Benign Cases Research Articles

The use of diagnostic complex robotic-assisted segmentectomy in the management of incidental and screen-detected pulmonary nodules.

Robotic-assisted thoracoscopic surgery (RATS) facilitates complex pulmonary segmentectomy which offers one-stage diagnostic and therapeutic management of small pulmonary nodules. We aimed to explore the potential advantages of a faster, simplified pathway and earlier diagnosis against the disadvantages of unnecessary morbidity in benign cases. In an observational study, patients with small, solitary pulmonary nodules deemed suspicious of malignancy by a multidisciplinary team were offered surgery without a pre or intraoperative biopsy. We report our initial experience with RATS complex segmentectomy (using >1 parenchymal staple line) to preserve as much functioning lung tissue as possible. Over a 4-year period, 245 RATS complex segmentectomies were performed; 140 right: 105 left. A median of 2 (1-4) segments was removed. There was no in-hospital mortality and no requirement for postoperative ventilation. Complications were reported in 63 (25.7%) cases, of which 36 (57.1%) were hospital-acquired pneumonia. A malignant diagnosis was found in 198 (81%) patients and a benign diagnosis in 47 (19%). The malignant diagnoses included: adenocarcinoma in 136, squamous carcinoma in 31 and carcinoid tumour in 15. The most frequent benign diagnosis was granulomatous inflammation in 18 cases. RATS complex segmentectomy offers a precise, safe and effective one-stop therapeutic biopsy in incidental and screen-detected pulmonary nodules.

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.

To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

Soluble Factors CD14, CD163, and Migration Inhibitory Factor Are Associated with Endometriosis-Related Infertility

Objectives: Myeloid cell-derived factors contribute to the immunopathology of endometriosis. Soluble CD14 (sCD14), CD163 (sCD163), and MIF serve as in vivo markers of myeloid function. However, these soluble molecules are largely unexplored in women with endometriosis-related infertility cases. We investigated three soluble markers, namely sCD14, sCD163, and MIF, in cases of infertility associated with endometriosis and correlated its level to the stage of endometriosis. Design: Eighty-seven women newly diagnosed with endometriosis or other benign gynecologic control cases linked to infertility were prospectively recruited and underwent diagnostic laparoscopy. Participants: Forty-four patients with endometriosis were included in this study, comprising 19 patients with early-endometriosis (stages I and II) and 25 late-endometriosis (stages III and IV) based on the revised American Society for Reproductive Medicine (rASRM) classification. The remaining 43 patients constituted a control group with infertility due to other causes. Methods: The levels of sCD14, sCD163, and MIF in serum and peritoneal fluid were assessed using ELISA. Results: Endometriosis women exhibited significantly higher serum levels of sCD163 and MIF levels compared to the control group. Both sCD163 and MIF levels displayed a positive correlation with the rASRM adhesion score. Moreover, the MIF level in serum had a positive correlation with the rASRM endometriosis score. In receiver operating characteristic analysis, serum sCD163 and MIF could significantly discriminate endometriosis and non-endometriosis in infertility cases. Limitations: Some limitations of the current study deserve to be underlined. First, the sensitive ELISA method was the sole-validated tool for detecting the markers in patient samples. Second, healthy or fertile women were not involved as the control group. Conclusions: The elevated systemic levels of sCD163 and MIF correlated with the severity of endometriosis. These soluble molecules have a potential diagnostic capacity as a non-invasive biomarker. Furthermore, our data warrants future studies on the underlying mechanism of sCD163 and MIF in endometriosis-related infertility.

Exploring the Diagnostic Challenges of Uterine Lesions in Frozen Section: A Comprehensive Study at a Tertiary Care Center.

