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Fulltext A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Diagnostics (Basel), 2023 Jul 21;13(14).
PMID: 37510187 DOI: 10.3390/diagnostics13142442

Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.
Fulltext Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

Khan MA, Alsulami M, Yaqoob MM, Alsadie D, Saudagar AKJ, AlKhathami M, et al.

Diagnostics (Basel), 2023 Jul 11;13(14).
PMID: 37510084 DOI: 10.3390/diagnostics13142340

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.
Fulltext Cat Scratch Disease-A Benign Disease with Thymic Hyperplasia Mimicking Lymphoma

Leong MH, Nabillah MJ, Rizuana IH, Asma A, Kew TY, Tan GC

Diagnostics (Basel), 2023 Jul 24;13(14).
PMID: 37510201 DOI: 10.3390/diagnostics13142457

Cat scratch disease (CSD) is a benign condition caused by the inoculation of Bartonella henselae. The imaging findings are non-specific, and it is difficult to diagnose the disease via imaging. However, imaging studies help exclude other differential diagnoses in diagnostic dilemmas. We encountered a case of a 17-year-old adolescent who presented with painful neck swelling. CT showed multiple bilateral cervical lymphadenopathies with triangular soft tissue mass at the anterior mediastinum likely to be thymic hyperplasia, which is unusual in CSD and was mistaken for a lymphoproliferative disorder. Tissue diagnosis with a thorough clinical history yielded the diagnosis of cat scratch disease, and follow-up imaging showed resolution of the cervical lymphadenopathy and thymic hyperplasia.
Fulltext Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race

Sheta A, Thaher T, Surani SR, Turabieh H, Braik M, Too J, et al.

Diagnostics (Basel), 2023 Jul 20;13(14).
PMID: 37510161 DOI: 10.3390/diagnostics13142417

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3-7% of males and 2-5% of females. In the United States alone, 50-70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.
Fulltext Detecting Diabetic Ketoacidosis with Infection: Combating a Life-Threatening Emergency with Practical Diagnostic Tools

Ahmad R, Narwaria M, Singh A, Kumar S, Haque M

Diagnostics (Basel), 2023 Jul 21;13(14).
PMID: 37510185 DOI: 10.3390/diagnostics13142441

BACKGROUND: Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes mellitus and can lead to patient demise if not immediately treated. From the recent literature, the diabetic ketoacidosis mortality rate, depending on age, is 2-5%. Insulin discontinuation and infection remain the two most common triggers for diabetic ketoacidosis. About 50% of cases of ketoacidosis result from bacterial infections like urinary tract infections and pneumonia. It is also important to diagnose the presence of infection in diabetic ketoacidosis patients to prevent the excessive use of antibiotics, which may lead to antibiotic resistance. Although performing bacterial culture is confirmatory for the presence or absence of bacterial infection, the time required to obtain the result is long. At the same time, emergency treatment needs to be started as early as possible.
METHODS: This narrative review examines various septic markers to identify the appropriate tools for diagnosis and to distinguish between diabetic ketoacidosis with and without infection. Electronic databases were searched using the Google engine with the keywords "Diabetes Mellitus", "Diabetic Ketoacidosis", "Infection with Diabetic Ketoacidosis", "biomarkers for infection in Diabetic Ketoacidosis", "Procalcitonin", "Inflammatory cytokines in DKA", "Lactic acidosis in DKA", and "White blood cell in infection in DKA".
RESULTS: This narrative review article presents the options for diagnosis and also aims to create awareness regarding the gravity of diabetic ketoacidosis with infection and emphasizes the importance of early diagnosis for appropriate management. Diabetes mellitus is a clinical condition that may lead to several acute and chronic complications. Acute diabetic ketoacidosis is a life-threatening condition in which an excess production of ketone bodies results in acidosis and hypovolemia. Infection is one of the most common triggers of diabetic ketoacidosis. When bacterial infection is present along with diabetic ketoacidosis, the mortality rate is even higher than for patients with diabetic ketoacidosis without infection. The symptoms and biomarkers of diabetic ketoacidosis are similar to that of infection, like fever, C reactive protein, and white blood cell count, since both create an environment of systemic inflammation. It is also essential to distinguish between the presence and absence of bacterial infection to ensure the appropriate use of antibiotics and prevent antimicrobial resistance. A bacterial culture report is confirmatory for the existence of bacterial infection, but this may take up to 24 h. Diagnosis needs to be performed approximately in the emergency room upon admission since there is a need for immediate management. Therefore, researching the possible diagnostic tools for the presence of infection in diabetic ketoacidosis patients is of great importance. Several of such biomarkers have been discussed in this research work.
Fulltext Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture

