderate, heavy. Airway change data and pulmonary function tests among the three groups of fibrotic ILA patients were compared with those of the control group by one-way ANOVA. Results: Mean skewness (2.58 ± 0.36) and kurtosis (7.64 ± 2.36) in the control group were significantly different from those of the fibrotic ILA patients (1.89 ± 0.37 and 3.62 ± 1.70, respectively, p < 0.001). In fibrotic ILA group, only heavy smokers had significantly increased Pi10 (mean increase 0.04, p = 0.013), increased airway wall thickness of the segmental bronchi (mean increase 0.06 mm, p = 0.005), and decreased lung diffusing capacity for carbon monoxide (p = 0.023). Conclusion: Pi10, as a biomaker of quantitative CT in fibrotic ILA patients, can reveal that smoking affects airway remodeling. Full article

(This article belongs to the Special Issue Quantitative Imaging in Oncology)

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11 pages, 1286 KiB

Open AccessEditor’s ChoiceArticle

Ipsilateral Recurrence of DCIS in Relation to Radiomics Features on Contrast Enhanced Breast MRI

and

Tomography 2022, 8(2), 596-606; https://doi.org/10.3390/tomography8020049 - 01 Mar 2022

Cited by 5 | Viewed by 2363

Abstract

The purpose of this retrospective study was to investigate the association between ipsilateral recurrence of ductal carcinoma in situ (DCIS) and radiomics features from DCIS and contralateral normal breast on contrast enhanced breast MR imaging. A total of 163 patients with DCIS who underwent preoperative MR imaging between January 2010 and December 2014 were included (training cohort; n = 117, validation cohort; n = 46). Radiomics features were extracted from whole tumor volume of DCIS on early dynamic T1-subtraction images and from the contralateral normal breast on precontrast T1 and early dynamic T1-subtraction images. After feature selection, a Rad-score was established by LASSO Cox regression model. Performance of Rad-score was evaluated by the receiver operating characteristic (ROC) curve and Kaplan Meier curve with log rank test. The Rad-score was significantly associated with ipsilateral recurrence free survival (RFS). The low-risk group with a low Rad-score showed higher ipsilateral RFS than the high-risk group with a high Rad-score in both training and validation cohorts (p < 0.01). The Rad-score based on radiomics features from DCIS and contralateral normal breast on breast MR imaging showed the potential for prediction of ipsilateral RFS of DCIS. Full article

(This article belongs to the Special Issue Quantitative Imaging in Oncology)

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