Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

BACKGROUND: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers. MATERIALS AND METHODS: A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index). RESULTS: Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses. CONCLUSIONS: Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.

Linear endoscopic ultrasound: Current uses and future perspectives in mediastinal examination.

This editorial elaborates on the current and future applications of linear endoscopic ultrasound (EUS), a substantial diagnostic and therapeutic modality for various anatomical regions. The scope of endosonographic assessment is broad and, among other factors, allows for the evaluation of the mediastinal anatomy and related pathologies, such as mediastinal lymphadenopathy and the staging of central malignant lung lesions. Moreover, EUS assessment has proven more accurate in detecting small lesions missed by standard imaging examinations, such as computed tomography or magnetic resonance imaging. We focus on its current uses in the mediastinum, including lung and esophageal cancer staging, as well as evaluating mediastinal lymphadenopathy and submucosal lesions. The editorial also explores future perspectives of EUS in mediastinal examination, including ultrasound-guided therapies, artificial intelligence integration, advancements in mediastinal modalities, and improved diagnostic approaches for various mediastinal lesions.

Ultrashort Oncologic Whole-Body [18F]FDG Patlak Imaging Using LAFOV PET.

Methods to shorten [18F]FDG Patlak PET imaging procedures ranging from 65-90 to 20-30 min after injection, using a population-averaged input function (PIF) scaled to patient-specific image-derived input function (IDIF) values, were recently evaluated. The aim of the present study was to explore the feasibility of ultrashort 10-min [18F]FDG Patlak imaging at 55-65 min after injection using a PIF combined with direct Patlak reconstructions to provide reliable quantitative accuracy of lung tumor uptake, compared with a full-duration 65-min acquisition using an IDIF. Methods: Patients underwent a 65-min dynamic PET acquisition on a long-axial-field-of-view (LAFOV) Biograph Vision Quadra PET/CT scanner. Subsequently, direct Patlak reconstructions and image-based (with reconstructed dynamic images) Patlak analyses were performed using both the IDIF (time to relative kinetic equilibrium between blood and tissue concentration (t*) = 30 min) and a scaled PIF at 30-60 min after injection. Next, direct Patlak reconstructions were performed on the system console using only the last 10 min of the acquisition, that is, from 55 to 65 min after injection, and a scaled PIF using maximum crystal ring difference settings of both 85 and 322. Tumor lesion and healthy-tissue uptake was quantified and compared between the differently obtained parametric images to assess quantitative accuracy. Results: Good agreement was obtained between direct- and image-based Patlak analyses using the IDIF (t* = 30 min) and scaled PIF at 30-60 min after injection, performed using the different approaches, with no more than 8.8% deviation in tumor influx rate value (Ki ) (mean difference ranging from -0.0022 to 0.0018 mL/[min × g]). When direct Patlak reconstruction was performed on the system console, excellent agreement was found between the use of a scaled PIF at 30-60 min after injection versus 55-65 min after injection, with 2.4% deviation in tumor Ki (median difference, -0.0018 mL/[min × g]; range, -0.0047 to 0.0036 mL/[min × g]). For different maximum crystal ring difference settings using the scan time interval of 55-65 min after injection, only a 0.5% difference (median difference, 0.0000 mL/[min × g]; range, -0.0004 to 0.0013 mL/[min × g]) in tumor Ki was found. Conclusion: Ultrashort whole-body [18F]FDG Patlak imaging is feasible on an LAFOV Biograph Vision Quadra PET/CT system without loss of quantitative accuracy to assess lung tumor uptake compared with a full-duration 65-min acquisition. The ultrashort 10-min direct Patlak reconstruction with PIF allows for its implementation in clinical practice.

AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method.

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available.

A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients.

BACKGROUND: In recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer. METHODS: Included in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results. RESULTS: Altogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = ex/ (1 + ex); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group. CONCLUSION: In this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients.

Differential diagnosis of benign and lung adenocarcinoma presenting as larger solid nodules and masses based on multiscale CT radiomics.

