Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic.

OBJECTIVE: To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia. METHODS: This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors. RESULTS: A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020. CONCLUSIONS: Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

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.

Influenza season influence on outcome of new nodules in the NELSON study.

We evaluated the impact of the influenza season on outcome of new lung nodules in a LDCT lung cancer screening trial population. NELSON-trial participants with ≥ 1 new nodule detected in screening rounds two and three were included. Outcome (resolution or persistence) of new nodules detected per season was calculated and compared. Winter (influenza season) was defined as 1st October to 31st March, and compared to the summer (hay-fever season), 1st April to 30th September. Overall, 820 new nodules were reported in 529 participants. Of the total new nodules, 482 (59%) were reported during winter. When considering the outcome of all new nodules, there was no statistically significant association between summer and resolving nodules (OR 1.07 [CI 1.00-1.15], p = 0.066), also when looking at the largest nodule per participant (OR 1.37 [CI 0.95-1.98], p = 0.094). Similarly, there was no statistically significant association between season and screen detected cancers (OR 0.47 [CI 0.18-1.23], p = 0.123). To conclude, in this lung cancer screening population, there was no statistically significant association between influenza season and outcome of new lung nodules. Hence, we recommend new nodule management strategy is not influenced by the season in which the nodule is detected.

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.

Fleischner Society Guideline Recommendations for Incidentally Detected Pulmonary Nodules and the Probability of Lung Cancer.

PURPOSE: The Fleischner Society aims to limit further evaluations of incidentally detected pulmonary nodules when the probability of lung cancer is <1% and to pursue further evaluations when the probability of lung cancer is ≥1%. To evaluate the internal consistency of guideline goals and recommendations, the authors evaluated stratum-specific recommendations and 2-year probabilities of lung cancer. METHODS: A retrospective cohort study (2005-2015) was conducted of individuals enrolled in one of two integrated health systems with solid nodules incidentally detected on CT. The 2017 Fleischner Society guidelines were used to define strata on the basis of smoking status and nodule size and number. Lung cancer diagnoses within 2 years of nodule detection were ascertained using cancer registry data. Confidence interval (CI) inspection was used to determine if stratum-specific probabilities of lung cancer were different than 1%. RESULTS: Among 5,444 individuals with incidentally detected lung nodules (median age, 66 years; 54% women; 57% smoked; median nodule size, 5.5 mm; 55% with multiple nodules), 214 (3.9%; 95% CI, 3.4%-4.5%) were diagnosed with lung cancer within 2 years. For 7 of 12 strata (58%), 2,765 patients (51%), and 194 lung cancer cases (91%), there was alignment between Fleischner Society goals and recommendations. Alignment was indeterminate for 5 strata (42%), 2,679 patients (49%), and 20 lung cancer cases (9%) because CIs for the probability of lung cancer spanned 1%. CONCLUSIONS: Fleischner Society guideline goals and recommendations align at least half the time. It is uncertain whether alignment of guideline goals and recommendations occurs more often.

Analysis of the interactions between small pulmonary nodules, clinical factors and the risk of malignancy in the chest following diagnosis of head and neck cancer.

BACKGROUND: This study aims to investigate radiological and clinical factors which predict malignancy in indeterminate pulmonary nodules in patients with head and neck cancer (HNC). METHODS: Prospective data were collected in 424 patients who were reviewed in the NHS Lothian HNC multidisciplinary meeting from May 2016 to May 2018. Staging and follow-up CT chest imaging were reviewed to identify and assess pulmonary nodules in all patients. RESULTS: About 61.8% of patients had at least one pulmonary nodule at staging CT. In total, 25 patients developed malignancy in the chest. Metastatic disease in the chest was significantly associated with unknown or negative p16 status (p < .0005). Pleural indentation and spiculation were associated with indeterminate nodules, subsequently being shown to represent metastatic disease (p > .0005 and p = .046, respectively). CONCLUSION: Negative or unknown p16 status was associated with an increased propensity to develop metastatic disease in the chest in patients with HNC.

