Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis.

OBJECTIVE: This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice. METHODS: A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software. RESULTS: The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93). CONCLUSIONS: Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

A Case Report of Primary Melanoma of the Urethra.

We illustrate a notable case of an 83-year-old male who presents to a community hospital with abdominal pain and hematuria. A few days after admission, an ulcerated lesion was found to be visible toward the ventral aspect of the penis, as well as bright red blood at the urethral meatus. An excisional biopsy of the urethral meatus, mid-urethra, and urethral tissue was done, and immunohistochemistry helped support the diagnosis of primary melanoma of the urethra. The pathophysiology and guidelines for treatment are discussed. Our purpose in putting forward this case is to present a rare diagnosis of primary melanoma of the male urethra and to emphasize the importance of early recognition to reduce the occurrence of invasive malignancy.

A multi-task model for reliable classification of thyroid nodules in ultrasound images.

Thyroid nodules are common, and patients with potential malignant lesions are usually diagnosed using ultrasound imaging to determine further treatment options. This study aims to propose a computer-aided diagnosis method for benign and malignant classification of thyroid nodules in ultrasound images. We propose a novel multi-task framework that combines the advantages of dense connectivity, Squeeze-and-Excitation (SE) connectivity, and Atrous Spatial Pyramid Pooling (ASPP) layer to enhance feature extraction. The Dense connectivity is used to optimize feature reuse, the SE connectivity to optimize feature weights, the ASPP layer to fuse feature information, and a multi-task learning framework to adjust the attention of the network. We evaluate our model using a 10-fold cross-validation approach based on our established Thyroid dataset. We assess the performance of our method using six average metrics: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC, which are 93.49, 95.54, 91.52, 91.63, 95.47, and 96.84%, respectively. Our proposed method outperforms other classification networks in all metrics, achieving optimal performance. We propose a multi-task model, DSMA-Net, for distinguishing thyroid nodules in ultrasound images. This method can further enhance the diagnostic ability of doctors for suspected cancer patients and holds promise for clinical applications.

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

Objective: To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density. Methods: A retrospective analysis was conducted on the clinical data of 130 individuals diagnosed with PNs based on pathological confirmation. The utilization of AI and physicians has been employed in the diagnostic process of distinguishing benign and malignant PNs. The CT images depicting PNs were integrated into AI-based software. The gold standard for evaluating the accuracy of AI diagnosis software and physician interpretation was the pathological diagnosis. Results: Out of 226 PNs screened from 130 patients diagnosed by AI and physician reading based on CT, 147 were confirmed by pathology. AI had a sensitivity of 94.69% and radiologists had a sensitivity of 85.40% in identifying PNs. The chi-square analysis indicated that the screening capacity of AI was superior to that of physician reading, with statistical significance (p < 0.05). 195 of the 214 PNs suggested by AI were confirmed pathologically as malignant, and 19 were identified as benign; among the 29 PNs suggested by AI as low risk, 13 were confirmed pathologically as malignant, and 16 were identified as benign. From the physician reading, 193 PNs were identified as malignant, 183 were confirmed malignant by pathology, and 10 appeared benign. Physician reading also identified 30 low-risk PNs, 19 of which were pathologically malignant and 11 benign. The physician readings and AI had kappa values of 0.432 and 0.547, respectively. The physician reading and AI area under curves (AUCs) were 0.814 and 0.798, respectively. Both of the diagnostic techniques had worthy diagnostic value, as indicated by their AUCs of >0.7. Conclusion: It is anticipated that the use of AI-based CT diagnosis in the detection of PNs would increase the precision in early detection of lung carcinoma, as well as yield more precise evidence for clinical management.

Cystic Pelvic Masses in Men: A Presentation of Uncommon Cases and a Literature Review.

