Does obstructive sleep apnea-induced intermittent hypoxia increase the incidence of solitary pulmonary nodules, thyroid nodules, and other disorders? A retrospective study based on 750 cardiovascular disease patients.

BACKGROUND: Obstructive sleep apnea (OSA) has been shown to be an important risk factor for cardiovascular disease (CVD), and intermittent hypoxia is an important pathogenetic factor for it. In the clinic, it was found that most CVD patients combined with OSA were also combined with solitary pulmonary nodules (SPN) or thyroid nodules (TN). Are these disorders related to intermittent hypoxia? One study showed that intermittent hypoxia is a pathogenic factor for lung cancer in mice, but there have been no clinical reports. So we conducted a retrospective study to explore whether intermittent hypoxia caused by OSA increases the incidence of SPN, TN, and other disorders. METHODS: We selected 750 patients with cardiovascular disease (CVD), who were divided into the control group and the OSA group according to the result of portable sleep monitoring. Retrospectively analyzed the comorbidities that patients with OSA are prone to and explored the correlation between OSA and those comorbidities. RESULTS: The incidence of SPN, TN, cervical spondylosis, and carotid-artery plaques was higher in the OSA group than in the control group. These diseases are significantly associated with OSA (p < 0.05), and their incidence increased with an elevated apnea-hypopnea index. After excluding interference from age, gender, BMI, smoking history, history of lung disease, and history of tumors, OSA showed a significant correlation with SPN. After excluding age, gender, BMI, and thyroid disease, OSA was associated with TN. Patients with comorbidities have lower nocturnal oxygen saturation and more extended periods of apnea. Logistic multiple regression results revealed that male, advanced age, obesity, CS, and nasal septum deviation were independent risk factors for OSA. CONCLUSIONS: Patients combined with OSA may further develop more comorbidities, such as SPN, TN, and carotid-artery plaques. It may be related to intermittent hypoxia caused by OSA.

Artificial intelligence's contribution to early pulmonary lesion detection in chest X-rays: insights from two retrospective studies on a Czech population.

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI's role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

The effect of combining different sampling tools on the performance of electromagnetic navigational bronchoscopy for the evaluation of peripheral lung lesions and factors associated with its diagnostic yield.

BACKGROUND: We assessed the performance of Electromagnetic navigational bronchoscopy (ENB) as a standalone diagnostic technique and the performance of different sampling tools used during the procedure. METHODS: We recruited 160 consecutive patients who underwent ENB for peripheral lung lesions (PLL) at a tertiary care centre. The diagnostic performance of ENB and sampling tools was assessed using a logistic regression model and a ROC-curve in which the dependent variable was diagnostic success. A multivariate model was built to predict diagnostic success before performing ENB to select the best candidates for the procedure. RESULTS: Most patients with PLLs in the study were male (65%), with a mean age of 67.9 years. The yield was 66% when the most common techniques were used together as suction catheter + transbronchial biopsy forceps (TBBx) + bronchoalveolar lavage + bronchial washing (p < 0.001) and increased to 69% when transbronchial needle aspiration (TBNA) and cytology brush were added (p < 0.001). Adding diagnostic techniques such as TBBx and TBNA resulted in an increase in diagnostic performance, with a statistically significant trend (p = 0.002). The logistic model area-under the ROC-curve for diagnostic success during ENB was 0.83 (95% CI:0.75-0.90; p < 0.001), and a logit value ≥ 0.12 was associated with ≥ 50% probability of diagnostic success. CONCLUSIONS: ENB, as a stand-alone diagnostic tool for the evaluation of PLLs when performed by experienced operators using a multi-modality technique, has a good diagnostic yield. The probability of having a diagnostic ENB could be assessed using the proposed model.

Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules.

INTRODUCTION: Solitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates. OBJECTIVE: We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics. METHODS: In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics. RESULTS AND CONCLUSION: GC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.

Establishment and validation of a clinical model for diagnosing solitary pulmonary nodules.

