The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation.

BACKGROUND: Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation. RESEARCH QUESTION: Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models? STUDY DESIGN AND METHODS: Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots. RESULTS: Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23. INTERPRETATION: The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.

Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals.

OBJECTIVE: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT. METHODS: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule. RESULTS: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017. CONCLUSION: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses. CLINICAL RELEVANCE STATEMENT: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice. KEY POINTS: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.

Clinical Characteristics of Human Pulmonary Dirofilariasis in Japan: An Uncommon Differential Diagnosis of a Solitary Pulmonary Nodule.

Human pulmonary dirofilariasis (HPD) is a zoonotic disease caused by Dirofilaria immitis. Most HPD cases are asymptomatic and are either detected during annual health checkups or incidentally identified during the investigation of other diseases, particularly primary or metastatic pulmonary lung cancers. However, the frequency and clinical features of Japanese patients with HPD remain unclear. We analyzed data from the Japanese Medical Abstract Society database and identified 69 cases between 1978 and 2022. The incidence of HPD increased until the 2000s but declined markedly in the 2010s. The incidence is higher in the southwestern region and lower in the northeastern region of Japan. Health checkups are the primary diagnostic opportunities. The Chugoku and Shikoku regions have had high incidence rates per population. The diagnosis of HPD using a noninvasive procedure is typically difficult because of the absence of specific clinical symptoms, and approximately 70% of the cases are detected using video-assisted thoracoscopic surgery. Climate change may increase the incidence of HPD in the northeastern region of Japan, and travel to countries with poor vector control may be a risk factor for HPD transmission. Physicians should consider this parasitic infectious disease when examining patients presenting with solitary lung nodules.

Incidence of Clinically Relevant Solitary Pulmonary Nodules Utilizing a Universal Health Care System.

INTRODUCTION: Solitary pulmonary nodules (SPNs) are common, but the clinical relevance of these nodules is unknown. Utilizing current screening guidelines, we sought to better characterize the national incidence of clinically important SPNs within the largest universal health care system in the nation. MATERIALS AND METHODS: TRICARE data were queried to identify SPNs for ages 18-64 years. SPNs that had been diagnosed within a year with no prior oncologic history were included to ensure true incidence. A proprietary algorithm was applied to determine clinically significant nodules. Further analysis characterized incidence by age grouping, gender, region, military branch, and beneficiary status. RESULTS: A total of 229,552 SPNs were identified with a 60% reduction seen after application of the clinical significance algorithm (N = 88,628). The incidence increased in each decade of life (all P < 0.01). Adjusted incident rate ratios were significantly higher for SPNs detected in the Midwest and Western regions. The incident rate ratio was also higher in females (1.05, confidence interval [CI] 1.018, P = 0.001) as well as non-active duty members (dependents = 1.4 and retired = 1.6, respectively, CIs 1.383-1.492 and 1.591-1.638, P < 0.01). The incidence calculated per 1,000 patients overall was 3.1/1,000. Ages 44-54 years had an incidence of 5.5/1,000 patients, which is higher than the previously reported incidence of < 5.0 nationally for the same age group. CONCLUSIONS: This analysis represents the largest evaluation of SPNs to date combined with clinical relevance adjustment. These data suggest a higher incidence of clinically significant SPNs starting at an age of 44 years in nonmilitary or retired women localized to the Midwest and Western regions of the United States.

Comparison of Brock University, Mayo Clinic and Herder models for pretest probability of cancer in solid pulmonary nodules.

OBJECTIVE: Risk analysis models, which are used in the diagnostic algorithm of incidental pulmonary nodules, are based on patient data from developed countries. Mayo Clinic, Brock University and Herder are among the most known models. We aim to compare the reliability of these models in patients with indeterminate solid nodules and to investigate the contribution of the predictors used to the model. METHODS: We analysed 305 patients who performed transthoracic needle biopsy and positron emission tomography/computed tomography for solid nodules, retrospectively. For all three models, the malignancy risk probabilities of patients were calculated, and patients were classified as low (<5%), moderate (60%) and high (<60%) risk groups. Later, the malignancy rates of each model in three different risk groups were compared within each other and among the models. RESULTS: The malignancy rate is 73% in 305 patients. In the Mayo Clinic and Herder models, the difference in the low-, medium- and high-risk groups is significant (p < 0.001). In the medium-risk group, the rate of malignancy is 96.8% in the Brock model. In the high-risk group, the rate of malignancy in Herder is 88.3% and the rate of malignancy in Mayo Clinic is 28.8%. The optimal cutoff values for the Mayo Clinic, Brock University, and Herder were 29.6, 13.4 and 70 (AUC, respectively; 0.71, 0.67 and 0.73). Age, smoking, gender, size, emphysema and spiculation increase the likelihood of malignancy. CONCLUSION: Close results were obtained in all three models. In the high-risk group, the Herder model has the highest reliability rate (odds ratio 3.3, confidence interval [1.1, 10.2]). Upper lobe predilection is not a reliable predictor.

