Using Immunohistochemistry to Manage Diagnostic Challenges in Pediatric Small Round Cell Tumours | Chapter 12 |Medical Science: Updates and Prospects Vol. 3

 

Background: Pediatric small round cell tumours (SRCTs) are a group of aggressive cancers that look very similar under the microscope, making them difficult to tell apart based on appearance alone. An accurate diagnosis is critical because each type requires different treatment. Immunohistochemistry (IHC) has emerged as an essential adjunct in the diagnostic workup, enabling precise lineage assignment through the detection of differentiation-specific antigens. Despite its widespread use, diagnostic ambiguity persists, particularly in resource-limited settings where antibody panels may be restricted or tissue preservation suboptimal.

 

Aim: This study aimed to test how effective a standard panel of immunohistochemistry (IHC) stains is at providing a definitive diagnosis for these challenging tumours.

 

Methods: A prospective study was conducted on 100 children with SRCTs. Cases were included if they showed histological evidence of a malignant SRCT on haematoxylin and eosin (H&E)-stained sections, had adequate formalin-fixed paraffin-embedded (FFPE) tissue for a complete immunohistochemical (IHC) workup. After an initial review under the microscope, all cases were tested with a targeted IHC panel designed to identify different tumour lineages (including CD99, myogenin, CD45, and synaptophysin).

 

Results: Initial microscopic examination failed to provide a specific diagnosis in 71% of cases, labelling them only as "undifferentiated." The IHC panel successfully resolved 99% of all cases, providing a specific diagnosis. The Ewing sarcoma family (50%) was the most common tumour, followed by embryonal rhabdomyosarcoma (17%).

 

Conclusion: A systematic IHC panel is a highly effective and essential tool for diagnosing pediatric SRCTs. It resolves the vast majority of ambiguous cases, ensuring that children receive the correct diagnosis as the crucial first step towards appropriate therapy. Study limitations include a single-centre, prospective design, which may introduce selection bias, as evidenced by a high proportion of bone and soft tissue tumours. Future investigation should involve multicenter cohorts with more diverse tumour types to validate the generalizability of these findings.

 

 

Author(s) Details

Divya Jain
Department of Pathology, Government Medical College, Alwar, India.

 

Achin Gupta
Department of Anaesthesia, Govt Medical College, Alwar, India.

 

Neeraj Raman
Department of Microbiology, Govt Medical College, Alwar, India.

 

Amandeep
Department of Pediatrics, Government Medical College, Alwar, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v3/6878

Current Evidence on the Image Quality and Diagnostic Effectiveness of Metal Artefact Reduction Algorithms in Dental CBCT: An Overview | Chapter 5 | Medical Science: Updates and Prospects Vol. 2

 

Dental cone-beam computed tomography (CBCT) has significantly enhanced diagnostic capabilities in maxillofacial imaging, offering high-resolution, three-dimensional views with relatively low radiation exposure. Compared with computed tomography, CBCT generally delivers a lower radiation dose when producing images for equivalent diagnostic objectives. However, the presence of metallic objects such as implants, restorations, and orthodontic appliances often introduces image-degrading artefacts that compromise diagnostic accuracy. Various metal artefact reduction (MAR) algorithms have been developed to counteract these limitations. In recent years, various post-processing methods have been developed to minimize the impact of metal artefacts in CBCT. Despite these advances, the clinical effectiveness of MAR in dental CBCT remains unclear. Most existing studies are in vitro, relying on phantoms or extracted specimens under controlled conditions that do not fully replicate the complexity of in vivo imaging. There is a critical need for well-designed clinical studies which can determine when and how MAR algorithms truly enhance diagnostic outcomes in different dental applications. This review synthesizes current evidence on the diagnostic effectiveness of MAR algorithms in dental CBCT, emphasizing their methodological principles, clinical outcomes, and limitations across different imaging contexts.

 

 

Author(s) Details

Seershika Reddy Y
Department of Oral Medicine and Radiology, Madha Dental College and Hospital, Kundrathur, Chennai-69, Tamil Nadu, India.

 

Kavitha M
Department of Oral Medicine and Radiology, Madha Dental College and Hospital, Kundrathur, Chennai-69, Tamil Nadu, India.

 

Niveditha B
Department of Oral Medicine and Radiology, Madha Dental College and Hospital, Kundrathur, Chennai-69, Tamil Nadu, India.

 

Devi S
Department of Oral Medicine and Radiology, Madha Dental College and Hospital, Kundrathur, Chennai-69, Tamil Nadu, India.

