Lung Nodules Research Articles

CD103+ cDC1 Dendritic Cell Vaccine Therapy for Osteosarcoma Lung Metastases.

We generated a CD103+DC vaccine using K7M3 OS cell lysates (cDCV) and investigated its ability to induce regression of primary tumors, established lung metastases, and a systemic immune response. A bilateral tumor model was used to assess cDCV therapy efficacy and systemic immunity induction. K7M3 cells were injected into mice bilaterally. Right-sided tumors received PBS (control) or cDCV. Left-sided tumors were untreated. Tumor growth was compared between the vaccine-treated and untreated tumor on the contralateral side and compared to the control group. The immune cell profiles of the tumors, and tumor-draining lymph nodes (TdLNs) and spleen were evaluated. To determine the efficacy of systemic cDCV therapy against established lung metastases, K7M3 cells were injected intratibially. Leg amputation was performed 5 weeks later. Mice were treated intravenously with PBS or cDCV and euthanized 6 weeks later. Lungs, TdLNs and spleen were collected. The number and size of the lung nodules were quantified. The immune cell profile of tumor, and lymph nodes and spleen were also evaluated. Using this same model, we evaluated the effect of cDCV + anti-CTLA-4. cDCV therapy inhibited the treated and untreated tumors and increased the number of T-cells in these tumors and the lymph nodes compared to control-treated mice. Systemic cDCV therapy administered following amputation decreased the size and number of lung metastases, and increased T-cell numbers in the tumor and lymph nodes. Combining anti-CTLA-4 with cDCV therapy increased cDCV efficacy against lung metastases. Intratumor cDCV generated a systemic immune response inhibiting the growth of both the treated and untreated tumors, with increased T-cells in the tumor and lymph nodes. Systemic cDCV was effective against established lung metastases. Efficacy was increased by anti-CTLA4. cDCVs may provide a novel therapeutic approach for relapsed/metastatic OS patients.

The pulmonary nodule: from incidental finding to pathological confirmation

As the number of CT examinations of the lungs increases, so does the prevalence of incidentally discovered pulmonary nodules. While most lung nodules are benign, the risk of malignancy significantly rises with the presence of risk factors and specific imaging features. Upon encountering an incidental nodule, efforts should focus on achieving an accurate pathological diagnosis, particularly to ascertain malignancy while minimizing the risks associated with unnecessary diagnostic procedures. A comprehensive understanding of the typical characteristics and behavior of malignant lung nodules, along with a detailed patient history and standardized clinical and imaging risk assessment, is crucial for determining the optimal diagnostic approach. Additionally, the decision regarding histologic confirmation should consider the patient's comorbidities, preferences, and the examiner's expertise. Emerging sampling technologies provide methods for addressing peripheral lung nodules with minimal risk of complications.

Automatic Lung Nodules Detection Using a Modified YOLOv5

Lung cancer diagnosis involves a detection strategy to determine the specific location of the abnormality and its likelihood whether cancerous or non-cancerous. In existing methods such as Endobronchial ultrasound-guided transbronchial needle extraction (EBUS-TBNA) method requires specific equipment and trained operators. The limits in biomarker discovery begin with sample collection, transportation, representative tissue processing, reference standards, assay sensitivity and specificity. Liquid biopsy method requires tissue biopsy for initial diagnosis and may increase the likelihood of false negatives and false positives. To overcome these challenges, initially in this paper, three detectors are trained for nodule detection i.e. You Only Look Once version 3 (YOLOv3), YOLOv4 and YOLOv5. YOLOv3 achieved precision of 72%, recall of 75%, mean Average Precision (mAP) of 70%, F1 Score of 0.73 and Giga Floating-Point Operations (GFLOPs) of 30. In contrast, YOLOv4 achieved 85% precision, 70% recall, 80% mAP, F1 score of 0.76 and 65 GFLOPs. On the other hand, YOLOv5 achieved precision of 90%, recall of 80%, mAP of 85%, F1 Score of 0.85 and FLOPs of 217. These three detectors also faced few challenges like complexity, have high computation time and low performance. So, to overcome the problems of YOLO based methods, a modified YOLOv5 model has been proposed for the automatic detection of lung nodules in CT scans. Key modifications include enhanced feature extraction layers and customized anchor boxes tailored for small nodule detection. These modifications demonstrate the model's potential for reliable and efficient lung cancer screening, aligning with the manuscript's focus on advancing detection techniques through customized YOLOv5 enhancements. The modified model achieves a 90% precision, 85% recall, 88% mAP, 0.87 F1 score and 35.2 GFLOPs. These results represent an improvement in accuracy and increase in sensitivity compared to the standard YOLOv5 model. Also, GFLOPs have been reduced which demonstrates low computing requirement for the proposed model. The proposed model could be further used as clinical tool for lung cancer diagnosis.