PURPOSE: To develop a better radiomic model for the differential diagnosis of benign and lung adenocarcinoma lesions presenting as larger solid nodules and masses based on multiscale computed tomography (CT) radiomics. MATERIALS AND METHODS: This retrospective study enrolled 205 patients with solid nodules and masses from Center 1 between January 2010 and February 2022 and Center 2 between January 2019 and February 2022. After applying the inclusion and exclusion criteria, we retrospectively enrolled 165 patients from two centers and assigned them to the training dataset (n = 115) or the test dataset (n = 50). Radiomics features were extracted from volumes of interest on CT images. A gradient boosting decision tree (GBDT) was used for data dimensionality reduction to perform the final feature selection. Four models were developed using clinical data, conventional imaging features and radiomics features, namely, the clinical and image model (CIM), the plain CT radiomics model (PRM), the enhanced CT radiomics model (ERM) and the combined model (CM). Model performance was evaluated to determine the best model for identifying benign and lung adenocarcinoma presenting as larger solid nodules and masses. RESULTS: In the training dataset, the areas under the curve (AUCs) for the CIM, PRM, ERM, and CM were 0.718, 0.806, 0.819, and 0.917, respectively. The differential diagnostic capability of the ERM was better than that of the PRM and the CIM. The CM was optimal. Intermediate and junior radiologists and respiratory physicians achieved improved obviously diagnostic results with the radiomics model. The senior radiologists showed slight improved diagnostic results after using the radiomics model. CONCLUSION: Radiomics may have the potential to be used as a noninvasive tool for the differential diagnosis of benign and lung adenocarcinoma lesions presenting as larger solid nodules and masses.

The Interval of Computed Tomography Follow-Ups after Advanced Non-Small-Cell Lung Cancer Surgery Did Not Show Any Relationship with Survival.

PURPOSE: There is limited evidence concerning the computed tomography (CT) follow-up interval to detect recurrence and second primary cancers after surgery for non-small-cell lung cancer (NSCLC). In this study, we aimed to investigate the impact of CT interval on survival after surgery. METHODS: This retrospective study analyzed the prognosis of 103 patients who underwent periodic CT after complete resection for pathological stage II-III NSCLC at a single institute between 2015 and 2020. The patients were stratified based on the follow-up CT intervals into the half-year group (Group H) and annual group (Group A). Additionally, the underlying differences in clinical backgrounds between the 2 groups were adjusted by propensity score matching. RESULTS: A total of 103 patients (Group H, 76 patients; Group A, 27 patients) were included in this study. The 5-year overall survival (OS) rates in the unmatched cohort were 83.5% and 95.2% in groups H and A, respectively ( P = 0.17). Among the matched cohort, 42 and 21 patients were in groups H and A. The 5-year OS rates of the matched cohort were 89.8% and 94.4% in groups H and A ( P = 0.45), with no significant difference. CONCLUSIONS: There was no association between CT intervals and postoperative survival.

Prognostic value of [18F]FDG PET/CT in metastatic hormone-sensitive prostate cancer at initial diagnosis: a retrospective cohort study.

INTRODUCTION: This retrospective study aimed to evaluate the prognostic value of [18F]FDG parameters in patients with visceral and bone metastatic hormone-sensitive prostate cancer (mHSPC). PATIENTS AND METHODS: This analysis included the mHSPC patients who underwent [18F]FDG PET/CT at the initial diagnosis. Baseline characteristics were analyzed, and the uptake of [18F]FDG was quantified using SUVmax. Kaplan-Meier and Cox proportional hazard regression analysis were employed to evaluate the correlation between SUVmax and patient survival. RESULTS: Among the 267 patients enrolled, 90 (33.7%) presented with visceral metastases and 177 (66.3%) had bone metastases. The median follow-up for the visceral metastasis group was 35.5 months (IQR 26-53.8 months). The median overall survival for patients with lung, liver, or both metastases were 30, 21 and 17 months, respectively. Patients exhibiting higher [18F]FDG uptake in metastatic lesions experienced shorter overall survival (OS) in comparison to those with lower [18F]FDG uptake, both in the visceral metastases group (17 vs. 31 months, p = 0.002) and the bone metastases group (27.5 vs. 34.5 months, p < 0.001). Cox regression analysis further revealed that increased [18F]FDG uptake in metastatic lesions emerged as a significant risk factor in both OS and progression-free survival (PFS). In contrast, the variability in [18F]FDG uptake in primary lesions did not provide a reliable indicator for predicting prognosis. CONCLUSIONS: In mHSPC patients, higher [18F]FDG uptake in metastatic lesions indicates shorter survival and increased risk of disease progression. The [18F]FDG SUVmax in primary tumors did not show significant prognostic value. Our study underscores the unique prognostic potential of [18F]FDG PET/CT in mHSPC patients, highlighting its importance in the management of both bone and visceral metastases.

