Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography

Incidental pulmonary nodules - current guidelines and management

BACKGROUND

 Due to the greater use of high-resolution cross-sectional imaging, the number of incidental pulmonary nodules detected each year is increasing. Although the vast majority of incidental pulmonary nodules are benign, many early lung carcinomas could be diagnosed with consistent follow-up. However, for a variety of reasons, the existing recommendations are often not implemented correctly. Therefore, potential for improvement with respect to competence, communication, structure, and process is described.

METHODS

 This article presents the recommendations for incidental pulmonary nodules from the current S3 guideline for lung cancer (July 2023). The internationally established recommendations (BTS guidelines and Fleischner criteria) are compared and further studies on optimized management were included after a systematic literature search in PubMed.

RESULTS AND CONCLUSION

 In particular, AI-based software solutions are promising, as they can be used in a support capacity on several levels at once and can lead to simpler and more automated management. However, to be applicable in routine clinical practice, software must fit well into the radiology workflow and be integrated. In addition, "Lung Nodule Management" programs or clinics that follow a high-quality procedure for patients with incidental lung nodules or nodules detected by screening have been established in the USA. Similar structures might also be implemented in Germany in a future screening program in which patients with incidental pulmonary nodules could be included.

KEY POINTS

  · Incidental pulmonary nodules are common but are often not adequately managed. · The updated S3 guideline for lung cancer now includes recommendations for incidental pulmonary nodules. · Competence, communication, structure, and process levels offer significant potential for improvement.

CITATION FORMAT

· Glandorf J, Vogel-Claussen J, . Incidental pulmonary nodules - current guidelines and management. Fortschr Röntgenstr 2024; 196: 582 - 590.

Approach to Pulmonary Nodules in Connective Tissue Disease

The assessment of pulmonary nodules is a common and often challenging clinical scenario. This evaluation becomes even more complex in patients with connective tissue diseases (CTDs), as a range of disease-related factors must also be taken into account. These diseases are characterized by immune-mediated chronic inflammation, leading to tissue damage, collagen deposition, and subsequent organ dysfunction. A thorough examination of nodule features in these patients is required, incorporating anatomic and functional information, along with patient demographics, clinical factors, and disease-specific knowledge. This integrated approach is vital for effective risk stratification and precise diagnosis. This review article addresses specific CTD-related factors that should be taken into account when evaluating pulmonary nodules in this patient group.

Imaging of Solid Pulmonary Nodules

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis

BACKGROUND

There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules.

METHODS

An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used.

RESULTS

Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively.

CONCLUSION

DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.

The use of diagnostic complex robotic-assisted segmentectomy in the management of incidental and screen-detected pulmonary nodules

OBJECTIVES

Robotic-assisted thoracoscopic surgery (RATS) facilitates complex pulmonary segmentectomy which offers one-stage diagnostic and therapeutic management of small pulmonary nodules. We aimed to explore the potential advantages of a faster, simplified pathway and earlier diagnosis against the disadvantages of unnecessary morbidity in benign cases.

METHODS

In an observational study, patients with small, solitary pulmonary nodules deemed suspicious of malignancy by a multidisciplinary team were offered surgery without a pre or intraoperative biopsy. We report our initial experience with RATS complex segmentectomy (using >1 parenchymal staple line) to preserve as much functioning lung tissue as possible.

RESULTS

Over a 4-year period, 245 RATS complex segmentectomies were performed; 140 right: 105 left. A median of 2 (1-4) segments was removed. There was no in-hospital mortality and no requirement for postoperative ventilation. Complications were reported in 63 (25.7%) cases, of which 36 (57.1%) were hospital-acquired pneumonia. A malignant diagnosis was found in 198 (81%) patients and a benign diagnosis in 47 (19%). The malignant diagnoses included: adenocarcinoma in 136, squamous carcinoma in 31 and carcinoid tumour in 15. The most frequent benign diagnosis was granulomatous inflammation in 18 cases.