Abstract A139: Patient and provider barriers and facilitators to lung cancer screening: I-STEP 2 Study

Abstract Purpose: While lung cancer is a leading cause of death in the U.S., annual lung cancer screening (LCS) rates remain low. After deployment of an implementation intervention to increase lung cancer screening (LCS) and referral, we used a qualitative approach to identify barriers and facilitators, and recommended supports to increase screening. Methods: Semi-structured interviews were conducted with patients (n=66) and providers (n=59) across 15 BJC Consortium hospitals. Interview domains included perceptions and experiences with LCS referral and completion, barriers and facilitators to LCS, and the impact of the COVID-19 pandemic on these processes. Patients were eligible if they had been referred for LCS. Providers were eligible if they were involved in the LCS decision-making and referral process. Interviews were audio-recorded, de-identified, and transcribed. Deductive and inductive codes were developed separately for patients and providers. Coded data were analyzed to determine themes. Results: Patients cited transportation, uncertainty surrounding insurance coverage for screening, and the COVID-19 pandemic as barriers to accessing LCS. Due to the Medicare and Medicaid requirement of a documented shared decision-making visit prior to providing an LCS referral, providers cited time constraints during patient encounters as a barrier to delivering optimal LCS care. Both patients and providers reported low general awareness of LCS (compared to other preventive screening services) as a barrier to utilization. Patients described many facilitators to LCS including fears regarding cancer risk, knowledge of the benefits of early cancer detection in improving cancer outcomes, provider recommendations, ease of LCS process, and receiving educational materials about LCS. Providers also describe access to evidence-based outreach and education materials regarding LCS, and implementation of clinical decision support tools (e.g. pre-screening for LCS eligibility and insurance authorization, integration of EMR prompts centered around LCS and nodule surveillance) as facilitators for screening. Patients and providers highlighted the importance of LCS navigators in facilitating communication and reducing barriers to LCS and surveillance – specifically, patients found one-on-one support and education with navigators to be encouraging as they underwent LCS. Conclusion: While there are some key barriers related to lung cancer screening and follow-up, there are many facilitators to screening that can be leveraged to increase referrals and enable successful follow-up management after screening. Recommended additions to basic lung screening intervention materials include provider education on how lung cancer screening referral processes are unique in comparison to other screenings, how providers can foster patient understanding of lung nodules, and local support and resources to help patients navigate barriers to lung screening. Citation Format: Amy Ayala, Meera Muthukrishnan, Sydney Beache, Michelle Silver, Courtney Harriss, Graham Colditz, Aimee James. Patient and provider barriers and facilitators to lung cancer screening: I-STEP 2 Study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A139.