Regularity and correlation analysis of regional lymph node metastasis in nonoperative patients with non-small cell lung cancer based on positron emission tomography/computed tomography images.

BACKGROUND: Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced, inoperable non-small cell lung cancer (NSCLC). Previous studies have mainly focused on examining local failure and recurrence patterns after surgery and the principles of lymph node metastasis (LNM) in surgical candidates with NSCLC. However, these studies were just only able to guide postoperative radiotherapy (PORT) and the patterns of LNM in patients with resected NSCLC was inadequate to represent that in locally advanced inoperable NSCLC patients for guiding target volume delineation of CCRT. In this study, we aimed to analyze the metastasis regularities and establish the correlations between different lymph node levels in NSCLC patients without any intervention using positron emission tomography/computed tomography (PET/CT) images. METHODS: Overall, 358 patients with N1-N3 NSCLC admitted in our hospital between 2018 and 2022 were retrospectively analyzed. The diagnosis of metastatic lymph nodes was reviewed and determined using the European Organization for Research and Treatment of Cancer standard and the standardized value of the PET/CT examination. Univariate and multivariate analysis were performed to investigate the correlations between the different levels were evaluated by using of the chi-square test and logistic regression model. RESULTS: The lymph nodes with the highest metastasis rates in patients with left lung cancer were in order as follows: 10L, 4L, 5, 4R, and 7; while in those with right lung cancer they were 10R, 4R, 7, 2R, and 1R. Notably, we found left lung patients were more likely to have contralateral hilar, mediastinal and supraclavicular lymph nodes involved, and the right lung group exhibited a higher propensity for ipsilateral mediastinum and supraclavicular lymph node invasion. Furthermore, correlation analysis revealed there were significant correlative patterns in the LNM across different levels. CONCLUSIONS: This study elucidated the patterns of primary LNM in patients with NSCLC who had not undergone surgery (without any treatment interventions) and the correlations between lymph node levels. These findings were expected to provide useful reference for target volume delineation in definitive concurrent chemoradiotherapy in locally advanced NSCLC patients.

Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizing 18F-deoxyglucose positron emission tomography imaging.

OBJECTIVE: To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma. METHODS: A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes. RESULTS: Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05). CONCLUSIONS: The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups.

Pericardial cyst unveiling: a case of unusual chest symptoms in a young woman with a family history of cancer: a case report and review of literature.

BACKGROUND: Pericardial cysts, though rare and benign, can present with various clinical symptoms depending on their size and location in the body. The detection of these cysts typically relies on imaging studies for a conclusive diagnosis, with surgical removal being the definitive treatment. CASE PRESENTATION: This case report details the clinical journey of a 32-year-old Iranian woman with a family history of breast and lung cancer, who experienced left-sided chest pain. Utilizing a combination of clinical history review, mammography, echocardiography, and computed tomography, a precise diagnosis of a 10 cm × 3.5 cm pericardial cyst was achieved. The patient underwent median sternotomy for complete cyst excision. CONCLUSIONS: While pericardial cysts are often asymptomatic and benign, they can lead to life-threatening complications. Hence, regular follow-up is advised, and in certain instances, minimally invasive interventions or surgery may be necessary.

Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma.

BACKGROUND: Concurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways. METHODS: Overall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing. RESULTS: The DL model exhibited favorable predictive performance, with an area under the curve of 0.86 (95% confidence interval [CI] 0.79-0·92) in the training cohort and 0.84 (95% CI 0.75-0.94) in the validation cohort. The DL model (low score vs. high score) was an independent predictive factor; it was significantly associated with progression-free survival and overall survival in both the training (hazard ratio [HR] = 0.54 [0.36-0.80], P = 0.002; 0.44 [0.28-0.68], P < 0.001) and validation cohorts (HR = 0.46 [0.24-0.88], P = 0.008; 0.30 [0.14-0.60], P < 0.001). The DL model was also positively related to the cell adhesion molecules, the P53 signaling pathway, and natural killer cell-mediated cytotoxicity. Single-cell analysis revealed that differentially expressed genes were enriched in different immune cells. CONCLUSION: The DL model demonstrated a strong predictive ability for determining the response in patients with NSCLC undergoing CCRT. Our findings contribute to understanding the potential biological mechanisms underlying treatment responses in these patients.

