Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers

BACKGROUND

Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.

METHODS

Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.

RESULTS

Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.

CONCLUSIONS

A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

[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.

Differential early diagnosis of benign versus malignant lung cancer using systematic pathway flux analysis of peripheral blood leukocytes

Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. This exploratory pilot study obtained peripheral blood samples from 139 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons. They were divided into three cohorts: original cohort (n = 99), control cohort (n = 10), and validation cohort (n = 40). Average RNAseq sequencing of leukocytes in these samples were conducted. Subsequently, data was integrated into artificial intelligence (AI)-based computational approach with system-wide gene expression technology to develop a rapid, effective, non-invasive immune index for early diagnosis of lung cancer. An immune-related index system, IM-Index, was defined and validated for the diagnostic application. IM-Index was applied to assess the malignancies of pulmonary nodules of 109 participants (original + control cohorts) with high accuracy (AUC: 0.822 [95% CI: 0.75-0.91, p < 0.001]), and to differentiate between phases of cancer immunoediting concept (odds ratio: 1.17 [95% CI: 1.1-1.25, p < 0.001]). The predictive ability of IM-Index was validated in a validation cohort with a AUC: 0.883 (95% CI: 0.73-1.00, p < 0.001). The difference between molecular mechanisms of adenocarcinoma and squamous carcinoma histology was also determined via the IM-Index (OR: 1.2 [95% CI 1.14-1.35, p = 0.019]). In addition, a structural metabolic behavior pattern and signaling property in host immunity were found (bonferroni correction, p = 1.32e - 16). Taken together our findings indicate that this AI-based approach may be used for "Super Early" cancer diagnosis and amend the current immunotherpay for lung cancer.

Pulmonary nodules detection based on multi-scale attention networks

Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of nodules in CT images is vital for lung cancer diagnosis. A 3D automatic detection system of pulmonary nodules based on multi-scale attention networks is proposed in this paper to use multi-scale features of nodules and avoid network over-fitting problems. The system consists of two parts, nodule candidate detection (determining the locations of candidate nodules), false positive reduction (minimizing the number of false positive nodules). Specifically, with Res2Net structure, using pre-activation operation and convolutional quadruplet attention module, the 3D multi-scale attention block is designed. It makes full use of multi-scale information of pulmonary nodules by extracting multi-scale features at a granular level and alleviates over-fitting by pre-activation. The U-Net-like encoder-decoder structure is combined with multi-scale attention blocks as the backbone network of Faster R-CNN for detection of candidate nodules. Then a 3D deep convolutional neural network based on multi-scale attention blocks is designed for false positive reduction. The extensive experiments on LUNA16 and TianChi competition datasets demonstrate that the proposed approach can effectively improve the detection sensitivity and control the number of false positive nodules, which has clinical application value.

Diagnosis of pulmonary nodules by DNA methylation analysis in bronchoalveolar lavage fluids

BACKGROUND

Lung cancer is the leading cause of cancer-related mortality. The alteration of DNA methylation plays a major role in the development of lung cancer. Methylation biomarkers become a possible method for lung cancer diagnosis.

RESULTS

We identified eleven lung cancer-specific methylation markers (CDO1, GSHR, HOXA11, HOXB4-1, HOXB4-2, HOXB4-3, HOXB4-4, LHX9, MIR196A1, PTGER4-1, and PTGER4-2), which could differentiate benign and malignant pulmonary nodules. The methylation levels of these markers are significantly higher in malignant tissues. In bronchoalveolar lavage fluid (BALF) samples, the methylation signals maintain the same differential trend as in tissues. An optimal 5-marker model for pulmonary nodule diagnosis (malignant vs. benign) was developed from all possible combinations of the eleven markers. In the test set (57 tissue and 71 BALF samples), the area under curve (AUC) value achieves 0.93, and the overall sensitivity is 82% at the specificity of 91%. In an independent validation set (111 BALF samples), the AUC is 0.82 with a specificity of 82% and a sensitivity of 70%.

CONCLUSIONS

This model can differentiate pulmonary adenocarcinoma and squamous carcinoma from benign diseases, especially for infection, inflammation, and tuberculosis. The model's performance is not affected by gender, age, smoking history, or the solid components of nodules.

