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A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research

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A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: closed (22 October 2021) | Viewed by 26168

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Dr. Christopher Lai

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Health and Social Sciences Cluster, Singapore Institute of Technology, SIT@Dover, 10 Dover Drive, Singapore 138683, Singapore
Interests: metabolic syndromes; ultrasonography and MRI in vascular, musculoskeletal and nervous systems; endothelial dysfunction and atherosclerosis in diabetes; radiography education development; computed tomography; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Suchart Kothan

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1. Center of Radiation Research and Medical Imaging, Chiang Mai University, Chiang Mai, Thailand
2. Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: magnetic resonance imaging; in vivo and in vitro nuclear magnetic resonance spectroscopy; paramagnetic nanoparticles; contrast agents; polyphenol and antioxidant; multidrug resistance; radiobiology; radiation shielding material; dosimetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging is an integral part of the diagnosis and treatment of diseases. From X-ray radiography, fluoroscopy, computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) to more recent hybrid imaging technologies such as SPECT-CT, SPECT-MRI, PET-CT, and PET-MRI imaging, the capabilities of medical imaging have radically expanded. Benefiting from the recent state-of-the-art rҽsҽarсh and evolutions in all aspects of radiation science and technology, there have been numerous advancements in medical imaging technology. Today, with the advancements in medical imaging technology together with the digital growth in artificial intelligence, healthcare workers can achieve greater procedural efficiency in the provision and execution of medical imaging services and patient care.

In view of the fast-paced evolution and multidisciplinary nature of medical imaging and radiation research and artificial intelligence research, this Special Issue aims to summarize recent updates and advancements in this special domain of medicine. All clinical, non-clinical and basic science works are welcomed. Original research and review papers will be considered.

Dr. Christopher Lai
Dr. Suchart Kothan
Guest Editors

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Keywords

medical imaging
molecular imaging
artificial intelligence
machine learning
deep learning
big data
paramagnetic nanoparticles
radiocontrast agents
radiation biology
radiation shielding
dosimetry
cancer
multidrug resistance
natural compounds and antioxidants
bioinformatics

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Published Papers (9 papers)

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Research

13 pages, 1366 KiB

Open AccessArticle

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

by Eva Y. W. Cheung, Anson C. M. Chau, Fuk Hay Tang and on behalf of the Alzheimer’s Disease Neuroimaging Initiative

Life 2022, 12(4), 514; https://doi.org/10.3390/life12040514 - 31 Mar 2022

Cited by 10 | Viewed by 2917

Abstract

This study aimed to build automated detection models—one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)—to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer’s Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. Method: 582 MRI T1-weighted images were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. Results: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. Conclusion: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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12 pages, 915 KiB

Open AccessArticle

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma

by Sai-Kit Lam, Jiang Zhang, Yuan-Peng Zhang, Bing Li, Rui-Yan Ni, Ta Zhou, Tao Peng, Andy Lai-Yin Cheung, Tin-Ching Chau, Francis Kar-Ho Lee, Celia Wai-Yi Yip, Kwok-Hung Au, Victor Ho-Fun Lee, Amy Tien-Yee Chang, Lawrence Wing-Chi Chan and Jing Cai

Life 2022, 12(2), 241; https://doi.org/10.3390/life12020241 - 6 Feb 2022

Cited by 13 | Viewed by 2836

Abstract

Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong’s test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest “corrected” AUC of 0.784 (BCa 95%CI: 0.673–0.859) and 0.723 (BCa 95%CI: 0.534–0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong’s test indicated that the R model performed significantly better than the C model in the QMH cohort (p < 0.0001), while demonstrating no significant difference compared to the RC model (p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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15 pages, 3097 KiB

Open AccessArticle

Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning

by Patiparn Kummanee, Wares Chancharoen, Kanut Tangtisanon and Todsaporn Fuangrod

Life 2021, 11(12), 1305; https://doi.org/10.3390/life11121305 - 27 Nov 2021

Cited by 3 | Viewed by 1828

Abstract

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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13 pages, 2232 KiB