 

Gurucharan R
Department of Oral Medicine and Radiology, Madha Dental College and Hospital, Kundrathur, Chennai-69, Tamil Nadu, India.

 

Vigneshwaran J
Department of Oral Medicine and Radiology, Madha Dental College and Hospital, Kundrathur, Chennai-69, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v2/6628

Enhanced Deep Learning Model for Accurate and Automated Detection of Hepatic Steatosis | Chapter 4 | Mathematics and Computer Science: Research Updates Vol. 8

 

Background: Hepatic Steatosis is one of the most prevalent liver disorders globally. Ultrasound imaging is widely used as the primary screening tool for Hepatic Steatosis. However, its diagnostic performance can vary significantly depending on the operator’s skill and the quality of the equipment. Recent advances in deep learning have brought new opportunities to medical imaging, providing automated, consistent, and quantitative assessments that reduce dependency on operator expertise.

 

Objectives: This study aims to develop a deep learning (DL)-based framework that enhances the detection and grading of Hepatic Steatosis from ultrasound images. The key goal is to achieve accuracy levels comparable to experienced radiologists while maintaining interpretability and efficiency for real-time use in clinical practice.

 

Methods: B-mode ultrasound images and cine clips were collected from patients, covering multiple liver views to capture diverse anatomical perspectives. Alongside imaging data, patient metadata such as age, body mass index (BMI), and comorbid conditions were also recorded to enrich the dataset. The proposed system employs a multi-view ultrasound preprocessing approach, followed by transfer learning to leverage existing feature representations. Attention-driven convolutional neural networks (CNNs) are then used to capture fine details across image regions. To ensure clinical usability, explainability modules are integrated, allowing transparent interpretation of model predictions.

 

Findings: Experimental evaluation demonstrated that the framework outperformed traditional single-view methods, offering improved sensitivity and specificity in detecting hepatic Steatosis. The performance was closely aligned with radiologist-level assessments. Furthermore, the system showed low latency, highlighting its suitability for near-real-time diagnostic applications.

 

Conclusion: Unlike conventional models that rely on a single static image, this study introduces a multi-view fusion strategy enhanced with attention mechanisms and explainability tools. This combination not only strengthens predictive accuracy but also ensures transparency and trustworthiness, critical factors for adoption in clinical settings. Despite the promising performance, challenges such as data variability, subtle early-stage disease patterns, and model interpretability remain. Addressing these limitations through larger, diverse datasets and explainable AI approaches will be essential for translating these models into clinical practice.

 

 

Author(s) Details

A. Sahaya Mercy
Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli-2, Affiliated to Bharathidasan University, Tamil Nadu, India.

 

G. Arockia Sahaya Sheela
Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli-2, Affiliated to Bharathidasan University, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6581

Artificial Intelligence in Pathology: Present and Future |Chapter 9 | Scientific Research, New Technologies and Applications Vol. 3

Artificial intelligence is the future and its use in pathology can create a tremendous impact on health care in different aspects. Its use is being initiated in the field of pathology and is on the rise with increasing acceptance. Pathology services will undergo a paradigm shift due to the implementation of computational pathology and the use of AI tools, becoming more effective and able to satisfy the demands of the precision medicine age. Moving AI models from research to clinical applications has been sluggish, not standing their success. There may be too much distance and neglect between the clinical setting and self-contained research. The merge of AI technologies into pathology has significantly impacted diagnostic precision and speed. Digital pathology platforms equipped with machine learning algorithms enable pathologists to analyze large volumes of histological images with enhanced accuracy. These systems have demonstrated remarkable capabilities in identifying subtle morphological features indicative of various diseases such as cancerous lesions or infectious conditions. Moreover, AI-driven image analysis tools can assist pathologists in differentiating between benign and malignant tumors by quantifying cellular characteristics beyond human visual perception.

 

Furthermore, AI-powered predictive models have the potential to refine prognostic assessments based on pathological findings. By leveraging vast datasets encompassing clinical outcomes and molecular profiles associated with specific diseases or tissue alterations, these algorithms can generate more tailored predictions regarding disease progression or treatment responsiveness. Through this approach, pathologists can offer more precise guidance on patient management while harnessing valuable insights from diverse sources for optimizing therapeutic intervention. The convergence of advanced image recognition techniques, virtual microscopy, and genomics data analysis could enable comprehensive profiling of individual disease phenotypes at an unprecedented level. In conclusion, AI technologies have already begun reshaping the landscape of modern pathology practices through improved diagnostic capabilities, enriched prognostic insights and envisaged pathways toward personalized healthcare delivery. The seamless integration of AI-driven solutions into daily laboratory workflows will undeniably propel pathology into a new era marked by heightened efficiency and unparalleled precision in diagnostics and therapeutic support.

