Benign Cases Research Articles

Deciphering the enigma of glomus tumors: A case series

Glomus tumors are rare contributing to less than two percent of all soft tissue tumors. Given the rarity of this tumor, there is a possibility of low suspicion and a missed clinical diagnosis. This case series suggests diagnostic and morphological clues for early diagnosis of this tumor.This retrospective study includes nine interesting cases of glomus tumors from January 2015 to January 2024. Data was tabulated and analysed by descriptive statistics using Microsoft Excel 2016. In this series the glomus tumors showed a mean age of (37.67± 17.5 years). Male to female ratio was 1.25:1. Most common sites were digital parts of extremities (5/9, 55.56%). Four of these digital locations were subungual (4/5, 80%). The left side (5/9, 55.56%) was the most common laterality. Duration of symptoms till diagnosis was established showed a median of three months [range was one month to nine years]. All nine cases presented with pain. The mean size of glomus lesions was 2.1±1.5 cm). Multiplicity was observed in (2/9, 22.22%) cases. Eight cases were benign (8/9, 88.89%) while one was malignant (1/9, 11.11%). Two out of these eight benign cases were glomangiomatosis. Excision biopsy was the surgical procedure performed in all of our benign cases while the malignant case was treated with amputation of the finger.Diagnosis of glomus tumors requires a high index of clinical suspicion, especially in extra-digital locations. Awareness is essential for early diagnosis and treatment of this lesion, as it is an extremely painful condition and pain is out of proportion to the size of the lesion. Magnetic Resonance Imaging (MRI) is the first investigation of choice for imaging. An early biopsy can confirm the diagnosis of a glomus tumor in time. Complete surgical excision is curative with a low risk of recurrence.

Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics.

A considerable portion of patients with fever of unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum of FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as the most prevalent etiology of FUO with associated lymphadenopathy. Distinguishing between benign and malignant lymph node lesions is a major challenge for physicians and an urgent clinical concern for patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing between malignant and benign lymph node lesions. This study utilizes PET/CT radiomics to differentiate between lymphoma and benign lymph node lesions in patients with FUO, aiming to improve diagnostic accuracy. Data were collected from 204 patients who underwent 18F-FDG PET/CT examinations for FUO, including 114 lymphoma patients and 90 patients with benign lymph node lesions. Patients were randomly divided into training and testing groups at a ratio of 7:3. A total of 15 effective features were obtained by the least absolute shrinkage and selection operator (LASSO) algorithm. Machine learning models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) algorithms. In the training group, the area under the curve (AUC) values for predicting lymphoma and benign cases by LR, SVM, RF, and KNN models were 0.936, 0.930, 0.998, and 0.938, respectively. There were statistically significant differences in AUC between the RF and other models (all P < 0.001). In the testing group, the AUC values for the four models were 0.860, 0.866, 0.915, and 0.891, respectively, with no statistically significant differences observed among them (all P > 0.05). The decision curve analysis (DCA) curves of the RF model outperformed those of the other three models in both the training and testing groups. PET/CT radiomics demonstrated promising performance in discriminating lymphoma from benign lymph node lesions in patients with FUO, with the RF model showing the best performance.

HPB SO22 - Clinical outcomes following radical cholecystectomies, a single centre experience in review

Abstract Background Gallbladder carcinoma (GBC) is a rare disease with dismal prognosis. The majority of cases are identified at an advanced state that preclude curative approaches which would usually necessitate removal of the gallbladder with en-block wedge hepatectomy of segment 4b/5 as well as periportal lymphadenectomy. The purpose of this study was to analyze our outcomes for patients undergoing a radical cholecystectomy for suspected GBC. Method A retrospective analysis of patients who underwent radical cholecystecomies at Queen’s Medical Centre, Nottingham, UK between 1st January 2014 and October 2023 was performed. Patients with concomitant primaries, incidental GBC or completion liver resection following index cholecystectomy were excluded. Results 41 patients were eligible for analysis, those were comprised of 13 males and 28 females with a median age of 67 (IQR 57-72) years. The incidence of malignancy on histology was 31.7% (GBC n=11, metastatic renal carcinoma n=1, metastatic melanoma n=1). Cystic margin assessment by frozen section was performed in 41% of cases. In cases with confirmed gallbladder cancer, the median tumour size was 30mm, positive periportal lymph nodes were identified in two patients while R0 resection was achieved in all cases. The median length of stay following surgery was 5 days (IQR 5-7) while Clavien Dindo complication grade ≥ III were experienced in 12% ( return to theatre for leak from repaired cholecysto-duodenal fistula n=1, IR drainage of post operative collection n=3, ITU stay for cardiac arrhythmia n=1). The pathologies encountered in benign cases (n=28) were compatible with chronic cholecystitis (53.5%, n=15), concurrent chronic cholecystitis and adenomyomatosis (21.4%, n=6), xanthogranulomatous cholecystitis (10.7%, n=3), adenomyomatosis (7.1%, n=2), follicular cholecystitis (3.5%, n=1), benign GB polyps (3.5%, n=1). Conclusion Radical cholecystectomy remains the standard of care for suspected GBC however sufficient preoperative counselling is prudent given likelihood of benign disease on histology.

