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Diagnostics
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Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders
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Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 27024

Special Issue Editors

Dr. Barbara Palumbo

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Guest Editor
Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Perugia, Italy
Interests: nuclear medicine; image-based diagnostics; artificial intelligence; PET/CT; SPECT; SPECT/CT; radiomics; oncology; neurodegenerative disorders
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Angela Spanu

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Guest Editor
Nuclear Medicine Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, Italy
Interests: nuclear medicine; image-based diagnostics; SPECT/CT; PET/CT; molecular imaging; oncology (breast cancer; lung cancer; thyroid cancer; neuroendocrine tumors; prostate cancer); radiomics; artificial intelligence; theranostics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luca Brunese

E-Mail Website
Guest Editor
Department of Medicine and Health Sciences “Vincenzo Tiberio”, Università degli Studi del Molise, Campobasso, Italy
Interests: artificial intelligence; image-based diagnostics; CT; MRI; radiology; radiomics; oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Bianconi

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti, 93-06125 Perugia, Italy
Interests: artificial intelligence; computational imaging; computer vision; image processing; medical image analysis; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Diagnostic imaging has experienced major changes in recent years. Radiological and nuclear medicine modalities represent the option of choice to investigate the main oncological and neurological diseases. The improving capabilities of the imaging devices and the increasing availability of storing, sharing and computing facilities have been generating larger and larger amounts of data. Consequently, there has been increasing attention on the development of computational methods for the extraction of objective imaging features (biomarkers) capable of correlating with disease phenotype, clinical outcome and/or response to treatment. The combined use of imaging data, biomarkers and artificial intelligence techniques makes it possible to build powerful predictive models which can assist the physician in the management of patients with a wide range of disorders, particularly oncological and neurological, ultimately leading to personalised treatment and better clinical outcome. However, there are still open challenges before these methods can be translated into clinical practice. Critical to this process, for instance, are standardisation, strong interdisciplinary cooperation, and the availability of centralised repositories of annotated data.  

This Special Issue wants to provide a forum to discuss challenges, discoveries and opportunities in the field, with specific focus on the diagnosis of oncological and neurological disorders by radiological and nuclear medicine modalities. We encourage the submission research papers as well as review articles; comparative evaluations and new datasets are also welcome.

Dr. Barbara Palumbo
Prof. Dr. Angela Spanu
Prof. Dr. Luca Brunese
Dr. Francesco Bianconi
Guest Editors

Manuscript Submission Information

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. Diagnostics 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 2600 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

  • Artificial intelligence in diagnostic imaging
  • Computer-assisted diagnosis and prognostication
  • Data mining and big data
  • Deep Learning
  • Image processing (including acquisition, segmentation and feature extraction)
  • Radiology
  • Nuclear Medicine
  • Imaging modalities (including CT, MRI, PET, PET/CT, PET/MRI, SPECT, SPECT/CT)
  • Oncological and neurological disorders
  • Personalised medicine

Published Papers (11 papers)