CONCLUSIONS

RATS complex segmentectomy offers a precise, safe and effective one-stop therapeutic biopsy in incidental and screen-detected pulmonary nodules.

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

BACKGROUND

Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs.

METHODS

Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data.

RESULTS

This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86-0.91), 78% (95%CI: 0.71-0.85), 4.10 (95%CI: 2.98-5.64), and 0.15 (95%CI: 0.12-0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88-0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85-0.96), 51% (95%CI: 0.37-0.66), 1.89 (95%CI: 1.36-2.62), and 0.16 (95%CI: 0.07-0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79-0.85).

CONCLUSIONS

These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

A metabolomics study on carcinogenesis of ground-glass nodules

Objective

This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer.

Material and Methods

Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure.

Results

Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples.

Conclusion

Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

Detection of Circulating Tumor DNA After Stereotactic Ablative Radiotherapy in Patients With Unbiopsied Lung Tumors (SABR-DETECT)

For patients with stage I/IIA non-small-cell lung cancer (NSCLC), surgical resection is the standard treatment. However, some of these patients are not candidates for surgery or refuse a surgical option. Definitive stereotactic ablative radiotherapy (SABR) is a standard approach in these patients. Approximately 15% of patients undergoing SABR for localized NSCLC will experience a recurrence within 2 years. Furthermore, many of these patients are deemed appropriate for SABR without a tissue diagnosis, based on the likelihood of malignancy which can be calculated by validated models. A liquid biopsy, detecting ctDNA, would be useful in early detection of recurrences, and documenting a cancer diagnosis in patients without a biopsy. This is a multi-institutional study enrolling patients with suspected stage I/IIA NSCLC and a pretreatment likelihood of malignancy of ≥60% using the validated models for patients without a tissue diagnosis, in cohort 1 (n = 45). The second cohort will consist of biopsied patients (n = 30-60). SABR will be delivered as per risk-adapted protocol. Plasma will be collected for ctDNA analysis prior to the first fraction of SABR, 24 to 72 hours after first fraction, and at 3, 6, 9, 12, 18, and 24-months. The patients will be followed up with imaging at 3, 6, 9, 12, 18, and 24-months. The primary objective is to assess whether a cancer detection liquid biopsy platform can predict recurrence of NSCLC. The secondary objectives are to assess the impact of SABR on detection rates of ctDNA in patients undergoing SABR and to correlate ctDNA positivity and pretreatment probability of malignancy (NCT05921474).

The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort

BACKGROUND

Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical prediction models were developed to inform physician assessment of pretest probability of nodule malignancy but have not been validated in a high-risk cohort of nodules for which biopsy was ultimately performed.

RESEARCH QUESTION

Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy?

STUDY DESIGN AND METHODS

We assembled a prospective cohort of 322 indeterminate pulmonary nodules in 282 patients referred to a tertiary medical center for diagnostic navigational bronchoscopy between 2017 and 2019. We calculated the probability of malignancy for each nodule using the Brock model, Mayo Clinic model, and Veterans Affairs (VA) model. On a subset of 168 patients who also had PET-CT scans before biopsy, we also calculated the probability of malignancy using the Herder model. The performance of the models was evaluated by calculating the area under the receiver operating characteristic curves (AUCs) for each model.

RESULTS

The study cohort contained 185 malignant and 137 benign nodules (57% prevalence of malignancy). The malignant and benign cohorts were similar in terms of size, with a median longest diameter for benign and malignant nodules of 15 and 16 mm, respectively. The Brock model, Mayo Clinic model, and VA model showed similar performance in the entire cohort (Brock AUC, 0.70; 95% CI, 0.64-0.76; Mayo Clinic AUC, 0.70; 95% CI, 0.64-0.76; VA AUC, 0.67; 95% CI, 0.62-0.74). For 168 nodules with available PET-CT scans, the Herder model had an AUC of 0.77 (95% CI, 0.68-0.85).

INTERPRETATION

Currently available clinical models provide insufficient discrimination between benign and malignant nodules in the common clinical scenario in which a patient is being referred for biopsy, especially when PET-CT scan information is not available.