Unclear cystic masses in the pelvis in male patients are a rare situation and could be of benign or malignant origin. The underlying diseases demand for specific diagnostic and therapeutic approaches. We present a case series of 3 male patients with different clinical symptoms (perineal pain, urinary retention and a large scrotal cyst) related to cystic lesions in the pelvic region. On all patients initial histopathological workup was unclear. All patients underwent surgery with complete resection of the tumor which revealed a broad spectrum of histopathological findings: unusual form of cystic adenocarcinoma of the prostate, malignant transformation of a dysontogenetic cyst, and finally a very rare diagnosis of a malignant tumor of the Cowper gland. This case series and literature review provide clues for a possible diagnostic and therapeutic approach in the case of unclear pelvic cystic masses and could support urologists during the therapy selection in the future.

Dynamic Contrast Enhanced MR Perfusion and Diffusion-Weighted Imaging of Marrow-Replacing Disorders of the Spine: A Comprehensive Review.

Significant advancements in cancer treatment have led to improved survival rates for patients, particularly in the context of spinal metastases. However, early detection and monitoring of treatment response remain crucial for optimizing patient outcomes. Although conventional imaging methods such as bone scan, PET, MR imaging, and computed tomography are commonly used for diagnosing and monitoring treatment, they present challenges in differential diagnoses and treatment response monitoring. This review article provides a comprehensive overview of the principles, applications, and practical uses of dynamic contrast-enhanced MR imaging and diffusion-weighted imaging in the assessment and monitoring of marrow-replacing disorders of the spine.

Exploration of CT imaging features of cystic pulmonary nodules and establishment of a prediction model for benign and malignant pulmonary nodules / 中国胸心血管外科临床杂志

@#Objective To explore the CT imaging features and independent risk factors for cystic pulmonary nodules and establish a malignant probability prediction model. Methods The patients with cystic pulmonary nodules admitted to the Department of Thoracic Surgery of the First People's Hospital of Neijiang from January 2017 to February 2022 were retrospectively enrolled. They were divided into a malignant group and a benign group according to the pathological results. The clinical data and preoperative chest CT imaging features of the two groups were collected, and the independent risk factors for malignant cystic pulmonary nodules were screened out by logistic regression analysis, so as to establish a prediction model for benign and malignant cystic pulmonary nodules. Results A total of 107 patients were enrolled. There were 76 patients in the malignant group, including 36 males and 40 females, with an average age of 59.65±11.74 years. There were 31 patients in the benign group, including 16 males and 15 females, with an average age of 58.96±13.91 years. Multivariate logistic analysis showed that the special CT imaging features such as cystic wall nodules [OR=3.538, 95%CI (1.231, 10.164), P=0.019], short burrs [OR=4.106, 95%CI (1.454, 11.598), P=0.008], cystic wall morphology [OR=6.978, 95%CI (2.374, 20.505), P<0.001], and the number of cysts [OR=4.179, 95%CI (1.438, 12.146), P=0.009] were independent risk factors for cystic lung cancer. A prediction model was established: P=ex/(1+ex), X=–2.453+1.264×cystic wall nodules+1.412×short burrs+1.943×cystic wall morphology+1.430×the number of cysts. The area under the receiver operating charateristic curve was 0.830, the sensitivity was 82.9%, and the specificity was 74.2%. Conclusion Cystic wall nodules, short burrs, cystic wall morphology, and the number of cysts are the independent risk factors for cystic lung cancer, and the established prediction model can be used as a screening method for cystic pulmonary nodules.

Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm.

PURPOSE: Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. METHODS: Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation (DWISD and ADCSD), DWI and ADC signal intensity ratio (DWISIR and ADCSIR), mean ADC and minimum ADC value (ADCmean and ADCmin) and ADC value standard deviation (ADCVSD). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. RESULTS: Two independent predictors of malignant nodules were identified by multivariate analysis: DWISIR (P = 0.007) and ADCmin (P < 0.001). The AUCs for multivariate prediction model, combined DWISIR and ADCmin thresholds model, combined DWISIR and ADCSIR thresholds model and ACR-TIRADS were 0.946 (0.896-0.996), 0.875 (0.759-0.991), 0.777 (0.648-0.907) and 0.722 (0.588-0.857). The combined DWISIR and ADCmin threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. CONCLUSION: Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined DWISIR and ADCmin thresholds model significantly reduced the unnecessary biopsy rate.