OBJECTIVES: The main purpose of this study was to develop and validate a clinical model for estimating the risk of malignancy in solitary pulmonary nodules (SPNs). METHODS: A total of 672 patients with SPNs were retrospectively reviewed. The least absolute shrinkage and selection operator algorithm was applied for variable selection. A regression model was then constructed with the identified predictors. The discrimination, calibration, and clinical validity of the model were evaluated by the area under the receiver-operating-characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: Ten predictors, including gender, age, nodule type, diameter, lobulation sign, calcification, vascular convergence sign, mediastinal lymphadenectasis, the natural logarithm of carcinoembryonic antigen, and combination of cytokeratin 19 fragment 21-1, were incorporated into the model. The prediction model demonstrated valuable prediction performance with an AUC of 0.836 (95% CI: 0.777-0.896), outperforming the Mayo (0.747, p = 0.024) and PKUPH (0.749, p = 0.018) models. The model was well-calibrated according to the calibration curves. The DCA indicated the nomogram was clinically useful over a wide range of threshold probabilities. CONCLUSION: This study proposed a clinical model for estimating the risk of malignancy in SPNs, which may assist clinicians in identifying the pulmonary nodules that require invasive procedures and avoid the occurrence of overtreatment.

[Research on "Augmented Reality" Assisted Precise Thoracoscopic Resection of Pulmonary Nodules].

To break with traditional preoperative localization, a porcine animal model to evaluate the safety of augmented reality (AR) assisted localization of solitary pulmonary nodules (SPN) was used. Before the experiment, Microsoft HoloLens AR system was used to bring the CT image into the laboratory after 3D reconstruction. The virtual model was fitted with real body surface markers, and the virtual positioning auxiliary line and auxiliary locator were used to perform puncture positioning before surgery. Data related to actual puncture path and expected planned path were recorded in the experiment. SPSS 26.0 was used to calculate the puncture accuracy under AR assisted positioning, and the results obtained were acceptable in segmentectomy or wedge pneumonectomy. Its feasibility in animal models will also be evaluated, and its safety and efficacy will need to be further studied in clinical trials.

A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population.

BACKGROUND: This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). METHODS: Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort. RESULTS: A total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed - 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models. CONCLUSIONS: The novel model in our study had a high clinical value in diagnose of MSPNs.

Feasibility and Safety of Radiofrequency Ablation Guided by Bronchoscopic Transparenchymal Nodule Access in Canines.

BACKGROUND: The treatment of pulmonary malignancies remains a challenge. The efficacy and safety of bronchoscopic radiofrequency ablation (RFA) for the treatment of lung cancer are not well elucidated. OBJECTIVE: This study aimed to evaluate the feasibility and safety of RFA guided by bronchoscopic transparenchymal nodule access (BTPNA) in vivo. METHODS: In an attempt to determine the parameters of RFA, we first performed RFA in conjunction with automatic saline microperfusion in the lung in vitro with various ablation energy (10, 15, 20, 25, and 30 W) and ablation times (3, 5, 8, and 10 min). The correlation between ablated area and RFA parameter was recorded and analyzed. Further, we conducted a canine study with RFA by BTPNA in vivo, observing the ablation effect and morphological changes in the lung assessed by chest CT and histopathologic examination at various follow-up time points (1 day, n = 3; 30 days, n = 4; 90 days, n = 4). The related complications were also observed and recorded. RESULTS: More ablation energy, but not ablation time, induced a greater range of ablation area in the lung. Ablation energy applied with 15 W for 3 min served as the appropriate setting for pulmonary lesions ≤1 cm. RFA guided by BTPNA was performed in 11 canines with 100% success rate. Inflammation, congestion, and coagulation necrosis were observed after ablation, which could be repaired within 7 days; subsequently, granulation and fibrotic scar tissue developed after 30 days. No procedure-related complication occurred during the operation or in the follow-up periods. CONCLUSION: The novel RFA system and catheter in conjunction with automatic saline microperfusion present a safe and feasible modality in pulmonary parenchyma. RFA guided by BTPNA appears to be well established with an acceptable tolerance; it might further provide therapeutic benefit in pulmonary malignancies.