Stereotactic body radiotherapy of central lung tumours using a 1.5 T MR-linac: First clinical experiences

Background

MRI-guidance may aid better discrimination between Organs at Risk (OARs) and target volumes in proximity of the mediastinum. We report the first clinical experiences with Stereotactic Body Radiotherapy (SBRT) of (ultra)central lung tumours on a 1.5 T MR-linac.

Materials and Methods

Patients with an (ultra)central lung tumour were selected for MR-linac based SBRT treatment. A T2-weighted 3D sequence MRI acquired during free breathing was used for daily plan adaption. Prior to each fraction, contours of Internal Target Volume (ITV) and OARs were deformably propagated and amended by a radiation oncologist. Inter-fractional changes in volumes and coverage of target volumes as well as doses in OARs were evaluated in offline and online treatment plans.

Results

Ten patients were treated and completed 60 Gy in 8 or 12 fractions. In total 104 fractions were delivered. The median time in the treatment room was 41 min with a median beam-on time of 8.9 min. No grade ≥3 acute toxicity was observed. In two patients, the ITV significantly decreased during treatment (58 % and 37 %, respectively) due to tumour shrinkage. In the other patients, 81 % of online ITVs were within ±15 % of the volume of fraction 1. Comparison with the pre-treatment plan showed that ITV coverage of the online plan was similar in 52 % and improved in 34 % of cases. Adaptation to meet OAR constraints, led to decreased ITV coverage in 14 %.

Conclusions

We describe the workflow for MR-guided Radiotherapy and the feasibility of using 1.5 T MR-linac for SBRT of (ultra) central lung tumours.

LIONS PREY: A New Logistic Scoring System for the Prediction of Malignant Pulmonary Nodules

OBJECTIVES

Classifying radiologic pulmonary lesions as malignant is challenging. Scoring systems like the Mayo model lack precision in predicting the probability of malignancy. We developed the logistic scoring system 'LIONS PREY' (Lung lesION Score PREdicts malignancY), which is superior to existing models in its precision in determining the likelihood of malignancy.

METHODS

We evaluated all patients that were presented to our multidisciplinary team between January 2013 and December 2020. Availability of pathological results after resection or CT-/EBUS-guided sampling was mandatory for study inclusion. Two groups were formed: Group A (malignant nodule; n = 238) and Group B (benign nodule; n = 148). Initially, 22 potential score parameters were derived from the patients' medical histories.

RESULTS

After uni- and multivariate analysis, we identified the following eight parameters that were integrated into a scoring system: (1) age (Group A: 64.5 ± 10.2 years vs. Group B: 61.6 ± 13.8 years; multivariate p-value: 0.054); (2) nodule size (21.8 ± 7.5 mm vs. 18.3 ± 7.9 mm; p = 0.051); (3) spiculation (73.1% vs. 41.9%; p = 0.024); (4) solidity (84.9% vs. 62.8%; p = 0.004); (5) size dynamics (6.4 ± 7.7 mm/3 months vs. 0.2 ± 0.9 mm/3 months; p < 0.0001); (6) smoking history (92.0% vs. 43.9%; p < 0.0001); (7) pack years (35.1 ± 19.1 vs. 21.3 ± 18.8; p = 0.079); and (8) cancer history (34.9% vs. 24.3%; p = 0.052). Our model demonstrated superior precision to that of the Mayo score (p = 0.013) with an overall correct classification of 96.0%, a calibration (observed/expected-ratio) of 1.1, and a discrimination (ROC analysis) of AUC (95% CI) 0.94 (0.92-0.97).

CONCLUSIONS

Focusing on essential parameters, LIONS PREY can be easily and reproducibly applied based on computed tomography (CT) scans. Multidisciplinary team members could use it to facilitate decision making. Patients may find it easier to consent to surgery knowing the likelihood of pulmonary malignancy. The LIONS PREY app is available for free on Android and iOS devices.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

Classification of Pulmonary Nodules in 2-[18F]FDG PET/CT Images with a 3D Convolutional Neural Network

Purpose

2-[18F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[18F]FDG PET images.

