Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. METHODS: Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. RESULTS: For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. CONCLUSION: This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.

BACKGROUND: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis. METHODS: 420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists' diagnoses. RESULTS: The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS. CONCLUSIONS: A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.

Advances in the Treatment of Pulmonary Nodules.

BACKGROUND: Early detection and accurate diagnosis of pulmonary nodules are crucial for improving patient outcomes. While surgical resection of malignant nodules is still the preferred treatment option, it may not be feasible for all patients. We aimed to discuss the advances in the treatment of pulmonary nodules, especially stereotactic body radiotherapy (SBRT) and interventional pulmonology technologies, and provide a range of recommendations based on our expertise and experience. SUMMARY: Interventional pulmonology is an increasingly important approach for the management of pulmonary nodules. While more studies are needed to fully evaluate its long-term outcomes and benefits, the available evidence suggests that this technique can provide a minimally invasive and effective alternative for treating small malignancies in selected patients. We conducted a systematic literature review in PubMed, designed a framework to include the advances in surgery, SBRT, and interventional pulmonology for the treatment of pulmonary nodules, and provided a range of recommendations based on our expertise and experience. KEY MESSAGES: As such, alternative therapeutic options such as SBRT and ablation are becoming increasingly important and viable. With recent advancements in bronchoscopy techniques, ablation via bronchoscopy has emerged as a promising option for treating pulmonary nodules. This study reviewed the advances of interventional pulmonology in the treatment of peripheral lung cancer patients that are not surgical candidates. We also discussed the challenges and limitations associated with ablation, such as the risk of complications and the potential for incomplete nodule eradication. These advancements hold great promise for improving the efficacy and safety of interventional pulmonology in treating pulmonary nodules.

Shape-Sensing Robotic-Assisted Bronchoscopy versus Computed Tomography-Guided Transthoracic Biopsy for the Evaluation of Subsolid Pulmonary Nodules.

INTRODUCTION: Lung cancer remains the leading cause of cancer death worldwide. Subsolid nodules (SSN), including ground-glass nodules (GGNs) and part-solid nodules (PSNs), are slow-growing but have a higher risk for malignancy. Therefore, timely diagnosis is imperative. Shape-sensing robotic-assisted bronchoscopy (ssRAB) has emerged as reliable diagnostic procedure, but data on SSN and how ssRAB compares to other diagnostic interventions such as CT-guided transthoracic biopsy (CTTB) are scarce. In this study, we compared diagnostic yield of ssRAB versus CTTB for evaluating SSN. METHODS: A retrospective study of consecutive patients who underwent either ssRAB or CTTB for evaluating GGN and PSN with a solid component less than 6 mm from February 2020 to April 2023 at Mayo Clinic Florida and Rochester. Clinicodemographic information, nodule characteristics, diagnostic yield, and complications were compared between ssRAB and CTTB. RESULTS: A total of 66 nodules from 65 patients were evaluated: 37 PSN and 29 GGN. Median size of PSN solid component was 5 mm (IQR: 4.5, 6). Patients were divided into two groups: 27 in the ssRAB group and 38 in the CTTB group. Diagnostic yield was 85.7% for ssRAB and 89.5% for CTTB (p = 0.646). Sensitivity for malignancy was similar between ssRAB and CTTB (86.4% vs. 88.5%; p = 0.828), with no statistical difference. Complications were more frequent in CTTB with no significant difference (8 vs. 2; p = 0.135). CONCLUSION: Diagnostic yield for SSN was similarly high for ssRAB and CTTB, with ssRAB presenting less complications and allowing mediastinal staging within the same procedure.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

[Common issues and solutions in intraoperative frozen pathological diagnosis of small pulmonary nodules].

With the development of chest CT screening, surgically resected lung tumors have shifted from predominantly large masses to predominantly small nodules. The intraoperative frozen diagnosis of pulmonary small nodules faces many challenges, such as the accurate understanding about the concepts of adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic adenocarcinoma, as well as their differential diagnosis with small size invasive adenocarcinoma, benign tumors (such as bronchiolar adenoma, sclerosing pneumocytoma, etc.), metastatic tumors and so on. This study summarizes some common problems encountered in the intraoperative frozen diagnosis of small pulmonary nodules in daily practice, focusing on the diagnosis and differential diagnosis of adenocarcinoma, in order to make the accurate intraoperative frozen diagnosis of small pulmonary nodules and diminish misdiagnosis.