Prevalence and consequences of non-adherence to an evidence-based approach for incidental pulmonary nodules.

IMPORTANCE: Distinguishing benign from malignant pulmonary nodules is challenging. Evidence-based guidelines exist, but their impact on patient-centered outcomes is unknown. OBJECTIVE: To understand if the evaluation of incidental pulmonary nodules that follows an evidence-based management strategy is associated with fewer invasive procedures for benign lesions and/or fewer delays in cancer diagnosis. DESIGN: Retrospective cohort study. SETTING: Large academic medical center. PARTICIPANTS: Adults (≥18 years age) with an incidental pulmonary nodule discovered between January 2012 and December 2014. Patients with calcified nodules, prior nodules, prior diagnosis of cancer, high suspicion for pulmonary metastasis, or limited life expectancy were excluded. EXPOSURE: Nodule management strategy (pre-specified based on evidence-based practices). OUTCOME: Composite of any invasive procedure for a benign nodule or delay in diagnosis in patients with cancer (>3 month delay once probability of cancer was >15%). RESULTS: Of 314 patients that met inclusion criteria, median age was 61, 46.5% were men, and 66.5% had current or former tobacco use. The mean nodule size was 10.3 mm, mean probability of cancer was 11.8%, and 14.3% of nodules were malignant. Evaluation followed an evidence-based strategy in 245 patients (78.0%), and deviated in 69 patients (22%). The composite outcome occurred in 26 (8.3%) patients. Among patients whose nodule evaluation was concordant with an evidence-based evaluation, 6.1% (15/245) experienced the composite outcome versus 15.9% (11/69) of patients with an evaluation that deviated from evidence-based recommendations (P<0.01). CONCLUSIONS AND RELEVANCE: At a large academic medical center, more than 1 in 5 patients with an incidental pulmonary nodule underwent evaluation that deviated from evidence-based practice recommendations. Nodule evaluation that deviated from an evidence-based strategy was associated with biopsy of benign lesions and delays in cancer diagnosis, suggesting a need to improve guideline uptake.

Prevalence and clinical characteristics of malignant lung nodules in tuberculosis endemic area in a single tertiary centre.

BACKGROUND: Lung nodule management remains a challenge to clinicians, especially in endemic tuberculosis areas. Different guidelines are available with various recommendations; however, the suitability of these guidelines for the Asian population is still unclear. Our study described the prevalence of malignant lung nodules among nodules measuring 2-30 mm, the demographic and characteristics of lung nodules between benign and malignant groups, and the clinician's clinical practice in managing lung nodules. METHOD: Retrospective review of lung nodules from the computed tomography archiving and communication system (PACS) database and clinical data from January 2019 to January 2022. The data was analysed by using chi square, mann whitney test and simple logistic regression. RESULTS: There were 288 nodules measuring 2-30 mm identified; 49 nodules underwent biopsy. Twenty-seven (55%) biopsied nodules were malignant, (prevalence of 9.4%). Among the malignant lung nodules, 74% were adenocarcinoma (n = 20). The commonest benign nodules were granuloma n = 12 (55%). In nodules > 8 mm, the median age of malignant and benign was 72 ± 12 years and 66 ± 16 years, respectively (p = 0.024). There was a significant association of benign nodules (> 8 mm) in subjects with previous or concurrent tuberculosis (p = 0.008). Benign nodules are also associated with nodule size ≤ 8 mm, without spiculation (p < 0.001) and absence of emphysema (p = 0.007). The nodule size and the presence of spiculation are factors to make the clinicians proceed with tissue biopsy. Spiculated nodules and increased nodule size had 11 and 13 times higher chances of undergoing biopsy respectively (p < 0.001).) Previous history of tuberculosis had a 0.874 reduced risk of progression to malignant lung nodules (p = 0.013). These findings implied that these three factors are important risk factors for malignant lung nodules. There was no mortality association between benign and malignant. Using Brock's probability of malignancy, nodules ≤ 8 mm had a low probability of malignancy. CONCLUSION: The prevalence of malignant lung nodules in our centre was comparatively lower than non-Asian countries. Older age, the presence of emphysema, and spiculation are associated with malignancy. Clinical judgment is of utmost importance in managing these patients. Fleishner guideline is still being used as a reference by our clinician.