Breast Cancer Classification Using Fine-Tuned SWIN Transformer Model on Mammographic Images

Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes. Mammography procedures are often used to detect early-stage cancer cells. This traditional method of mammography while valuable has limitations in its potential for false positives and negatives, patient discomfort, and radiation exposure. Therefore, there is a probe for more accurate techniques required in detecting breast cancer, leading to exploring the potential of machine learning in the classification of diagnostic images due to its efficiency and accuracy. This study conducted a comparative analysis of pre-trained CNNs (ResNet50 and VGG16) and vision transformers (ViT-base and SWIN transformer) with the inclusion of ViT-base trained from scratch model architectures to effectively classify mammographic breast cancer images into benign and malignant cases. The SWIN transformer exhibits superior performance with 99.9% accuracy and a precision of 99.8%. These findings demonstrate the efficiency of deep learning to accurately classify mammographic breast cancer images for the diagnosis of breast cancer, leading to improvements in patient outcomes.

Preliminary assessment of the diagnostic accuracy of cutaneous T-cell lymphoma through deep sequencing of T-cell receptor gamma gene.

The diagnostic challenges in early mycosis fungoides (MF) and other cutaneous T-cell lymphomas (CTCL) persist despite advancements in molecular methods. The study aims to provide a preliminary assessment of next-generation sequencing in analyzing T-cell receptor gamma (TRG) sequences for distinguishing CTCL from benign inflammatory disorders. Skin samples from CTCL and benign inflammatory skin disorders proven clinicopathologically were assessed for TRG by NGS. Our study analyzed skin samples from a total of 36 subjects, comprising 22 cases of CTCL, including 14 MF and 8 other CTCLs, alongside 14 cases of benign inflammatory skin disorders. According to the LymphoTrack criteria, monoclonality was detected in 75.0 % of the overall 24 CTCL patients. Specifically, in MF cases, 10 out of 14 were identified as monoclonality, with all four non-monoclonal cases being in the patch stage. For the other CTCL, 6 out of 8 cases displayed monoclonality. Among the overall 22 CTCL patients, 11 had multiple biopsies, with 9 displaying the same dominant clone across different sites. Among the 14 benign cases, only the case with erythrodermic psoriasis exhibited monoclonality. Our decision tree analysis suggests that a high frequency of the most abundant clone, its ratio to the third most abundant clone, and TRG V-I segment usage are effective markers aiding in diagnosing CTCL. Combination of the clone frequencies, and TRG V segment usage may enhance diagnosis for MF and other CTCLs, aiding in differentiating them from benign conditions. However, molecular diagnosis for patch-stage MF remains challenging.

The genomic landscape of papillary thyroid carcinoma on next-generation sequencing in patients undergoing total thyroidectomy.