 

Author (s) Details

Saqib Ahmed
Department of Pathology, Shri Guru Ram Rai Institute of Medical and Health Sciences and Hospital, Dehradun, Uttarakhand, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v3/1831

 

Correlation between MRI and Ultrasound Findings in Rotator Cuff Tear of Shoulder Joint for Accuracy Assessment of Diagnostic Tests |Chapter 2 | Medical Science: Recent Advances and Applications Vol. 2

Background: Magnetic resonance imaging (MRI) is a fine modality in diagnosing rotator cuff tears (RCT). With the speedy development and advancement of technology of ultrasound, rotator cuff tears have ameliorated significantly and reached levels comparable to (MRI).

Aim: The aim of the study is to assess the accuracy of ultrasound for the diagnosis of rotator cuff tears with respect to magnetic resonance imaging, to identify partial thickness rotator cuff tears, full thickness rotator cuff tears and tendinopathic changes; and to evaluate the sensitivity and specificity of US in diagnosing them with respect to magnetic resonance imaging.

Methods: It is a prospective study conducted at the Department of Radiodiagnosis in Narayana Medical College and Hospital, Nellore. A total of 30 patients who were suspected of having rotator cuff tear or tendinosis and planning to undergo an MRI of the shoulder were included in this study. Shoulder ultrasound was performed either before or after the MRI scan on the same day. The findings of ultrasound were compared and correlated with the findings of MRI.

Results: The study included 23 male and 7 female patients. The highest number of cases was found in the age group of 18-30 years, with a total number of 12 cases accounting for 40 %. The majority of the cases (19 cases) presented with the duration of symptoms between one to six months constituting about 63.3% of all cases. The Sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy for diagnosis of rotator cuff tear were 93%, 73%, 77%, 91%, and 83%, respectively.

Conclusion: Sensitivity (SN) for diagnosis of rotator cuff tear was good and had a higher negative predictive value (NPV). Consequently, the operator of ultrasound even though having a short tenure of experience in performing an ultrasound of the shoulder had good sensitivity in diagnosing tears; and was able to eliminate them with sureness. The study was limited by the involvement of a single ultrasound operator. Further research is recommended to assess the diagnostic accuracy of multiple ultrasonography operators with varying levels of expertise in detecting rotator cuff tears.

 

Author (s) Details

K S Vedaraju
Department of Radiodiagnosis, Narayana Medical College & Hospital, Nellore, Andhra Pradesh, India.

 

Basam Pavani Reddy
Department of Radiodiagnosis, Narayana Medical College & Hospital, Nellore, Andhra Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/msraa/v2/5001

Artificial Intelligence in Dental Implant Identification: A Comprehensive Overview | Chapter 4 | Medical Science: Trends and Innovations Vol. 7

Background: Dental implantology has significantly transformed the field of restorative dentistry, providing patients with long-term, functional, and aesthetic solutions for missing teeth. As the demand for implants increases globally, the need for effective and accurate implant fixture identification has become more crucial.

Aim: This review aims to explore the role of artificial intelligence (AI) in dental implant identification, focusing on its applications, benefits, and challenges in clinical practice. The study examines AI-driven tools and their impact on diagnostic accuracy, clinical decision-making, and treatment planning.

Methodology: A comprehensive literature review was conducted using Medline (PubMed) and Google Scholar databases in January 2025. The search targeted studies and reviews on AI applications in dental implant identification, analyzing technological advancements and their clinical implications. A total of 28 relevant articles were selected for assessment.

Results: AI-powered tools, such as Spotimplant.com, Implantif.ai, and AI2D, have demonstrated high accuracy in identifying dental implants from radiographic images. Studies have shown that AI-based systems can improve identification precision by up to 25% compared to traditional methods. These technologies streamline the identification process, reduce human error, and enhance treatment planning. However, challenges remain, including database limitations, difficulties in complex cases, and the need for regulatory compliance.

Conclusion: AI-driven implant identification offers significant advantages in improving diagnostic accuracy and clinical efficiency. While current AI tools present challenges related to data quality, regulatory frameworks, and integration into clinical workflows, ongoing advancements are expected to enhance their reliability and applicability. Future research should focus on expanding AI training datasets, optimizing deep learning models, and integrating AI into digital dental workflows for personalized treatment planning.