CT characteristics of solid pulmonary nodules of never smokers versus smokers: A population-based study.

PURPOSE: Aim was to assess CT characteristics of lung nodules in never and former smokers compared to current smokers in a population-based setting. METHOD: We included individuals aged 45-60 years taking part in the ImaLife (Imaging in Lifelines) study, with at least one solid lung nodule (≥30 mm3) on low-dose chest CT. Qualitative (location, shape, margin, nodule type, attached structures) and quantitative (count, diameter, volume) nodule characteristics were evaluated. Based on Fleischner criteria, 'high risk' nodules were defined. To examine the association between smoking status and nodule CT characteristics of participants, multi-level multinomial logistic regression corrected for clustering of nodules within participants was performed, where all odds ratios (aORs) were adjusted for age and sex. RESULTS: Overall, 1,639 individuals (median age: 55.0, IQR:50.5-58.5, 50.5% men) were included, with 42.1% never smokers, 35.3% former smokers and 22.6% current smokers. A total of 3,222 solid nodules were identified; 39.7% of individuals had multiple nodules. Nodule size, location, type and attachment were similar for never compared to current smokers. The odds of nodules with an irregular shape and irregular margin was lower in never smokers (aOR:0.64, 95 %CI:0.44-0.93; aOR:0.60, 95 %CI:0.41-0.88, respectively) and former smokers (aOR:0.61, 95 %CI:0.41-0.90; aOR:0.57, 95 %CI:0.38-0.85, respectively) compared to current smokers. The odds of a detected nodule being 'high risk' was similar for never versus current smokers (never smokers: aOR = 0.90; 95% CI:0.73-1.11). CONCLUSIONS: CT-based characteristics of solid lung nodules in never and former smokers differed only slightly from current smokers. Among individuals with solid nodules, 'high-risk' nodules were equally common in never smokers and current smokers.

Evaluating the Patient With a Pulmonary Nodule: A Review.

IMPORTANCE: Pulmonary nodules are identified in approximately 1.6 million patients per year in the US and are detected on approximately 30% of computed tomographic (CT) images of the chest. Optimal treatment of an individual with a pulmonary nodule can lead to early detection of cancer while minimizing testing for a benign nodule. OBSERVATIONS: At least 95% of all pulmonary nodules identified are benign, most often granulomas or intrapulmonary lymph nodes. Smaller nodules are more likely to be benign. Pulmonary nodules are categorized as small solid (<8 mm), larger solid (≥8 mm), and subsolid. Subsolid nodules are divided into ground-glass nodules (no solid component) and part-solid (both ground-glass and solid components). The probability of malignancy is less than 1% for all nodules smaller than 6 mm and 1% to 2% for nodules 6 mm to 8 mm. Nodules that are 6 mm to 8 mm can be followed with a repeat chest CT in 6 to 12 months, depending on the presence of patient risk factors and imaging characteristics associated with lung malignancy, clinical judgment about the probability of malignancy, and patient preferences. The treatment of an individual with a solid pulmonary nodule 8 mm or larger is based on the estimated probability of malignancy; the presence of patient comorbidities, such as chronic obstructive pulmonary disease and coronary artery disease; and patient preferences. Management options include surveillance imaging, defined as monitoring for nodule growth with chest CT imaging, positron emission tomography-CT imaging, nonsurgical biopsy with bronchoscopy or transthoracic needle biopsy, and surgical resection. Part-solid pulmonary nodules are managed according to the size of the solid component. Larger solid components are associated with a higher risk of malignancy. Ground-glass pulmonary nodules have a probability of malignancy of 10% to 50% when they persist beyond 3 months and are larger than 10 mm in diameter. A malignant nodule that is entirely ground glass in appearance is typically slow growing. Current bronchoscopy and transthoracic needle biopsy methods yield a sensitivity of 70% to 90% for a diagnosis of lung cancer. CONCLUSIONS AND RELEVANCE: Pulmonary nodules are identified in approximately 1.6 million people per year in the US and approximately 30% of chest CT images. The treatment of an individual with a pulmonary nodule should be guided by the probability that the nodule is malignant, safety of testing, the likelihood that additional testing will be informative, and patient preferences.