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.

Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals.

Introduction: To investigate the pulmonary nodules detected by low-dose computed tomography (LDCT), identified factors affecting the size and number of pulmonary nodules (single or multiple), and the pulmonary nodules diagnosed and management as lung cancer in healthy individuals. Methods: A retrospective analysis was conducted on 54,326 healthy individuals who received chest LDCT screening. According to the results of screening, the detection rates of pulmonary nodules, grouped according to the size and number of pulmonary nodules (single or multiple), and the patients' gender, age, history of smoking, hypertension, and diabetes were statistically analyzed to determine the correlation between each factor and the characteristics of the nodules. The pulmonary nodules in healthy individuals diagnosed with lung cancer were managed with differently protocols. Results: The detection rate of pulmonary nodules was 38.8% (21,055/54,326). The baseline demographic characteristics of patients with pulmonary nodules were: 58% male and 42% female patients, 25.7% smoking and 74.3% nonsmoking individuals, 40-60 years old accounted for 49%, 54.8% multiple nodules, and 45.2% single nodules, and ≤5-mm size accounted for 80.4%, 6-10 mm for 18.2%, and 11-30 mm for 1.4%. Multiple pulmonary nodules were more common in hypertensive patients. Diabetes is not an independent risk factor for several pulmonary nodules. Of all patients with lung nodules, 26 were diagnosed with lung cancer, accounting for 0.1% of all patients with pulmonary nodules, 0.6% with nodules ≥5 mm, and 2.2% with nodules ≥8 mm, respectively. Twenty-six patients with lung cancer were treated with surgical resection (57.7%), microwave ablation (MWA, 38.5%), and follow-up (3.8%). Conclusions: LDCT was suitable for large-scale pulmonary nodules screening in healthy individuals, which was helpful for the early detection of suspicious lesions in the lung. In addition to surgical resection, MWA is an option for early lung cancer treatment.

A cross-sectional study of psychological burden in Chinese patients with pulmonary nodules: Prevalence and impact on the management of nodules.

BACKGROUND: Uncertainty after the detection of pulmonary nodules (PNs) can cause psychological burden. We designed this study to quantitatively evaluate the prevalence, severity and possible impact of this burden on the preference of patients for management of nodules. METHODS: The Hospital Anxiety and Depression Scale (HADS) was used to evaluate psychological burden in patients. An independent t-test and a Mann-Whitney U test were used to determine the significance of differences between groups in continuous variables. A chi-square test was used to determine the significance of difference between groups in categorical variables. RESULTS: A total of 334 inpatients diagnosed with PNs were included in the study. A total of 17.96% of the participates screened positive for anxiety and 14.67% for depression. Female patients had significantly higher positive rates of both anxiety and depression screenings than male patients (21.57% vs. 12.31%, p = 0.032 and 18.05% vs. 9.30%, p = 0.028, respectively). Among patients screened positive for anxiety, the proportion of those who chose more aggressive management was significantly higher (34/60 vs. 113/274, p = 0.029). The rate of benign or precursor disease resected was significantly higher in patients with more aggressive management (46.94% vs. 9.63%, p < 0.01). CONCLUSIONS: Anxiety and depression are common in Chinese patients with PNs. Patients with positive HADS anxiety screening results are more likely to adopt more aggressive management that leads to a higher rate of benign or precursor disease resected/biopsied. This study alerts clinicians to the need to assess and possibly treat emotional responses.