Open AccessArticle

Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound

by Nonhlanhla Chambara, Shirley Yuk Wah Liu, Xina Lo and Michael Ying

Life 2021, 11(11), 1148; https://doi.org/10.3390/life11111148 - 27 Oct 2021

Cited by 2 | Viewed by 1999

Abstract

The value of computer-aided diagnosis (CAD) and computer-assisted techniques equipped with different TIRADS remains ambiguous. Parallel diagnosis performances of computer-assisted subjective assessments and CAD were compared based on AACE, ATA, EU, and KSThR TIRADS. CAD software computed the diagnosis of 162 thyroid nodule sonograms. Two raters (R₁ and R₂) independently rated the sonographic features of the nodules using an online risk calculator while blinded to pathology results. Diagnostic efficiency measures were calculated based on the final pathology results. R₁ had higher diagnostic performance outcomes than CAD with similarities between KSThR (SEN: 90.3% vs. 83.9%, p = 0.57; SPEC: 46% vs. 51%, p = 0.21; AUROC: 0.76 vs. 0.67, p = 0.02), and EU (SEN: 85.5% vs. 79%, p = 0.82; SPEC: 62% vs. 55%, p = 0.27; AUROC: 0.74 vs. 0.67, p = 0.06). Similarly, R₂ had higher AUROC and specificity but lower sensitivity than CAD (KSThR-AUROC: 0.74 vs. 0.67, p = 0.13; SPEC: 61% vs. 46%, p = 0.02 and SEN: 75.8% vs. 83.9%, p = 0.31, and EU-AUROC: 0.69 vs. 0.67, p = 0.57, SPEC: 64% vs. 55%, p = 0.19, and SEN: 71% vs. 79%, p = 0.51, respectively). CAD had higher sensitivity but lower specificity than both R₁ and R₂ with AACE for 114 specified nodules (SEN: 92.5% vs. 88.7%, p = 0.50; 92.5% vs. 79.3%, p = 0.02, and SPEC: 26.2% vs. 54.1%, p = 0.001; 26.2% vs. 62.3%, p < 0.001, respectively). All diagnostic performance outcomes were comparable for ATA with 96 specified nodules. Computer-assisted subjective interpretation using KSThR is more ideal for ruling out papillary thyroid carcinomas than CAD. Future larger multi-center and multi-rater prospective studies with a diverse representation of thyroid cancers are necessary to validate these findings. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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18 pages, 1599 KiB

Open AccessArticle

Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer’s Related Neurodegenerative Diseases—A Resting State fMRI Study

by Eva Y. W. Cheung, Y. F. Shea, Patrick K. C. Chiu, Joseph S. K. Kwan and Henry K. F. Mak

Life 2021, 11(10), 1108; https://doi.org/10.3390/life11101108 - 19 Oct 2021

Cited by 14 | Viewed by 3029

Abstract

Previous studies have demonstrated that functional connectivity (FC) of different brain regions in resting state function MRI were abnormal in patients suffering from mild cognitive impairment (MCI) and Alzheimer’s disease (AD) when comparing to healthy controls (HC) using seed based, independent component analysis (ICA) or small world network techniques. A new technique called voxel-mirrored homotopic connectivity (VMHC) was used in the current study to evaluate the value of interhemispheric functional connectivity (IFC) as a diagnostic tool to differentiate vascular dementia (VD) from other Alzheimer’s related neurodegenerative diseases. Eighty-three participants were recruited from the university hospital memory clinic. A multidisciplinary panel formed by a neuroradiologist and two geriatricians classified the participants into VD (13), AD (16), MCI (29), and HC (25) based on clinical history, Montreal Cognitive Assessment Hong Kong version (HK‑MoCA) neuropsychological score, structural MRI, MR perfusion, and 18-F Flutametamol (amyloid) PET-CT findings of individual subjects. We adopted the calculation method used by Kelly et al. (2011) and Zuo et al. (2010) in obtaining VMHC maps. Specific patterns of VMHC maps were obtained for VD, AD, and MCI to HC comparison. VD showed significant reduction in VMHC in frontal orbital gyrus and gyrus rectus. Increased VMHC was observed in default mode network (DMN), executive control network (ECN), and the remaining salient network (SN) regions. AD showed a reduction of IFC in all DMN, ECN, and SN regions; whereas MCI showed VMHC reduction in vSN, and increased VMHC in DMN and ECN. When combining VMHC values of relevant brain regions, the accuracy was improved to 87%, 92%, and 83% for VD, AD, and MCI from HC, respectively, in receiver operating characteristic (ROC) analysis. Through studying the VMHC maps and using VMHC values in relevant brain regions, VMHC can be considered as a reliable diagnostic tool for VD, AD, and MCI from HC. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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13 pages, 1395 KiB