Introduction Uterine masses are commonly submitted for frozen section, to guide the surgeon, regarding the type and extent of the procedure during surgery. Despite the technical difficulties in processing, sectioning, and staining of frozen section samples, it remains a fairly reliable intraoperative tool. Aim This study aims to analyze the diverse spectrum of uterine masses sent for frozen sections for two years. In addition, it aims to analyze the histomorphology of the uterine masses sent for the frozen section and correlate it with that of the routine histopathological findings, thereby justifying the diagnostic value of the frozen section with this study. Furthermore, the study aims to classify the lesions into benign and malignant, quantify their frequency, and listthe most common lesions seen in the uterine mass specimens sent for frozen section analysis. Methodology This retrospective descriptive study includes data from January 2021to December 2022, retrieved from the archives of the Department of Pathology at Saveetha Medical College.This study includes a total of 76 cases, including all the uterine masses sent for frozen section analysis during the study period. Results Of the total of 76 cases received, 17 (22.4%) were malignant and 59 (77.6%) were benign. Of the malignant cases reported, the most common was endometrial carcinoma, and the least common entities encountered were carcinosarcoma and leiomyosarcoma. Of the benign cases, benign endometrial polyp was the most common endometrial lesion and leiomyoma with and without degeneration was the most common myometrial lesion encountered. Of the 50 cases of leiomyoma encountered, 16 had extensive degenerative changes. The most common degeneration seen in the fibroid was hyaline degeneration, and the least common was xanthogranulomatous degeneration. Conclusions The intraoperative frozen section analysis is a very important diagnostic tool, but we need to be aware of its limitations. The accuracy, sensitivity, and specificity rates were found to be high. Thus, frozen section diagnoses can be very valuable in the clinical management of uterine tumors. Careful gross examination, sampling from representative areas, and good communication between the pathologist and surgeon may help in avoiding its limitations.

Mange in farmed rabbits

In this study we determined occurrence of mange in breeding rabbits on 1368 commercial farms in Portugal and Spain during 1996-2022. We obtained our information by carrying out 11 737 visits to 1334 doe farms, 11 farms only with growers, and 23 artificial insemination (AI) centres. The median size of the visited doe farms was 450 does (minimum to maximum: 100–2500 does) and 1175 does (ranging from100 to 6000 does) in 1996 and 2022, respectively. AI was used on 9% of the farms visited in 1996 and 95% in 2022. For our diagnoses we used (1) clinical observations on all visited farms to detect sarcoptic mange; (2) examination of the outer ear of breeding rabbits on a subset of farms to assess the prevalence of otodectic clinical mange (OCM); and (3) the examination of breeding rabbits and youngstock does (2.5 to 5.5 mo old) on a subset of 72 farms during 2018 to estimate prevalence of body mange compatible with cheyletiellosis. They were mainly clinical diagnoses, supported sometimes by a laboratorial confirmation. Over the course of the 27-yr clinical study, the cumulative incidence of sarcoptic mange was low; we recorded a total of 13 affected doe farms. The percentage of farms affected by OCM dropped from 55% in 1996 to 28% in 2022. OCM mean prevalence for the period 1996-2022 and 95% binomial confidence interval (CI) were 3.2% (95% CI [3.1-3.3%]), and 3.9% (95% CI [3.7-4.1%]) in does and bucks, respectively. We observed an improvement over time; the OCM yearly mean prevalence decreased from 7% in 1996 to 2.3% in 2022 in females and from 7.2% to 2.2% in males, respectively. This progress was compatible with the use of semen coming from AI centres; biosecurity measures and medical management also contributed. Genetic type was predisposing risk factor for OCM. Enabling risk factors were year and season (more affected in summer). Hair and skin disorders along the back, compatible with cheyletiellosis, were also assessed during 2018; we detected 50% of positive farms with various prevalence results in females, males or young does. In this study, we describe protocols observed in the control of benign and severe cases of mange in rabbitries, which included the use of ivermectin and synthetic acaricides. Ivermectin was used on 36% of doe farms visited during 2018-2022; we suggest that it should be used less often to lower its negative impact on the environment.