Methods

One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[18F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used.

Results

The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455-1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives.

Conclusion

A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[18F]FDG PET images.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13139-023-00821-6.

Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5-30 mm pulmonary nodules: a retrospective analysis

Background

The diagnosis of benign and malignant solitary pulmonary nodules based on personal experience has several limitations. Therefore, this study aims to establish a nomogram for the diagnosis of benign and malignant solitary pulmonary nodules using clinical information and computed tomography (CT) results.

Methods

Retrospectively, we collected clinical and CT characteristics of 1,160 patients with pulmonary nodules in Guang'an People's Hospital and the hospital affiliated with North Sichuan Medical College between 2019 and 2021. Among these patients, data from 773 patients with pulmonary nodules were used as the training set. We used the least absolute shrinkage and selection operator (LASSO) to optimize clinical and imaging features and performed a multivariate logistic regression to identify features with independent predictive ability to develop the nomogram model. The area under the receiver operating characteristic curve (AUC), C-index, decision curve analysis, and calibration plot were used to evaluate the performance of the nomogram model in terms of predictive ability, discrimination, calibration, and clinical utility. Finally, data from 387 patients with pulmonary nodules were utilized for validation.

Results

In the training set, the predictors for the nomogram were gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction, selected through LASSO and logistic regression analysis. The resulting model had a C-index of 0.842 (95% CI [0.812-0.872]) and AUCs of 0.842 (95% CI [0.812-0.872]). In the validation set, the C-index was 0.856 (95% CI [0.811-0.901]), and the AUCs were 0.844 (95% CI [0.797-0.891]). Results from the calibration curve and clinical decision curve analyses indicate that the nomogram has a high fit and clinical benefit in both the training and validation sets.

Conclusion

The establishment of a nomogram for predicting the benign or malignant diagnosis of solitary pulmonary nodules by this study has shown good efficacy. Such a nomogram may help to guide the diagnosis, follow-up, and treatment of patients.

Navigational Bronchoscopy versus Computed Tomography-guided Transthoracic Needle Biopsy for the Diagnosis of Indeterminate Lung Nodules: protocol and rationale for the VERITAS multicenter randomized trial

Background

Lung nodule incidence is increasing. Many nodules require biopsy to discriminate between benign and malignant etiologies. The gold-standard for minimally invasive biopsy, computed tomography-guided transthoracic needle biopsy (CT-TTNB), has never been directly compared to navigational bronchoscopy, a modality which has recently seen rapid technological innovation and is associated with improving diagnostic yield and lower complication rate. Current estimates of the diagnostic utility of both modalities are based largely on non-comparative data with significant risk for selection, referral, and publication biases.

Methods

The VERITAS trial (na V igation E ndoscopy to R each Indeterminate lung nodules versus T ransthoracic needle A spiration, a randomized controlled S tudy) is a multicenter, 1:1 randomized, parallel-group trial designed to ascertain whether electromagnetic navigational bronchoscopy with integrated digital tomosynthesis is noninferior to CT-TTNB for the diagnosis of peripheral lung nodules 10-30 mm in diameter with pre-test probability of malignancy of at least 10%. The primary endpoint is diagnostic accuracy through 12 months follow-up. Secondary endpoints include diagnostic yield, complication rate, procedure duration, need for additional invasive diagnostic procedures, and radiation exposure.

Discussion

The results of this rigorously designed trial will provide high-quality data regarding the management of lung nodules, a common clinical entity which often represents the earliest and most treatable stage of lung cancer. Several design challenges are described. Notably, all nodules are centrally reviewed by an independent interventional pulmonology and radiology adjudication panel relying on pre-specified exclusions to ensure enrolled nodules are amenable to sampling by both modalities while simultaneously protecting against selection bias favoring either modality. Conservative diagnostic yield and accuracy definitions with pre-specified criteria for what non-malignant findings may be considered diagnostic were chosen to avoid inflation of estimates of diagnostic utility.