 

Author (s) Details

 

Hanen Boukhris
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Ghada Bouslama
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Hajer Zidani
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Kawther Bel Haj Salah
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Souha BenYoussef
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Please see the book here:- https://doi.org/10.9734/bpi/msti/v7/4409

Determining the Role of Ki67 and p16INK4a Biomarkers on Conventional Cell Blocks to Differentiate Post Radiation Dysplasia from Cervical Cancer in Post Therapeutic Surveillance Cytology | Chapter 02 | Issues and Developments in Medicine and Medical Research Vol. 2

 Introduction: Although pap tests are successful in detecting abnormal cervical cytology prior to treatment, they are ineffective after treatment due to radiation-induced alterations. Post-therapy Papanicolaou (pap) tests have a low diagnostic accuracy because it is difficult to distinguish benign from malignant lesions due to post-radiation cellular alterations, also known as post-radiation dysplasia.

The usefulness of the biomarkers p16INK4a and Ki67on conventional cell blocks (CCBs) in post-therapeutic surveillance of cervical cancer to detect residual disease and site recurrence was investigated in this work. We've also looked into using CCBs as a key screening tool.

Patients who were diagnosed with cervical cancer less than a year ago were followed in this cross-sectional study between April 2018 and April 2019. For CCBs, we used typical pap smears and samples in 10% neutral buffered formalin. Ki67 and p16INK4a were used as primary antibodies in immunohistochemistry on all cell blocks.

Recurrences and residual disease were identified in 8 patients out of a total of 35. For detecting cervical cancer, pap, cell blocks, and p16INK4a had sensitivity, specificity, and diagnostic accuracy of 75 percent, 74.07 percent, and 88.57 percent; 100 percent, 88.89 percent, 91.43 percent; and 37.50 percent, 96.30 percent, and 82.86 percent, respectively. We discovered that the Ki67 labelling index of 20% had a diagnostic accuracy of 100%.

Conclusion: Ki67 labelling index of 20% on CCBs may distinguish persistent and recurrent cancer from post-radiation dysplasia in post-therapy surveillance cytology (p value 0.001). In addition, we discovered that CCBs have a higher diagnostic accuracy than pap tests (Mac Nemar p value, 0.027). We did not find p16INK4A to be very beneficial as a biomarker for recurrence/residual illness evaluation.

Author(S) Details

F. S. Desai
Department of Surgical Pathology, Himalaya Cancer Hospital and Research Center, Vadodara, Gujarat, India.

Rajesh Korant
Department of Radiation Oncology, Himalaya Cancer Hospital and Research center, Vadodara, Gujarat, India.

Mehul Gohil
Department of Radiation Oncology, Himalaya Cancer Hospital and Research center, Vadodara, Gujarat, India.

Lisam Shanjukumar Singh
Department of Biotechnology, Cancer Biology Division, Manipur University, Imphal, Manipur, India.

View Book:- https://stm.bookpi.org/IDMMR-V2/article/view/5431

Diagnostic Accuracy and Pitfalls in Fine Needle Aspiration Cytology of Salivary Gland Lesions: An Advanced Study Approach | Chapter 12 | Recent Developments in Medicine and Medical Research Vol. 10

 The goal was to assess clinical data in a population from the Mexican state of Guanajuato as a suspected case of COVID-19 with a positive rRT-PCR result reported till October 2, 2020.

Introduction: One of the main concerns since the start of the new coronavirus pandemic in Wuhan, China, at the end of 2019, has been getting a correct diagnosis. Fever is the most common symptom of COVID-19 infection, however it can also be found in other viral infections.

Study Design: This is a cross-sectional study based on data from the General Epidemiological Directorate's National Epidemiological Surveillance System and the Mexican Secretary of Health's National Epidemiological Surveillance System.

Sample registries from confirmed and discarded COVID-19 cases will be kept in the database until October 2, 2020.

Methodology: A total of 100,919 registries were examined. The results of the rRT-PCR test were missing in 810 of them, thus they were eliminated. A confirmed case of COVID-19 is a person who has a positive rRT-PCR test for SARS-CoV-2, regardless of the clinical data presented. A suspected case of COVID-19 is a person who has a positive rRT-PCR test for SARS-CoV-2 and is accompanied by at least one of the following: myalgia, arthralgia, odynophagia, chills, chest pain, rhinorrhea, an Age, sex, and clinical data were all recorded, as well as the SARS-CoV-2 rRT-PCR result. The effect of clinical data on positive rRT-PCR was investigated using logistic regression.

A total of 100,109 registries were examined. SARS-CoV-2 was detected in 41,734 of them. Fever (OR 1.72, CI95 percent 1.68 to 1.77), cough (OR 1.70, CI95 percent 1.66 to 1.74), and odynophagia (OR 1.71, CI95 percent 1.66 to 1.75) were all found to have a larger effect on a positive rRT-PCR test. The result of the rRT-PCR test was unaffected by cyanosis.

