Journal Description
Diagnostics
Diagnostics
is an international, peer-reviewed, open access journal on medical diagnosis published semimonthly online by MDPI. The British Neuro-Oncology Society (BNOS), the International Society for Infectious Diseases in Obstetrics and Gynaecology (ISIDOG) and the Swiss Union of Laboratory Medicine (SULM) are affiliated with Diagnostics and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Medicine, General & Internal)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: LabMed.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.7 (2022)
Latest Articles
Role of Myostatin in Rheumatoid Arthritis: A Review of the Clinical Impact
Diagnostics 2024, 14(11), 1085; https://doi.org/10.3390/diagnostics14111085 - 23 May 2024
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects synovial joints and that frequently involves extra-articular organs. A multiplicity of interleukins (IL) participates in the pathogenesis of RA, including IL-6, IL-1β, transforming growth factor-beta (TGF-β), and tumor necrosis factor (TNF)-α; immune cells
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Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects synovial joints and that frequently involves extra-articular organs. A multiplicity of interleukins (IL) participates in the pathogenesis of RA, including IL-6, IL-1β, transforming growth factor-beta (TGF-β), and tumor necrosis factor (TNF)-α; immune cells such as monocytes, T and B lymphocytes, and macrophages; and auto-antibodies, mainly rheumatoid factor and anti-citrullinated protein antibodies (ACPAs). Skeletal muscle is also involved in RA, with many patients developing muscle wasting and sarcopenia. Several mechanisms are involved in the myopenia observed in RA, and one of them includes the effects of some interleukins and myokines on myocytes. Myostatin is a myokine member of the TGF-β superfamily; the overproduction of myostatin acts as a negative regulator of growth and differentiates the muscle fibers, limiting their number and size. Recent studies have identified abnormalities in the serum myostatin levels of RA patients, and these have been found to be associated with muscle wasting and other manifestations of severe RA. This review analyzes recent information regarding the relationship between myostatin levels and clinical manifestations of RA and the relevance of myostatin as a therapeutic target for future research.
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(This article belongs to the Special Issue Molecular Expression and Diagnosis of Rheumatology)
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Open AccessCase Report
Uterine Arteriovenous Malformation: Diagnostic and Therapeutic Challenges
by
Luisa Clavero Bertomeu, Laura Castro Portillo and Cristina Fernández-Conde de Paz
Diagnostics 2024, 14(11), 1084; https://doi.org/10.3390/diagnostics14111084 - 23 May 2024
Abstract
Uterine arteriovenous malformations are a rare cause of puerperal haemorrhage, but their incidence is increasing due to both improved diagnosis and the more frequent use of uterine surgery in recent years. The use of ultrasound, both B-mode and Doppler, is recommended for diagnosis
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Uterine arteriovenous malformations are a rare cause of puerperal haemorrhage, but their incidence is increasing due to both improved diagnosis and the more frequent use of uterine surgery in recent years. The use of ultrasound, both B-mode and Doppler, is recommended for diagnosis and follow-up, as it has been shown to be the simplest and most cost-effective method. Endometrial thickening associated with an anechoic and vascular intramiometrial structure is very useful for diagnosis and can help to exclude other causes of dysfunctional bleeding. Pulsed Doppler shows low-resistance vessels and high pulsatility indices with a high peak systolic velocity (PSV). In a healthy myometrium, the vessels have a peak systolic velocity of 9–40 cm/s and a resistance index between 0.6 and 0.8, whereas in the case of AVMs, the systolic and diastolic velocities are 4–6 times higher (PSV 25–110 cm/s with a mean of 60 cm/s and a resistance index of 0.27–0.75 with a mean of 0.41). For treatment, we must individualise each case, taking into account haemodynamic stability, the patient’s reproductive wishes, and the severity of the AVM as assessed by its size and PSV.