[Chinese expert consensus on diagnosis and treatment of pulmonary nodules(2024)].

Lung cancer is the leading cause of the incidence and mortality of malignant tumors in China. The 5-year survival rate released for China in 2018 was 19.7%. The 5-year survival rate for stage Ⅰ patients is 77%-92%. Early diagnosis and treatment of lung cancer are key to improving the 5-year overall survival rate and prognosis of lung cancer patients. Therefore, experts from the Academic Group of Lung Cancer in Chinese Thoracic Society and Chinese Alliance Against Lung Cancer Expert Group formulated the Chinese Expert Consensus on the Diagnosis and Treatment of Lung Nodules in 2015 to standardize the diagnosis and treatment of lung nodules. In 2018, this consensus was updated to formulate the Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2018 edition), and widely applied in multiple branch centers of Chinese Alliance Against Lung Cancer, proposing the Intelligent Treatment of Million Early Lung Cancer Project. Based on applied experience of the expert consensus in recent years, with reference to the latest evidence has been updated, Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2024 edition) was formulated. The updated content of this consensus mainly includes the following aspects: (1) Define the screening age of high-risk lung cancer populations in China based on the national conditions; (2) Propose definition of "difficult-to-determine pulmonary nodules" to avoid delaying the diagnosis and treatment; (3) Evaluate pulmonary nodules assisted by artificial intelligence (AI) imaging-assisted diagnostic system and propose human-machine MDT to avoid the limitations of AI; (4) Evaluate pulmonary nodules by routine and individualized evaluations for different populations, and make recommendations based on evidence-based management guidelines for different types and sizes of pulmonary nodules. In the updated consensus, 18 consensus points were recommended as a reference for clinical management of pulmonary nodules to improve the 5-year overall survival rate and the prognosis of lung cancer in China.

Novel electromagnetic navigation bronchoscopy system for the diagnosis of peripheral pulmonary nodules: a prospective, multicentre study.

BACKGROUND: Traditional electromagnetic navigation bronchoscopy (ENB) is a real-time image-guided system and used with thick bronchoscopes for the diagnosis of peripheral pulmonary nodules (PPNs). A novel ENB that could be used with thin bronchoscopes was developed. This study aimed to evaluate the diagnostic yield and the experience of using this ENB system in a real clinical scenario. METHODS: This multicentre study enrolled consecutive patients with PPNs adopting ENB from March 2019 to August 2021. ENB was performed with different bronchoscopes, ancillary techniques and sampling instruments according to the characteristics of the nodule and the judgement of the operator. The primary endpoint was the diagnostic yield. The secondary endpoints included the diagnostic yield of subgroups, procedural details and complication rate. RESULTS: In total, 479 patients with 479 nodules were enrolled in this study. The median lesion size was 20.9 (IQR, 15.9-25.9) mm. The overall diagnostic yield was 74.9% (359/479). A thin bronchoscope was used in 96.2% (461/479) nodules. ENB in combination with radial endobronchial ultrasound (rEBUS), a guide sheath (GS) and a thin bronchoscope was the most widely used guided method, producing a diagnostic yield of 74.1% (254/343). The median total procedural time was 1325.0 (IQR, 1014.0-1676.0) s. No severe complications occurred. CONCLUSION: This novel ENB system can be used in combination with different bronchoscopes, ancillary techniques and sampling instruments with a high diagnostic yield and safety profile for the diagnosis of PPNs, of which the combination of thin bronchoscope, rEBUS and GS was the most common method in clinical practice. TRIAL REGISTRATION NUMBER: NCT03716284.

A combined diagnostic model based on circulating tumor cell in patients with solitary pulmonary nodules.