Application of Quantitative Parameters of Contrast-Enhanced Ultrasound in Common Benign and Malignant Lesions in Pediatric Livers: A Preliminary Study.

We aimed to investigate the diagnostic utility of quantitative parameters of contrast-enhanced ultrasound (CEUS) for benign and malignant liver lesions in pediatric patients. This was a single-center retrospective analysis of children with liver lesions who underwent CEUS at our hospital between July 2019 and February 2023. The CEUS perfusion patterns for all lesions were qualitatively analyzed using histopathology, contrast-enhanced magnetic resonance imaging, contrast-enhanced computed tomography, or long-term clinical follow-up as reference standards. The CEUS images were quantitatively analyzed using SonoLiver® software (TomTec Imaging Systems, Munich, Germany) to obtain data regarding quantitative parameters and dynamic vascular pattern (DVP) parametric images, including rise time (RT), time to peak (TTP), mean transit time (mTT), and maximum intensity (IMAX). Statistical analysis was carried out using Student's t-test and receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic value of quantitative parameters. A total of 53 pediatric cases were included in this study, and 88.57% (31/35) of malignant lesions exhibited hyper-enhancement with rapid washout patterns; the same proportion of DVP parametric images exhibited washout patterns. Conversely, 94.44% (17/18) of benign lesions showed hyper-enhancement with slow washout patterns, and the same proportion of DVP parametric images showed no-washout patterns. RT, TTP, and mTT were significantly shorter in the malignant group than in the benign group (p < 0.05), while IMAX showed no significant difference (p > 0.05). ROC analysis indicated that mTT < 113.34 had the highest diagnostic value, with an area under the curve of 0.82. CEUS quantitative analysis had an accuracy of 98.11%, while qualitative analysis had an accuracy of 92.45%, with no statistically significant difference (p > 0.05). Quantitative analysis of CEUS provides valuable assistance in differentiating benign and malignant liver lesions in children. Among all quantitative parameters, mTT holds promise as a potentially valuable tool for identifying liver tumors.

Radiomics to Differentiate Malignant and Benign Breast Lesions: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis.

Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings.  A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67).  Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.

Differential Diagnosis of Benign and Malignant Pulmonary Nodules in CT Images Based on Multitask Learning.

BACKGROUND: Artificial intelligence-based aided diagnostic systems for pulmonary nodules can be divided into subtasks such as nodule detection, segmentation, and benign and malignant differentiation. Most current studies are limited to single-target tasks. However, aided diagnosis aims to distinguish benign from malignant pulmonary nodules, which requires the fusion of multiple-scale features and comprehensive discrimination based on the results of multiple learning tasks. OBJECTIVE: This study focuses on the aspects of model design, network structure, and constraints and proposes a novel model that integrates the learning tasks of pulmonary nodule detection, segmentation, and classification under weakly supervised conditions. METHODS: The main innovations include the following three aspects: (1) a two-dimensional sequence detection model based on a ConvLSTM (Convolutional Long Short-Term Memory) network and U-shaped structure network is proposed to obtain the context space features of image slices fully; (2) a differential diagnosis of benign and malignant pulmonary nodules based on multitask learning is proposed, which uses the annotated data of different types of tasks to mine the potential common features among tasks; and (3) an optimization strategy incorporating prior knowledge of computed tomography images and dynamic weight adjustment of multiple tasks is proposed to ensure that each task can efficiently complete training and learning. RESULTS: Experiments on the LIDC-IDRI and LUNA16 datasets showed that our proposed method achieved a final competition performance metric score of 87.80% for nodule detection and a Dice similarity coefficient score of 83.95% for pulmonary nodule segmentation. CONCLUSION: The cross-validation results of the LIDC-IDRI and LUNA16 datasets show that our model achieved 87.80% of the final competition performance metric score for nodule detection and 83.95% of the DSC score for pulmonary nodule segmentation, representing the optimal result for that dataset.