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(This article belongs to the Special Issue Current Challenges and Perspectives of Ultrasound)
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Open AccessArticle
Assessing the Utility of Acoustic Radiation Force Impulse in the Evaluation of Non-Alcoholic Fatty Liver Disease with Severe Obesity or Steatosis
by
Yeo Wool Kang, Yang Hyun Baek, Jong Hoon Lee, Young Hoon Roh, Hee Jin Kwon, Sang Yi Moon, Min Kook Son and Jin Sook Jeong
Diagnostics 2024, 14(11), 1083; https://doi.org/10.3390/diagnostics14111083 - 22 May 2024
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) encompasses a heterogeneous spectrum ranging from simple steatosis to fibrosis and cirrhosis. Fibrosis, associated with long-term overall mortality and liver-related events, requires evaluation. Traditionally, liver biopsy has been the gold standard for diagnosing fibrosis. However, its invasive
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Background: Non-alcoholic fatty liver disease (NAFLD) encompasses a heterogeneous spectrum ranging from simple steatosis to fibrosis and cirrhosis. Fibrosis, associated with long-term overall mortality and liver-related events, requires evaluation. Traditionally, liver biopsy has been the gold standard for diagnosing fibrosis. However, its invasive nature, potential complications, and sampling variability limit widespread use. Consequently, various non-invasive tests have been developed as alternatives for diagnosing fibrosis in NAFLD patients. Aim: This study aimed to compare the accuracy of non-invasive tests (NITs) and evaluate the diagnostic accuracy of acoustic radiation force impulse (ARFI), one of the point shear wave techniques, compared to conventional methods, assessing its effective role in diagnosis. Methods: This is a retrospective study; a total of 136 patients diagnosed with fatty liver disease through ultrasonography were enrolled. The anthropometric data of the patients were collected on the day of admission and blood tests, measurements of ARFI, and a point shear test were conducted using abdominal ultrasound; a biopsy was performed the following day. In addition, we calculated the aspartate aminotransferase-to-platelet ratio index (APRI) index based on four factors (FIB-4) and the NAFLD fibrosis score (NFS). Subsequently, we assessed the diagnostic accuracy of NITs within various subgroups based on the extent of obesity, steatosis, or NAFLD activity score. Results: ARFI has been shown to have the highest diagnostic value among various NITs, with AUROC values of 0.832, 0.794, 0.767, and 0.696 for ARFI, APRI, FIB-4, and NFS, respectively. In the morbidly obese subgroup, the AUROC values of ARFI, APRI, FIB-4, and NFS were 0.805, 0.769, 0.736, and 0.674. In the group with severe steatosis or non-alcoholic steatohepatitis (NASH), the AUROC values were 0.679, 0.596, 0.661, and 0.612, respectively, for severe steatosis and 0.789, 0.696, 0.751, and 0.691, respectively, for NASH. Conclusion: In conclusion, ARFI is not affected by various factors and maintains diagnostic accuracy compared to serum NITs. Therefore, we can recommend ARFI as a valuable diagnostic test to screen for advanced fibrosis in patients with NAFLD.
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(This article belongs to the Special Issue Imaging Diagnosis of Liver Diseases)
Open AccessArticle
ChatGPT’s Efficacy in Queries Regarding Polycystic Ovary Syndrome and Treatment Strategies for Women Experiencing Infertility
by
Belgin Devranoglu, Tugba Gurbuz and Oya Gokmen
Diagnostics 2024, 14(11), 1082; https://doi.org/10.3390/diagnostics14111082 - 22 May 2024
Abstract
This study assesses the efficacy of ChatGPT-4, an advanced artificial intelligence (AI) language model, in delivering precise and comprehensive answers to inquiries regarding managing polycystic ovary syndrome (PCOS)-related infertility. The research team, comprising experienced gynecologists, formulated 460 structured queries encompassing a wide range
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This study assesses the efficacy of ChatGPT-4, an advanced artificial intelligence (AI) language model, in delivering precise and comprehensive answers to inquiries regarding managing polycystic ovary syndrome (PCOS)-related infertility. The research team, comprising experienced gynecologists, formulated 460 structured queries encompassing a wide range of common and intricate PCOS scenarios. The queries were: true/false (170), open-ended (165), and multiple-choice (125) and further classified as ‘easy’, ‘moderate’, and ‘hard’. For true/false questions, ChatGPT-4 achieved a flawless accuracy rate of 100% initially and upon reassessment after 30 days. In the open-ended category, there was a noteworthy enhancement in accuracy, with scores increasing from 5.53 ± 0.89 initially to 5.88 ± 0.43 at the 30-day mark (p < 0.001). Completeness scores for open-ended queries also experienced a significant improvement, rising from 2.35 ± 0.58 to 2.92 ± 0.29 (p < 0.001). In the multiple-choice category, although the accuracy score exhibited a minor decline from 5.96 ± 0.44 to 5.92 ± 0.63 after 30 days (p > 0.05). Completeness scores for multiple-choice questions remained consistent, with initial and 30-day means of 2.98 ± 0.18 and 2.97 ± 0.25, respectively (p > 0.05). ChatGPT-4 demonstrated exceptional performance in true/false queries and significantly improved handling of open-ended questions during the 30 days. These findings emphasize the potential of AI, particularly ChatGPT-4, in enhancing decision-making support for healthcare professionals managing PCOS-related infertility.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Open AccessArticle
Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification
by
Derek Wu, Delaney Smith, Blake VanBerlo, Amir Roshankar, Hoseok Lee, Brian Li, Faraz Ali, Marwan Rahman, John Basmaji, Jared Tschirhart, Alex Ford, Bennett VanBerlo, Ashritha Durvasula, Claire Vannelli, Chintan Dave, Jason Deglint, Jordan Ho, Rushil Chaudhary, Hans Clausdorff, Ross Prager, Scott Millington, Samveg Shah, Brian Buchanan and Robert Arntfieldadd
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Diagnostics 2024, 14(11), 1081; https://doi.org/10.3390/diagnostics14111081 - 22 May 2024
Abstract
Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the
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Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce—compared to other medical imaging data—we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model’s performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.