BACKGROUND: Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore imperative to identify novel biomarkers and prediction models supporting early diagnosis of SPNs. This study combined folate receptor-positive circulating tumor cells (FR+ CTC) with serum tumor biomarkers, patient demographics and clinical characteristics to develop a prediction model. METHODS: A total of 898 patients with a solitary pulmonary nodule who received FR+ CTC detection were randomly assigned to a training set and a validation set in a 2:1 ratio. Multivariate logistic regression was used to establish a diagnostic model to differentiate malignant and benign nodules. The receiver operating curve (ROC) and the area under the curve (AUC) were calculated to assess the diagnostic efficiency of the model. RESULTS: The positive rate of FR+ CTC between patients with non-small cell lung cancer (NSCLC) and benign lung disease was significantly different in both the training and the validation dataset (p < 0.001). The FR+ CTC level was significantly higher in the NSCLC group compared with that of the benign group (p < 0.001). FR+ CTC (odds ratio, OR, 95% confidence interval, CI: 1.13, 1.07-1.19, p < 0.0001), age (OR, 95% CI: 1.06, 1.01-1.12, p = 0.03) and sex (OR, 95% CI: 1.07, 1.01-1.13, p = 0.01) were independent risk factors of NSCLC in patients with a solitary pulmonary nodule. The area under the curve (AUC) of FR+ CTC in diagnosing NSCLC was 0.650 (95% CI, 0.587-0.713) in the training set and 0.700 (95% CI, 0.603-0.796) in the validation set, respectively. The AUC of the combined model was 0.725 (95% CI, 0.659-0.791) in the training set and 0.828 (95% CI, 0.754-0.902) in the validation set, respectively. CONCLUSIONS: We confirmed the value of FR+ CTC in diagnosing SPNs and developed a prediction model based on FR+ CTC, demographic characteristics, and serum biomarkers for differential diagnosis of solitary pulmonary nodules.

Serum lncRNA THRIL predicts benign and malignant pulmonary nodules and promotes the progression of pulmonary malignancies.

BACKGROUND: This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs. METHODS: The study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL's significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene. RESULTS: There were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a. CONCLUSION: The detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.

Pulmonary Tuberculosis with Elevated CEA and Positive PET-CT.

BACKGROUND: Pulmonary tuberculosis presenting as solitary pulmonary nodules in imaging is sometimes difficult to differentiate from lung cancer and is more likely to be misdiagnosed when accompanied by elevated CEA and positive PET-CT findings. METHODS: By reporting a case of misdiagnosed lung cancer, which was confirmed to be pulmonary tuberculosis by lung biopsy, a joint literature analysis was performed to raise clinicians' awareness of isolated nodules in the lung. RESULTS: With a series of ancillary tests, we initially considered the nodule to be malignant, and the lung biopsy pathology eventually confirmed pulmonary tuberculosis. CONCLUSIONS: When chest imaging suggests the presence of malignant features in solitary pulmonary nodules, invasive procedures can be performed appropriately to clarify the nature of the lesion. The diagnosis cannot be made blindly to ensure that no incorrect diagnosis is made nor wrong treatment given.

[Echinococcosis Detected by a Solitary Shadow in the Lung].

Here we report a rare case of pulmonary coin lesion due to echinococcosis. An woman in her 60s who has no symptom was found a nodular shadow of the left lung incidentally. Since the nodule was enlarging, surgical treatment was done. Pathologically, it was diagnosed as an echinococcosis of the lung. It was pulmonary solitary echinococcosis without any lesion in other organs.

Role of biomarkers in lung nodule evaluation.

PURPOSE OF REVIEW: Worldwide, lung cancer is the leading cause of cancer mortality. Much of this mortality is thought to be secondary to detection in later stages, where treatment options and survivability are limited. The goals of lung nodule evaluation are to expedite the diagnosis and treatment of patients with malignant nodules and to minimize unnecessary diagnostic procedures in those with benign nodules. However, the differentiation between benign and malignant has been challenging and is further complicated by the benefits of early diagnosis competing with potential morbidity of invasive diagnostic procedures. RECENT FINDINGS: Biomarkers have the potential to improve estimates of pretest probability of malignancy in pulmonary nodules, especially in the intermediate-risk subgroup. Four biomarkers have undergone extensive validation and are available for clinical use, and we will discuss each in this review. SUMMARY: The application of biomarkers to lung cancer risk assessment has the potential to improve cancer probability assessments, which in turn can reduce unnecessary invasive testing and/or reduce delays in diagnosis and treatment.

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.

Multiomics Analysis Reveals Distinct Immunogenomic Features of Lung Cancer with Ground-Glass Opacity.