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Research

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13 pages, 7115 KiB  
Article
Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis
by Pierpaolo Alongi, Riccardo Laudicella, Francesco Panasiti, Alessandro Stefano, Albert Comelli, Paolo Giaccone, Annachiara Arnone, Fabio Minutoli, Natale Quartuccio, Chiara Cupidi, Gaspare Arnone, Tommaso Piccoli, Luigi Maria Edoardo Grimaldi, Sergio Baldari and Giorgio Russo
Diagnostics 2022, 12(4), 933; https://doi.org/10.3390/diagnostics12040933 - 8 Apr 2022
Cited by 14 | Viewed by 3970
Abstract
Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial [...] Read more.
Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosis of AD, also by comparing the results with following Amyloid-PET and final clinical diagnosis. Methods: From July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clinical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD. Results: A total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (n = 6) and AD (n = 5) were found. Among them, two higher-order features (original_glcm_Idmn and original_glcm_Id), extracted from the limbic enthorinal cortical area (ROI-1) in the FDG-PET/CT images, predicted the positivity of Amyloid-PET/CT scans with maximum values of sensitivity (SS), specificity (SP), precision (PR) and accuracy (AC) of 84.92%, 75.13%, 73.75%, and 79.56%, respectively. Conversely, for the prediction of the clinical-instrumental final diagnosis of AD, the best performance was obtained by two higher-order features (original_glcm_MCC and original_glcm_Maximum Probability) extracted from ROI-2 (frontal cortex) with a SS, SP, PR and AC of 75.16%, 80.50%, 77.68%, and 78.05%, respectively, and by one higher-order feature (original_glcm_Idmn) extracted from ROI-3 (medial Temporal cortex; SS = 80.88%, SP = 76.85%, PR = 75.63%, AC = 78.76%. Conclusions: The results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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14 pages, 1989 KiB  
Article
A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
by Ayten Kayi Cangir, Kaan Orhan, Yusuf Kahya, Ayse Uğurum Yücemen, İslam Aktürk, Hilal Ozakinci, Aysegul Gursoy Coruh and Serpil Dizbay Sak
Diagnostics 2022, 12(2), 416; https://doi.org/10.3390/diagnostics12020416 - 5 Feb 2022
Cited by 8 | Viewed by 1806
Abstract
Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud [...] Read more.
Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud platform (Huiying Medical Technology Co., Ltd., Beijing, China) was used for managing the data, clinical data, and subsequent radiomics analysis. Two hand-crafted radiomics models are prepared in this study: the first model includes the data regarding all of the patients to differentiate between the groups; the second model includes 78 PCTs and 38 PHs without signs of fat tissue. The separation of the training and validation datasets was performed randomly using an (8:2) ratio and 620 random seeds. The results revealed that the MLP method (RF) was best for PH (AUC = 0.999) and PCT (AUC = 0.999) for the first model (AUC = 0.836), and PC (AUC = 0.836) in the test set for the second model. Radiomics tumor features derived from CT images are useful to differentiate the carcinoid tumors from hamartomas with high accuracy. Radiomics features may be used to differentiate PHs from PCTs with high levels of accuracy, even without the presence of fat on the CT. Advances in knowledge: CT-based radiomic holds great promise for a more accurate preoperative diagnosis of solitary pulmonary nodules (SPNs). Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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12 pages, 3158 KiB  
Article
Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices—A Feasibility Study
by Philipp Sager, Lukas Näf, Erwin Vu, Tim Fischer, Paul M. Putora, Felix Ehret, Christoph Fürweger, Christina Schröder, Robert Förster, Daniel R. Zwahlen, Alexander Muacevic and Paul Windisch
Diagnostics 2021, 11(9), 1676; https://doi.org/10.3390/diagnostics11091676 - 14 Sep 2021
Cited by 1 | Viewed by 2009
Abstract
Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas [...] Read more.
Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then externally validated on contrast-enhanced and non-contrast-enhanced slices from a different institution. Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results: The model achieved an accuracy of 0.928 (95% CI: 0.869–0.987) on contrast-enhanced slices and 0.795 (95% CI: 0.702–0.888) on non-contrast-enhanced slices from the external validation cohorts. The implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the focus of the model was not limited to the contrast-enhancing tumor but to a larger area of the cerebellum and the cerebellopontine angle. Conclusions: Single-slice predictions might constitute a computationally inexpensive alternative to training 2.5/3D-CNNs for certain detection tasks in medical imaging even without the use of segmentations. Head-to-head comparisons between 2D and more sophisticated architectures could help to determine the difference in accuracy, especially for more difficult tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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17 pages, 646 KiB  
Article
Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study
by Francesco Bianconi, Mario Luca Fravolini, Isabella Palumbo, Giulia Pascoletti, Susanna Nuvoli, Maria Rondini, Angela Spanu and Barbara Palumbo
Diagnostics 2021, 11(7), 1224; https://doi.org/10.3390/diagnostics11071224 - 6 Jul 2021
Cited by 7 | Viewed by 2214
Abstract
Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be [...] Read more.
Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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11 pages, 891 KiB  
Article
Adding Value of MRI over CT in Predicting Peritoneal Cancer Index and Completeness of Cytoreduction
by Chia-Ni Lin, Weh-Shih Huang, Tzu-Hao Huang, Chao-Yu Chen, Cheng-Yi Huang, Ting-Yao Wang, Yu-San Liao and Li-Wen Lee
Diagnostics 2021, 11(4), 674; https://doi.org/10.3390/diagnostics11040674 - 8 Apr 2021
Cited by 13 | Viewed by 2893
Abstract
Background: This study aimed to investigate the adding value of MRI over CT for preoperative cytoreductive surgery with hyperthermic intraperitoneal chemotherapies (CRS/HIPEC). Methods: Imaging and intraoperative peritoneal cancer index (PCI) were calculated in 62 patients with peritoneal metastasis. Predictive models for the completeness [...] Read more.
Background: This study aimed to investigate the adding value of MRI over CT for preoperative cytoreductive surgery with hyperthermic intraperitoneal chemotherapies (CRS/HIPEC). Methods: Imaging and intraoperative peritoneal cancer index (PCI) were calculated in 62 patients with peritoneal metastasis. Predictive models for the completeness of cytoreductive score using PCI data were established using decision tree algorithms. Results: In gastric cancer patients, a large discrepancy and poor agreement was appreciated between CT and surgical PCI, and a nonsignificant difference was noted between MRI and surgical PCI. In colon cancer patients, a better agreement and higher correlation with a smaller error was observed in PCI score using MRI than in that using CT. However, the addition of MRI to CT was limited for appendiceal and ovarian cancer patients. For predicting incomplete cytoreduction, CT models yielded inadequate accuracy while MRI models were more accurate with fair discrimination ability. Conclusions: CT was suitable for estimating PCI and surgery outcome in appendiceal and ovarian cancer patients, while further MRI in addition to CT was recommended for colon and gastric cancer patients. However, for classifying patients with peritoneal carcinomatosis into complete and incomplete cytoreduction, MRI was more effective than CT. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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22 pages, 2707 KiB  
Article
On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis
by Antonella Santone, Maria Paola Belfiore, Francesco Mercaldo, Giulia Varriano and Luca Brunese
Diagnostics 2021, 11(2), 293; https://doi.org/10.3390/diagnostics11020293 - 12 Feb 2021
Cited by 9 | Viewed by 2268
Abstract
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose [...] Read more.
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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Review