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(This article belongs to the Special Issue Explainable Artificial Intelligence for Trustworthy Machine Learning and Deep Learning Models in Healthcare)
Open AccessArticle
Analytical Evaluation of Point-of-Care Finecare™ Procalcitonin Rapid Quantitative Test in Sepsis Population as Compared with Elecsys® BRAHMS Procalcitonin Immunoassay
by
Mohd Zulfakar Mazlan, Wan Norlina Wan Azman, Najib Majdi Yaacob, Tan Say Koon and Nurul Khaiza Yahya
Diagnostics 2024, 14(11), 1080; https://doi.org/10.3390/diagnostics14111080 - 22 May 2024
Abstract
The study compared two plasma procalcitonin (PCT) assays, the point of care (POC) Finecare™ Procalcitonin Rapid Quantitative Test and the Elecsys® BRAHMS PCT immunoassay, in sepsis ICU patients. Forty-one plasma samples were analyzed, showing a strong correlation (r = 0.98) and no
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The study compared two plasma procalcitonin (PCT) assays, the point of care (POC) Finecare™ Procalcitonin Rapid Quantitative Test and the Elecsys® BRAHMS PCT immunoassay, in sepsis ICU patients. Forty-one plasma samples were analyzed, showing a strong correlation (r = 0.98) and no significant difference in PCT values. The mean POC PCT value was 4.46 ng/mL (SD 8.68), and for laboratory BRAHMS PCT, it was 4.67 ng/mL (SD 10.03). The study found a strong linear relationship between plasma POC PCT and laboratory BRAHMS PCT (r = 0.98). Different regression methods showed varying intercepts and slopes: Ordinary Least Squares had an intercept of 0.49 and a slope of 0.85; Deming regression showed an intercept of 0.43 and a slope of 0.86; Passing–Bablok regression showed an intercept of 0.02 and a slope of 1.08. Precision results for cut-offs of 0.5 ng/mL were a coefficient of variation (CV) of 5%, and for 2.5 ng/mL, the CV was 2.5%. The Pearson correlation coefficient (r) for linearity was ≥0.99. The study revealed no significant difference between the POC Finecare™ PCT and Elecsys® BRAHMS PCT immunoassay in sepsis samples from ICU patients, supported by strong correlation, minimal bias, a consistent CV, and linearity.
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(This article belongs to the Special Issue Sepsis at the Crossroads: Innovations in Critical Care Management, Diagnostic Tools, and Treatment Paradigms)
Open AccessSystematic Review
Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review
by
Sanjeev B. Khanagar, Farraj Albalawi, Aram Alshehri, Mohammed Awawdeh, Kiran Iyer, Barrak Alsomaie, Ali Aldhebaib, Oinam Gokulchandra Singh and Abdulmohsen Alfadley
Diagnostics 2024, 14(11), 1079; https://doi.org/10.3390/diagnostics14111079 - 22 May 2024
Abstract
Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency
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Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp–tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.
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(This article belongs to the Special Issue Advances in the Diagnosis of Oral and Maxillofacial Disease)
Open AccessReview
Laboratory Tests, Bacterial Resistance, and Treatment Options in Adult Patients Hospitalized with a Suspected Urinary Tract Infection
by
Paul Froom and Zvi Shimoni
Diagnostics 2024, 14(11), 1078; https://doi.org/10.3390/diagnostics14111078 - 22 May 2024
Abstract
Patients treated for systemic urinary tract infections commonly have nonspecific presentations, and the specificity of the results of the urinalysis and urine cultures is low. In the following narrative review, we will describe the widespread misuse of urine testing, and consider how to
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Patients treated for systemic urinary tract infections commonly have nonspecific presentations, and the specificity of the results of the urinalysis and urine cultures is low. In the following narrative review, we will describe the widespread misuse of urine testing, and consider how to limit testing, the disutility of urine cultures, and the use of antibiotics in hospitalized adult patients. Automated dipstick testing is more precise and sensitive than the microscopic urinalysis which will result in false negative test results if ordered to confirm a positive dipstick test result. There is evidence that canceling urine cultures if the dipstick is negative (negative leukocyte esterase, and nitrite) is safe and helps prevent the overuse of urine cultures. Because of the side effects of introducing a urine catheter, for patients who cannot provide a urine sample, empiric antibiotic treatment should be considered as an alternative to culturing the urine if a trial of withholding antibiotic therapy is not an option. Treatment options that will decrease both narrower and wider spectrum antibiotic use include a period of watching and waiting before antibiotic therapy and empiric treatment with antibiotics that have resistance rates > 10%. Further studies are warranted to show the option that maximizes patient comfort and safety.