Clinical value of peripheral blood miR-21 and miR-486 combined with CT forearly cancer diagnosis in pulmonary nodulessmoking

PurposeThis study aimed to investigate the clinical significance of combining peripheral blood miR-21 and miR-486 with CT for the early cancer diagnosis in pulmonary nodules.MethodsA total of 215 patients diagnosed with isolated pulmonary nodules with a history of smoking were selected as researchsubjects. 30 healthy volunteers with a history of smoking were recruitedas the control group.The selection of subjectswas based on the presence of isolated pulmonary nodules detected on chest CT scans. The training set consisted of 65 patients with lung nodules and 30 healthy smokers, while the verification setincluded 150 patients with lung nodules.ResultsCompared with the control group, the plasma expression level of miR-210 was significantly higher in the group of patients with benign pulmonary nodules (P < 0.05). The level of miR-486-5p was lower in patients with malignant pulmonary nodules compared to those with benign pulmonary nodules (P < 0.05). Moreover, the plasma level of miR-210was higher in patients with malignant pulmonary nodules compared to those with benign pulmonary nodules and healthy smokers (P < 0.05). The combination of miR-21 and miR-486 yielded an AUC of 0.865, which was significantly higher than any other gene combination (95%CI: 0.653–0.764, P < 0.05).ConclusionsThis study offered preliminary evidence supporting the use of peripheral blood miR-21 and miR-486, combined with CT scans, as potential biomarkers for the early cancer diagnosis in lung nodules.

H-marker via bronchoscopy under LungPro navigation combined with cone-beam computed tomography for locating multiple pulmonary ground-glass nodules: A case report and literature review.

Pulmonary ground-glass nodules (GGNs) pose challenges in intraoperative localization due to their primarily nonsolid composition. This report highlights a novel approach using H-marker deployment guided by LungPro navigation combined with cone-beam computed tomography (CBCT) for precise localization of multiple GGNs. A 55-year-old female patient presented at Sir-Run-Run-Shaw Hospital, Zhejiang University School of Medicine, in June 2021, requiring thoracoscopic surgery for the management of multiple GGNs in her right lung. She had a recent history of thoracoscopic wedge resection for a lesion in her lower left lung 3 months prior. Computed tomography scans revealed the presence of 3 mixed GGNs in the right lung, with further confirmation identifying these as solitary pulmonary nodules, necessitating surgical intervention. The patient underwent thoracoscopic surgery, during which the multiple nodules in her right lung were precisely localized utilizing an H-marker implanted bronchoscopically under the guidance of LungPro navigation technology, with CBCT providing additional confirmation of nodule positioning. This innovative combination of technologies facilitated accurate targeting of the lesions. Postoperative histopathological analysis confirmed the nodules to be microinvasive adenocarcinomas. Radiographic examination with chest X-rays demonstrated satisfactory lung expansion, indicating effective lung function preservation following the procedure. Follow-up assessments have shown no evidence of tumor recurrence, suggesting successful treatment. The employment of H-marker implantation guided by the LungPro navigation system with CBCT confirmation presents a feasible and efficacious strategy for localizing multiple pulmonary GGNs. To further validate its clinical utility and safety, large-scale, multicenter, prospective studies are warranted. This approach holds promise in enhancing the precision and outcomes of surgeries involving GGNs.

Comparison of electromagnetic navigation bronchoscopy localization and CT-guided percutaneous localization in resection of lung nodules: A protocol for systematic review and meta-analysis.

This study aimed to evaluate the efficacy and safety between electromagnetic navigational bronchoscopy (ENB) and computed tomography (CT)-guided percutaneous localization before resection of pulmonary nodules. Pubmed, Embase, Web of Science, and the Cochrane Library databases were searched from January 1, 2000 to April 30, 2022, for relevant studies. Two reviewers conducted the search, selection, and extraction of data from eligible studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The primary outcome was the localization success rate, and the secondary outcomes were the pneumothorax incidence and localization time. The meta-analysis was performed by Review Manager 5.4. The protocol for the meta-analysis was registered on PROSPERO (Registration number: CRD42022345972). Five cohort studies comprising 441 patients (ENB group: 185, CT group: 256) were analyzed. Compared with the CT-guided group, the ENB-guided group was associated with lower pneumothorax incidence (relative ratio = 0.16, 95% confidence interval [CI]: 0.04-0.65, P = .01). No significant differences were found in location success rates (relative ratio = 1.01, 95% CI: 0.98-1.05, P = .38) and localization time (mean difference = 0.99, 95% CI: -5.73 to 7.71, P = .77) between the ENB group and CT group. Both ENB and CT-guided are valuable technologies in localizing lung nodules before video-assisted thoracoscopic surgery based on current investigations. ENB achieved a lower pneumothorax rate than the CT-guided group. In our opinion, there is no perfect method, and decision-making should be given the actual circumstances of each institute. Future prospective studies in the form of a randomized trial are needed to confirm their clinical value.

GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification

PurposeCurrently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tackle these concerns, this investigation introduces a novel approach: the 3D Global Coordinated Attention Wide Inverted ResNet Network (GC-WIR). This network aims to achieve precise classification of benign and malignant pulmonary nodules, leveraging its merits of heightened efficiency, parsimonious parameterization, and robust stability.MethodsWithin this framework, a 3D Global Coordinate Attention Mechanism (3D GCA) is designed to compute the features of the input images by converting 3D channel information and multi-dimensional positional cues. By encompassing both global channel details and spatial positional cues, this approach maintains a judicious balance between flexibility and computational efficiency. Furthermore, the GC-WIR architecture incorporates a 3D Wide Inverted Residual Network (3D WIRN), which augments feature computation by expanding input channels. This augmentation mitigates information loss during feature extraction, expedites model convergence, and concurrently enhances performance. The utilization of the inverted residual structure imbues the model with heightened stability.ResultsEmpirical validation of the GC-WIR method is performed on the LUNA 16 dataset, yielding predictions that surpass those generated by previous models. This novel approach achieves an impressive accuracy rate of 94.32%, coupled with a specificity of 93.69%. Notably, the model’s parameter count remains modest at 5.76M, affording optimal classification accuracy.ConclusionFurthermore, experimental results unequivocally demonstrate that, even under stringent computational constraints, GC-WIR outperforms alternative deep learning methodologies, establishing a new benchmark in performance.

The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules

To compare the diagnostic performance between plain CT-based model and plain plus contrast CT-based modelin the classification of malignancy for solitary solid pulmonary nodules. Between January 2012 and July 2021, 527 patients with pathologically confirmed solitary solid pulmonary nodules were collected at dual centers with similar CT examinations and scanning parameters. Before surgery, all patients underwent both plain and contrast-enhanced chest CT scans. Two clinical characteristics, fifteen plain CT characteristics, and four enhanced characteristics were used to develop two logistic regression models: model 1 (plain CT only) and model 2 (plain + contrast CT). The diagnostic performance of the two models was assessed separately in the development and external validation cohorts using the AUC. 392 patients from Center A were included in the training cohort (median size, 20.0 [IQR, 15.0–24.0] mm; mean age, 55.8 [SD, 9.9] years; male, 53.3%). 135 patients from Center B were included in the external validation cohort (median size, 20.0 [IQR, 16.0–24.0] mm; mean age, 56.4 [SD, 9.6] years; male, 51.9%). Preoperative patients with 201 malignant (adenocarcinoma, 148 [73.6%]; squamous cell carcinoma, 35 [17.4%]; large cell carcinoma,18 [9.0%]) and 326 benign (pulmonary hamartoma, 118 [36.2%]; sclerosing pneumocytoma, 35 [10.7%]; tuberculosis, 104 [31.9%]; inflammatory pseudonodule, 69 [21.2%]) solitary solid pulmonary nodules were gathered from two independent centers. The mean sensitivity, specificity, accuracy, PPV, NPV, and AUC (95%CI) of model 1 (Plain CT only) were 0.79, 0.78, 0.79, 0.67, 0.87, and 0.88 (95%CI, 0.82–0.93), the model 2 (Plain + Contrast CT) were 0.88, 0.91, 0.90, 0.84, 0.93, 0.93 (95%CI, 0.88–0.98) in external validation cohort, respectively. A logistic regression model based on plain and contrast-enhanced CT characteristics showed exceptional performance in the evaluation of malignancy for solitary solid lung nodules. Utilizing this contrast-enhanced CT model would provide recommendations concerning follow-up or surgical intervention for preoperative patients presenting with solid lung nodules.

Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules

Introduction Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction. Methods Patients with stage 0–IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN. Results Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules. Conclusions Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.

Mechanism study of serum extracellular nano-vesicles miR-412-3p targeting regulation of TEAD1 in promoting malignant biological behavior of sub-centimeter lung nodules.

To investigate the impact and potential mechanisms of serum extracellular nano-vesicles (sEVs) miR-412-3p released from sub-centimeter lung nodules with a diameter of ⩽ 10mm on the malignant biological function of micro-nodular lung cancer (mnLC). A total of 87participants were included and divided into a mnLC group (n= 30), a benign lung nodule (BLN) group (n= 27), and a healthy people control group (n= 30). Transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA) and Western blot (WB) were used to measure the morphological characteristics and surface markers of sEVs. In vitro analysis, real-time quantitative polymerase chain reaction (RT-qPCR), CCK-8 cell proliferation assay, clone formation assay, Transwell, stem cell sphere-forming assay, and WB assay were conducted to verify the effect of miR-412-3p/TEAD1 signaling axis on the biological function of lung cancer cells through, respectively. Further validation was conducted using the serum sEVs of the participants. The expression level of sEVs-miR-412-3p in the mnLC group was significantly higher than that in the BLN and healthy groups (P< 0.01). In lung cancer cell lines, miR-412-3p can negatively regulate the targeted gene TEAD1. The miR-412-3p/TEAD1 signaling axis is involved in promoting the EMT signaling pathway and regulating the malignant biological functions of lung cancer cell proliferation, migration, and stemness (P< 0.05). In addition, sEVs in the mnLC group significantly promoted lung cancer cell proliferation, migration, and stemness compared to the BLN and healthy groups, inhibited the expression of E-cadherin and TEAD1 in lung cancer cells, and promoted the expression of N-cadherin and Vimentin (P< 0.05). sEVs-miR-412-3p could promote the biological process of EMT, and lead to the occurrence of malignant biological behavior in sub-centimeter lung nodules. This provides evidence for the miR-412-3p/TEAD1 signaling axis as a potential therapeutic target for mnLC.

Developing Role of Artificial Intelligence in Radiology in the UK

Artificial intelligence (AI) is revolutionising radiological diagnosis in the UK, promising to enhance the accuracy, efficiency, and accessibility of healthcare. The integration of AI into radiology is particularly timely, as the National Health Service (NHS) faces increasing demand for imaging services, coupled with a shortage of radiologists. AI technologies, including deep learning algorithms and machine learning systems, are being developed to assist in interpreting complex medical images such as X-rays, CT scans, and MRIs. One of the key benefits of AI in radiology is its ability to quickly and accurately detect abnormalities. For instance, AI algorithms can identify early signs of diseases like cancer, strokes, and fractures, often with a precision that rivals or exceeds human expertise. This has the potential to significantly reduce diagnostic errors, expedite treatment plans, and improve patient outcomes. For example, AI tools are already in use in the UK to flag lung nodules on CT scans, assisting radiologists in early cancer detection. AI also offers efficiency gains. By automating routine tasks, such as identifying normal scans or prioritizing urgent cases, AI can help streamline workflows, reduce waiting times, and alleviate the burden on overworked radiologists. This is critical, as delays in diagnosis can have serious consequences for patient care. However, the widespread adoption of AI in radiology is not without challenges. Concerns about data privacy, algorithmic transparency, and the potential for over-reliance on AI must be carefully managed. It is crucial to strike a balance where AI complements, rather than replaces, the expertise of radiologists. Ultimately, AI's role in radiological diagnosis in the UK is poised to grow, offering a future where healthcare is not only faster and more accurate but also more equitable for patients across the country.