The combination of CTCs and CEA can help guide the management of patients with SPNs suspected of being lung cancer.

OBJECTIVE: Solitary pulmonary nodules (SPNs) is a common radiographic finding and require further evaluation because of the possibility of lung cancer. This study aimed to determine the sensitivity and specificity of circulating tumour cells (CTCs) as a marker for the diagnosis of SPNs and the integration of CTCs, carcinoembryonic antigen (CEA) and imaging findings to improve the sensitivity and specificity of diagnosis in patients with SPNs suspected of being lung cancer. METHOD: For the serum biomarker assay, the concentration of CEA was measured by an automated electrochemiluminescence analyzer. CTCs were collected from 6 ml of blood by the SE i-FISH method, which detects the gene copy number in eight chromosomes and the tumour-associated antigen CK18. RESULTS: With a threshold of 6 CTC units, the method showed a sensitivity of 67.1% and a specificity of 56.5% in the diagnosis of NSCLC, especially in the upper lobe, in which the diagnostic strength was the highest (P < 0.01). CTCs, CEA and nodule type had the highest diagnostic efficacy (area under the curve, 0.827; 95% confidence interval, 0.752-0.901) in patients with SPNs being suspected lung cancer. Combining CTCs (cut-off value 12 units) with CEA (1.78 ng/ml), the method showed a sensitivity of 77.8% and a specificity of 90% in the diagnosis of NSCLC, especially in the upper lobe, subsolid nodules and nodules ≥8 mm. CONCLUSIONS: Our results demonstrated that CTCs are feasible diagnostic biomarkers in patients with SPNs, especially in the upper lobe. Furthermore, CTCs combined with CEA showed higher diagnostic efficacy in the upper lobe, subsolid nodules and nodules ≥8 mm.

Pulmonary nodules precision localization techniques.

The advent of helical high-resolution CT scanners, the application of screening programs and the follow-up of patient with oncological history, led to an increasing number of diagnosis of small pulmonary nodule (less than 10 mm in maximum diameter), partially solid nodule or completely ground glass ones. Their management is controversial. Excisional biopsy by mean of video-assisted thoracic surgery is often a viable choice but to locate these lesions intraoperatively can be impossible without the aid of preoperative or intraoperative localization techniques. In this brief review we will analyze the benefit of adopting localization techniques prior to pulmonary resection for small pulmonary lesions and face the advantages and problems with the main techniques described in the literatures.

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial.

BACKGROUND: Estimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs. METHODS: Data from the UK Lung Cancer Screening trial were analysed. Multivariable logistic regression models were used to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline and at 3-month and 12-month repeat screening. RESULTS: Of 1994 participants who underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median follow-up of 4 years. Covariates that predict lung cancer in our model included female gender, asthma, bronchitis, asbestos exposure, history of cancer, early and late onset of family history of lung cancer, smoking duration, FVC, nodule type (pure ground-glass and part-solid) and volume as measured by semiautomated volumetry. The final model incorporating all predictors had excellent discrimination: area under the receiver operating characteristic curve (AUC 0.885, 95% CI 0.880 to 0.889). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected AUC 0.882, 95% CI 0.848 to 0.907). The risk model had a good calibration (goodness-of-fit χ[8] 8.13, p=0.42). CONCLUSIONS: Our model may be used in estimating the probability of lung cancer in nodules detected at baseline and at 3 months and 12 months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programmes. TRIAL REGISTRATION NUMBER: 78513845.

Incidental Pulmonary Nodules Are Common on CT Coronary Angiogram and Have a Significant Cost Impact.