Lim CC, Ling AHW, Chong YF, Mashor MY, Alshantti K, Aziz ME

Diagnostics (Basel), 2023 Jul 14;13(14).
PMID: 37510120 DOI: 10.3390/diagnostics13142377

Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difficult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of five main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median filter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format files for deep learning. The MRI images were then transformed to fit the CNN's requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefficients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly defined borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efficacy.
Fulltext Exploring Extended White Blood Cell Parameters for the Evaluation of Sepsis among Patients Admitted to Intensive Care Units

Ho SF, Tan SJ, Mazlan MZ, Iberahim S, Lee YX, Hassan R

Diagnostics (Basel), 2023 Jul 21;13(14).
PMID: 37510189 DOI: 10.3390/diagnostics13142445

Sepsis is a major cause of mortality and morbidity in intensive care units. This case-control study aimed to investigate the haematology cell population data and extended inflammatory parameters for sepsis management. The study included three groups of patients: sepsis, non-sepsis, and healthy controls. Patients suspected of having sepsis underwent a Sequential Organ Failure Assessment (SOFA) evaluation and had blood drawn for blood cultures, complete peripheral blood counts (CBC), and measurements of various markers such as C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6). We observed significant changes in numerous CBC parameters and extended inflammation parameters (EIPs), in addition to significant biochemical analysis markers CRP and IL-6 in sepsis cohorts. Multiple logistic regression analyses showed that combining different CBC parameters and EIPs were effective to profile these patients. Two different models have been developed using white blood cell counts and their extended parameters. Our findings indicate that the absolute counts of white blood cells, and the EIPs which reflect their activation states, are important for the prediction and assessment of sepsis, as the body responds to an insult that triggers an immune response. In an emergency situation, having timely updates on patient conditions becomes crucial for guiding the management process. Identifying trends in these specific patient groups will aid early diagnosis, complementing clinical signs and symptoms, especially as CBC is the most commonly ordered test in a diagnostic workup.
Differential Protein Expression Patterns of HOXA13 and HOXB13 Are Associated with Bladder Cancer Progression

Chin FW, Hussin H, Chau DM, Ong TA, Yunus R, Abdul Razack AH, et al.

Diagnostics (Basel), 2023 Aug 09;13(16).
PMID: 37627895 DOI: 10.3390/diagnostics13162636

Bladder cancer is a common urological cancer and has the highest recurrence rate of any cancer. The aim of our study was to profile and characterize the protein expression of homeobox A13 (HOXA13) and homeobox B13 (HOXB13) genes in Malaysian bladder cancer patients. The protein expression of HOXA13 and HOXB13 in formalin-fixed paraffin-embedded (FFPE) bladder cancer tissues was determined by immunohistochemistry (IHC) analysis. The association between HOXA13/HOXB13 protein expression and demographic/clinicopathological characteristics of the bladder cancer patients was determined by chi-square analysis. Approximately 63.6% of the bladder cancer tissues harbored high HOXA13 expression. High HOXA13 expression was significantly associated with non-muscle invasive bladder cancer, lower tumor grade, higher number of lymph node metastases, and recurrence risk. In contrast, low HOXB13 expression (including those with negative expression) was observed in 71.6% of the bladder cancer tissues analyzed. Low HOXB13 expression was significantly associated with muscle-invasive bladder cancer, higher tumor stage, tumor grade, and metastatic risk. Both HOXA13 and HOXB13 protein expression were found to be associated with bladder tumorigenesis. The putative oncogenic and tumor suppressive roles of HOXA13 and HOXB13, respectively, suggest their potential utility as biomarkers in bladder cancer.
Homeobox Gene Expression Dysregulation as Potential Diagnostic and Prognostic Biomarkers in Bladder Cancer