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(This article belongs to the Special Issue Recent Advances in Clinical Biochemical Testing)
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Open AccessBrief Report
Associations of Brain Arteriovenous Malformation-Related Factors with Epileptic Seizure Presentations
by
Kymbat Mukhtarova, Chingiz Nurimanov, Elena Zholdybayeva, Yerbol Makhambetov and Serik Akshulakov
Diagnostics 2024, 14(11), 1077; https://doi.org/10.3390/diagnostics14111077 - 22 May 2024
Abstract
Background: Arteriovenous malformations (AVMs) are abnormal tangles of arteries and veins that connect directly without an intervening capillary bed. Epileptic seizures are the second most common symptom in patients with brain AVMs, occurring in 30 to 50% of cases. However, the exact mechanism
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Background: Arteriovenous malformations (AVMs) are abnormal tangles of arteries and veins that connect directly without an intervening capillary bed. Epileptic seizures are the second most common symptom in patients with brain AVMs, occurring in 30 to 50% of cases. However, the exact mechanism of epileptic seizure development in AVMs remains unclear. In this study, we aimed to investigate the factors associated with epileptic seizures in patients with brain arteriovenous malformation (AVMs) in Kazakhstan. Methods: A case–control study was conducted, which included 163 patients diagnosed with brain AVMs. Demographic and clinical data were collected and analyzed, and multivariate logistic regression was built to assess the factors associated with seizures in brain AVMs. Results: from this rupture of vessels OR = 0.36 95% CI (0.14–0.91, a medium-to-high Spetzler-Martin score (III–V) OR = 6.16 (2.14–17.69) and OR = 3.05 (1.08–8.68), respectively), location in brain cortex (frontal lobe OR = 6.16 (2.04–18.54), parietal lobe OR = 9.37 (3.26–26.91), temporal lobe OR = 4.57 (1.56–13.36), occipital lobe OR = 0.27 (0.08–0.91), and the presence of hemiparesis OR = 0.12 (0.02–0.66) in adverse outcomes were statistically significantly associated with the presence of epileptic seizures in brain arteriovenous malformations patients. Conclusions: To conclude, this contributed to model factors associated with brain arteriovenous malformations that are linked to epileptic seizures.
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(This article belongs to the Special Issue Cerebrovascular Lesions: Diagnosis and Management)
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Open AccessCommunication
Nationwide Real-World Data of Microsatellite Instability and/or Mismatch Repair Deficiency in Cancer: Prevalence and Testing Patterns
by
Elena Fountzilas, Theofanis Papadopoulos, Eirini Papadopoulou, Cedric Gouedard, Helen P. Kourea, Pantelis Constantoulakis, Christina Magkou, Maria Sfakianaki, Vassiliki Kotoula, Dimitra Bantouna, Georgia Raptou, Angelica A. Saetta, Georgia Christopoulou, Dimitris Hatzibougias, Electra Michalopoulou-Manoloutsiou, Eleni Siatra, Eleftherios Eleftheriadis, Evangelia Kavoura, Loukas Kaklamanis, Antigoni Sourla, George Papaxoinis, Kitty Pavlakis, Prodromos Hytiroglou, Christina Vourlakou, Petroula Arapantoni-Dadioti, Samuel Murray, George Nasioulas, Grigorios Timologos, George Fountzilas and Zacharenia Saridakiadd
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Diagnostics 2024, 14(11), 1076; https://doi.org/10.3390/diagnostics14111076 - 22 May 2024
Abstract
Determination of microsatellite instability (MSI)/mismatch repair (MMR) status in cancer has several clinical implications. Our aim was to integrate MSI/MMR status from patients tested in Greece to assess the prevalence of MSI-high (MSI-H)/deficient MMR (dMMR) per tumor type, testing patterns over time and
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Determination of microsatellite instability (MSI)/mismatch repair (MMR) status in cancer has several clinical implications. Our aim was to integrate MSI/MMR status from patients tested in Greece to assess the prevalence of MSI-high (MSI-H)/deficient MMR (dMMR) per tumor type, testing patterns over time and concordance between MSI and MMR status. We retrospectively recorded MSI/MMR testing data of patients with diverse tumor types performed in pathology and molecular diagnostics laboratories across Greece. Overall, 18 of 22 pathology and/or molecular diagnostics laboratories accepted our invitation to participate. In the 18 laboratories located across the country, 7916 tumor samples were evaluated for MSI/MMR status. MSI/MMR testing significantly increased in patients with colorectal cancer (CRC) and other tumor types overtime (p < 0.05). The highest prevalence was reported in endometrial cancer (47 of 225 patients, 20.9%). MSI-H/dMMR was observed in most tumor types, even in low proportions. Among 904 tumors assessed both for MSI and MMR status, 21 had discordant results (overall discordance rate, 2.3%). We reported MSI-H/dMMR prevalence rates in patients with diverse cancers, while demonstrating increasing referral patterns from medical oncologists in the country overtime. The anticipated high rate of concordance between MSI and MMR status in paired analysis was confirmed.