BACKGROUND: Computed tomography (CT) coronary angiogram (CTCA) is commonly used for diagnostic evaluation of low-moderate risk patients due to its excellent performance and cost-effectiveness. However, previous cost analyses have not factored in the burden of management of pulmonary nodules, which are a common occurrence. We sought to describe the frequency and characteristics of lung nodules on CTCA in an Australian tertiary hospital, and to assess cost impacts. METHODS: Consecutive CTCAs performed in the calendar year 2012 were retrospectively identified from the imaging department database. Subjects were excluded if they were under the age of 35, had known malignancy or findings identified prior to CTCA. Patients were stratified on smoking history and nodule size. RESULTS: Of the 2479 CTCAs included, full-field imaging revealed nodules in 358 patients (13.9%). The nodules were generally small (73% <6mm), multiple (63%) and in the lower lobe (83.4%). There was no significant difference when stratified for smoking, with 60% of nodules detected in never-smokers. A minimum of 445 subsequent scans was required for nodule surveillance, resulting in an additional overall cost of $63.62 per CTCA. Limited-Field-of-View (L-FOV) would have identified only 22 nodules, with a cost of $6.14 for every CTCA performed, a cost saving of $57 per patient. CONCLUSIONS: Indeterminate pulmonary nodules are a common incidental finding on CTCA and prevalence appears to be independent of smoking status. There is a consequent significant cost burden that has not previously been recognised. Use of L-FOV reduces the number of nodules identified, with a significant cost benefit, but this has to be balanced against the ethical and medico-legal issues inherent in not reconstructing the irradiated lung.

Combination diagnosis of multi-slice spiral computed tomography and secretary phospholipase A2-IIa for solitary pulmonary nodules.

INTRODUCTION: This study was aimed to compare the diagnostic value of multi-slice spiral computed tomography (CT) and secretary phospholipase A2-IIa (sPLA2-IIa) in differentiating between malignant and benign solitary pulmonary nodules (SPNs). METHODS: A total of 223 patients with SPNs (91 patients with malignant SPNs and 132 patients with benign SPNs) were included from Weihai Central Hospital during October 2014 to December 2016. SPN diagnosis was confirmed in all patients using needle biopsy, surgery and bronchoscopy. The patients were managed with dynamic multi-slice CT scans, and their sPLA2-IIa levels were also detected. By selecting the area of interest of focus, the perfusion parameters of multi-slice CT targeting the focus were obtained. RESULTS: The levels of MTT, PS, BV, BF and sPLA2-IIa significantly increased with increasing severity of SPNs (P<.05). Notably, BV (area under the ROC curve [AUC]=0.915; 95%CI: 0.88-0.95; sensitivity=91.21%; specificity=78.79%) showed a higher potential to discriminate patients with malignant SPNs from those with benign SPNs than did BF (AUC=0.712; 95%CI: 0.65-0.78; sensitivity=72.50%; specificity=59.10%), PS (AUC=0.772; 95%CI: 0.71-0.84; sensitivity=65.93%; specificity=82.58%) and MTT (AUC=0.600; 95%CI: 0.52-0.68; sensitivity=52.75%; specificity=78.03%). Finally, the combined diagnostic value of BV and sPLA2-IIa was quite ideal (AUC=0.947; 95%CI: 0.92-0.97; sensitivity=85.70%; specificity=92.70%) for malignant and benign SPNs. CONCLUSIONS: The combined diagnostic value of BV and sPLA2-IIa appeared as a desirable detection method for malignant and benign SPNs.

A Novel Method for In Vivo Imaging of Solitary Lung Nodules Using Navigational Bronchoscopy and Confocal Laser Microendoscopy.

Solitary pulmonary nodules (SPN) have become increasingly prevalent and diagnostic management remains challenging. We demonstrate a novel technique in which probe-based confocal endomicroscopy (pCLE) could be performed to microimage SPN in vivo and in real-time. Two confocal wavelengths (488 and 660 nm with methylene blue (MB)) were used for elastin network and cellular imaging, respectively using pCLE in conjunction with r-EBUS and virtual navigation. In the first case, the 1-mm Alveoflex was used to image a metastatic melanoma in a subcentimetric nodule in the right middle lobe. In the next case, a malignant 2-cm nodule in the posterior segment of the upper lobe was imaged using the smaller 0.6-mm Cholangioflex. Lastly, we present a benign case revealing confocal characteristics of a nodular lipid pneumonitis. This reports for the first time the feasibility and utility of pCLE in vivo microimaging of SPN using either the Alveoflex or Cholangioflex miniprobes in addition to 660 nm/MB imaging.