Rationale: Ground-glass opacity (GGO)-associated lung cancers are common and radiologically distinct clinical entities known to have an indolent clinical course and superior survival, implying a unique underlying biology. However, the molecular and immune characteristics of GGO-associated lung nodules have not been systemically studied. Objectives: To provide mechanistic insights for the treatment of these radiologically distinct clinical entities. Methods: We initiated a prospective cohort study to collect and characterize pulmonary nodules with GGO components (nonsolid and part-solid nodules) or without GGO components, as precisely quantified by using three-dimensional image reconstruction to delineate the molecular and immune features associated with GGO. Multiomics assessment conducted by using targeted gene panel sequencing, RNA sequencing, TCR (T-cell receptor) sequencing, and circulating tumor DNA detection was performed. Measurements and Main Results: GGO-associated lung cancers exhibited a lower tumor mutation burden than solid nodules. Transcriptomic analysis revealed a less active immune environment in GGO components and immune pathways, decreased expression of immune activation markers, and less infiltration of most immune-cell subsets, which was confirmed by using multiplex immunofluorescence. Furthermore, T-cell repertoire sequencing revealed lower T-cell expansion in GGO-associated lung cancers. HLA loss of heterozygosity was significantly less common in lung adenocarcinomas with GGO components than in those without. Circulating tumor DNA analysis suggested that the release of tumor DNA to the peripheral blood was correlated with the tumor size of non-GGO components. Conclusions: Compared with lung cancers presenting with solid lung nodules, GGO-associated lung cancers are characterized by a less active metabolism and a less active immune microenvironment, which may be the mechanisms underlying their indolent clinical course. Clinical trial registered with www.clinicaltrials.gov (NCT03320044).

Pulmonary coin lesion caused by Dirofilaria immitis - a report of two cases with a minireview of the literature.

Dirofilariasis is a rare zoonosis, transmitted from infested dogs or other carnivorous animals to humans via mosquitoes. Two male patients with a solitary, peripheral, well-defined, coin-like pulmonary lesion in the right upper lobe were presented. Rapid enlargement of the lesion within a few months suggested malignancy, resulting in surgical removal. Microscopic examination of the resected lung revealed necrotic circumscribed lesions with embolized parasites in the vessels. Both parasites were females of the species Dirofilaria immitis. They represent the first reported cases of pulmonary dirofilariasis in Slovenia. Awareness of this entity is important in the differential diagnosis of pulmonary coin lesions.

[The multi-dimensional molecular characteristics of the indolent pulmonary ground-glass nodules].

With the dramatically increasing detection rate of ground-glass nodules (GGN), exact understanding and treatment strategy of them has become the hottest issue currently. More and more studies have begun to explore the underlying mechanisms of their indolent characteristics and favorable prognosis from the perspectives of molecular evolution and immune microenvironment. GGN has different dominating gene mutations at different evolutional stages. The pure GGN has a lower tumor mutation burden and genomic instability, while a gradually evolutionary feature of genomic mutation along with the pathological progression can be observed. GGN has less infiltration of immune cells, and they are under the pressure of immune surveillance with weakened immune escape. With the increase of solid components, an inhibitory immune microenvironment is gradually established and immune escape is gradually enhanced, leading to rapid tumor growth. Further exploration of the molecular characteristics of GGN will help to more precisely distinguish these highly heterogeneous lesions, which could be helpful to make personalized treatment plans.

[The diagnostic value of machine-learning-based model for predicting the malignancy of solid nodules in multiple pulmonary nodules].

Objective: To examine the efficiacy of a machine learning diagnostic model specifically for solid nodules in multiple pulmonary nodules constructed by combining patient clinical information and CT features. Methods: Totally 446 solid nodules resected from 287 patients with multiple pulmonary nodules in Department of Thoracic Surgery, Peking University People's Hospital from January 2010 to December 2018 were included. There were 117 males and 170 females, aging (61.4±9.9) yeras (range: 33 to 84 years). The nodules were randomly divided into training set (228 patients with 357 nodules) and test set (59 patients with 89 nodules) by a ratio of 4∶1. The extreme gradient boosting (XGBoost) algorithm was used to generate a predictive model (PKU-ML model) on the training set. The accuracy was verified on the test set and compared with previous published models. Finally, an independent single solid nodule set (155 patients, 95 males, aging (62.3±8.3) years (range: 37 to 77 years)) was used to evaluate the accuracy of the model for predictive value of single solid nodules. Area of receiver operating characteristic curve (AUC) was used to evaluate diagnostic values of models. Results: In the training set, the AUC of the PKU-ML model was 0.883 (95%CI: 0.849 to 0.917). In the test set, the performance of the PKU-ML model (AUC=0.838, 95%CI: 0.754 to 0.921) was better than the models designed for single pulmonary nodules (Brock model: AUC=0.709, 95%CI: 0.603 to 0.816, P=0.04; Mayo model: AUC=0.756, 95%CI: 0.656 to 0.856, P=0.01; VA model: AUC=0.674, 95%CI: 0.561 to 0.787, P<0.01), similar with PKUPH model (AUC=0.750, 95%CI: 0.649 to 0.851, P=0.07). In the independent single solid nodules set, the PKU-ML model also achieved good performance (AUC=0.786, 95%CI: 0.701 to 0.872). Conclusion: The machine learning based PKU-ML model can better predict the malignancy of solid nodules in multiple pulmonary nodules, and also achieved a good performance in predicting the malignancy of single solid pulmonary nodules compared to mathematical models.