Chin FW, Chan SC, Veerakumarasivam A

Diagnostics (Basel), 2023 Aug 10;13(16).
PMID: 37627900 DOI: 10.3390/diagnostics13162641

Homeobox genes serve as master regulatory transcription factors that regulate gene expression during embryogenesis. A homeobox gene may have either tumor-promoting or tumor-suppressive properties depending on the specific organ or cell lineage where it is expressed. The dysregulation of homeobox genes has been reported in various human cancers, including bladder cancer. The dysregulated expression of homeobox genes has been associated with bladder cancer clinical outcomes. Although bladder cancer has high risk of tumor recurrence and progression, it is highly challenging for clinicians to accurately predict the risk of tumor recurrence and progression at the initial point of diagnosis. Cystoscopy is the routine surveillance method used to detect tumor recurrence. However, the procedure causes significant discomfort and pain that results in poor surveillance follow-up amongst patients. Therefore, the development of reliable non-invasive biomarkers for the early detection and monitoring of bladder cancer is crucial. This review provides a comprehensive overview of the diagnostic and prognostic potential of homeobox gene expression dysregulation in bladder cancer.
A Rare Case of Post-Traumatic Cervical Ligamentous Tear Complicated by Vertebral Arteriovenous Fistula (vAVF), with Successful Endovascular Treatment

Fazdlin ARN, Rizuana IH, Ch'ng LS

Diagnostics (Basel), 2023 Aug 16;13(16).
PMID: 37627952 DOI: 10.3390/diagnostics13162693

Post-traumatic vertebral arteriovenous fistula (vAVF) caused by motor vehicle accidents (MVA) is a rare condition in which there is abnormal communication between the vertebral artery and its adjacent veins. In a post-MVA setting, it is commonly associated with vertebral body fracture. In this paper, we report a case of a 19-year-old girl with a complete C2/C3 anterior and posterior ligament tear post MVA without any cervical bony injury. Initial plain computed tomography (CT) cervical scan showed a prevertebral hematoma. A CT angiogram (CTA) raised the suspicion of a pseudo-aneurysm at the right posterior C3 vertebral body. Further imaging with magnetic resonance imaging (MRI) demonstrated traumatic AVF at the C2/C3 level involving the V2/V3 right vertebral artery to the vertebral venous plexus. Digital Subtraction Angiography (DSA) further revealed a transected right vertebral artery at the C2/C3 level with an arteriovenous fistula and an enlarged vertebral venous plexus. The fistulous communication was successfully occluded with coils from a cranial and caudal approach to the transected segment right vertebral artery, with a total of eight coils. Post-MVA vertebral arteriovenous fistula (vAVF) is a rare sequela of vertebral bony injury at the cervical region, and is an even rarer association with an isolated ligamentous injury, whereby endovascular treatment with ipsilateral vertebral artery closure is a feasible treatment of vAVF.
Fulltext A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images

Khan MM, Chowdhury MEH, Arefin ASMS, Podder KK, Hossain MSA, Alqahtani A, et al.

Diagnostics (Basel), 2023 Jul 31;13(15).
PMID: 37568900 DOI: 10.3390/diagnostics13152537

Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.
Fulltext Non-Invasive Prenatal Testing (NIPT): Reliability, Challenges, and Future Directions

Jayashankar SS, Nasaruddin ML, Hassan MF, Dasrilsyah RA, Shafiee MN, Ismail NAS, et al.

Diagnostics (Basel), 2023 Aug 02;13(15).
PMID: 37568933 DOI: 10.3390/diagnostics13152570

Non-invasive prenatal testing was first discovered in 1988; it was primarily thought to be able to detect common aneuploidies, such as Patau syndrome (T13), Edward Syndrome (T18), and Down syndrome (T21). It comprises a simple technique involving the analysis of cell-free foetal DNA (cffDNA) obtained through maternal serum, using advances in next-generation sequencing. NIPT has shown promise as a simple and low-risk screening test, leading various governments and private organizations worldwide to dedicate significant resources towards its integration into national healthcare initiatives as well as the formation of consortia and research studies aimed at standardizing its implementation. This article aims to review the reliability of NIPT while discussing the current challenges prevalent among different communities worldwide.
Fulltext Lessons from a 3-Year Review of PSMA PET-CT in a Tertiary Setting: Can We Fine Tune Referral Criteria by Identifying Factors Predicting Positivity and Negativity?