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review.

BACKGROUND: Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. METHODS: We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. RESULTS: A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice. CONCLUSIONS: The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020161559.

Computed tomography features of COVID-19 in children: A systematic review and meta-analysis.

BACKGROUND: There are few reports on the chest computed tomography (CT) imaging features of children with coronavirus disease 2019 (COVID-19), and most reports involve small sample sizes. OBJECTIVES: To systematically analyze the chest CT imaging features of children with COVID-19 and provide references for clinical practice. DATA SOURCES: We searched PubMed, Web of Science, and Embase; data published by Johns Hopkins University; and Chinese databases CNKI, Wanfang, and Chongqing Weipu. METHODS: Reports on chest CT imaging features of children with COVID-19 from January 1, 2020 to August 10, 2020, were analyzed retrospectively and a meta-analysis carried out using Stata12.0 software. RESULTS: Thirty-seven articles (1747 children) were included in this study. The heterogeneity of meta-analysis results ranged from 0% to 90.5%. The overall rate of abnormal lung CT findings was 63.2% (95% confidence interval [CI]: 55.8%-70.6%), with a rate of 61.0% (95% CI: 50.8%-71.2%) in China and 67.8% (95% CI: 57.1%-78.4%) in the rest of the world in the subgroup analysis. The incidence of ground-glass opacities was 39.5% (95% CI: 30.7%-48.3%), multiple lung lobe lesions was 65.1% (95% CI: 55.1%-67.9%), and bilateral lung lesions was 61.5% (95% CI: 58.8%-72.2%). Other imaging features included nodules (25.7%), patchy shadows (36.8%), halo sign (24.8%), consolidation (24.1%), air bronchogram signs (11.2%), cord-like shadows (9.7%), crazy-paving pattern (6.1%), and pleural effusion (9.1%). Two articles reported 3 cases of white lung, another reported 2 cases of pneumothorax, and another 1 case of bullae. CONCLUSIONS: The lung CT results of children with COVID-19 are usually normal or slightly atypical. The lung lesions of COVID-19 pediatric patients mostly involve both lungs or multiple lobes, and the common manifestations are patchy shadows, ground-glass opacities, consolidation, partial air bronchogram signs, nodules, and halo signs; white lung, pleural effusion, and paving stone signs are rare. Therefore, chest CT has limited value as a screening tool for children with COVID-19 and can only be used as an auxiliary assessment tool.

A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population.

PURPOSE: Lung cancer is the leading cause of cancer death and there have been clinical prediction models. This study aimed to evaluate the diagnostic performance of published models and create new models to evaluate the probability of malignant solitary pulmonary nodules (SPNs) in Chinese population. METHODS: We consecutively enrolled 2061 patients with SPNs from West China Hospital between January 2008 and December 2016, each SPN was pathologically confirmed. First, four published prediction models, Mayo clinic model, Veterans Affairs (VA) model, Brock model and People's Hospital of Peking University (PEH) model were validated in our patients. Then, utilizing logistic regression, decision tree and random forest (RF), we developed three new models and internally validated them. RESULTS: Area under the receiver operating characteristic curve (AUC) values of four published models were as follows: Mayo 0.705 (95% CI 0.658-0.752, n = 726), VA 0.64 6 (95% CI 0.598-0.695, n = 800), Brock 0.575 (95% CI 0.502-0.648, n = 550) and PEH 0.675 (95% CI 0.627-0.723, n = 726). Logistic regression model, decision tree model and RF model were developed, AUC values of these models were 0.842 (95% CI 0.778-0.906), 0.734 (95% CI 0.647-0.821), 0.851 (95% CI 0.789-0.914), respectively. CONCLUSION: The four published lung cancer prediction models do not apply to our population, and we have established new models that can be used to predict the probability of malignant SPNs.

Development and clinical application of deep learning model for lung nodules screening on CT images.

Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.

Benign Solitary Pulmonary Necrotic Nodules: How Effectively Does Pathological Examination Explain the Cause?