Open AccessEditor’s ChoiceArticle

Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change

by Cheng Xue, Fuk-Hay Tang, Christopher W. K. Lai, Lars J. Grimm and Joseph Y. Lo

Life 2021, 11(8), 747; https://doi.org/10.3390/life11080747 - 26 Jul 2021

Cited by 6 | Viewed by 2666

Abstract

Background: The strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable. Methods: This study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images. During the registration process, the mechanical properties of the breast tissues were individually assigned using an optimization process, which allowed the model to become patient specific. Evaluation and results: The proposed method has been extensively tested on two datasets collected from two independent institutions, one from America and another from Hong Kong. Conclusions: Our method can accurately predict the deformation of breasts from the supine to prone position for both the Hong Kong and American samples, with a small target registration error of lesions. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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11 pages, 999 KiB

Open AccessArticle

Waist Circumference and BMI Are Strongly Correlated with MRI-Derived Fat Compartments in Young Adults

by Duanghathai Pasanta, Khin Thandar Htun, Jie Pan, Montree Tungjai, Siriprapa Kaewjaeng, Sirirat Chancharunee, Singkome Tima, Hong Joo Kim, Jakrapong Kæwkhao and Suchart Kothan

Life 2021, 11(7), 643; https://doi.org/10.3390/life11070643 - 1 Jul 2021

Cited by 21 | Viewed by 2792

Abstract

Young adulthood is increasingly considered as a vulnerable age group for significant weight gain, and it is apparent that there is an increasing number of new cases of metabolic syndrome developing among this population. This study included 60 young adult volunteers (18–26 years old). All participants obtained a calculated total abdominal fat percentage, subcutaneous fat percentage, and visceral fat percentage using a semiautomatic segmentation technique from T1-weighted magnetic resonance imaging (MRI) images of the abdomen. The results show strongest correlation between abdominal fat and BMI (r = 0.824) followed by subcutaneous fat (r = 0.768), and visceral fat (r = 0.633) respectively, (p < 0.001 for all, after having been adjusted for age and gender). Among anthropometric measurements, waist circumference showed strong correlation with all fat compartments (r = 0.737 for abdominal, r = 0.707 for subcutaneous fat, and r = 0.512 for visceral fat; p < 0.001 for all). The results obtained from examining the blood revealed that there was a moderate positive correlation relationship between all fat compartments with triglyceride, high-density lipoprotein, and fasting glucose levels (p < 0.05 for all). This study suggests that both BMI and waist circumference could be used to assess the fat compartments and treatment targets to reduce the risk of metabolic disorders and health risks in the young adult population. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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18 pages, 2236 KiB

Open AccessEditor’s ChoiceArticle

Identification of Metabolic Phenotypes in Young Adults with Obesity by ¹H NMR Metabolomics of Blood Serum

by Khin Thandar Htun, Jie Pan, Duanghathai Pasanta, Montree Tungjai, Chatchanok Udomtanakunchai, Sirirat Chancharunee, Siriprapa Kaewjaeng, Hong Joo Kim, Jakrapong Kaewkhao and Suchart Kothan