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning

BackgroundContrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help improve the constrains, this study aims to develop an end-to-end method based on deep learning to make malignancy diagnosis of liver lesions using CEUS.MethodsA total of 420 focal liver lesions with 136 benign cases and 284 malignant cases were included. A deep learning model based on a two-dimensional convolution neural network, a long short-term memory (LSTM), and a linear classifier (with sigmoid) was developed to analyze the CEUS loops from different contrast imaging phases. For comparison, a 3D-CNN based method and a machine-learning (ML)-based time-intensity curve (TIC) method were also implemented for performance evaluation.ResultsResults of the 4-fold validation demonstrate that the mean AUC is 0.91, 0.88, and 0.78 for the proposed method, the 3D-CNN based method, and the ML-based TIC method, respectively.ConclusionsThe proposed CNN-LSTM method is promising in making malignancy diagnosis of liver lesions in CEUS without any additional manual features selection.

Deciphering the shift from benign to active relapsing-remitting multiple sclerosis: Insights into T regulatory cell dysfunction and apoptosis regulation

BackgroundRelapsing-remitting multiple sclerosis (RRMS), a common demyelinating disease among young adults, follows a benign course in 10–15% of cases, where patients experience minimal neurological disability for a decade following disease onset. However, there is potential for these benign cases to transition into a clinically active, relapsing state. ObjectiveTo elucidate the biological mechanisms underlying the transition from benign to active RRMS using gene expression analysis. MethodsWe employed complementary-DNA microarrays to examine peripheral-blood gene expression patterns in patients with benign MS, defined as having a disease duration exceeding 10 years and an Expanded Disability Status Scale (EDSS) score of ≤3.0. We compared the gene expression pattern between patients who switched to active disease (Switching BMS) with those who maintained a benign state (Permanent-BMS) during an additional 5-year follow-up. ResultsWe identified two primary mechanisms linked to the transition from benign MS to clinically active disease. The first involves the suppression of regulatory T cell activity, and the second pertains to the dysfunction of nuclear receptor 4 A family-dependent apoptosis. These mechanisms collectively contribute to an augmented autoimmune response and increased disease activity. ConclusionsThe intricate gene regulatory networks that operate in switching-BMS are related to suppression of immune tolerance and aberrant apoptosis. These findings may lead to new therapeutic targets to prevent the escalation to active disease.

A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis

Timely identification of lung nodules, which are precursors to lung cancer, and their evaluation can significantly reduce the incidence rate. Computed Tomography (CT) is the primary technique used for lung cancer screening due to its high resolution. Identifying white, spherical shadows as lung nodules in CT images is essential for accurately detecting lung cancer. Convolutional Neural Network (CNN)-based methods have performed better than traditional techniques in various medical image applications. However, challenges still need to be addressed due to insufficient annotated datasets, significant intra-class variations, and substantial inter-class similarities, which hinder their practical use. Manually labeling the position of nodules on CT slices is critical for distinguishing between benign and malignant cases, but it is an unreliable and time-consuming process. Insufficient data and class imbalance are the primary factors that may result in overfitting and below-par performance. The paper presents a novel Deep Learning (DL) framework to detect and classify lung cancer in input CT images. It introduces a 3D-VNet architecture for accurate segmentation of pulmonary nodules and a 3D-ResNet architecture designed for their classification. The segmentation model achieves a Dice Similarity Coefficient (DSC) of 99.34% on the LUNA16 dataset while reducing false positives to 0.4%. The classification model shows performance metrics with accuracy, sensitivity, and specificity of 99.2%, 98.8%, and 99.6%, respectively. The 3D-VNet network outperforms previous segmentation methods by accurately calibrating lung nodules of various sizes and shapes with excellent robustness. The classification model’s metrics show that the suggested method outperforms current approaches regarding accuracy, specificity, sensitivity and F1-Score.

Histopathological Spectrum of Thyroid Neoplasm - A Retrospective Study Done at Tertiary Cancer Care Hospital