COVID-19 has no clinical data that can be used to diagnose it. In confirmed instances, the clinical data is similar to that of other respiratory viral infections.

Author(S) Details

Crysle Saldanha
Department of Pathology, Father Muller Medical College, Mangalore, India.

Hilda Fernandes
Department of Pathology, Father Muller Medical College, Mangalore, India.

View Book:- https://stm.bookpi.org/RDMMR-V10/article/view/4634

Study on Invasive Procedures of the Chest Lesions: Are they Must be Performed and Why?| Chapter 9 | Issues and Development in Health Research Vol. 4

 Introduction: Lung cancer is one of the leading causes of death in the globe. MDCT is discovering an increasing number of lung and mediastinal lesions, and histological diagnosis is frequently necessary to establish the optimal treatment choice. The goals of this article are to detail invasive techniques for chest lesions, including indications, contraindications, technical features, and diagnostic accuracy of percutaneous lung biopsies. Fine-needle aspiration biopsy (FNAB) and core-needle biopsy (CNB) are the methods of choice for collecting tissue specimens in patients with lung lesions. Histology diagnoses are commonly used to guide treatment techniques. FNAB biopsy is conducted in 85 of the 97 patients in our study when logistically feasible or when other procedures (such as bronchoscopy with lavage) are equivocal, and CNB is performed in 12 of the 97 patients. Disposable needles sized 19-22G were used. Results: All 76 patients, ages 21 to 79, who had lung lesions with a diameter of 2.0 cm or smaller had FNAB under CT control. FNAB under US control is conducted in 13 individuals because to the superficial location of the lesions. All patients' tissue samples are cytologically and histologically examined. It is computed the diagnostic sensitivity and accuracy, as well as the sort of complications that occurred. The overall sensitivity, specificity, and accuracy of CNB improved slightly. Conclusion: Percutaneous FNAB and CNB are safe procedures for evaluating focal pulmonary lesions for diagnostic purposes. Although some problems, such as pneumothorax and pulmonary haemorrhage, are uncommon, others, such as air embolism and metastatic seeding, can have serious effects.

Author(s) Details

Assoc. Prof. Dr. A. Hilendarov
Medical University-Plovdiv, Мedical Faculty, Department of Diagnostic Imaging, Plovdiv, Bulgaria.

Dr. A. Georgiev
Medical University-Plovdiv, Мedical Faculty, Department of Diagnostic Imaging, Plovdiv, Bulgaria.

Dr. A. Chervenkov
Medical University-Plovdiv, Мedical Faculty, Department of Diagnostic Imaging, Plovdiv, Bulgaria.

View Book :- https://stm.bookpi.org/IDHR-V4/article/view/3377

Study on Fine Needle Aspiration Cytology and Histopathology in the Diagnosis of Breast Lump: A Comparative Approach | Chapter 5 | New Frontiers in Medicine and Medical Research Vol. 6

 1.To compare the results of fine needle aspiration cytology (FNAC) and histopathological study of biopsy in detecting breast cancer in suspicious breast lumps in an outpatient setting; 2.To investigate the accuracy of FNAC in diagnosing various breast lumps; 3.To determine whether cancer patients can be managed solely based on FNAC diagnosis without histological study.

Methodology: This was a hospital-based retrospective study that used data from the Department of Pathology at Mysore Medical College in Mysore during August and September 2016.

The study lasted two months, and the sample size was 50 patients.

Results: FNAC had a sensitivity of 50%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 88.37 percent in this study. FNAC is very specific in this scenario, with a strong positive predictive value.

Conclusion: Benign breast neoplasms are more common than malignant breast neoplasms. The current investigation found a high specificity and maximum positive predictive value for FNAC correlation with histology. When compared to biopsy, FNAC has a lower sensitivity. However, in an outpatient context, FNAC can be utilised as a suggestive diagnostic (one stop) for breast cancer evaluation. The accuracy of the FNAC allows you to decide whether or not to proceed with surgery. In the vast majority of situations, it bridges the gap between clinical evaluation and ultimate surgical pathological diagnosis. It allows the doctor to make a diagnosis in a high percentage of cases with little time and money spent, and to avoid unneeded surgery in many circumstances.

Author (S) Details

K. B. Chetan
Department of Pathology, Mysore Medical College and Research Institute, Karnataka, India.

N. Sreenivas
Department of Pathology, Mysore Medical College and Research Institute, Karnataka, India.

View Book :- https://stm.bookpi.org/NFMMR-V6/article/view/2763