Evaluation of undiagnosed solitary lung nodules according to the probability of malignancy in the American College of Chest Physicians (ACCP) evidence-based clinical practice guidelines.

BACKGROUND: This study retrospectively investigated the clinical significance of undiagnosed solitary lung nodules removed by surgical resection. PATIENTS AND METHODS: We retrospectively collected data on the age, smoking, cancer history, nodule size, location and spiculation of 241 patients who had nodules measuring 7 mm to 30 mm and a final diagnosis established by histopathology. We compared the final diagnosis of each patient with the probability of malignancy (POM) which was proposed by the American College of Chest Physicians (ACCP) guidelines. RESULTS: Of the 241 patients, 203 patients were diagnosed to have a malignant lung tumor, while 38 patients were diagnosed with benign disease. There were significant differences in the patients with malignant and benign disease in terms of their age, smoking history, nodule size and spiculation. The mean value and the standard deviation of the POM in patients with malignant tumors were 51.7 + 26.1%, and that of patients with benign lesions was 34.6 + 26.7%. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.67. The best cut-off value provided from the ROC curve was 22.6. When the cut-off value was set at 22.6, the sensitivity was 83%, specificity 52%, positive predictive value 90%, negative predictive value 36% and accuracy 77%, respectively. CONCLUSIONS: The clinical prediction model proposed in the ACCP guidelines showed unsatisfactory results in terms of the differential diagnosis between malignant disease and benign disease of solitary lung nodules in our study, because the specificity, negative predictive value and AUC were relatively low.

Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes.

OBJECTIVE: To explore the role of the texture features of images in the diagnosis of solitary pulmonary nodules (SPNs) in different sizes. MATERIALS AND METHODS: A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes: ≤10, 11-20, and >20 mm. Their texture features were segmented and extracted. The differences in the image features between benign and malignant SPNs were compared. The SPNs in these three groups were determined and analyzed with the texture features of images. RESULTS: These 379 SPNs were successfully segmented using the 2D Otsu threshold method and the self-adaptive threshold segmentation method. The texture features of these SPNs were obtained using the method of grey level co-occurrence matrix (GLCM). Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. The entropy, contrast, energy, homogeneity, and correlation were 3.5597±0.6470, 0.5384±0.2561, 0.1921±0.1256, 0.8281±0.0604, and 0.8748±0.0740 in the benign SPNs and 3.8007±0.6235, 0.6088±0.2961, 0.1673±0.1070, 0.7980±0.0555, and 0.8550±0.0869 in the malignant SPNs (all P<0.05). The sensitivity, specificity, and accuracy of the texture features of images were 83.3%, 90.0%, and 86.8%, respectively, for SPNs sized ≤10 mm, and were 86.6%, 88.2%, and 87.1%, respectively, for SPNs sized 11-20 mm and 94.7%, 91.8%, and 93.9%, respectively, for SPNs sized >20 mm. CONCLUSIONS: The entropy and contrast of malignant pulmonary nodules have been demonstrated to be higher in comparison to those of benign pulmonary nodules, while the energy, homogeneity correlation of malignant pulmonary nodules are lower than those of benign pulmonary nodules. The texture features of images can reflect the tissue features and have high sensitivity, specificity, and accuracy in differentiating SPNs. The sensitivity and accuracy increase for larger SPNs.

An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule.

BACKGROUND: Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs. METHODS: We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups. RESULTS: The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups. CONCLUSION: Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm.

Objective: In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm. Methods: A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085). Conclusion: the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules' (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier. Two medical experts provided their malignancy likelihood scores, taking into account the patient's clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29-95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings.

Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules.

Background: This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs). Method: We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). Result: The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively. Conclusion: Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.