Thyroid lesions are fairly common &amp; have a wide spectrum of diseases ranging from functional, immune mediated to neoplastic lesions. All thyroidectomy specimens were included in the study period of 1.5 years from September 2019 to February 2021 at our institute, a total of 10,200 biopsies were sent to our department for histopathological examination, out of which 7200 were diagnosed as malignant cases, of which 408 (5.6%) cases were thyroid cancer (TC) cases. One hundred forty-five (35.53%) out of 408 TC cases underwent thyroid surgeries, fifty consecutive cases who fulfilled the inclusion criteria were registered in our study. All grossed, processed, stained sections of haematoxylin and eosin were reviewed and observed under microscope. Average age of the patients suffering from TC was found 44.80 years. Female preponderance seen with F:M-1.38:1. Most common location of tumor was Right lobe of thyroid (32%) followed by Left Lobe (26%). Malignant lesions (96%) predominated over Benign lesions (4%). Papillary carcinoma thyroid was the most common malignancy (72%) followed by Follicular carcinoma (14%). Both benign cases were of Trabaecular Adenoma (4%). Majority of tumors were Unifocal (62%) followed by Mutlifocal (38%). Keywords: Thyroid Neoplasm, Benign and Malignant lesions

O160: Novel Monitoring of General Surgeons' Intraoperative Cognitive Load following Surgical Sabermetrics Principles

Abstract Introduction Surgeons strive to continually enhance performance. Currently, assessment relies on subjective, retrospective methods-hindering real-time improvement and error prevention. Learning from sports, surgeons can utilise surgical sabermetrics for data-driven analysis. Cognitive load(CogL) impacts performance, with overload impairing skills and judgement. Sabermetrics can be leveraged to measure CogL objectively(e.g.Electrodermal activity(EDA)) and subjectively. This study aimed to combine these measures and explore their relationship with surgeon factors and patient outcomes. Methods Fourteen surgeons conducting 58 benign surgery cases participated. Surgeons’ intraoperative EDA was captured using a digital sensor. Subjective data collected included (i)Surgery Task-Load-Index(SURG-TLX)), (ii)mood, (iii)sleep-disturbance and (iv)risk-aversion. EDA underwent decomposition into (a)tonic (background autonomic arousal) and (b) phasic(response to events) components. Patient outcomes included (i)30-day morbidity and (ii)case difficulty. Results Overall, mean phasic-EDA was 0.038μS and mean tonic-EDA was 0.483μS(ranges 0-1:1 indicating highest CogL). Mean SURG-TLX was 47.128(/120, where 120 is the highest workload). Negative mood related to changes in tonic-EDA(p&amp;lt;0.05,F=11.84). EDA climbed throughout each case (Tonic:p&amp;lt;0.05,F=2.035;Phasic:p&amp;lt;0.05,F=1.626). SURG-TLX were weakly correlated with sleep(p&amp;lt;0.05,ϱ=-0.273) and moderately correlated with case duration(p&amp;lt;0.05, ϱ=0.598), but not EDA(p&amp;gt;0.50). Discussion This study is the first in the UK to report combined CogL measures gathered during live surgery. Awareness of factors that affect CogL crucially allows for the management of factors to prevent overload and to inform targeted interventions for performance improvement. Using digital sensors to measure CogL provides opportunity for automated, real-time performance assessment, advanced feedback, and behavioural modifications.

Epidemiology and classification for canine and feline mammary gland tumors: a histopathological survey of 437 mammary gland tumor biopsies performed in a secondary care hospital in Chiang Mai, Thailand from 2012 to 2019.