Thyroidectomy is increasingly performed for suspected malignancy. This cohort study aimed to identify genetic markers associated with malignancy and determine the molecular landscape of papillary thyroid carcinoma (PTC) through next-generation sequencing (NGS) in patients undergoing total thyroidectomy. Among 116 surgical candidates, 103 patients (age=42.9±13.7years; Male: Female=14:89) with benign or malignant thyroid nodules were eligible. Live thyroid tissue samples harvested intraoperatively with adequate DNA and RNA yield were subjected to NGS on the Illumina NovaSeq 6000 platform for genomic and transcriptomic analysis, respectively. Histopathology comprised 20 malignant (19.4%) and 83 benign (80.6%) cases, including 16 PTC (15.5%) cases. On NGS, single nucleotidepolymorphisms (SNPs) in NTRK1 at NC_000001.11:156879016 on chromosome 1 and ALK at NC_000002.12:29717663 on chromosome 2 were frequent in malignant lesions (p<0.05). A SNP in ALK at NC_000002.12:29193706 was consistently a homozygous alternate alleleacross the cohort. DNA-sequencing of PTC lesions identified recurrentsomatic mutations in BRAF (100%), ALK (56.3%), RET (18.8%), PIK3CA (12.5%), NTRK1 (12.5%), NTRK2 (87.5%), NTRK3(12.5%), NRAS(6.3%), and PTCH1 (31.3%) genes. RNA sequencing revealednovelfusiongenes, including MKRN1-BRAF, RN7SL1-CDH1, IRF2BPL-MED12, MED12-IRF2BPL, CPM-MDM2, and AC005895.3-PDGFRB. Inreceiveroperative characteristics analysis, the AUCs ofALKmutationpredictingrecurrence and metastases were 0.818 and 0.783. This Indian study identified novel somatic mutations and fusion genes in PTC, revealing a distinct genomic landscape with implications in precision diagnostics and personalized therapies. NGS with intraoperative live sampling shows promise in prognostication and therapeutic optimization of advanced/metastatic PTC cases. CTRI/2020/09/027607dt 04/09/2020; REF/2020/08/036119.

Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights.

Breast cancer (BCa) poses a severe threat to women's health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the radiologist's experience. Therefore, we propose a novel, mammogram image-based BCa explainable AI (BCaXAI) model with a deep learning-based framework for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet V2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model's decision-making process, fostering trust and confidence in the AI-based system. Based on using the DDSM and CBIS-DDSM mammogram datasets, BCaXAI achieved exceptional performance, surpassing traditional models such as ResNet50 and VGG16. The model demonstrated superior accuracy (98.53%), recall (98.53%), precision (98.40%), F1-score (98.43%), and AUROC (0.9933), highlighting its effectiveness in distinguishing between benign and malignant cases. These promising results could alleviate the diagnostic subjectivity that might arise as a result of the experience-variability between different radiologists, as well as minimize the need for repetitive biopsy procedures.

Validation of the 2019 American Society of Colposcopy and Cervical Pathology Online Cervical Cancer Screening Program via 9 Large-Cohort Data of Chinese Women.

The aim of the study is to validate the applicability and performance of the 2019 US risk-based guideline for Chinese women. We analyzed 10,055 cases with data on human papillomavirus testing, cytology, and pathologically confirmed diagnosis (analysis-set). According to the 2019 US risk-based guideline, we recorded the risk value and triage recommendations for each case. Then, we assessed the concordance of the guideline triage recommendations with pathology diagnosis for the relevant case from the real-world projects. Among the analysis-set, 9,495 cases with an estimated risk value were identified as analysis cohort while the remaining 560 cases were cataloged as "special cases." Among the analysis cohort, 960 and 526 cases were pathologically confirmed as cervical intraepithelial neoplasia (CIN) 2+ and CIN3+, respectively. The US risk-based guideline recommended colposcopy or more aggressive interventions (Colp+) for 86.8% (833/960) of the CIN2+ and 95.8% (504/526) of the CIN3+ cases, with 87.1% sensitivity and 82.5% specificity for CIN3+ cases (AUC = 0.926, p < .0001). The US risk-based recommended no-Colp for 98.0% (6,142/6,269) of the pathologically confirmed CIN1 or benign cases. In addition, 97.3% (545/560) of the "special cases" cases were recommended as Colp+. The 2019 US risk-based guideline works well with satisfied clinical sensitivity for CIN2+ and CIN3+ and seems applicable for cervical cancer screening in China.