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(This article belongs to the Section Pathology and Molecular Diagnostics)
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Open AccessArticle
Improved Positron Emission Tomography Quantification: Evaluation of a Maximum-Likelihood Scatter Scaling Algorithm
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Nanna Overbeck, Sahar Ahangari, Maurizio Conti, Vladimir Panin, Aleena Azam, Sorel Kurbegovic, Andreas Kjær, Liselotte Højgaard, Kirsten Korsholm, Barbara Malene Fischer, Flemming Littrup Andersen and Thomas Lund Andersen
Diagnostics 2024, 14(11), 1075; https://doi.org/10.3390/diagnostics14111075 - 22 May 2024
Abstract
Incorrect scatter scaling of positron emission tomography (PET) images can lead to halo artifacts, quantitative bias, or reconstruction failure. Tail-fitted scatter scaling (TFSS) possesses performance limitations in multiple cases. This study aims to investigate a novel method for scatter scaling: maximum-likelihood scatter scaling
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Incorrect scatter scaling of positron emission tomography (PET) images can lead to halo artifacts, quantitative bias, or reconstruction failure. Tail-fitted scatter scaling (TFSS) possesses performance limitations in multiple cases. This study aims to investigate a novel method for scatter scaling: maximum-likelihood scatter scaling (MLSS) in scenarios where TFSS tends to induce artifacts or are observed to cause reconstruction abortion. [68Ga]Ga-RGD PET scans of nine patients were included in cohort 1 in the scope of investigating the reduction of halo artifacts relative to the scatter estimation method. PET scans of 30 patients administrated with [68Ga]Ga-uPAR were included in cohort 2, used for an evaluation of the robustness of MLSS in cases where TFSS-integrated reconstructions are observed to fail. A visual inspection of MLSS-corrected images scored higher than TFSS-corrected reconstructions of cohort 1. The quantitative investigation near the bladder showed a relative difference in tracer uptake of up to 94.7%. A reconstruction of scans included in cohort 2 resulted in failure in 23 cases when TFSS was used. The lesion uptake values of cohort 2 showed no significant difference. MLSS is suggested as an alternative scatter-scaling method relative to TFSS with the aim of reducing halo artifacts and a robust reconstruction process.
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(This article belongs to the Section Medical Imaging and Theranostics)
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Open AccessArticle
Evaluating Diagnostic Clarity: The Comparative Efficacy of BlueStain in Serous Effusion Cytology under the International System for Reporting Serous Fluid Cytopathology Reporting Framework
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Paula Melo Alves, Maria Teresa Azevedo, Fernando Ferreira, Ebru Tastekin, Sule Canberk and Fernando C. Schmitt
Diagnostics 2024, 14(11), 1074; https://doi.org/10.3390/diagnostics14111074 - 22 May 2024
Abstract
Serous effusion cytology is a pivotal diagnostic and staging tool in clinical pathology, valued for its simplicity and cost-effectiveness. Staining techniques such as Giemsa and Papanicolaou are foundational, yet the search for rapid and efficient alternatives continues. Our study assesses the efficacy of
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Serous effusion cytology is a pivotal diagnostic and staging tool in clinical pathology, valued for its simplicity and cost-effectiveness. Staining techniques such as Giemsa and Papanicolaou are foundational, yet the search for rapid and efficient alternatives continues. Our study assesses the efficacy of an in-house-developed BlueStain, a toluidine blue variant, within the International System for Reporting Serous Fluid Cytopathology (TIS), aiming to optimize diagnostic clarity and resource use. Materials and Methods: This section provides details on the cohort of 237 patients with serous effusions, the ethical approval process, sample collection, and staining procedures with BlueStain, Papanicolaou, and Giemsa. It also describes the microscopic evaluation criteria, scoring system, and statistical methods used to compare the stains. Results: BlueStain demonstrated notable performance, particularly in identifying malignant cells, presenting a competitive alternative to the Papanicolaou stain, which, despite higher quality indices in other categories, requires more resources and time. The study revealed that BlueStain might offer a valuable balance between quality and efficiency, especially in cases where rapid diagnostic turnaround is essential. Conclusions: Our findings suggest that BlueStain is a viable staining method in the context of serous effusions, capable of providing detailed cytomorphological analysis. While traditional stains hold their place for their established diagnostic clarity, BlueStain offers a rapid and resource-optimized alternative. The absence of definitive diagnostic criteria in the atypical category and the inherent sample heterogeneity underscores the necessity for adaptable staining methods like BlueStain. The study highlights the potential trade-offs between detail and practicality in staining techniques, advocating for further research into innovative methods that do not compromise diagnostic precision for cost and time efficiency.