Metastatic disease resulting from mammary gland tumors (MGTs) is a known cause of death among dogs and cats. Keys to successful prevention and management strategies involve the accurate recording of diagnostic data. This retrospective study reviewed the epidemiology and classification of canine mammary gland tumors (CMTs) and feline mammary gland tumors (FMTs), as well as the factors including sex, age, and breed related to the occurrence of these tumors. Accordingly, 1,736 tumor biopsy cases were reported from 2012 to 2019 at Chiang Mai University Small Animal Hospital, Thailand, with 1,639 canine tumor biopsy cases and 97 feline tumor biopsy cases. The proportion of CMTs was reported at 24.5% (401/1,639) for all canine tumor biopsy cases. Benign and malignant tumors were reported at 14.5% (58/401) and 85.5% (343/401) for all CMT cases, respectively. The mean age of dogs affected by benign CMTs was 9.0±3.0 years, which was significantly lower than for malignant CMTs at 9.9±2.8 years (P=0.0239). According to histopathological classification, benign mixed tumors and simple carcinoma types were highest among benign and malignant CMT cases, respectively. Moreover, female dogs were at significantly higher risk of developing mammary gland tumors (OR = 45.8, 95% CI [3.9-86.0], P<0.0001) than male dogs, as well as older dogs (>8 years) (OR = 1.7, 95% CI [1.2-2.2], P=0.0001) compared to young ones (≤8 years). The proportion of FMTs was 37.1% (36/97) for all feline tumor biopsy cases. Benign and malignant tumors for all FMTs were reported at 16.7% (6/36) and 83.3% (30/36), respectively. According to histopathological classifications, adenoma and simple carcinoma were present in the highest proportion among benign and malignant FMTs, respectively. Female cats were at a significantly higher risk of developing mammary gland tumors than male cats (OR=25.7, 95% CI [3.9-272.8], P<0.0001). There was a high proportion of MGT cases compared with other tumor cases reported in a secondary care hospital in Chiang Mai, Thailand from 2012 to 2019, and malignant tumor biopsies have been more frequently observed than benign tumor biopsies in both CMT and FMT cases. The resulting data originating from this study can be an aid for veterinary oncologists in better educating clients and planning treatment and prevention strategies and it can be used as a basis for further experimental studies in the oncology section.

Clinicopathological Analysis of Postmenopausal Bleeding and Endometrium

Highlights: 1. Malignancies in postmenopausal bleeding are not very common, yet it is necessary to evaluate the etiology and implement appropriate treatment strategies.2. The findings of this study highlight the need for early detection of benign, premalignant, or malignant cases to provide more effective management, prevent the development of cancer, and improve the prognosis of the condition. Abstract Bleeding from the genital organ after a year of menopause is called postmenopausal bleeding. The causes may be either benign or malignant, originating from genital (uterine or extrauterine) and extragenital sites. About 3% of postmenopausal women suffer from uterine cancer. This present study aimed to analyze the clinical significance of postmenopausal bleeding concerning the source, associated risk factors, and various endometrial pathologies, including malignant and premalignant conditions. This retrospective study was conducted over four years and featured postmenopausal bleeding patients who met the inclusion criteria. Different causes of bleeding were noted and managed. Data collection on the history, clinical examination, blood test, and endometrial biopsy was performed on women with endometrial bleeding. The collected data were analyzed using standard descriptive statistics and presented using frequency tables. A total of 88 women were admitted, of whom 73 (82.95%) had endometrial bleeding and 15 (17.05%) experienced bleeding from other sites. The endometrial bleeding group mostly consisted of women aged &gt;50–60 years (45.20%). Hypertension (26.03%), diabetes (21.91%), obesity (43.83%), and nulliparous (5.48%) were the risk factors present among the patients. The majority of the patients (41.10%) were within five years of menopause. The transvaginal ultrasound findings indicated that 56.16% of the women had an endometrial thickness of &gt;10 mm, while 9.59% had an endometrial thickness of ≤4 mm. Upon histological investigation, endometrial hyperplasia (34.24%), atrophy (38.36%), and cancer (4.11%) were detected. Furthermore, a prevalence of 16.44% was identified as proliferative endometrium, whereas polyps were found in 6.84% of cases. In conclusion, postmenopausal bleeding is mostly benign, but it may raise concerns about the possibility of malignancy, which can be distressing for women. Disregarding the amount and frequency, postmenopausal bleeding requires a thorough evaluation, primarily because adequate management can prevent the progression of many premalignant cases to be endometrial cancer.

Exploration of Preliminary Objective Triage by Menopause Score and CA 125 Result Prior to Accelerating Fast-Track Booking for Suspected Ovarian Cancer-A Role for the Pathway Navigator?