Tongue texture may contribute to the assessment of malignant risk of thyroid nodules.

In the present study, it was aimed to evaluate whether there is an objective tongue image indicator that could be used to evaluate malignant risk of thyroid nodules through a cross sectional study. From December 2018 to December 2020, the TFDA-1 digital tongue-face diagnostic instrument was used to collect the tongue images. TDAS 2.0 software was used for tongue image analysis. A standardized database was constructed by combining patient physical examination results and tongue image analysis results. The relationship between tongue image index and TI-RADS classification of thyroid nodules was tested. A total of 5,900 cases were collected and 4,615 cases were included in the present study after excluding 154 cases due to incomplete information, 1,221 cases with thyroid nodules were separated into 417 cases TI-RADS 2 group, 693 cases in TI-RADS 3 group and 111 cases in TI-RADS 4 group. Without considering confounding factors, tongue image indexes zhiCon, zhiASM, zhiENT, zhiMEAN, zhiClrB, zhiClrR, zhiClrG, zhiClrI, zhiClrL and zhiClrY were significantly different among the three groups (P<0.05). Excluding the influence of age, sex, body mass index, smoking and drinking, the results of one-way variance linear trend analysis showed that the values of zhiCon, zhiENT and zhiMEAN increased with the increasing TI-RADS category, while the values of zhiASM decreased with the increase of TI-RADS category. Tongue texture index may be helpful for differentiating the benign and malignant of thyroid nodules.

Fine-Needle Aspiration Cytology in the Diagnosis of Salivary Gland Tumors Before the Milan System: A Ten-Year Experience From a Tertiary Care Center in Greece.

Objective The objective of this study was to determine the diagnostic value of fine-needle aspiration cytology (FNAC) for salivary gland tumors. Methodology A retrospective file analysis of patients with salivary gland pathology, attending the Department of Oral and Maxillofacial Surgery of a tertiary care center in Athens, Greece, over a 10-year-long period, was conducted. Sensitivity, specificity, accuracy, positive prognostic value (PPV), and negative prognostic value (NPV) of FNAC for benign and malignant tumors separately were assessed and compared with histology. Results A total of 82 patients (46 male and 36 female) with salivary gland tumors, submitted to both FNAC and histology, were included. The mean age was 55 years. A total of 73 tumors were histologically diagnosed as benign and nine as malignant. FNAC identified 62 benign and seven malignant tumors but was inconclusive in 13 cases. The most common diagnosis of both histology and FNAC was pleomorphic adenoma. FNAC sensitivity, specificity, accuracy, PPV, and NPV were 98.3% and 100%, 87.5% and 100%, 97.1% and 100%, 98.3% and 100%, and 87.5% and 100% for benign and malignant tumors, respectively. Conclusions FNAC is highly sensitive but moderately specific for the preoperative identification of benign salivary gland tumors. Its use as an initial diagnostic modality is warranted, thanks to its safeness, rapidity, and lack of pain.

Meta analysis of the diagnostic value of circulating miRNA in benign and malignant pulmonary nodules.