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(This article belongs to the Section Pathology and Molecular Diagnostics)
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Open AccessArticle
Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning
by
Raphael Diener, Alexander W. Renz, Florian Eckhard, Helmar Segbert, Nicole Eter, Arnim Malcherek and Julia Biermann
Diagnostics 2024, 14(11), 1073; https://doi.org/10.3390/diagnostics14111073 - 22 May 2024
Abstract
In order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The
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In order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The clinical annotation of datasets is usually performed at the expense of the data volume, which results in poorer algorithm performance. This study aimed to evaluate the application of an MLA for the automated classification of physiological optic discs (PODs), glaucomatous optic discs (GODs), and glaucoma-suspected optic discs (GSODs). Annotation of the data to the three groups was based on the diagnosis made in clinical practice by a glaucoma specialist. Color fundus photographs and 14 types of metadata (including visual field testing, retinal nerve fiber layer thickness, and cup–disc ratio) of 1168 eyes from 584 patients (POD = 321, GOD = 336, GSOD = 310) were used for the study. Machine learning (ML) was performed in the first step with the color fundus photographs only and in the second step with the images and metadata. Sensitivity, specificity, and accuracy of the classification of GSOD vs. GOD and POD vs. GOD were evaluated. Classification of GOD vs. GSOD and GOD vs. POD performed in the first step had AUCs of 0.84 and 0.88, respectively. By combining the images and metadata, the AUCs increased to 0.92 and 0.99, respectively. By combining images and metadata, excellent performance of the MLA can be achieved despite having only a small amount of data, thus supporting ophthalmologists with glaucoma diagnosis.
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(This article belongs to the Special Issue Artificial Intelligence in Ophthalmology)
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Open AccessInteresting Images
Navigating the Labyrinth: When the “Mesenterium Commune” Turns Colonoscopy into an Endoscopic Rollercoaster
by
Giacomo Emanuele Maria Rizzo, Silvia Ferriolo, Lucio Carrozza, Gabriele Rancatore and Mario Traina
Diagnostics 2024, 14(11), 1072; https://doi.org/10.3390/diagnostics14111072 - 22 May 2024
Abstract
These images involved the case of a 51-year-old woman who had a history of chronic abdominal pain, iron deficiency, and diarrhoea but no blood or mucus in her stool. She had never undergone major abdominal surgery, and her past medical evaluation diagnosed her
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These images involved the case of a 51-year-old woman who had a history of chronic abdominal pain, iron deficiency, and diarrhoea but no blood or mucus in her stool. She had never undergone major abdominal surgery, and her past medical evaluation diagnosed her with celiac disease, leading to the adoption of a gluten-free diet alleviating most of her gastrointestinal symptoms. However, years later, her abdominal pain returned, so she underwent an abdominal ultrasound, revealing non-specific bowel loop dilation, and a weakly positive faecal occult blood test led to a colonoscopy. Despite many efforts to advance the scope beyond the transverse colon, colonoscopy was arduous and not complete, even after several changes in decubitus and abdominal compressions. Therefore, a virtual colonoscopy was conducted, revealing no intraluminal masses, but the entire colon was located on the left side of the abdomen. Indeed, the results showed sigma and that most of the colon was curled up in the small pelvis. This rare anatomical variant, known as “Mesenterium commune” (MC), is a type of gut malrotation that develops in childhood due to a lack of omphalomesenteric loop rotation during the embryonic period. This condition can lead to episodes of intestinal obstruction, potentially resulting in an acute abdomen and leading to surgical correction. Symptoms include chronic recurring abdominal pain, nausea, vomiting, and occasionally bloody stools. Few cases of this extremely rare condition have been reported in the literature so far.