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.

A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules

Addressing the high false-positive rate of conventional low-dose computed tomography (LDCT) for lung cancer diagnosis, the efficacy of incorporating blood-based noninvasive testing for assisting practicing clinician's decision making in diagnosis of pulmonary nodules (PNs) is investigated. In this prospective observative study, next generation sequencing- (NGS-) based cell-free DNA (cfDNA) mutation profiling, NGS-based cfDNA methylation profiling, and blood-based protein cancer biomarker testing are performed for patients with PNs, who are diagnosed as high-risk patients through LDCT and subsequently undergo surgical resections, with tissue sections pathologically examined and classified. Using pathological classification as the gold standard, statistical and machine learning methods are used to select molecular markers associated with tissue's malignant classification based on a 98-patient discovery cohort (28 benign and 70 malignant), and to construct an integrative multianalytical model for tissue malignancy prediction. Predictive models based on individual testing platforms have shown varying levels of performance, while their final integrative model produces an area under the receiver operating characteristic curve (AUC) of 0.85. The model's performance is further confirmed on a 29-patient independent validation cohort (14 benign and 15 malignant, with power > 0.90), reproducing AUC of 0.86, which translates to an overall sensitivity of 80% and specificity of 85.7%.

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms

The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

BACKGROUNDCurrent clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests.METHODWe developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules.RESULTSA 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones.CONCLUSIONThis study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions.FUNDINGThe National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.

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.

Estimation of malignancy of pulmonary nodules at CT scans: Effect of computer-aided diagnosis on diagnostic performance of radiologists

OBJECTIVES

To develop a computer-aided diagnosis (CAD) system for distinguishing malignant from benign pulmonary nodules on computed tomography (CT) scans, and to assess whether the diagnostic performance of radiologists with different experiences can be improved with the assistant of CAD.

MATERIALS AND METHODS

A total of 857 malignant nodules from 601 patients and 426 benign nodules from 278 patients were retrospectively collected from four hospitals. In this study, we exploited convolutional neural network in the framework of deep learning to classify whether a nodule was benign or malignant. A total of 745 malignant nodules and 370 benign nodules were used as the training data of our CAD system. The remaining 112 malignant nodules and 56 benign nodules were used as the test data. The participants were two senior chest radiologists, two secondary chest radiologists, and two junior radiology residents. The readers estimated the likelihood of malignancy of pulmonary nodules first without and then with CAD output. Receiver-operating characteristic (ROC) curve was used to evaluate readers' diagnostic performance.

RESULTS

When a threshold level of 58% was used to estimate the likelihood of malignancy, the sensitivity, specificity, and diagnostic accuracy values of our CAD scheme alone were 93.8%, 83.9%, and 90.5%, respectively. For all six readers, the mean area under the ROC curve (A_z ) values without and with CAD system were 0.913 and 0.938, respectively. For each reader, there is a large difference in A_z values that assessed without and with CAD system. With CAD output, the readers made correct changes an average of 15.7 times and incorrect changes an average of 2 times.

CONCLUSION

Our CAD system significantly improved the diagnostic performance of readers regardless of their experience levels for assessment of the likelihood of malignancy of pulmonary nodules.

Management of Lung Nodules and Lung Cancer Screening During the COVID-19 Pandemic: CHEST Expert Panel Report

BACKGROUND

The risks from potential exposure to coronavirus disease 2019 (COVID-19), and resource reallocation that has occurred to combat the pandemic, have altered the balance of benefits and harms that informed current (pre-COVID-19) guideline recommendations for lung cancer screening and lung nodule evaluation. Consensus statements were developed to guide clinicians managing lung cancer screening programs and patients with lung nodules during the COVID-19 pandemic.

METHODS

An expert panel of 24 members, including pulmonologists (n = 17), thoracic radiologists (n = 5), and thoracic surgeons (n = 2), was formed. The panel was provided with an overview of current evidence, summarized by recent guidelines related to lung cancer screening and lung nodule evaluation. The panel was convened by video teleconference to discuss and then vote on statements related to 12 common clinical scenarios. A predefined threshold of 70% of panel members voting agree or strongly agree was used to determine if there was a consensus for each statement. Items that may influence decisions were listed as notes to be considered for each scenario.