Pant V, Vinjamuri S, Zanial AZ, Naeem F

Diagnostics (Basel), 2023 Jul 31;13(15).
PMID: 37568904 DOI: 10.3390/diagnostics13152542

AIM OF THE STUDY: To draw inferences from a retrospective evaluation of PSMA PET CT scans performed for the evaluation of biochemical recurrence.
MATERIAL AND METHODS: A retrospective analysis of 295 PSMA PET CT scans spanning 3 years between 2020 and 2022 was undertaken.
RESULTS: Of 295 PET CT scans, 179 were positive, 66 were negative and 50 had indeterminate findings. In the positive group, 67 had radical prostatectomy and PSMA avid lesions were seen most commonly in pelvic lymph nodes. The remaining 112 positive scans were in the non-radical prostatectomy group; 25 had recurrence only in the prostate, 17 had recurrence involving the prostate bed; 28 had no recurrence in the prostate gland, while 42 had recurrence in the prostate as well as in extra-prostatic sites. Overall, in the non-prostatectomy group, 75% of the population was harboring a PSMA avid lesion in the prostate gland while in the remaining 25% of the population, recurrence did not involve the prostate gland. The majority of indeterminate findings were seen in small pelvic or retroperitoneal lymph nodes or skeletal regions (ribs/others) and in nine patients indeterminate focus was seen in the prostate bed only. Follow-up PSMA PET CT was helpful in prior indeterminate findings and unexplained PSA rise.
CONCLUSION: A higher recurrence in the prostate bed while evaluating biochemical recurrence prompts the following: question: should prostatectomy be offered more proactively? Follow-up PSMA PET CT is helpful for indeterminate findings; a PSA rise of 0.7 ng/mL in 6 months can result in positive PSMA PET CT while negative scans can be seen up to a 2 ng/mL PSA rise in 6 months.
Fulltext Motion Capture Technologies for Ergonomics: A Systematic Literature Review

Salisu S, Ruhaiyem NIR, Eisa TAE, Nasser M, Saeed F, Younis HA

Diagnostics (Basel), 2023 Aug 04;13(15).
PMID: 37568956 DOI: 10.3390/diagnostics13152593

Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.
A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images

Alsalatie M, Alquran H, Mustafa WA, Zyout A, Alqudah AM, Kaifi R, et al.

Diagnostics (Basel), 2023 Aug 25;13(17).
PMID: 37685299 DOI: 10.3390/diagnostics13172762

One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.
Fulltext Prognostic Value of TNFR2 and STAT3 among High-Grade Serous Ovarian Cancer Survivors According to Platinum Sensitivity

Silva Raju J, Abd Aziz NH, Atallah GA, Teik CK, Shafiee MN, Mohd Saleh MF, et al.

Diagnostics (Basel), 2021 Mar 16;11(3).
PMID: 33809542 DOI: 10.3390/diagnostics11030526

This study's goal was to determine the protein expression level of tumour necrosis factor receptor 2 (TNFR2) and signal transducer and activator of transcription 3 (STAT3) in high-grade serous ovarian cancer (HGSC) tissues in relation to the platinum-based chemotherapy response and the prognosis outcome. A total of 25 HGSC patients underwent primary surgical debulking followed by first-line adjuvant platinum-based chemotherapy. Tissue microarray (TMA) slides were constructed utilising archived formalin fixed paraffin embedded (FFPE). The protein expression of TNFR2 and STAT3 were analysed using immunohistochemistry (IHC) staining and subsequently were correlated to the clinicopathological characteristics, platinum sensitivity as well as the duration of progression-free survival. About 14 out of 25 patients (56.0%) were platinum-sensitive. The progression free survival was significantly longer in the platinum-sensitive (PS) group when compared to those with the platinum-resistant group (PR), p = 0.0001. Among patients with TNFR2 strong expression on ovarian tissue, there was a significantly longer progression-free survival interval of 540 days in the PS group compared to PR, p = 0.0001. Patients with STAT3 expression also showed significantly better progression-free survival of 660 days in the PS group when compared to the PR group, p = 0.0001. In conclusion, patients with strong TNFR2 and STAT3 expression in the ovarian tissue had significantly longer progression-free survival interval in the PS group. Nevertheless, further research with a larger number of tissues may be required to demonstrate further significant differences.
Fulltext Microsatellite Instability in Colorectal Cancer Liquid Biopsy-Current Updates on Its Potential in Non-Invasive Detection, Prognosis and as a Predictive Marker