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(This article belongs to the Special Issue Advances in the Diagnostic Imaging of Gastrointestinal Diseases)
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The Epidemiological Features and Pathogen Spectrum of Respiratory Tract Infections, Istanbul, Türkiye, from 2021 to 2023
by
Nuran Karabulut, Sema Alaçam, Esranur Şen, Mehmet Karabey and Nurhayat Yakut
Diagnostics 2024, 14(11), 1071; https://doi.org/10.3390/diagnostics14111071 - 22 May 2024
Abstract
Respiratory tract infections (RTIs) can lead to both recurrent seasonal epidemic outbreaks and devastating pandemics. The aim of this study was to evaluate the epidemiologic characteristics and pathogen spectrum of RTIs using a multiplex RT-PCR panel. A total of 9354 cases with suspected
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Respiratory tract infections (RTIs) can lead to both recurrent seasonal epidemic outbreaks and devastating pandemics. The aim of this study was to evaluate the epidemiologic characteristics and pathogen spectrum of RTIs using a multiplex RT-PCR panel. A total of 9354 cases with suspected RTIs between February 2021 and July 2023 were included in this study. A total of 11,048 nasopharyngeal and oropharyngeal samples from these patients were analyzed for 23 respiratory tract pathogens using multiplex RT-PCR. H. influenzae and S. pneumoniae were considered as colonizing bacteria. At least one pathogen was detected in 70.66% of the samples; viral pathogens were detected in 48.41% of the samples, bacterial pathogens were detected in 16.06% of the samples, and viral + bacterial pathogens were detected in 35.53% of the samples. The most frequently detected viral pathogen was rhinovirus/enterovirus (RV/EV) (19.99%). Interestingly, in 2021, respiratory syncytial virus A/B showed atypical activity and replaced RV/EV as the most prevalent pathogen. Human bocavirus, H. influenzae, and S. pneumoniae were detected at higher rates in males (p: 0.038, p: 0.042, and p: 0.035, respectively), while SARS-CoV-2 and B. pertussis were detected at higher rates in females (p < 0.001 and p: 0.033). RTIs were found at higher rates in children (p < 0.001). SARS-CoV-2 and human coronaviruses 229E were detected at higher rates in adults (p < 0.001 and p: 0.001). This comprehensive study with a large sample size investigating RTI pathogens was the first in Türkiye. Understanding the current viral circulation using multiplex RT-PCR panels enables clinicians to predict the most likely pathogens affecting patients and contributes to patient management, in addition to anticipating potential threats.
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(This article belongs to the Special Issue Laboratory Diagnosis of Infectious Diseases)
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Prediction of Seropositivity in Suspected Autoimmune Encephalitis by Use of Radiomics: A Radiological Proof-of-Concept Study
by
Jacob Stake, Christine Spiekers, Burak Han Akkurt, Walter Heindel, Tobias Brix, Manoj Mannil and Manfred Musigmann
Diagnostics 2024, 14(11), 1070; https://doi.org/10.3390/diagnostics14111070 - 21 May 2024
Abstract
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral
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In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases. Images were obtained at our tertiary care center and at various secondary care centers in North Rhine-Westphalia, Germany. The sample was randomly split into training data and independent test data. A total of 107 radiomic features were extracted from bilateral regions of interest (ROIs). Automated machine learning (AutoML) was used to identify the most promising machine learning algorithms. Feature selection was performed using recursive feature elimination (RFE) and based on the determination of the most important features. Selected features were used to train various machine learning algorithms on 100 different data partitions. Performance was subsequently evaluated on independent test data. Our radiomics approach was able to predict the presence of autoantibodies in the independent test samples with a mean AUC of 0.90, a mean accuracy of 0.83, a mean sensitivity of 0.8, 4 and a mean specificity of 0.82, with Lasso regression models yielding the most promising results. These results indicate that radiomics-based machine learning could be a promising tool in predicting the presence of autoantibodies in suspected AE patients. Given the implications of seropositivity for definitive diagnosis of suspected AE cases, this may expedite diagnostic workup even before results from specialized laboratory testing can be obtained. Furthermore, in conjunction with recent publications, our results indicate that characterization of AE subtypes by use of radiomics may become possible in the future, potentially allowing physicians to tailor treatment in the spirit of personalized medicine even before laboratory workup is completed.
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(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study
by
Naoki Kawahara, Hiroshi Kobayashi, Tomoka Maehana, Kana Iwai, Yuki Yamada, Ryuji Kawaguchi, Junko Takahama, Nagaaki Marugami, Hirotaka Nishi, Yosuke Sakai, Hirokuni Takano, Toshiyuki Seki, Kota Yokosu, Yukihiro Hirata, Koyo Yoshida, Takafumi Ujihira and Fuminori Kimura
Diagnostics 2024, 14(11), 1069; https://doi.org/10.3390/diagnostics14111069 - 21 May 2024
Abstract
Background: Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted
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Background: Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study. Methods: After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study. Results: The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%). Conclusions: MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.