OBJECTIVE: A meta-analysis was conducted to assess the impact of miRNAs in circulation on diagnosing benign and malignant pulmonary nodules (BPNs and MPNs). METHODS: Electronic databases such as Embase, PubMed, Web of Science, and The Cochrane Library were utilized for diagnostic tests of circulating miRNAs to diagnose BPNs and MPNs from the library creation to February 2023. Meta-analysis of the included literature was performed using Stata 16, Meta-Disc 1.4, and Review Manager 5.4 software. This study determined the combined sensitivity, specificity, diagnostic ratio (DOR), positive/negative likelihood ratios (PLR/NLR), as well as value of area under the receiver operating characteristic (ROC) curve. RESULTS: This meta-analysis included 14 publications and 17 studies. According to our findings, the pooled sensitivity for miRNA in diagnosing benign and malignant pulmonary nodules was 0.82 [95% CI (0.74, 0.88)], specificity was 0.84 [95% CI (0.79, 0.88)], whereas the DOR was 22.69 [95% CI (13.87, 37.13)], PLR was 5.00 [95% CI (3.87, 6.46)], NLR was 0.22 [95% CI (0.15, 0.32)], and the area under the working characteristic curve (AUC) of the subject was 0.89 [95% CI (0.86, 0.91)]. CONCLUSION: Circulating miRNAs could be used with sensitivity, specificity, DOR, PLR, NLR, and AUC as biomarkers to diagnose pulmonary nodules (PNs). However, more research is needed to determine the optimum miRNA combinations for diagnosing PNs due to the significant heterogeneity on previous studies.

Multi-Biomarkers Panel in Identifying Benign and Malignant Lung Diseases and Pathological Types of Lung Cancer.

With the discovery of many tumor markers, there are new strategies for the early diagnosis and treatment of lung cancer and the prediction of prognosis. We examined the multi-protein markers panel (4MP, consisting of Pro-SFTPB, CA125, Cyfra21-1, and CEA) diagnosis performance in differentiating benign and malignant lung diseases and identifying pathological types of lung cancer. Meantime, the complementary performance of three conventional tumor markers (NSE, SCC, and Pro-GRP) for 4MP was assessed. A total of 294 patients with lung cancer or benign lung disease are contained in this study. The AUCs of 4MP and 7MP (NSE, SCC, Pro-GRP, and 4MP) in distinguishing benign lung disease and lung cancer were 0.808 and 0.832, respectively. In distinguishing SQCLC and SCLC, the AUCs were 0.716 and 0.985, respectively. In distinguishing LADC and SCLC, the AUCs were 0.849 and 0.998, respectively. This study demonstrated that 4MP can distinguish lung cancer from benign disease. Traditional biomarkers NSE, SCC, and Pro-GRP can significantly improve the performance of 4MP in the differentiation of LADC, SQCLC, and SCLC, which is expected to contribute to the accurate diagnosis and personalized treatment of patients.

The Fallacies of the Breast MRI: A Case Study.

Magnetic resonance imaging (MRI) of breasts using diffusion-weighted imaging and dynamic contrast enhancement is now well-established imaging for the evaluation and characterization of suspicious breast lesions, where it has become a problem-solving tool. Breast lesions are characterized according to their morphological features and enhancement characteristics. Breast MRI is helpful in the evaluation of breast lesions in patients with dense breasts and women with breast implants and to differentiate scars and recurrence. However, this technique has its own limitations, a few of which are elucidated in the present case report.

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks.

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model's learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model's feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model's classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model's performance will be compared with state-of-the-art approaches in other existing systems' accuracy, precision, recall, and F1 score.

Adenoid Cystic Carcinoma of the Oral Cavity- A Case Report Comparing the Differences and Similarities Between a Child and an Adult Patient.

Adenoid cystic carcinoma is a malignant neoplasm primarily of the salivary gland, which can also involve lacrimal glands and other exocrine glands. Adenoid cystic carcinoma rarely presents in the buccal mucosa and young children, and among the major salivary glands, it rarely occurs in the sublingual gland. We are presenting two cases of Grade 1- adenoid cystic carcinoma. One in the buccal mucosa of an eight-year-old boy and another in the sublingual gland of a 50-year-old female patient. The site and age of occurrence can make a huge difference in diagnosis and treatment planning due to the unpredictability of the lesion. Proper diagnosis, treatment planning, and appropriate treatment help improve the lesion's prognosis. Even though such lesions rarely occur, awareness among the Oral and maxillofacial fraternity is very important in providing proper patient care.

High-flow Nasal Cannula versus Conventional Ventilation in Laryngeal Surgery: A Systematic Review and Meta-analysis.