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

Integrating Whole Slide Imaging and Artificial Intelligence Provides Higher Accuracy in Thyroid Cytology

Abstract Introduction/Objective The aim of the study is to evaluate the potential advantages of integrating Whole Slide Imaging (WSI) and Artificial Intelligence (AI) to increase efficiency and diagnostic accuracy in thyroid cytology, especially in indeterminate lesions. Methods/Case Report A retrospective database search from 2000-2023 was performed, and the concordance with the pathologists was evaluated based on the manual or AI-based Region of Interest (ROI) identification, the best- suited AI, algorithm design, color enhancement, and combination of AI and Electronic Medical Records (EMR). Results (if a Case Study enter NA) The cohort included 3285 WSI and 121,384 ROI. In 109 WSI with manually diagnosed ROI, Deep learning successfully predicted benign and malignant cases, including 11 indeterminate cases, with zero false positives or negatives for 35% of cases. In the second study with 908 WSI, 4494 ROI were identified with Convolutional Neural Network (CNN). A higher area under the curve (AUC) resulted when EMR and Machine Learning algorithm (MLA) were integrated: EMR: 0.931, MLA: 0932, EMR+ MLA; 0.962, Sensitivity: 92.0%, Specificity 90.5%. In the next study with 145 WSI, the CNN-identified ROI was presented to the same pathologist after 117 days. Perfect concordance was seen between the two methods (K score 0.924, time to diagnosis;81.59 Second). In 360 WSI, YOLOV4 algorithm was trained to detect the ROI, and detection and classification rates were used to evaluate the accuracy. It had a precision of 81.84% (3.16% more than the single detection networks of 78.68%). In the last study with 964 WSI, color augmentation improved the AUC for indeterminate cases from 88.3% to 96.2%. Conclusion CNN is a better-suited AI for whole slide imaging due to its compatibility with image data and ability to automatically detect important features without human supervision. It also removes the component of subjective bias in interpretation. Color augmentation and integration of EMR can overcome the unique challenges in thyroid indeterminate cytology.

Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset

Aim: The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis. Background: Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives. Objective: This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances. Method: In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively. Result: Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis. Conclusion: This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.

7847 Radiomic Ultrasound Checkup of Thyroid Nodules Before Radiodiodine Therapy

Abstract Disclosure: A.A. Trukhin: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). A.V. Manaev: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). S.M. Zakharova: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). M.S. Sheremeta: None. E.A. Troshina: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). Background: Over 83 years radioiodine therapy (RIT) of autonomous thyroid nodules still have uncovered pages, according to its complexity and thyroid nodules variety. There are two main criteria to allow RIT – no malignancy and presence of iodine uptake, the last one could be checked by Tc-99m-pertachnetate or I-123, I-131 iodine scintigraphy. Case with thyroid nodules first undergo ultrasound (US) TI-RADS classification, then fine needle aspiration stratification according to the Bethesda system. Publications applied such algorithm shows 75-85% accuracy in malignancy determination, that in 15-25% can lead to improper RIT application. Aim: Method development for thyroid nodules ultrasound checkup before I-131 administration and I-131 24h uptake prognosis. Methods: Study includes 150 nodules (65 benign, 60 malignant, 25 autonomous benign thyroid nodules). Both US longitudinal and transversal thyroid nodule projections were obtained via GE Voluson E8 (36% benign cases / 27% malignant cases) and GE Logiq E (64% benign cases / 73% malignant cases) in cinematic loop regime. Autonomous thyroid nodules underwent I-131 pharmacokinetics examination with 24h uptake determination and RIT. Statistical textural features were obtained through spatial adjacency matrix. Results: Analysis of 16 statistical textural features shows weak correlation between 11 statistical textural features E (Energy), S (Entropy), LU (Local homogeneity), MP (Maximum probability), TR (Matrix trace), CORR (Correlation), IMC (information measure of correlation), DV (Difference variance), DE (Difference entropy), SE (Sum entropy), SA (Average value of the sum). ROC-analysis using 11 statistical textural features shows the best model for benign/malignance determination with AUC = 0.83, Se = 74%, Sp = 73%, PPV = 75%, NPV = 72%, Acc = 74%. I-131 pharmacokinetics examination shows strong correlation SA to I-131 24h uptake. Conclusions: The performed study demonstrates the possibility of using textural statistical features of ultrasound images as instrument for thyroid nodules ultrasound checkup before I-131 administration, for extra FNA prescription and prognosis the I-131 24h important in exact therapeutic I-131 activity calculation. Presentation: 6/2/2024

HMGA2-positive salivary gland neoplasms with prominent trabecular/canalicular morphology: a focus on carcinomas arising within this phenotype.