RESULTS

Twelve statements related to baseline and annual lung cancer screening (n = 2), surveillance of a previously detected lung nodule (n = 5), evaluation of intermediate and high-risk lung nodules (n = 4), and management of clinical stage I non-small cell lung cancer (n = 1) were developed and modified. All 12 statements were confirmed as consensus statements according to the voting results. The consensus statements provide guidance about situations in which it was believed to be appropriate to delay screening, defer surveillance imaging of lung nodules, and minimize nonurgent interventions during the evaluation of lung nodules and stage I non-small cell lung cancer.

CONCLUSIONS

There was consensus that during the COVID-19 pandemic, it is appropriate to defer enrollment in lung cancer screening and modify the evaluation of lung nodules due to the added risks from potential exposure and the need for resource reallocation. There are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care.

A multi-feature image retrieval scheme for pulmonary nodule diagnosis

Deep analysis of radiographic images can quantify the extent of intra-tumoral heterogeneity for personalized medicine.In this paper, we propose a novel content-based multi-feature image retrieval (CBMFIR) scheme to discriminate pulmonary nodules benign or malignant. Two types of features are applied to represent the pulmonary nodules. With each type of features, a single-feature distance metric model is proposed to measure the similarity of pulmonary nodules. And then, multiple single-feature distance metric models learned from different types of features are combined to a multi-feature distance metric model. Finally, the learned multi-feature distance metric is used to construct a content-based image retrieval (CBIR) scheme to assist the doctors in diagnosis of pulmonary nodules. The classification accuracy and retrieval accuracy are used to evaluate the performance of the scheme.The classification accuracy is 0.955 ± 0.010, and the retrieval accuracies outperform the comparison methods.The proposed CBMFIR scheme is effective in diagnosis of pulmonary nodules. Our method can better integrate multiple types of features from pulmonary nodules.

[Computer instruments for the management of isolated pulmonary nodule. Detectability and prediction of malignancy]

Lung cancer remains the most common cause of cancer deaths in the world, but its mortality can be significantly reduced by diagnosis and early detection. Computerized resources were developed to assist radiologists in their management of the large volume of thoracic images to be analyzed. Their objective is the detection of pulmonary nodules with high sensitivity and a low rate of false-positives and the ability to differentiate benign and malignant nodules. The volume of a pulmonary nodule and its volume doubling time are essential to nodule management. Computer aided detection or diagnosis (CAD) software are not currently used in clinically settings on a routine basis . Significant advances are expected due to the implementation of the artificial intelligence systems who will probably be integrated into the multidisciplinary management of any pulmonary nodule.

Novelties in imaging in pulmonary fibrosis and nodules. A narrative review

In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes. In addition, new recommendations for classification of pulmonary nodules have been released. Radiological images are of major interest for automated and standardized analysis and so in both cases software tools using artificial intelligence were developed for visualization and quantification of the disease. These tools have been validated by human readers and demonstrated their capabilities. This review summarizes the new recommendations for classification of pulmonary fibrosis and nodules and reviews the capabilities of radiomics within these two entities.

Hepatitis B Virus-associated Vasculitis: Multiple Cavitary Masses in the Lung Mimicking Granulomatous Polyangiitis

Hepatitis B virus (HBV) is one of the main causes of polyarteritis nodosa (PAN). We herein report a rare case of HBV-associated vasculitis presenting with multiple pulmonary nodules, mimicking granulomatous polyangiitis (GPA), with no abnormalities of the ear, nose, or kidney. A surgical lung biopsy revealed geographic necrosis surrounded by palisading granuloma and capillaritis. Because the HBV surface antigen was positive with a serum HBV-DNA level of 2.9 log10 copies/mL, we first treated the patient with entecavir and 2 weeks of prednisone 50 mg/day. The pulmonary nodules resolved, and seroconversion was observed after one month.