Evaluación de nódulos pulmonares con TC mediante el sistema Lung-RADS. Valor clínico / Evaluation of pulmonary nodules on CT using the LUNG-RADS system. Clinical assessment

Resumen Introducción: Inspirado en el sistema BI-RADS (Breast Imaging Reporting), el American College of Radiology (ACR) desarrolló el sistema Lung-RADS, con la intención de realizar informes estandarizados sobre los nódulos pulmonares detectados en el En Argentina y en muchos lugares del mundo, no se realiza el Lung Cancer Screening (LCS) debido a los altos costos; sin embargo, en las TC de tórax los hallazgos incidentales de nódulos pulmonares, es frecuente. Para evaluarlos, existen diversos sistemas basados en caracte rísticas que permiten establecer un seguimiento. Entre ellos, Guía Fleischner, British Thoracic Society Guidelines y Lung-RADS, siendo este último el único que posee una categorización numérica. En este artículo se estudia la utilidad del Lung-RADS, como método de diagnóstico y seguimiento en la clasificación de los nódulos pulmonares. Objetivo: Evaluación del nódulo pulmonar diagnosticado en la TC de Tórax, mediante el uso del Lung-RADS para determinar su valor clínico, comparando la correlación entre esta clasificación y la malignidad o benignidad en el examen histopatológico. Material y Método: Estudio descriptivo, estadístico, observacional, retrospectivo y prospectivo.Se estudiaron un total de 100 pacientes adultos, de ambos sexos, con diagnóstico de nódulo pulmonar, comprendidos entre Enero del año 2017 hasta Diciembre del 2019, de los cuales se excluyeron aquellos que no tuvieron un seguimiento. Estudio tomográfico realizado en tomógrafo de 128 hileras de detectores. Las variables estudiadas incluyeron: sexo y edad de los pacientes, tamaño y densidad del nódulo, malignidad de la lesión en el estudio anatomopatológico, categoría del Lung-RADS y terapéutica realizada y sugerida. Para el análisis descriptivo, se utilizaron frecuencias relativas (porcentajes) y absolutas (número de casos) para las variables cualitativas; y para las variables cuantitativas se utilizó media y desvío estándar, y rango de valores mínimomáximo. Para las pruebas de hipótesis, se realizaron pruebas de Chi cuadrado para las variables cualitativas. Para las variables cuantitativas se realizaron, en primer lugar, pruebas de Shapiro Wilks y de Kolmogorov. Resultados: En 100 pacientes en los que se aplicó el Lung-RADS para determinar seguimiento y tratamiento, se identificaron diferentes tipos de escenarios tanto en el comportamiento como en el seguimiento de los mismos, algunos con necesidad de recategorización y cambios en conducta diagnóstica y tratamiento. En cuanto al análisis estadístico se analizó la asociación entre la Clasificación Lung- RADS obtenida y la presencia o ausencia de malignidad en el examen anatomopatológico obteniendo resultados estadísticamente significativos (p-valor <0,0001) para esta asociación. Discusión: Actualmente se utiliza en sistema Lung-RADS y las guías de recomendaciones de los nódulos pulmonares de la Sociedad Fleischner. Ambas tienen criterios similares y se basan en la sospecha morfológica de malignidad, que incluye la densidad del nódulo (sólido, parcialmente sólido o en vidrio esmerilado), tamaño y, cuando está disponible, el crecimiento o evolución, que se aplican en distintos grupos de pacientes. La determinación del puntaje Lung-RADS ha demostrado su utilidad en este estudio, dada la correlación patológica del nódulo, con resultado estadísticamente aceptable y buena correlación con la decisión de seguimiento y tratamiento. Conclusión: La aplicación del sistema Lung-RADS en serie ha demostrado un buen manejo de seguimiento de los mismos posibilitando, en algunos casos, la realización de resecciones quirúrgicas y, en otros, una conducta expectante con cierta seguridad evitando, en muchas oportunidades, la adopción de tratamientos agresivos innecesarios.