Pleomorphic adenoma (PA) with a prominent trabecular/canalicular morphology has consistent HMGA2 protein expression, and association with HMGA2 fusions. We report our experience with this subtype, with emphasis on the carcinomas that can arise in this context. A retro- and prospective review (2013-2024) of major salivary gland tumours with prominent trabecular/canalicular morphology was performed. Twenty-one parotid tumours met the criteria: 14 benign (66.7%), six carcinomas (28.6%), and one of uncertain behaviour (4.7%). HMGA2 immunohistochemistry (IHC) was performed on all cases. Next-generation sequencing was successfully performed on 18. Seven benign cases had a conventional PA component. In all cases, the tumour cells in these trabecular/canalicular areas demonstrated variable papillary thyroid carcinoma-like nuclear changes, including chromatin clearing, overcrowding, membrane irregularities, and intranuclear pseudoinclusions. Benign tumours were well-demarcated, whereas carcinomas demonstrated either a multinodular pattern of invasion or subtle infiltration. Two carcinomas showed increased cytologic atypia and architectural complexity and one had perineural invasion. By IHC, all were positive for HMGA2. In the trabecular/canalicular areas, there was consistent strong expression of CAM5.2, S-100, and SOX-10 and variable expression of p63 but negative p40. HMGA2 alterations were detected in 16 of 18 cases (89%). Follow-up was available on two carcinomas, with one being locally recurrent. While most HMGA2-positive salivary gland neoplasms with a prominent trabecular/canalicular growth pattern are benign, they, like traditional PAs, may give rise to carcinomas that can locally recur. These carcinomas can be deceptively bland, subtly infiltrative, or have a multinodular pattern of invasion.

A Combination of Cell Findings Improves the Accuracy of Voided Urine Cytology

Abstract Introduction: Urine cytology is a morphological diagnostic test that is, patient-friendly and easy to sample but subjective in morphological evaluation. This study aims to evaluate the effect of combining cell findings to assess urine cytology. Materials and Methods: Thirty cell findings found in high-grade urothelial carcinoma (HGUC) were selected for morphological abnormalities, each with detailed definitions. All 66 HGUC and 30 benign cases were examined for morphological abnormalities. The number of cases with at least one abnormal cell finding deviating from normal cells was used. The HGUC and benign cases were compared using significance difference tests. Using the cell findings extracted by the exact tests, discriminant analysis was performed to determine the valid cell findings for HGUC diagnosis. Results: There was a difference in the detection rate of abnormal findings between HGUC and benign cases. Additionally, 20 cell findings were detected significantly more frequently in patients with HGUC. Furthermore, the discriminant analysis revealed that six cellular findings (high nucleus-to-cytoplasm ratio, hyperchromasia, eccentric nuclei, nuclear protrusion, unevenly distributed chromatin, and irregular nuclear shape) showed high accuracy in confirming the diagnosis of HGUC. Conclusions: Five- to six-cell findings were individually valid; however, the combination of cell findings is crucial for an objective and accurate diagnosis of HGUC.

Hybrid Approach of Multiclassification for Lung Cancer Types Identification

As a widespread health concern, lung cancer requires sophisticated detection techniques for a precise classification. This research proposes an integrated framework for improving how well lung cancer is detected by combining processing methods for images and machine learning. The dataset, which includes normal cases, large-cell carcinoma, squamous cell carcinoma, and adenocarcinoma, allows for a detailed investigation of various forms of lung cancer. K-means clustering is the initial stage of the approach of segmenting complex images. Utilizing the VGG16 model, features are extracted that are important for classification. Several classification models, such as SVM, Logistic Regression, and Feedforward Neural Network, are trained using the combined features from clustering and VGG16. This research goes beyond the dichotomy of benign and malignant cases, investigating in more detail the subtypes of large-cell carcinoma, squamous cell carcinoma, and adenocarcinoma. Including different classes of lung cancer increases the granularity of the classification process, improving diagnostic accuracy and clinical insights. The results highlight how well the suggested strategy works, which is a major advancement in the accurate identification of distinct kinds of lung cancer.

Post-operative breast imaging: a management dilemma. Can mammographic artificial intelligence help?

BackgroundImaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy.MethodsThis cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes.ResultsMammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy “POM” score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%.ConclusionApplying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers.

Correlation between radiological imaging and histopathological findings in patients with breast lump

Background: Breast cancer is the most common cancer among women globally and the second leading cause of cancer-related deaths, after lung cancer. Early detection via routine screening is crucial, as not all breast lumps are cancerous and benign lumps do not always progress to cancer. To improve diagnostic accuracy, a triple assessment approach is used, combining radiological imaging, pathological diagnosis, and clinical examination. Techniques such as mammography, ultrasonography, and fine needle aspiration cytology (FNAC) are key to this evaluation. This study assesses the breast imaging reporting and data system (BIRADS) scoring system’s diagnostic accuracy by comparing radiological techniques with histopathological findings. Methods: The study involved 100 patients with breast lumps, categorised into three groups based on BIRADS scores: group A (BIRADS I-III), group B (BIRADS IV), and group C (BIRADS V). Data analysis using chi-square and student’s t tests were done. Results: In our study we found that accuracy of BIRADS score: sensitivity: 88.9%, specificity: 89.5%, positive predictive value: 97.3%, negative predictive value: 65.4% and diagnostic accuracy: 81%. Conclusions: The study demonstrated a high incidence of fibroadenoma (66%), others (3%) as benign cases and invasive ductal carcinoma (31%) malignant cases. The study highlights the importance of accurate diagnosis and targeted screening, especially in high-risk groups such as postmenopausal women and those with a family history of breast cancer. The findings affirm the high sensitivity and specificity of the BIRADS system as a valuable diagnostic tool.

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

Ultrasound Multimodality Examination Improves the Diagnostic Efficiency of Non-Mass-Like Breast Lesions

This study is focused on ultrasound multimodality examination, which refers to the combined use of three ultrasound examination modalities, ultrasound (US), acoustic radiation force impulse (ARFI) imaging, and contrast-enhanced ultrasound (CEUS). The purpose of this study is to analyze the value of applying ultrasound multimodality examination in the differential diagnosis of benign and malignant breast non-mass-like lesions (NMLs). Cases of breast NMLs were analyzed retrospectively, and the nature of all the lesions was verified by pathological examination. Based on the gray-scale ultrasound image characteristics, the cases were classified into types Ⅰ to Ⅴ, and type Ⅰ and type Ⅱ were further classified into 4 subtypes, Ⅰa, Ⅰb, Ⅱa, and Ⅱb, according to whether there was also calcification, and the proportion of malignant cases in each subtype was statistically analyzed. Logistic regression models of US, US+ARFI, US+CEUS, and US+ARFI+CEUS for the diagnosis of malignant cases were established, ROC curves were drawn, the area under the curve (AUC) was calculated, and comparisons were made accordingly. The detection rate of malignant NMLs without calcification (atypical malignant NMLs) by the combination examination of US, ARFI, and CEUS was analyzed. A total of 407 cases were included in the study. All subjects were female, aged 22 to 81 years, with the average age being (47.0±11.0) years. There were 220 benign cases and 187 malignant cases. Ranked from the highest to the lowest, the malignancy proportion of the different types was Ⅰb>Ⅱb>Ⅲ>Ⅴ>Ⅰa>Ⅱa>Ⅳ. The malignant proportion of the low echo area with calcification was significantly higher than that of the lesions without calcification. The AUC (95% confidence interval [CI]) for diagnosing malignant cases with the logistic regression models of US, US+ARFI, US+CEUS, and US+ARFI+CEUS were 0.895 (0.862-0.927), 0.908 (0.878-0.937), 0.921 (0.893-0.948), and 0.927 (0.902-0.952), respectively. Comparison of the AUC of the 4 regression models showed significant differences (P<0.001). The detection rate of US for NMLs without calcification was 80.7%. When US was used in combination with ARFI and CEUS, 86.4% of the malignant NMLs lesions without calcification could be detected if the lesion CEUS score was 4 or 5 points or if shear-wave velocity (SWV)≥4.28 m/s. Breast NMLs with calcification show high risks of malignancy, and a pathological examination is always recommended for a conclusive diagnosis. Ultrasound multimodality examination can improve the diagnostic accuracy of breast NML without calcification.