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19 pages, 322 KiB  
Review
An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology
by Jeffrey Liu, Bino Varghese, Farzaneh Taravat, Liesl S. Eibschutz and Ali Gholamrezanezhad
Diagnostics 2022, 12(6), 1351; https://doi.org/10.3390/diagnostics12061351 - 30 May 2022
Cited by 6 | Viewed by 2153
Abstract
Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate [...] Read more.
Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
22 pages, 386 KiB  
Review
Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review
by Alfonso Reginelli, Valerio Nardone, Giuliana Giacobbe, Maria Paola Belfiore, Roberta Grassi, Ferdinando Schettino, Mariateresa Del Canto, Roberto Grassi and Salvatore Cappabianca
Diagnostics 2021, 11(10), 1796; https://doi.org/10.3390/diagnostics11101796 - 29 Sep 2021
Cited by 27 | Viewed by 4036
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, [...] Read more.
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

Other

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2 pages, 173 KiB  
Reply
Reply to Perrella et al. Coming Back to the Basics. Comment on “Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors. Diagnostics 2022, 12, 416”
by Ayten Kayi Cangir, Kaan Orhan and Aysegul Gursoy Coruh
Diagnostics 2023, 13(23), 3490; https://doi.org/10.3390/diagnostics13233490 - 21 Nov 2023
Viewed by 503
Abstract
We thank to Dr. Perrella and and his fellow authors for your kind letter and thoughtful comments [...] Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
2 pages, 186 KiB  
Comment
Coming Back to the Basics. Comment on Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors. Diagnostics 2022, 12, 416
by Armando Perrella, Giulio Bagnacci, Nunzia Di Meglio, Vito Di Martino, Cristiana Bellan, Luca Luzzi, Maria Antonietta Mazzei and Luca Volterrani
Diagnostics 2023, 13(23), 3489; https://doi.org/10.3390/diagnostics13233489 - 21 Nov 2023
Cited by 1 | Viewed by 531
Abstract
We read with great interest the article by Cangir et al., “A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors”, published on 5 February 2022 [...] Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
13 pages, 1395 KiB  
Systematic Review
Diagnostic Performance of the Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson’s Disease: An Updated Systematic Review and Meta-Analysis
by Seongken Kim, Chong Hyun Suh, Woo Hyun Shim and Sang Joon Kim
Diagnostics 2022, 12(1), 12; https://doi.org/10.3390/diagnostics12010012 - 22 Dec 2021
Cited by 8 | Viewed by 2930
Abstract
Progressive supranuclear palsy (PSP) and Parkinson’s disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and [...] Read more.
Progressive supranuclear palsy (PSP) and Parkinson’s disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and EMBASE was performed to identify original articles evaluating the diagnostic performance of the MRPI in differentiating PSP from PD published up to 20 February 2021. The pooled sensitivity, specificity, and 95% CI were calculated using the bivariate random-effects model. The area under the curve (AUC) was calculated using a hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression was performed to explain the effects of heterogeneity. A total of 14 original articles involving 484 PSP patients and 1243 PD patients were included. In all studies, T1-weighted images were used to calculate the MRPI. Among the 14 studies, nine studies used 3D T1-weighted images. The pooled sensitivity and specificity for the diagnostic performance of the MRPI in differentiating PSP from PD were 96% (95% CI, 87–99%) and 98% (95% CI, 91–100%), respectively. The area under the HSROC curve was 0.99 (95% CI, 0.98–1.00). Heterogeneity was present (sensitivity: I2 = 97.29%; specificity: I2 = 98.82%). Meta-regression showed the association of the magnet field strength with heterogeneity. Studies using 3 T MRI showed significantly higher sensitivity (100%) and specificity (100%) than those of studies using 1.5 T MRI (sensitivity of 98% and specificity of 97%) (p < 0.01). Thus, the MRPI could accurately differentiate PSP from PD and support the implementation of appropriate management strategies for patients with PSP. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)
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