Understanding the patient journey to diagnosis of lung cancer.

OBJECTIVE: This research describes the clinical pathway and characteristics of two cohorts of patients. The first cohort consists of patients with a confirmed diagnosis of lung cancer while the second consists of patients with a solitary pulmonary nodule (SPN) and no evidence of lung cancer. Linked data from an electronic medical record and the Louisiana Tumor Registry were used in this investigation. MATERIALS AND METHODS: REACHnet is one of 9 clinical research networks (CRNs) in PCORnet®, the National Patient-Centered Clinical Research Network and includes electronic health records for over 8 million patients from multiple partner health systems. Data from Ochsner Health System and Tulane Medical Center were linked to Louisiana Tumor Registry (LTR), a statewide population-based cancer registry, for analysis of patient's clinical pathways between July 2013 and 2017. Patient characteristics and health services utilization rates by cancer stage were reported as frequency distributions. The Kaplan-Meier product limit method was used to estimate the time from index date to diagnosis by stage in lung cancer cohort. RESULTS: A total of 30,559 potentially eligible patients were identified and 2929 (9.58%) had primary lung cancer. Of these, 1496 (51.1%) were documented in LTR and their clinical pathway to diagnosis was further studied. Time to diagnosis varied significantly by cancer stage. A total of 24,140 patients with an SPN were identified in REACHnet and 15,978 (66.6%) had documented follow up care for 1 year. 1612 (10%) had no evidence of any work up for their SPN. The remaining 14,366 had some evidence of follow up, primarily office visits and additional chest imaging. CONCLUSION: In both cohorts multiple biopsies were evident in the clinical pathway. Despite clinical workup, 70% of patients in the lung cancer cohort had stage III or IV disease. In the SPN cohort, only 66% were identified as receiving a diagnostic work-up.

The Prevalence of Benign Pathology Following Major Pulmonary Resection for Suspected Malignancy.

BACKGROUND: In the era of lung cancer screening with low-dose computed tomography, there is concern that high false-positive rates may lead to an increase in nontherapeutic lung resection. The aim of this study is to determine the current rate of major pulmonary resection for ultimately benign pathology. MATERIALS AND METHODS: A single-institution, retrospective analysis of all patients > 18 y who underwent major pulmonary resection between 2013 and 2018 for suspected malignancy and had benign final pathology was performed. RESULTS: Of 394 major pulmonary resections performed for known or presumed malignancy, 10 (2.5%) were benign. Of these 10, the mean age was 61.1 y (SD 14.6). Most were current or former smokers (60%). Ninety percent underwent a fluorodeoxyglucose positron emission tomography scan. Median nodule size was 27 mm (IQR 21-35) and most were in the right middle lobe (50%). Preoperative biopsy was performed in four (40%) but were nondiagnostic. Video-assisted thoracoscopic lobectomy (70%) was the most common surgical approach. Final pathology revealed three (30%) infectious, three (30%) inflammatory, two (20%) fibrotic, and two (20%) benign neoplastic nodules. Two (20%) patients had perioperative complications, both of which were prolonged air leaks, one (10%) patient was readmitted within 30 d, and there was no mortality. CONCLUSIONS: A small percentage of patients (2.5% in our series) may undergo major pulmonary resection for unexpectedly benign pathology. Knowledge of this rate is useful to inform shared decision-making models between surgeons and patients and evaluation of thoracic surgery program performance.