Life 2021, 11(6), 574; https://doi.org/10.3390/life11060574 - 18 Jun 2021

Cited by 15 | Viewed by 3215

Abstract

(1) Since the obesity prevalence rate has been consistently increasing, it is necessary to find an effective way to prevent and treat it. Although progress is being made to reduce obesity in the young adult population, a better understanding of obesity-related metabolomics and related biochemical mechanisms is urgently needed for developing appropriate screening strategies. Therefore, the aim of this study is to identify the serum metabolic profile associated with young adult obesity and its metabolic phenotypes. (2) Methods: The serum metabolic profile of 30 obese and 30 normal-weight young adults was obtained using proton nuclear magnetic resonance spectroscopy (¹H NMR). ¹H NMR spectra were integrated into 24 integration regions, which reflect relative metabolites, and were used as statistical variables. (3) Results: The obese group showed increased levels of lipids, glucose, glutamate, N-acetyl glycoprotein, alanine, lactate, 3 hydroxybutyrate and branch chain amino acid (BCAA), and decreased levels of choline as compared with the normal-weight group. Non-hyperlipidemia obese adults showed lower levels of lipids and lactate, glutamate, acetoacetate, N-acetyl glycoprotein, isoleucine, and higher levels of choline and glutamine, as compared with hyperlipidemic obese adults. (4) Conclusions: This study reveals valuable findings in the field of metabolomics and young adult obesity. We propose several serum biomarkers that distinguish between normal weight and obese adults, i.e., glutamine (higher in the normal group, p < 0.05), and lactate, BCAAs, acetoacetate and 3-hydroxybutyrate (higher in the obese group, p < 0.05). In addition, visceral fat and serum TG, glutamate, acetoacetate, N-acetyl glycoprotein, unsaturated lipid, isoleucine, and VLDL/LDL are higher (p < 0.05) in the obese with hyperlipidemia. Therefore, they can be used as biomarkers to identify these two types of obesity. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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13 pages, 3397 KiB

Open AccessArticle

Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method

by Yi Ling Eileen Goh, Zhen Yu Lee and Christopher Lai

Life 2021, 11(6), 516; https://doi.org/10.3390/life11060516 - 3 Jun 2021

Viewed by 2734

Abstract

(1) Background: Mammographic breast density (MBD) and older age are classical breast cancer risk factors. Normally, MBDs are not evenly distributed in the breast, with different women having different spatial distribution and clustering patterns. The presence of MBDs makes tumors and other lesions challenging to be identified in mammograms. The objectives of this study were: (i) to quantify the amount of MBDs—in the whole (overall), different sub-regions, and different zones of the breast using an image segmentation method; (ii) to investigate the spatial distribution patterns of MBD in different sub-regions of the breast. (2) Methods: The image segmentation method was used to quantify the overall amount of MBDs in the whole breast (overall percentage density (PD)), in 48 sub-regions (regional PDs), and three different zones (zonal PDs) of the whole breast, and the results of the amount of MBDs in 48 sub-regional PDs were further analyzed to determine its spatial distribution pattern in the breast using Moran’s I values (spatial autocorrelation). (3) Results: The overall PD showed a negative correlation with age (p = 0.008); the younger women tended to have denser breasts (higher overall PD in breasts). We also found a higher proportion (p < 0.001) of positive autocorrelation pattern in the less dense breast group than in the denser breast group, suggesting that MBDs in the less dense breasts tend to be clustered together. Moreover, we also observed that MBDs in the mature women (<65 years old) tended to be clustered in the middle zone, while in older women (>64 years old) they tended to be clustered in both the posterior and middle zones. (4) Conclusions: There is an inverse relationship between the amount of MBD (overall PD in the breast) and age, and a different clustering pattern of MBDs between the older and mature women. Full article

(This article belongs to the Special Issue A New Era of Multidisciplinary Radiology: Fast Growing of Medical Radiation/Imaging/Artificial Intelligence Research)

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