Value of 18F-FDG PET/CT-based radiomics features for differentiating primary lung cancer and solitary lung metastasis in patients with colorectal adenocarcinoma

Objective To investigate the value and applicability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in differentiating primary lung cancer (PLC) from solitary lung metastasis (SLM) in patients with colorectal cancer (CRC). Materials and methods This retrospective study included 103 patients with CRC and solitary pulmonary nodules (SPNs). The least absolute shrinkage and selection operator (LASSO) was used to screen for optimal radiomics features and establish a PET/CT radiomics model. PET/CT Visual and complex models (combining radiomics with PET/CT visual features) were developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the predictive value and diagnostic efficiency of the models. Results The AUC of the PET/CT radiomics model for differentiating PLC from SLM was 0.872 (95% CI: 0.806–0.939), which was not different from that of the visual (0.829 [95% CI: 0.749–0.908; p = .352]). However, the AUC of the complex model (0.936 [95% CI:0.892–0.981]) was significantly higher than that of the PET/CT radiomics (p = .005) and visual model (p = .001). The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of PET/CT radiomics for differentiating PLC from SLM were 0.720, 0.887, 0.806, 0.857, and 0.770, respectively. Conclusion PET/CT radiomics can effectively distinguish PLC and SLM in patients with CRC and SPNs and guide the implementation of personalized treatment.

A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset

Recently, intelligent analysis of lung nodules with the assistant of computer aided diagnosis (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on Computed Tomography (CT) images from one single period, while ignoring the cross spatio-temporal features associated with the progression of nodules contained in imaging data from various captured periods of lung cancer. If the evolution patterns of nodules across various periods in the patients’ CT sequences can be explored, it will play a crucial role in guiding the precise screening identification of lung cancer. Therefore, a cross spatio-temporal lung nodule dataset with pathological information for nodule identification and diagnosis is constructed, which contains 317 CT sequences and 2,295 annotated nodules from 109 patients. This comprehensive database is intended to drive research in the field of CAD towards more practical and robust methods, and also contribute to the further exploration of precision medicine related field. To ensure patient confidentiality, we have removed sensitive information from the dataset.

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography.

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

Lung nodule detection using Eyrie Flock-based Deep Convolutional Neural Network

PROBLEM: Lung cancer is a dangerous and deadly disease with high mortality and reduced survival rates. However, the lung nodule diagnosis performance is limited by its heterogeneity in terms of texture, shape, and intensity. Furthermore, the high degree of resemblance between the lung nodules and the tissues that surround the lung nodules makes the building of a reliable detection model more difficult. Moreover, there are several methods for diagnosing and grading lung nodules; still, the accuracy of detection with the variations in intensity is a challenging task. AIM &amp; METHODS: For the detection of lung nodules and grading, this research proposes an Eyrie Flock Optimization-based Deep Convolutional Neural Network (Eyrie Flock-DeepCNN). The proposed Eyrie Flock Optimization integrates the fishing characteristics of Eyrie’s and the flocking characteristics of Tusker to accelerate the convergence speed which inturns enhance the training process and improve the generalization performance of the DeepCNN model. In the Eyrie Flock optimization, two optimal issues are considered: (i) segmenting the lung nodule and (ii) fine-tuning hyperparameters of Deep CNN. RESULTS: The capability of the newly developed method is evaluated by the terms of Specificity, Sensitivity, and Accuracy, attaining 98.96%, 95.21%, and 94.12%, respectively. CONCLUSION: Efficiently utilized the Deep CNN along with the help of the Eyrie Flock optimization algorithm which enhances the efficiency of the classifier and convergence of the model.

Incidentally Detected ALK-Positive Histiocytosis with EML4::ALK Fusion in a Solitary Pulmonary Nodule Following COVID-19 Infection: A Rare Case Report.