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(This article belongs to the Special Issue Advances in Diagnosis of Gynecological Cancers)
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Clinical Efficacy and Safety of an Automatic Closed-Suction System in Mechanically Ventilated Patients with Pneumonia: A Multicenter, Prospective, Randomized, Non-Inferiority, Investigator-Initiated Trial
by
Dong-Hyun Joo, Hyo Chan Park, Joon Han Kim, Seo Hee Yang, Tae Hun Kim, Hyung-Jun Kim, Myung Jin Song, Sung Yoon Lim, Sung A Kim, Hee Won Bae, Yoon Hae Ahn, Si Mong Yoon, Jimyung Park, Hong Yeul Lee, Jinwoo Lee, Sang-Min Lee, Jung Chan Lee and Young-Jae Cho
Diagnostics 2024, 14(11), 1068; https://doi.org/10.3390/diagnostics14111068 - 21 May 2024
Abstract
Endotracheal suctioning is an essential but labor-intensive procedure, with the risk of serious complications. A brand new automatic closed-suction device was developed to alleviate the workload of healthcare providers and minimize those complications. We evaluated the clinical efficacy and safety of the automatic
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Endotracheal suctioning is an essential but labor-intensive procedure, with the risk of serious complications. A brand new automatic closed-suction device was developed to alleviate the workload of healthcare providers and minimize those complications. We evaluated the clinical efficacy and safety of the automatic suction system in mechanically ventilated patients with pneumonia. In this multicenter, randomized, non-inferiority, investigator-initiated trial, mechanically ventilated patients with pneumonia were randomized to the automatic device (intervention) or conventional manual suctioning (control). The primary efficacy outcome was the change in the modified clinical pulmonary infection score (CPIS) in 3 days. Secondary outcomes were the frequency of additional suctioning and the amount of secretion. Safety outcomes included adverse events or complications. A total of 54 participants, less than the pre-determined number of 102, were enrolled. There was no significant difference in the change in the CPIS over 72 h (−0.13 ± 1.58 in the intervention group, −0.58 ± 1.18 in the control group, p = 0.866), but the non-inferiority margin was not satisfied. There were no significant differences in the secondary outcomes and safety outcomes, with a tendency for more patients with improved tracheal mucosal injury in the intervention group. The novel automatic closed-suction system showed comparable efficacy and safety compared with conventional manual suctioning in mechanically ventilated patients with pneumonia.
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(This article belongs to the Special Issue Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management)
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A New Quantitative Tool for the Ultrasonographic Assessment of Tendons: A Reliability and Validity Study on the Patellar Tendon
by
Isabel Albarova-Corral, José Segovia-Burillo, Miguel Malo-Urriés, Izarbe Ríos-Asín, Jesús Asín, Jorge Castillo-Mateo, Zeus Gracia-Tabuenca and Mario Morales-Hernández
Diagnostics 2024, 14(11), 1067; https://doi.org/10.3390/diagnostics14111067 - 21 May 2024
Abstract
Ultrasound is widely used for tendon assessment due to its safety, affordability, and portability, but its subjective nature poses challenges. This study aimed to develop a new quantitative analysis tool based on artificial intelligence to identify statistical patterns of healthy and pathological tendons.
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Ultrasound is widely used for tendon assessment due to its safety, affordability, and portability, but its subjective nature poses challenges. This study aimed to develop a new quantitative analysis tool based on artificial intelligence to identify statistical patterns of healthy and pathological tendons. Furthermore, we aimed to validate this new tool by comparing it to experts’ subjective assessments. A pilot database including healthy controls and patients with patellar tendinopathy was constructed, involving 14 participants with asymptomatic (n = 7) and symptomatic (n = 7) patellar tendons. Ultrasonographic images were assessed twice, utilizing both the new quantitative tool and the subjective scoring method applied by an expert across five regions of interest. The database contained 61 variables per image. The robustness of the clinical and quantitative assessments was tested via reliability analyses. Lastly, the prediction accuracy of the quantitative features was tested via cross-validated generalized linear mixed-effects logistic regressions. These analyses showed high reliability for quantitative variables related to “Bone” and “Quality”, with ICCs above 0.75. The ICCs for “Edges” and “Thickness” varied but mostly exceeded 0.75. The results of this study show that certain quantitative variables are capable of predicting an expert’s subjective assessment with generally high cross-validated AUC scores. A new quantitative tool for the ultrasonographic assessment of the tendon was designed. This system is shown to be a reliable and valid method for evaluating the patellar tendon structure.
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(This article belongs to the Special Issue Ultrasound Diagnosis and Guided Intervention of Musculoskeletal/Neuromuscular Pathology 2023)
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Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform
by
Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine and Houman Sotoudeh
Diagnostics 2024, 14(11), 1066; https://doi.org/10.3390/diagnostics14111066 - 21 May 2024
Abstract
Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and
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Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland–Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform’s low inter-user variability and the high accuracy of its T1c and FLAIR AI models.
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(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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