Aims: We investigated the histopathological features of solitary pulmonary necrotic nodules (NNs) of undetermined cause. We combined our findings with those obtained using other methods to determine how well the etiological factors were explained. Methods: We screened patients who underwent surgery to treat solitary pulmonary granulomatous and nongranulomatous NNs of undetermined cause. The NN sizes and features of both the NNs and adjacent parenchyma were evaluated. Histochemical analyses included Ehrlich-Ziehl-Neelsen (EZN), Grocott, and Gram staining. Polymerase chain reaction (PCR) was used to detect tuberculous and nontuberculous mycobacteria, panfungal DNA, Nocardia, Francisella tularensis types A and B, and actinomycetes. Results: The NNs were granulomatous in 78.9% and nongranulomatous in 21% of the 114 patients included. EZN staining or PCR was positive for Mycobacterium in 53.5% of all NNs: 62.2% of granulomatous and 20.8% of nongranulomatous NNs. We found a weak but significant correlation between granulomatous NNs and Bacillus positivity and a significant correlation between granulomas surrounding the NNs and the presence of multiple necroses. The NN etiology was determined via histopathological, histochemical, and PCR analyses in 57% of patients but remained undetermined in 42.9%. Conclusion: The causes of both granulomatous and nongranulomatous NNs can be determined by pathological examination. Granulomatous necrosis and granulomas in the adjacent parenchyma are important for differential diagnosis. When both features are present, they strongly support a diagnosis of tuberculosis, even in the absence of bacilli.

A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating.

BACKGROUND: To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions. METHODS: A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self-verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out-of-group verification was performed by k-fold cross-validation. RESULTS: There were statistically significant differences in age, maximum standardized uptake value (SUVmax ), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUVmax , size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e-x ), x = - 5.583 + 0.039 × age + 0.477 × SUVmax + 0.139 × size + 1.537 × lobulation - 1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883-0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K-fold cross-validation showed that the training accuracy was 0.899 ± 0.011, and the predictive accuracy was 0.873 ± 0.053. CONCLUSIONS: The risk factors for malignant nodules included age, SUVmax , size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions.

CT Lung Screening in Patients with Laryngeal Cancer.

Laryngeal cancer (LC) patients who meet the age and smoking criteria of the U.S. Preventive Services Task Force (USPSTF) for annual CT lung screening were analysed for pulmonary nodules (PN) detection and secondary lung cancer (SLC) diagnosis. This is a retrospective chart review of LC patients treated at Johns Hopkins Hospital from January 2010 to December 2017. The study population included patients who met USPSTF criteria by age and smoking history for annual chest screening and were followed for at least 3 consecutive years. A total of 998 LC patients' records were reviewed, of which 151 met the inclusion criteria. Inadequate follow-up period (37% of excluded cases) was the most common reason for exclusion, followed by not meeting USPSTF age criteria (27% excluded cases). In seventy-eight patients (n = 78, 52% of analysed patients) PN were reported. Nine individuals (6% of analysed patients) were diagnosed with SLC. Age over 70 (p = 0.003) was an independent predictor of malignancy. White race and smoking history over 40 pack-years were positively associated with a pulmonary nodule detection (p = 0.037 and p = 0.044, respectively). The incidence of PN and SLC in patients with LC is high. Many patients with laryngeal cancer meet the formal guidelines for USPSTF screening, and should be screened annually according to evidence-based medicine for the early detection of secondary lung cancers.

Evaluation of models for predicting the probability of malignancy in patients with pulmonary nodules.

OBJECTIVES: The post-imaging, mathematical predictive model was established by combining demographic and imaging characteristics with a pulmonary nodule risk score. The prediction model provides directions for the treatment of pulmonary nodules. Many studies have established predictive models for pulmonary nodules in different populations. However, the predictive factors contained in each model were significantly different. We hypothesized that applying different models to local research groups will make a difference in predicting the benign and malignant lung nodules, distinguishing between early and late lung cancers, and between adenocarcinoma and squamous cell carcinoma. In the present study, we compared four widely used and well-known mathematical prediction models. MATERIALS AND METHODS: We performed a retrospective study of 496 patients from January 2017 to October 2019, they were diagnosed with nodules by pathological. We evaluate models' performance by viewing 425 malignant and 71 benign patients' computed tomography results. At the same time, we use the calibration curve and the area under the receiver operating characteristic curve whose abbreviation is AUC to assess one model's predictive performance. RESULTS: We find that in distinguishing the Benign and the Malignancy, Peking University People's Hospital model possessed excellent performance (AUC = 0.63), as well as differentiating between early and late lung cancers (AUC = 0.67) and identifying lung adenocarcinoma (AUC = 0.61). While in the identification of lung squamous cell carcinoma, the Veterans Affairs model performed the best (AUC = 0.69). CONCLUSIONS: Geographic disparities are an extremely important influence factors, and which clinical features contained in the mathematical prediction model are the key to affect the precision and accuracy.