Evaluation of pulmonary nodules on CT using the Lung-RADS system. Clinical value

Abstract Introduction: Inspired by the BI-RADS system (Breast Imaging Reporting and Data System), the American College of Radiology (ACR) developed the Lung-RADS system, for the purpose of making standardized reports on lung nodules detected in lung cancer screening (LCS). In Argentina and in many other parts of the world the LCS is not performed due to high costs; however, in chest CT scans pulmonary nodules frequently appear as incidental findings. There are multiple systems to evaluate them based on a series of features that allow subsequent follow-up. Some of them are the Fleischner Guidelines, the British Thoracic Society Guidelines and the Lung-RADS system, the latter being the only one with numerical categorization. In this article we study the usefulness of the Lung- RADS, as a diagnostic, follow-up method for the classification of pulmonary nodules. Objective: Evaluation of the pulmonary nodule diagnosed on chest CT scan, using the Lung-RADS system to determine its clinical importance, comparing the correlation between this classification and the malignancy or benignancy in the histopathological examination. Material and Method: Descriptive, statistical, observational, retrospective and prospective study. A total of 100 adult patients, both men and women, with a diagnosis of pulmonary nodule were studied between January 2017 and December 2019. Patients without follow-up were excluded. Studies were performed with a 128-slice scanner. The variables under evaluation were: patients' sex and age, size and density of the nodule, malignancy of the lesion found in the anatomopathological study, Lung-RADS category and treatment performed and suggested. For the descriptive analysis we used relative frequencies (percentages) and absolute frequencies (number of cases) for qualitative variables; and mean and standard deviation as well as range of minimum-maximum values for the quantitative variables. For hypothesis tests, Chi-Square tests were performed for qualitative variables. For quantitative variables, Shapiro Wilks and Kolmogorov tests were performed. Results: In 100 patients in whom Lung-RADS was applied to determine follow-up and treatment, different types of scenarios could be identified regarding the approach and follow-up: some needed recategorization and changes in the diagnostic approach and treatment. As for the statistical analysis, we analyzed the association between the Lung-RADS classification obtained and the presence or absence of malignancy in the anatomopathological examination, and obtained statistically significant results (p-value <0.0001) for this association. Discussion: The Lung-RADS system and the Fleischner Society Guidelines on pulmonary nodules are used at present. Both have similar criteria and are based on the morphological suspicion of malignancy that includes the density of the nodule (solid, partially solid or ground-glass), the size and, when available, growth or evolution, which can be applied in different groups of patients. Determining the Lung-RADS score has proven its usefulness in this study, based on the pathological correlation of the nodule, with a statistically acceptable result and a good correlation with the treatment and follow-up decision. Conclusion: The application of the Lung-RADS system to this series of patients has shown a good management of patients' follow-up, with surgical resections in some cases and an expectant approach in others, providing certain security and mostly avoiding the use of unnecessary aggressive treatments.

Using a risk model for probability of cancer in pulmonary nodules.

BACKGROUND: Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide. METHODS: A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video-assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm. RESULTS: The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient. CONCLUSIONS: Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.

Development and Validation of Machine Learning-based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts.

PURPOSE: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning-based model to estimate the malignant probability of MPNs to guide decision-making. EXPERIMENTAL DESIGN: A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort. RESULTS: The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854-0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859-0.916), higher than those of the Brock model [0.806 (0.771-0.838)], PKU model [0.780 (0.743-0.817)], Mayo model [0.739 (0.697-0.776)], and VA model [0.682 (0.640-0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815-0.915)] was higher than that of surgeons [0.790 (0.711-0.852), 0.741 (0.662-0.804), and 0.727 (0.650-0.788)], radiologist [0.748 (0.671-0.814)], and the CADx system [0.757 (0.682-0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity. CONCLUSIONS: After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.

Clinical significance of indeterminate pulmonary nodules on the survival of 364 patients with nonmetastatic, high-grade, localized osteosarcoma: A 12-year retrospective cohort study.