We hereby report a patient with ALK-positive histiocytosis with localized lung involvement. A 47-year-old woman presented with a solitary pulmonary nodule in the left upper lobe, 7 months after COVID infection. A well-defined 15 mm yellow mass was found in trisegmentectomy specimen. Histopathological examination revealed that the mass was composed of epithelioid and spindle cells with foamy cytoplasms. No necrosis, pleomorphism, or nuclear atypia was detected. The cells were positive for CD68, CD163, PU.1, ALK and negative for KRT, smooth muscle actin (SMA), S100, Melan-A, CD34, STAT6, and BRAF VE1. Flourescence in situ hybridization demonstrated ALK gene rearrangement, and next generation sequencing confirmed EML4::ALK fusion. Lung involvement in ALK-positive histiocytosis is characterized by the presence of pulmonary nodules, which can be seen in all forms of the disease. However, lung involvement is rarely seen in single-system ALK-positive histiocytosis. Our report represents the fourth documented instance of localized lung involvement in ALK-positive histiocytosis, an exceedingly rare occurrence, and it is the third instance with available molecular data.

Navigating the Diagnosis and Treatment of Extra-Adrenal Pheochromocytoma Presenting with Chronic Abdominal Pain: A Clinical Conundrum

Background: Extra-adrenal pheochromocytomas, or paragangliomas, are rare neuroendocrine tumors that present diagnostic challenges due to their nonspecific symptoms and radiologic features.Objective: To report a case of extra-adrenal pheochromocytoma presenting as chronic abdominal pain and hepatic masses, emphasizing the diagnostic and therapeutic challenges.Methods: A 36-year-old male with a history of diabetes and hypertension presented with weight loss and abdominal pain. Initial evaluation included comprehensive physical examination, laboratory tests, ultrasonography, and CT imaging of the abdomen and chest. Partial hepatectomy was performed, and the diagnosis was confirmed through histopathological and immunohistochemical analysis.Results: Imaging identified three mixed echogenic hepatic masses, with the largest measuring 5.6 x 5.4 cm. Enlarged para-aortic lymph nodes were observed, the largest being 4.0 x 3.5 cm. CT chest revealed a mass on the chest wall (7.0 x 3.0 cm) with metastatic lung nodules. Immunohistochemistry confirmed paraganglioma with positivity for chromogranin A and neuron-specific enolase.Conclusion: This case underscores the importance of considering paragangliomas in differential diagnoses of atypical abdominal masses and highlights the critical role of histopathological confirmation in guiding management.

Attribute and Malignancy Analysis of Lung Nodule on Chest CT with Cause-and-Effect Logic

Attribute and Malignancy Analysis of Lung Nodule on Chest CT with Cause-and-Effect Logic

Plasma Circulating HPV DNA Surveillance in a Patient With Nasopharyngeal Cancer.

Nearly one third of nasopharyngeal carcinomas (NPCs) in the United States are associated with human papillomavirus (HPV). Surveillance for Epstein-Barr virus (EBV)-negative subtypes is customarily based solely on imaging and physical examinations. We present a case of HPV-positive NPC using serial circulating tumor HPV DNA (ctHPVDNA) as a biomarker of disease status. A 58-year-old Caucasian female initially presented with T1N1M0 EBV-negative p16-positive squamous cell carcinoma of the nasopharynx and was treated with concurrent chemoradiation. Regional nodal recurrence identified 7 months post-radiotherapy was treated with salvage left neck dissection. Surveillance was supplemented using a commercially available polymerase chain reaction (PCR)-based ctHPVDNA assay. Rising ctHPVDNA levels at 9 and 10 months post salvage surgery prompted positron emission tomography (PET). Biopsy confirmed recurrence in avid right hilar and paraoesophageal lymph nodes. Following definitive radiotherapy to the involved nodes and concurrent pembrolizumab, posttreatment ctHPVDNA decreased to baseline, but then increased after 6 cycles of pembrolizumab. Follow-up PET found left mediastinal recurrence outside of the prior treatment field, which was treated with concurrent chemoradiation with cetuximab. Again, ctHPVDNA level dropped to baseline but increased 3 months postradiation. PET scan showed a left lung nodule, and the patient received systemic therapy. Plasma ctHPVDNA monitoring correlated well with disease activity in our patient with HPV-positive NPC. ctHPVDNA detected disease earlier than standard surveillance methods and allowed for earlier intervention. Larger studies are needed to validate the utility of HPV biomarker surveillance in NPC.