Increased Serum Sedimentation and Positive Tuberculosis Antibody Combined Multiple Pulmonary Nodules in Chest CT in a Middle-Aged Patient Firstly Misdiagnosed as Tuberculosis Proved as Sarcoidosis by CT Guided Percutaneous Lung Puncture Biopsy: a Case Report and Literature Review

BACKGROUND

Tuberculosis is a common infectious disease in developing countries. Tuberculosis and sarcoidosis are difficult to differentiate. We presented an adult case with increased serum sedimentation and positive tuberculosis antibody combined with multiple pulmonary nodules in chest CT in a middle-aged patient firstly misdiagnosed as tuberculosis proved as sarcoidosis by CT guided percutaneous lung puncture biopsy.

METHODS

Appropriate laboratory tests are carried out. The chest CT scan, bronchoscopy CT guided percutaneous lung puncture biopsy were performed for diagnosis.

RESULTS

Serum sedimentation was increased and tuberculosis antibody was positive. The chest CT scan showed multiple pulmonary nodules in both lungs and multiple lymphadenopathy. The bronchoscopy demonstrated no abnormality. Pathology of CT guided percutaneous lung puncture biopsy showed non-caseous multiple granulomatous lesions and acid-fast staining was negative.

CONCLUSIONS

When a patient has multiple pulmonary nodules and lymphadenopathy without obvious tuberculosis poisoning symptoms, physicians should pay attention to tuberculosis, sarcoidosis, and lung cancer. Pathology is crucial for the ultimate diagnosis.

Detection of pulmonary nodules: a clinical study protocol to compare ultra-low dose chest CT and standard low-dose CT using ASIR-V

INTRODUCTION

Lung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of inducing malignancies in healthy individuals. Therefore, lowering the radiation dose is one of the main objectives for radiologists. The aim of this study is to demonstrate that an ultra-low dose (ULD) chest CT protocol, using recently introduced hybrid iterative reconstruction (ASiR-V, GE medical Healthcare, Milwaukee, Wisconsin, USA), is as performant as a standard 'low dose' (LD) CT to detect non-calcified lung nodules ≥4 mm.

METHODS AND ANALYSIS

The total number of patients to include is 150. Those are referred for non-enhanced chest CT for detection or follow-up of lung nodule and will undergo an additional unenhanced ULD CT acquisition, the dose of which is on average 10 times lower than the conventional LD acquisition. Total dose of the entire exam (LD+ULD) is lower than the French diagnostic reference level for a chest CT (6.65 millisievert). ULD CT images will be reconstructed with 50% and 100% ASiR-V and LD CT with 50%. The three sets of images will be read in random order by two pair of radiologists, in a blind test, where patient identification and study outcomes are concealed. Detection rate (sensitivity) is the primary outcome. Secondary outcomes will include concordance of nodule characteristics; interobserver reproducibility; influence of subjects' characteristics, nodule location and nodule size; and concordance of emphysema, coronary calcifications evaluated by visual scoring and bronchial alterations between LD and ULD CT. In case of discordance, a third radiologist will arbitrate.

ETHICS AND DISSEMINATION

The study was approved by the relevant ethical committee. Each study participant will sign an informed consent form.

TRIAL REGISTRATION NUMBER

NCT03305978; Pre-results.

Ectopic thyroid microfollicular adenoma in the lung: A case report

INTRODUCTION

Ectopic thyroid occurs due to aberrant development of the thyroid gland during its migration to the pretracheal region. Intrapulmonary ectopic thyroid is extremely rare and its benign transformation (microfollicular adenoma) has never been reported. This paper reports a case of ectopic thyroid microfollicular adenoma in the lung mimicking metastatic pelvic tumors.

PATIENT CONCERNS

A 76-year old female presented to our hospital because of transient unconsciousness. Pelvic ultrasound (US) and chest computed tomography (CT) showed pelvic tumors and pulmonary nodules.

DIAGNOSIS AND INTERVENTIONS

The patient underwent pelvic tumors resection and CT-guided fine-needle aspiration cytology (FNAC) at the largest pulmonary nodule. Pathological description revealed bilateral ovarian serous cystadenoma and endometrioma in pelvic, and ectopic thyroid microfollicular adenoma in lung. In view of the patient's age and physical conditions, it is unanimously decided by the physicians and the family members of the patient to closely follow up this benign pulmonary lesion.