Trial registration

ClinicalTrials.gov NCT04250194.

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.

[Prevention, Diagnosis, Therapy, and Follow-up of Lung Cancer - Interdisciplinary Guideline of the German Respiratory Society and the German Cancer Society - Abridged Version]

The current S3 Lung Cancer Guidelines are edited with fundamental changes to the previous edition based on the dynamic influx of information to this field:The recommendations include de novo a mandatory case presentation for all patients with lung cancer in a multidisciplinary tumor board before initiation of treatment, furthermore CT-Screening for asymptomatic patients at risk (after federal approval), recommendations for incidental lung nodule management , molecular testing of all NSCLC independent of subtypes, EGFR-mutations in resectable early stage lung cancer in relapsed or recurrent disease, adjuvant TKI-therapy in the presence of common EGFR-mutations, adjuvant consolidation treatment with checkpoint inhibitors in resected lung cancer with PD-L1 ≥ 50%, obligatory evaluation of PD-L1-status, consolidation treatment with checkpoint inhibition after radiochemotherapy in patients with PD-L1-pos. tumor, adjuvant consolidation treatment with checkpoint inhibition in patients withPD-L1 ≥ 50% stage IIIA and treatment options in PD-L1 ≥ 50% tumors independent of PD-L1status and targeted therapy and treatment option immune chemotherapy in first line SCLC patients.Based on the current dynamic status of information in this field and the turnaround time required to implement new options, a transformation to a "living guideline" was proposed.

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer

Objective

The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data.

Methods

A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively.

Results

The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly.

Conclusion

The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

Circulating proteome for pulmonary nodule malignancy

BACKGROUND

Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules.

METHODS

Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated.

RESULTS

We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001).

CONCLUSIONS

Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

[18F]FDG-PET-Based Personalized Radiotherapy Dose Prescription

PET imaging with 2'-deoxy-2'-[18F]fluoro-D-glucose ([18F]FDG) has become one of the pillars in the management of malignant diseases. It has proven value in diagnostic workup, treatment policy, follow-up, and as prognosticator for outcome. [18F]FDG is widely available and standards have been developed for PET acquisition protocols and quantitative analyses. More recently, [18F]FDG-PET is also starting to be appreciated as a decision aid for treatment personalization. This review focuses on the potential of [18F]FDG-PET for individualized radiotherapy dose prescription. This includes dose painting, gradient dose prescription, and [18F]FDG-PET guided response-adapted dose prescription. The current status, progress, and future expectations of these developments for various tumor types are discussed.

Incidental Pulmonary Nodules and Lung Cancer Screening

Incidental nodules and lung cancer screening nodules are causes of concern and anxiety for the patients. Both these require diligent follow up according to their respective guidelines.

Differentiating Lung Nodules Due to Coccidioides from Those Due to Lung Cancer Based on Radiographic Appearance

BACKGROUND

Coccidioidomycosis (cocci) is an endemic fungal disease that can cause asymptomatic or post-symptomatic lung nodules which are visible on chest CT scanning. Lung nodules are common and can represent early lung cancer. Differentiating lung nodules due to cocci from those due to lung cancer can be difficult and lead to invasive and expensive evaluations.

MATERIALS AND METHODS

We identified 302 patients with biopsy-proven cocci or bronchogenic carcinoma seen in our multidisciplinary nodule clinic. Two experienced radiologists who were blinded to the diagnosis read the chest CT scans and identified radiographic characteristics to determine their utility in differentiating lung cancer nodules from those due to cocci.

RESULTS

Using univariate analysis, we identified several radiographic findings that differed between lung cancer and cocci infection. We then entered these variables along with age and gender into a multivariate model and found that age, nodule diameter, nodule cavitation, presence of satellite nodules and radiographic presence of chronic lung disease differed significantly between the two diagnoses. Three findings, cavitary nodules, satellite nodules and chronic lung disease, have sufficient discrimination to potentially be useful in clinical decision-making.