The 28-days-to-diagnosis pathway is the current expected standard of care for women with symptoms of ovarian cancer in the UK. However, the anticipated conversion rate of symptoms to cancer is only 3%, and use of the pathway is increasing. A rapid triage at the moment of receipt of the referral might allow resources to be allocated more appropriately. In secondary care, multidisciplinary teams (MDTs) use the risk of malignancy index (RMI) score, (multiply menopausal status pre = 1 or post = 3 × ultrasound score = 0 - 3 × the CA 125 level), using a score of >200, to triage urgency and management in possible ovarian cancer cases. The most powerful determinant of the RMI score variables is CA 125 level, an objective number. Could a simple modification of the RMI score retain a high sensitivity for cancer whilst improving specificity and, consequently, decrease the morbidity of false-positive classification? To test this hypothesis, a retrospective evaluation of an ovarian two-week-wait telephone clinic of one consultant gynaecological oncologist was undertaken. Enquiry re menopause status was scored as one for pre- and three for postmenopausal or uncertain. CA 125 levels of >67 u/mL for premenopausal and >23 u/mL for postmenopausal women were used to precipitate urgent cross-sectional imaging requests and MDT opinions. These CA 125 cut thresholds were calculated using an assumption that the RMI imaging score, regardless of whether the result was available, could be three. We contemplate that women who did not exceed a provisional RMI score of >200 might be informed they are extremely unlikely to have cancer, removed from the malignancy tracker and appropriate follow-up arranged. One hundred and forty consecutive cases were analysed; 43% were deemed premenopausal and 57% postmenopausal. Twenty of the women had cancer, eighteen (90%) of whom had an RMI > 200. One hundred and twenty were benign, and only twenty-three (19%) classified as urgent cases in need of accelerated referral to imaging. In contrast, CA 125 > 35 u/mL, whilst retaining the sensitivity of 90%, misclassified 36 (30%) of the benign cases. It is possible that a telephone triage via a questionnaire determining menopausal status and the CA 125 result could offer a sensitivity for cancer of 90% and urgent expert review of under 20% of benign cases. This rapid initial telephone assessment could be presented by a trained pathway navigator, physician associate or nurse specialist. Substantial savings in NHS cancer services resources, anxieties all around and reduced patient morbidity may occur as a result.

Evaluating the comprehensive diagnosis efficiency of lung cancer, including measurement of SHOX2 and RASSF1A gene methylation

Methylation of the promoters of SHOX2 and RASSF1A (LungMe®) exhibits promise as a potential molecular biomarker for diagnosing lung cancer. This study sought to assess the aberrant methylation of SHOX2 and RASSF1A in broncho-exfoliated cells (BEC) and compare it with conventional cytology, histology examination, immunohistochemistry, and serum tumor markers to evaluate the overall diagnostic efficiency for lung cancer. This study recruited 240 patients, including 185 malignant cases and 55 benign cases. In our observation, we noted a slight reduction in the detection sensitivity, however, the ΔCt method exhibited a significant enhancement in specificity when compared to Ct judgment. Consequently, the ΔCt method proves to be a more appropriate approach for interpreting methylation results. The diagnostic sensitivity of cytology and histology was in ranged from 20.0%-35.1% and 42.9%-80%, respectively, while the positive detection rate of LungMe® methylation ranged from 70.0% to 100%. Additionally, our findings indicate a higher prevalence of SHOX2( +) among patients exhibiting medium and high expression of Ki67 (P < 0.01), as opposed to those with low expression of Ki67, but RASSF1A methylation did not show this phenomenon (P = 0.35). Furthermore, CEA, SCCA, and CYFRA21-1 showed positive detection rates of 48.8%, 26.2%, and 55.8%, respectively. Finally, we present a comprehensive lung cancer diagnostic work-up, including LumgMe® methylation. The combined analysis of SHOX2 and RASSF1A methylation serves as a powerful complement and extension to conventional methods, enhancing the accuracy of a lung cancer diagnosis with satisfactory sensitivity and specificity.

Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models.