High-flow nasal cannula (HFNC) is an emerging option for maintaining oxygenation in patients undergoing laryngeal surgery, as an alternative to traditional tracheal ventilation and jet ventilation (JV). However, the data on its safety and efficacy is sparse. This study aims to aggregate the current data and compares the use of HFNC with tracheal intubation and jet ventilation in adult patients undergoing laryngeal surgery. We searched PubMed, MEDLINE (Medical Literature Analysis and Retrieval System Online, or MEDLARS Online), Embase (Excerpta Medica Database), Google Scholar, Cochrane Library, and Web of Science. Both observational studies and prospective comparative studies were included. Risk of bias was appraised with the Cochrane Collaboration Risk of Bias in Non-Randomized Studies - of Interventions (ROBINS-I) or RoB2 tools and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for case series. Data were extracted and tabulated as a systematic review. Summary statistics were performed. Meta-analyses and trial sequential analyses of the comparative studies were performed. Forty-three studies (14 HFNC, 22 JV, and seven comparative studies) with 8064 patients were included. In the meta-analysis of comparative studies, the duration of surgery was significantly reduced in the THRIVE (Transnasal Humidified Rapid-Insufflation Ventilatory Exchange) group, but the number of desaturations, need for rescue intervention, and peak end-tidal CO2 were significantly increased compared to the conventional ventilation group. The evidence was of moderate certainty and there was no evidence of publication bias. In conclusion, HFNC may be as effective as tracheal intubation in oxygenation during laryngeal surgery in selected adult patients and reduces the duration of surgery but conventional ventilation with tracheal intubation may be safer. The safety of JV was comparable to HFNC.

[Establishment and Verification of Benign and Malignant Prediction Model of 
Subcentimeter Pulmonary Ground Glass Nodules Based on HRCT].

BACKGROUND: Pre-operative accuracy of subcentimeter ground glass nodules (SGGNs) is a difficult problem in clinical practice, but there are few clinical studies on the benign and malignant prediction model of SGGNs. The aim of this study was to help identify benign and malignant lesions of SGGNs based on the imaging features of high resolution computed tomography (HRCT) and the general clinical data of patients, and to build a risk prediction model. METHODS: This study retrospectively analyzed the clinical data of 483 patients with SGGNs who underwent surgical resection and were confirmed by histology from the First Affiliated Hospital of University of Science and Technology of China from August 2020 to December 2021. The patients were divided into the training set (n=338) and the validation set (n=145) according to 7:3 random assignment. According to the postoperative histology, they were divided into adenocarcinoma group and benign lesion group. The independent risk factors and models were analyzed by univariate analysis and multivariate Logistic regression. The receiver operator characteristic (ROC) curve was constructed to evaluate the model differentiation, and the calibration curve was used to evaluate the model consistency. The clinical application value of the decision curve analysis (DCA) evaluation model was drawn, and the validation set data was substituted for external verification. RESULTS: Multivariate Logistic analysis screened out patients' age, vascular sign, lobular sign, nodule volume and mean-CT value as independent risk factors for SGGNs. Based on the results of multivariate analysis, Nomogram prediction model was constructed, and the area under ROC curve was 0.836 (95%CI: 0.794-0.879). The critical value corresponding to the maximum approximate entry index was 0.483. The sensitivity was 76.6%, and the specificity was 80.1%. The positive predictive value was 86.5%, and the negative predictive value was 68.7%. The benign and malignant risk of SGGNs predicted by the calibration curve was highly consistent with the actual occurrence risk after sampling 1,000 times using Bootstrap method. DCA showed that patients showed a positive net benefit when the predictive probability of the predicted model probability was 0.2 to 0.9. CONCLUSIONS: Based on preoperative medical history and preoperative HRCT examination indicators, the benign and malignant risk prediction model of SGGNs was established to have good predictive efficacy and clinical application value. The visualization of Nomogram can help to screen out high-risk groups of SGGNs, providing support for clinical decision-making.