[Surgical Resection of High-grade Fetal Adenocarcinoma of the Lung:Report of a Case].

A 79-year-old woman was revealed to have an abnormal shadow in the right upper lung field by a chest radiography at the time of medical examination. Contrast-enhanced chest computed tomography( CT) revealed a solid, irregularly-shaped nodule with pleural indentation and total/solid diameter of 26 mm in the S3 segment of the right upper lobe. A diagnosis could not be made with bronchoscopy, although positron emission tomography( PET)-CT showed accumulation of 18F-fluoro-2-deoxy-D-glucose( FDG) in the same area. The lung cancer in the right upper lobe was considered to be cT1cN0M0 stage ⅠA3, and surgery (thoracoscopic right upper lobectomy ND2a-1) was performed for diagnostic and therapeutic purposes. The histopathological diagnosis was high-grade fetal adenocarcinoma of the lung with metastasis to the #12 lymph node, pT1cN1M0 stage ⅡB. Currently, 3.5 years postoperatively, the patient has shown no apparent metastasis or recurrence. In future, the epidemiology and treatment methods of high-grade fetal adenocarcinoma of the lung should be established by accumulating more cases.

The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.

BACKGROUND: Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. METHODS: A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. RESULTS: Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. CONCLUSION: This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.

Extremely Rare Coexistence of Peripherally Located Mucous Gland Adenoma and Pulmonary Chondroid Hamartoma.

Pulmonary mucous gland adenomas (MGAs) originating in mucous-secreting cells in the bronchi are extremely rare benign tumours. Pulmonary chondroid hamartomas (PCHs) are the most common benign neoplasms of mesenchymal origin of the lung. This study reports an unusual case where MGA and PCH coexisted in a peripheral intra-parenchymal location. A patient with a 1-cm non-specific nodule in the left lung on a computed tomography scan underwent wedge resection. Microscopically, mesenchymal elements consisting of fat and cartilage tissue were observed. Mucous glands were present around these mesenchymal elements. No cellular atypia or mitosis was observed. This allowed for complete treatment without the need for a segmentectomy.

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

PURPOSE: This 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. METHODS: A 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. RESULTS: Compared 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). CONCLUSIONS: This 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.

Is clinical target volume necessary for locally advanced non-small cell lung cancer treated with 4D-CT intensity-modulated radiation therapy.

BACKGROUND: A dosimetric evaluation is still lacking in terms of clinical target volume (CTV) omission in stage III patients treated with 4D-CT Intensity-Modulated Radiation Therapy (IMRT). METHODS: 49 stage III NSCLC patients received 4D-CT IMRT were reviewed. Target volumes and organs at risk (OARs) were re-delineated. Four IMRT plans were conducted retrospectively to deliver different prescribed dose (74 Gy-60 Gy), and with or without CTV implementation. Dose and volume histogram (DVH) parameters were collected and compared. RESULTS: In the PTV-g 60 Gy plan (PTV-g refers to the PTV generated from the internal gross tumor volume), only 5 of 49 patients had the isodose ≥ 50 Gy line covering at least 95% of the PTV-c (PTV-c refers to the PTV generated from the internal CTV) volume. When the prescribed dose was elevated to 74 Gy to the PTV-g, 33 of 49 patients could have the isodose ≥ 50 Gy line covering at least 95% of the PTV-c volume. In terms of OARs protection, the SIB-IMRT plan showed the lowest value of V5, V20, and mean dose of lung, had the lowest V55 of esophagus, and the lowest estimated radiation doses to immune cells (EDIC). The V20, V30, and mean dose of heart was lower in the simultaneous integrated boost (SIB) IMRT (SIB-IMRT) plan than that of the PTV-c 60 Gy plan. CONCLUSIONS: CTV omission was not suitable for stage III patients when the prescribed dose to PTV-g was 60 Gy in the era of 4D-CT IMRT. CTV omission plus high dose to PTV-g (74 Gy for example) warranted further exploration. The SIB-IMRT plan had the best protection to normal tissue including lymphocytes, and might be the optimal choice.