This research compares traditional machine learning classification methods to innovative techniques that use deep learning models. Established usual classification models such as k-Nearest Neighbors (kNN), Gradient Boosting, Support Vector Machine (SVM), Neural Network, CN2 rule inducer, Naive Bayes, Stochastic Gradient Descent (SGD), and Tree, and deep learning models such as Neural Decision Forest and Multilayer Perceptron used. The investigation, which was carried out using the Orange and Python tools, evaluates their diagnostic effectiveness in breast cancer detection. The evaluation uses UCI's publicly accessible Wisconsin Diagnostic Data Set, enabling transparency and accessibility in the study approach. The mean radius ranges from 6.981 to 28.110, while the mean texture runs from 9.71 to 39.28 in malignant and benign cases. Gradient boosting and CN2 rule inducer classifiers outperform SVM in accuracy and sensitivity, whereas SVM has the lowest accuracy and sensitivity at 88%. The CN2 rule inducer classifier achieves the greatest ROC curve score for benign and malignant breast cancer datasets, with an AUC score of 0.98%. MLP displays distinguish positive and negative classes, with a higher AUC-ROC of 0.9959. with accuracy of 96.49%, precision of 96.57%, recall of 96.49%, and an F1-Score of 96.50%. Among the most commonly used classifier models, CN2 rule and GB performed better than other models. However, MLP from deep learning produced the greatest overall performance.

Benign Breast Disease among Patients Visiting the Breast and Endocrine Clinic of a Tertiary Care Centre.

Breast diseases encompass a wide range of conditions, including benign and malignant disorders. Given the significant burden of breast-related health issues in the community, there is a critical need to understand the prevalence. This study aimed to find the prevalence of benign breast diseases among patients visiting the breast and endocrine clinic of a tertiary care centre. A descriptive cross-sectional study was conducted among patients presenting to the breast and endocrine clinic from 1 January 2022 to 1 January 2023 after obtaining ethical approval from the Institutional Review Committee. A convenience sampling method was used. The point estimate was calculated at a 95% Confidence Interval. Among 979 patients, the prevalence of benign disease was 937 (95.71%) (94.44-96.98, 95% Confidence Interval). Mastalgia was the most frequent diagnosis 416 (44.40%), followed by fibroadenoma 137 (14.62%), benign lumps 84 (8.96%), and mastitis 64 (6.83%) and the most common symptoms reported by benign cases were pain in the breast 692 (73.85%) and breast lump 483 (51.55%). The prevalence of benign breast diseases was found to be similar to other studies done in similar settings. benign; breast diseases; mastalgia; prevalence.

Can ultrasound shear wave elastography differentiate between malignant and benign pleural effusions?

BackgroundMalignant pleural effusion is a common and unfortunately serious condition that is related to life poor quality and mortality. The majority of malignant pleural effusion is metastatic in nature, with the most encountered primary neoplasm in men is the lung cancer while in women is the breast cancer. Ultrasound elastography, with its ability of evaluating tissue stiffness, has been used to differentiate malignant from benign diseases. Shear wave elastography (SWE) has been explored in several organs, like the liver, breast, thyroid, lymph nodes and prostate, with the pleura can now be included among the growing list of SWE applications. Shear wave elastography may help in the differentiation of transudative and exudative types of pleural effusions.ResultsIn this study, a total of 36 patients were included, 50% revealed malignant pleural effusions (10/18 mesothelioma) and (8/18 metastatic), while the 50% revealed benign pleural effusions (9/18 transudates) and (9/18 exudates). By using sensitivity analysis test of the SWE values with V1 represents the minimum value, V2 the maximum value to calculate the cutoff values that will be used in our final diagnosis in order to differentiate benign and malignant cases. V1 shear wave elastography can significantly predict the malignant pleural effusion with p value 0.001 using a cutoff 48 kPa with calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were as follows 94.4%, 88.9%, 89.5% & 94.4% respectively with diagnostic accuracy of 92%. SWE maximum value (V2) can significantly predict the malignant pleural effusion with p value 0.001 using a cutoff 54.2 kPa, with calculated sensitivity, specificity, PPV and NPV were as follows 94.4%, 94.4%, 94.4% and 94.4% respectively with diagnostic accuracy 94.4%. So, through comparing both cutoff values, we concluded that the cutoff value 2 is better with greater specificity, PPV and NPV in relation to the final diagnosis.ConclusionsCombination of both conventional transthoracic ultrasound and SWE quantitative and qualitative abilities in assessment of both pleural and parenchymal lesions can result in high diagnostic accuracy. Shear wave elastography alone is a sensitive, specific, useful, cost-effective, widely available and noninvasive tool for assessment of tissue stiffness and in matters of high sensitivity. 54.2 kPa can be used as the cutoff value to distinguish MPE from benign pleural disease.