BACKGROUND AND OBJECTIVES: We investigated the clinical significance of indeterminate pulmonary nodules (IPNs) in patients diagnosed with nonmetastatic, high-grade localized osteosarcoma. METHODS: We retrospectively analyzed the clinical data of 364 patients with nonmetastatic, high-grade localized osteosarcoma. Based on pulmonary computed tomography findings at presentation, the patients were categorized into the no-nodules and the IPNs group and were further categorized into subgroups based on age (<18 and ≥18 years). We performed an intergroup comparison of event-free survival (EFS) and overall survival (OS). RESULTS: At presentation, 276 (75.8%) patients showed no nodules, and 88 (24.2%) patients showed IPNs. The EFS and OS were similar between adults with IPNs (n = 54 [30.5%]) and without nodules (n = 123 [69.5%]) (p = .200 and p = .609, respectively). No significant intergroup difference in OS was observed in pediatric patients (p = .093). However, pediatric patients with IPNs (n = 34 [18.2%]) had poorer EFS than those without nodules (n = 153 [81.8%]) (p = .016). Multivariate analyses confirmed that IPNs were independently associated with poorer EFS in pediatric patients (hazard ratio 1.788, 95% confidence interval 1.092-2.926, p = .021). CONCLUSIONS: This study showed that IPNs at presentation did not affect the survival of adults with nonmetastatic, high-grade localized osteosarcoma but were associated with poorer EFS in pediatric patients.

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.

Pulmonary nodular and cystic light chain deposition disease: A retrospective review of 10 cases.

INTRODUCTION: Light chain deposition disease (LCDD) rarely involves the lungs. We report clinical and radiologic findings of pulmonary LCDD. METHODS: We retrospectively identified patients with biopsy-proven pulmonary LCDD seen at Mayo Clinic (Rochester, Minnesota) from January 1997 through December 2018. Demographic, clinical, and imaging features were analyzed. RESULTS: We identified 10 patients with pulmonary LCDD (median age at diagnosis, 55 years; range, 39-77 years). Eight patients were women and 7 were never-smokers. Dyspnea (n = 3) and chest pain (n = 3) were the most common respiratory symptoms. Associated conditions included Sjögren syndrome (n = 6), sarcoidosis (n = 1), and limited scleroderma (n = 1). Eight patients had mucosa-associated lymphoid tissue (MALT) lymphoma. Among the 9 patients with chest computed tomography (CT) images, 8 (89%) had cysts. Cysts were predominantly distributed in the lower lung and were round or oval. All patients had multiple cysts (5 patients had 1-5 cysts, 3 had >20 cysts). The median diameter of the largest cyst was 18 mm (range, 5-68 mm). All 9 patients had solid nodules (3 had >10 nodules). Five patients had subsolid nodules. The median diameter of the largest solid nodules was 13 mm (range, 6-26 mm). Positron emission tomography-CT images were available for 8 patients. The median maximum standardized uptake value of the most avid pulmonary nodule was 2.2 (range, 1.9-6.0). Two patients died during a median follow-up of 2.3 years (range, 0.5-9.9 years). CONCLUSIONS: Pulmonary LCDD is characterized by cysts and nodules. The disease is associated with MALT lymphoma, especially in the setting of Sjögren syndrome.

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines. METHODS: A dataset of incidentally detected pulmonary nodules measuring 5-15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. There were 1397 nodules in 1187 patients, of which 234 nodules in 229 (19.3%) patients were cancer. Model discrimination and performance statistics at predefined score thresholds were compared between the Brock model and the LCP-CNN. RESULTS: The area under the curve for LCP-CNN was 89.6% (95% CI 87.6 to 91.5), compared with 86.8% (95% CI 84.3 to 89.1) for the Brock model (p≤0.005). Using the LCP-CNN, we found that 24.5% of nodules scored below the lowest cancer nodule score, compared with 10.9% using the Brock score. Using the predefined thresholds, we found that the LCP-CNN gave one false negative (0.4% of cancers), whereas the Brock model gave six (2.5%), while specificity statistics were similar between the two models. CONCLUSION: The LCP-CNN score has better discrimination and allows a larger proportion of benign nodules to be identified without missing cancers than the Brock model. This has the potential to substantially reduce the proportion of surveillance CT scans required and thus save significant resources.

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.