Long-term cancer risk associated with lung nodules observed on low-dose screening CT scans.

OBJECTIVE: Non-calcified nodules (NCNs) associated with false positive low-dose CT (LDCT) lung cancer screens have been attributed to various causes. Some, however, may represent lung cancer precursors. An association of NCNs with long-term lung cancer risk would provide indirect evidence of some NCNs being cancer precursors. METHODS: LDCT arm participants in the National Lung Screening Trial (NLST) received LDCT screens at baseline and years 1-2. The relationship between NCNs found on LDCT screens and subsequent lung cancer diagnosis over different time periods was examined at the person and lobe level. For the latter, a lobe had a cancer outcome only if the cancer was located in the lobe. Separate analyses were performed on baseline and post-baseline LDCT findings; for the latter, those with baseline NCNs were excluded and only new (non-pre-existing) NCNs examined. Raw and adjusted rate-ratios (RRs) were computed for presence of NCNs and subsequent lung cancer risk; adjusted RRs controlled for demographic and smoking factors. RESULTS: 26,309 participants received the baseline LDCT screen. Over median 11.3 years follow-up, 1675 lung cancers were diagnosed. Adjusted RRs for time periods 0-4, 4-8 and 8-12 years following the baseline screen were 5.1 (95 % CI:4.4-5.9), 1.5 (95 % CI:1.3-1.9) and 1.5 (95 % CI:1.2-1.8) at the person-level and 14.7 (95 % CI:12.6-17.2), 2.6 (95 % CI: 2.0-3.4) and 2.2 (95 % CI:1.6-2.9) at the lobe-level. 18,585 participants were included in the post-baseline analysis. Adjusted RRs for periods 0-4, 4-8 and 8-11 years were 5.6 (95 % CI: 4.5-7.0), 1.9 (95 % CI: 1.3-2.7) and 1.6 (95 % CI: 0.9-2.9) at the person-level and 19.6 (95 % CI:14.9-25.3), 2.5 (95 % CI:1.3-4.7) and 3.3 (95 % CI:1.4-7.6) at the lobe-level. Raw RRs were similar. CONCLUSION: NCNs are associated with excess long-term lung cancer risk, suggesting that some may be lung cancer precursors.

Multidisciplinary Team-Based Management of Incidentally Detected Lung Nodules.

BACKGROUND: Each year, > 1.5 million Americans are diagnosed with an incidentally detected lung nodule. Practice guidelines attempt to balance the benefit of early detection of lung cancer with the risks of diagnostic testing, but adherence to guidelines is low. The goal of this study was to determine guideline adherence rates in the setting of a multidisciplinary nodule clinic and describe reasons for nonadherence as well as associated outcomes. METHODS: This cohort study included 3 years of follow-up of patients aged ≥ 35 years with an incidentally detected lung nodule evaluated in a multidisciplinary clinic that used the 2005 Fleischner Society Guidelines. RESULTS: Among 113 patients, 67% (95% CI, 58-76) were recommended a guideline-concordant nodule evaluation; 7.1% (95% CI, 3.1-13) and 26% (95% CI, 18-25) were recommended less or more intense evaluation, respectively. In contrast, 58% (95% CI, 48-67), 22% (95% CI, 18-25), and 23% (95% CI, 16-32) received a guideline-concordant, less intense, or more intense evaluation. The most common reason for recommending guideline-discordant care was concern for two different diagnoses that would each benefit from early detection and treatment. A majority of lung cancer diagnoses (88%) occurred in patients who received guideline-concordant care. There were no lung cancer cases in those who received less intense nodule care. CONCLUSIONS: A multidisciplinary nodule clinic may serve as a system-level intervention to promote guideline-concordant care, while also providing a multidisciplinary basis by which to deviate from guidelines to address the needs of a heterogeneous patient population.