Tieng FYF, Abu N, Lee LH, Ab Mutalib NS

Diagnostics (Basel), 2021 Mar 18;11(3).
PMID: 33803882 DOI: 10.3390/diagnostics11030544

Colorectal cancer (CRC) is the third most commonly-diagnosed cancer in the world and ranked second for cancer-related mortality in humans. Microsatellite instability (MSI) is an indicator for Lynch syndrome (LS), an inherited cancer predisposition, and a prognostic marker which predicts the response to immunotherapy. A recent trend in immunotherapy has transformed cancer treatment to provide medical alternatives that have not existed before. It is believed that MSI-high (MSI-H) CRC patients would benefit from immunotherapy due to their increased immune infiltration and higher neo-antigenic loads. MSI testing such as immunohistochemistry (IHC) and PCR MSI assay has historically been a tissue-based procedure that involves the testing of adequate tissue with a high concentration of cancer cells, in addition to the requirement for paired normal tissues. The invasive nature and specific prerequisite of such tests might hinder its application when surgery is not an option or when the tissues are insufficient. The application of next-generation sequencing, which is highly sensitive, in combination with liquid biopsy, therefore, presents an interesting possibility worth exploring. This review aimed to discuss the current body of evidence supporting the potential of liquid biopsy as a tool for MSI testing in CRC.
Fulltext Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate

Huqh MZU, Abdullah JY, Al-Rawas M, Husein A, Ahmad WMAW, Jamayet NB, et al.

Diagnostics (Basel), 2023 Sep 22;13(19).
PMID: 37835768 DOI: 10.3390/diagnostics13193025

INTRODUCTION: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals.
METHODS: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen.
RESULTS: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study's output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%.
CONCLUSION: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.
Fulltext Perspectives on the Application of Cytogenomic Approaches in Chronic Lymphocytic Leukaemia

Wan Mohamad Zamri WN, Mohd Yunus N, Abdul Aziz AA, Zulkipli NN, Sulong S

Diagnostics (Basel), 2023 Mar 03;13(5).
PMID: 36900108 DOI: 10.3390/diagnostics13050964

Chronic lymphocytic leukaemia (CLL) is a haematological malignancy characterised by the accumulation of monoclonal mature B lymphocytes (positive for CD5+ and CD23+) in peripheral blood, bone marrow, and lymph nodes. Although CLL is reported to be rare in Asian countries compared to Western countries, the disease course is more aggressive in Asian countries than in their Western counterparts. It has been postulated that this is due to genetic variants between populations. Various cytogenomic methods, either of the traditional type (conventional cytogenetics or fluorescence in situ hybridisation (FISH)) or using more advanced technology such as DNA microarrays, next generation sequencing (NGS), or genome wide association studies (GWAS), were used to detect chromosomal aberrations in CLL. Up until now, conventional cytogenetic analysis remained the gold standard in diagnosing chromosomal abnormality in haematological malignancy including CLL, even though it is tedious and time-consuming. In concordance with technological advancement, DNA microarrays are gaining popularity among clinicians as they are faster and better able to accurately diagnose the presence of chromosomal abnormalities. However, every technology has challenges to overcome. In this review, CLL and its genetic abnormalities will be discussed, as well as the application of microarray technology as a diagnostic platform.
Fulltext Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

Nazarudin AA, Zulkarnain N, Mokri SS, Zaki WMDW, Hussain A, Ahmad MF, et al.

Diagnostics (Basel), 2023 Feb 16;13(4).
PMID: 36832237 DOI: 10.3390/diagnostics13040750

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu's thresholding with the Chan-Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu's thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan-Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan-Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan-Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan-Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu's thresholding and the Chan-Vese method enhanced the segmentation of ultrasound images.

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