CONCLUSIONS

Careful evaluation of the three obtained radiographic findings can significantly improve our ability to differentiate benign coccidioidomycosis infection from lung cancer in an endemic region for the fungal disease. Using these data may significantly reduce the cost and risk associated with distinguishing the cause of lung nodules in these patients by preventing unnecessary invasive studies.

A lung cancer risk warning model based on tongue images

Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods. Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets. Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively. Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.

High risk lung nodule: A multidisciplinary approach to diagnosis and management

Pulmonary nodules are often discovered incidentally during CT scans performed for other reasons. While the vast majority of nodules are benign, a small percentage may represent early-stage lung cancer with the potential for curative treatments. With the growing use of CT for both clinical purposes and lung cancer screening, the number of pulmonary nodules detected is expected to increase substantially. Despite well-established guidelines, many nodules do not receive proper evaluation due to a variety of factors, including inadequate coordination of care and financial and social barriers. To address this quality gap, novel approaches such as multidisciplinary nodule clinics and multidisciplinary boards may be necessary. As pulmonary nodules may indicate early-stage lung cancer, it is crucial to adopt a risk-stratified approach to identify potential lung cancers at an early stage, while minimizing the risk of harm and expense associated with over investigation of low-risk nodules. This article, authored by multiple specialists involved in nodule management, delves into the diagnostic approach to lung nodules. It covers the process of determining whether a patient requires tissue sampling or continued surveillance. Additionally, the article provides an in-depth examination of the various biopsy and therapeutic options available for malignant lung nodules. The article also emphasizes the significance of early detection in reducing lung cancer mortality, especially among high-risk populations. Furthermore, it addresses the creation of a comprehensive lung nodule program, which involves smoking cessation, lung cancer screening, and systematic evaluation and follow-up of both incidental and screen-detected nodules.

Comparison of long-term outcomes of stereotactic body radiotherapy (SBRT) via Helical tomotherapy for early-stage lung cancer with or without pathological proof

BACKGROUND

Stereotactic body radio therapy (SBRT) has emerged as a standard treatment option for nonsurgical candidates with early-stage non-small cell lung cancer (NSCLC). Pathological proof is sometimes difficult to obtain in patients with solitary pulmonary nodules (SPNs). We aimed to compare the clinical outcomes of stereotactic body radiotherapy via helical tomotherapy (HT-SBRT) for early-stage lung cancer patients with or without a pathological diagnosis.

METHODS

Between June 2011 and December 2016, we treated 119 lung cancer patients with HT-SBRT, including 55 with a clinical diagnosis and 64 with a pathological diagnosis. Survival outcomes, including local control (LC), progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS), were compared between two cohorts with and without a pathological diagnosis.

RESULTS

The median follow-up for the whole group was 69 months. Patients with a clinical diagnosis were significantly older (p = 0.002). No significant differences were observed between the clinical and pathological diagnosis cohorts in terms of the long-term outcome, with 5-year LC, PFS, CSS, and OS of 87% versus 83% (p = 0.58), 48% versus 45% (p = 0.82), 87% versus 84% (p = 0.65), and 60% versus 63% (p = 0.79), respectively. Recurrence patterns and toxicity were also similar.

CONCLUSIONS

Empiric SBRT appears to be a safe and effective treatment option in a multidisciplinary setting when patients with SPNs highly suggestive of malignancy are unable/refuse to obtain a definitive pathological diagnosis.

Patient and Nodule Characteristics Associated With a Lung Cancer Diagnosis Among Individuals With Incidentally Detected Lung Nodules

BACKGROUND

Pulmonary nodules are a common incidental finding on CT imaging. Few studies have described patient and nodule characteristics associated with a lung cancer diagnosis using a population-based cohort.

RESEARCH QUESTION

Does a relationship exist between patient and nodule characteristics and lung cancer among individuals with incidentally detected pulmonary nodules, and can this information be used to create exploratory lung cancer prediction models with reasonable performance characteristics?