Artificial Intelligence in Dermoscopy: Enhancing Diagnosis to Distinguish Benign and Malignant Skin Lesions.

This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google's platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google's servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model's proficiency in distinguishing between benign and malignant skin lesions. The use of Google's Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model's robustness and facilitate its integration into clinical practice.

Validity of ultrasound with color Doppler to differentiate between benign and malignant ovarian tumours.

To assess the utility of ultrasound and color Doppler and the Accuracy of International Ovarian Tumor Analysis (IOTA) group classification in the preoperative evaluation of ovarian neoplasms to assess benign or malignant histopathology in the diagnosis of ovarian tumors. This observational longitudinal prospective analysis of 60 patients was performed over a period of 2 years (2017- 2019). The mean age of the patients was 43.75 years. Ultrasonography of ovarian masses were evaluated, and cancer antigen-125 (CA-125) levels were evaluated. Based on the IOTA classification, the B and M features of adnexal masses were studied. Color Doppler imaging was performed to evaluate the patterns of vascularity and indices. Sixty patients with 35 benign, 23 malignant, and two borderline lesions were included in the study. In malignant lesions, 17 women (73.9%) were above the age of 45. The CA-125 cut off was ≥35 internatioal units/mL. Based on the IOTA classification, 27/35 (77.1%) benign cases, were correctly identified as benign, 6/35 (17.1%) benign cases were incorrectly identified as malignant, and two (5.7%) were found to be inconclusive. In the malignant group, 17 of the 23 patients were identified as having malignancy. Color Doppler showed three (18.8%) benign tumors had a pulsatality index (PI) of <0.8 and 21 malignant tumors had a PI of <0.8. Four benign tumors had an resistive index (RI) of <0.6 and 100% of malignant tumors had an RI <0.6. The IOTA classification is a reliable scoring system for adnexal masses, and color Doppler can help to minimize interobserver variation.

Eyelid Tumors at Razi and Farabi Hospitals, Tehran, Iran

Background: Eyelid tumors are common in the daily practice of skin tumors and are composed of benign and malignant lesions. Methods: This retrospective case series study collected histopathologic reports of eyelid tumors of patients treated between 2022 and 2023 in Razi and Farabi hospitals (Tehran, Iran). Patient data, including patient age, sex, location of the lesion, and recurrence, were collected. Descriptive and inferential statistics were used to characterize the subgroups. Results: The sample was composed of 154 eyelid tumor specimens, including 72 (47%) malignant cases, 80 (52%) benign cases, and 2 (1%) premalignant cases. Malignant tumors included 81.9% basal cell carcinomas (BCC), 15.3% basosquamous cell carcinomas (BSC), and 1.4% squamous cell carcinoma (SCC). The benign eyelid lesions were comprised of 20% intradermal melanocytic nevus, 11.3% epidermal cyst, 7.6% dermoid cyst, 10% seborrheic keratosis, 6.3% xanthelasma, 2.5% wart, 3.8% compound nevus, 5% fibroconnective tissue, 3.8% squamous papilloma, 3.8% neurofibroma, 2.5% comedone,2.5% apocrine hidrocystoma, 2.5% foreign body type granuloma, and 1.3% of others (arteriovenous hemangioma, cavernous hemangioma, cutaneous horn, etc.) The premalignant lesions were 2 cases of actinic keratosis. Benign lesions had an equal frequency of occurrence in the upper and lower lids. Malignant lesions were seen more in the lower lid. Premalignant lesions were seen in the lower lid. The mean age at diagnosis was 45.8 ± 23.06 years for patients with benign lesions and 63.5 ± 16.7 years for patients with malignant lesions. The mean age for patients with premalignant lesions was 81.5 ± 9.19 years (P &lt; 0.001). Conclusions: In our study, benign lesions were seen predominantly in young persons and malignant lesions were mostly seen in elderly persons. There is an equal occurrence of malignant lesions in males and females, and recurrence after excision was infrequent.