OUTCOMES

During the 12-month follow-up, no pelvic tumor recurrence or metastasis was found. CT review of pulmonary nodules showed no remarkable changes. The patient was asymptomatic and euthyroid after being discharged from the hospital.

CONCLUSION

Ectopic thyroid microfollicular adenoma in the lung is extremely rare and can be easily mistaken for pulmonary metastases from other sites. The case reported in this paper highlights that ectopic intrapulmonary thyroid tumor should not be overlooked.

An unusual case report of multiple pulmonary leiomyomatous hamartoma

RATIONALE

Multiple pulmonary leiomyomatous hamartoma (MPLH) is an extremely rare benign disease that mostly occurs in women of reproductive age.

PATIENT CONCERNS

A 32-year-old female patient recently diagnosed with multiple bilateral pulmonary nodules. She has the symptoms of dry cough, chest tightness, dyspnea on exertion. Chest X-ray identified multiple bilateral pulmonary nodules in the lung, and the diameter of the largest nodule was about 3.1 cm.

DIAGNOSES

Pathology confirmed the diagnosis of MPLH based on morphology and immunohistochemical staining.

INTERVENTIONS

The patient presented with multiple well-defined nodular shadows in chest computed tomography (CT), atypical image and symptoms were detected. Positron emission tomography/CT scan showed mild fluorine-18 fluorodeoxyglucose uptake in the lesions and no abnormal foci in any other parts of her body. She subsequently underwent a video-assisted thoracoscopic surgery with wedge resection of the biggest one of the nodules. Then the patient given symptomatic treatment, without hormone, no further treatment was prescribed.

OUTCOMES

The patient is in the good general condition and without obvious pulmonary symptoms after the follow-up of 1 year, chest CT scan showed no significant changes in the sizes and locations of her bilateral pulmonary nodules.

LESSONS

Due to its rare presentation, the primary MPLH may be undiagnosed. Awareness of main morphologic and immunohistochemical features of MPLH is critical for the recognition of this uncommon disease.

Electromagnetic Transthoracic Nodule Localization for Minimally Invasive Pulmonary Resection

BACKGROUND

Increased use of chest computed tomography and the institution of lung cancer screening have increased the detection of ground-glass and small pulmonary nodules. Intraoperative localization of these lesions via a minimally invasive thoracoscopic approach can be challenging. We present the feasibility of perioperative transthoracic percutaneous nodule localization using a novel electromagnetic navigation platform.

METHODS

This is a multicenter retrospective analysis of a prospectively collected database of patients who underwent perioperative electromagnetic transthoracic nodule localization before attempted minimally invasive resection between July 2016 and March 2018. Localization was performed using methylene blue or a mixture of methylene blue and the patient's blood (1:1 ratio). Patient, nodule, and procedure characteristics were collected and reported.

RESULTS

Thirty-one nodules were resected from 30 patients. Twenty-nine of 31 nodules (94%) were successfully localized. Minimally invasive resection was successful in 93% of patients (28/30); 7% (2/30) required conversion to thoracotomy. The median nodule size was 13 mm (interquartile range 25%-75%, 9.5-15.5), and the median depth from the surface of the visceral pleura to the nodule was 10 mm (interquartile range 25%-75%, 5.0-15.9). Seventy-one percent (22/31) of nodules were malignant. No complications associated with nodule localization were reported.

CONCLUSIONS

The use of intraoperative electromagnetic transthoracic nodule localization before thoracoscopic resection of small and/or difficult to palpate lung nodules is safe and effective, potentially eliminating the need for direct nodule palpation. Use of this technique aids in minimally invasive localization and resection of small, deep, and/or ground-glass lung nodules.

Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study

PURPOSE

To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules.

MATERIALS AND METHODS

A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUC_CV) was calculated together with its 95% confidence interval.

RESULTS

The AUC_CV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis.

CONCLUSIONS

The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients.

KEY POINTS

• Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non-small cell lung cancer. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.