STUDY DESIGN AND METHODS

We conducted a retrospective cohort study of adults older than 18 years with lung nodules of any size incidentally detected by chest CT imaging between 2005 and 2015. All patients had at least 2 years of complete follow-up. To evaluate the relationship between patient and nodule characteristics and lung cancer, we used binomial regression. We used logistic regression to create prediction models, and we internally validated model performance using bootstrap optimism correction.

RESULTS

Among 7,240 patients with a median age of 67 years, 56% of whom were women, with a median BMI of 28 kg/m2, 56% of whom were ever smokers, 31% of whom had prior nonlung malignancy, with a median nodule size 5.6 mm, 57% of whom had multiple nodules, and 40% of whom had an upper lobe nodule, 265 patients (3.7%; 95% CI, 3.2%-4.1%) had a diagnosis of lung cancer. In a multivariate analysis, age, sex, BMI, smoking history, and nodule size and location were associated with a lung cancer diagnosis, whereas prior malignancy and nodule number and laterality were not. We were able to construct two prediction models with an area under the curve value of 0.75 (95% CI, 0.72-0.80) and reasonable calibration.

INTERPRETATION

Lung cancer is uncommon among individuals with incidentally detected lung nodules. Some, but not all, previously identified factors associated with lung cancer also were associated with this outcome in this sample. These findings may have implications for clinical practice, future practice guidelines, and the development of novel lung cancer prediction models for individuals with incidentally detected lung nodules.

Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study

BACKGROUND

Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies.

RESEARCH QUESTION

This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer?

STUDY DESIGN AND METHODS

In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data.

RESULTS

A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86.

INTERPRETATION

Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer.

CLINICAL TRIAL REGISTRATION

The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

Lung cancer screening

Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.

Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT

OBJECTIVES

This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT.

METHODS

A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately.

RESULTS

Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts.

CONCLUSIONS

We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment.

KEY POINTS

• Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.

Lung nodules: sorting the wheat from the chaff

Pulmonary nodules are a common finding on CT scans of the chest. In the United Kingdom, management should follow British Thoracic Society Guidelines, which were published in 2015. This review covers key aspects of nodule management also looks at new and emerging evidence since then.

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

BACKGROUND

Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.

RESEARCH QUESTION

What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?

STUDY DESIGN AND METHODS

This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.

RESULTS

Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).

INTERPRETATION

An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier; No.: NCT02013063.

Morphologic Severity of Atypia Is Predictive of Lung Cancer Diagnosis

In cytologic analysis of lung nodules, specimens classified as atypia cannot be definitively diagnosed as benign or malignant. Atypia patients are typically subject to additional procedures to obtain repeat samples, thus delaying diagnosis. We evaluate morphologic categories predictive of lung cancer in atypia patients. This retrospective study stratified patients evaluated for primary lung nodules based on cytologic diagnoses. Atypia patients were further stratified based on the most severe verbiage used to describe the atypical cytology. Logistic regressions and receiver operator characteristic curves were performed. Of 129 patients with cytologic atypia, 62.8% later had cytologically or histologically confirmed lung cancer and 37.2% had benign respiratory processes. Atypia severity significantly predicted final diagnosis even while controlling for pack years and modified Herder score (p = 0.012). Pack years, atypia severity, and modified Herder score predicted final diagnosis independently and while adjusting for covariates (all p < 0.001). This model generated a significantly improved area under the curve compared to pack years, atypia severity, and modified Herder score (all p < 0.001) alone. Patients with severe atypia may benefit from repeat sampling for cytologic confirmation within one month due to high likelihood of malignancy, while those with milder atypia may be followed clinically.

Current investigative modalities for detecting and staging lung cancers: a comprehensive summary

This narrative review compares the advantages and drawbacks of imaging and other investigation modalities which currently assist with lung cancer diagnosis and staging, as well as those which are not routinely indicated for this. We examine plain film radiography, computed tomography (CT) (alone, as well as in conjunction with positron emission tomography (PET)), magnetic resonance imaging (MRI), ultrasound, and newer techniques such as image-guided bronchoscopy (IGB) and robotic bronchoscopy (RB). While a chest X-ray is the first-line imaging investigation in patients presenting with symptoms suggestive of lung cancer, it has a high positive predictive value (PPV) even after negative X-ray findings, which calls into question its value as part of a potential national screening programme. CT lowers the mortality for high-risk patients when compared to X-ray and certain scoring systems, such as the Brock model can guide the need for further imaging, like PET-CT, which has high sensitivity and specificity for diagnosing solitary pulmonary nodules as malignant, as well as for assessing small cell lung cancer spread. In practice, PET-CT is offered to everyone whose lung cancer is to be treated with a curative intent. In contrast, MRI is only recommended for isolated distant metastases. Similarly, ultrasound imaging is not used for diagnosis of lung cancer but can be useful when there is suspicion of intrathoracic lymph node involvement. Ultrasound imaging in the form of endobronchial ultrasonography (EBUS) is often used to aid tissue sampling, yet the diagnostic value of this technique varies widely between studies. RB is another novel technique that offers an alternative way to biopsy lesions, but further research on it is necessary. Lastly, thoracic surgical biopsies, particularly minimally invasive video-assisted techniques, have been used increasingly to aid in diagnosis and staging.

Preoperative computed tomography-guided localization for multiple lung nodules: a Meta-analysis

PURPOSE

Approximately 20% of patients with lung nodules (LNs) have multiple LNs (MLNs). This meta-analysis was performed to assess the safety and efficacy of computed tomography (CT)-guided localization of MLNs in comparison with those of single LN (SLN) localization.

MATERIAL AND METHODS

The PubMed, Embase, and Cochrane Library were searched to collect relevant articles published till February 2022. The meta-analysis was performed using the RevMan v5.3.

RESULTS

In total, seven studies met the inclusion criteria for this meta-analysis. No significant difference was observed between patients with MLNs and SLN in terms of pooled successful localization rate based on LNs (p = 0.64) and patients (p = 0.06). The pooled duration of localization was significantly shorter and the pooled pneumothorax and lung hemorrhage rates were significantly lower in the SLN group than in the MLNs group (p < 0.00001 for all). The pooled duration of hospital stay was comparable between the MLNs and SLN groups (p = 0.96). Significant heterogeneity was observed in the endpoints of duration of localization (I² = 75%) and pneumothorax (I² = 53%).

CONCLUSIONS

CT-guided simultaneous MLN localization is clinically safe and effective, despite requiring a longer procedural time and having higher incidence of pneumothorax and lung hemorrhage than SLN localization.

A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules

BACKGROUND

Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk.

METHODS

502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores.

FINDINGS

499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention.

INTERPRETATION

The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models.

FUNDING

This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).

EarlyCDT Lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation

BACKGROUND

EarlyCDT Lung (Oncimmune Holdings plc, Nottingham, UK) is a blood test to assess malignancy risk in people with solid pulmonary nodules. It measures the presence of seven lung cancer-associated autoantibodies. Elevated levels of these autoantibodies may indicate malignant disease. The results of the test might be used to modify the risk of malignancy estimated by existing risk calculators, including the Brock and Herder models.

OBJECTIVES

The objectives were to determine the diagnostic accuracy, clinical effectiveness and cost-effectiveness of EarlyCDT Lung; and to develop a conceptual model and identify evidence requirements for a robust cost-effectiveness analysis.

DATA SOURCES

MEDLINE (including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE), EMBASE, Cochrane Central Register of Controlled Trials, Science Citation Index, EconLit, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database ( NHS EED ) and the international Health Technology Assessment database were searched on 8 March 2021.

REVIEW METHODS

A systematic review was performed of evidence on EarlyCDT Lung, including diagnostic accuracy, clinical effectiveness and cost-effectiveness. Study quality was assessed with the quality assessment of diagnostic accuracy studies-2 tool. Evidence on other components of the pulmonary nodule diagnostic pathway (computerised tomography surveillance, Brock risk, Herder risk, positron emission tomography-computerised tomography and biopsy) was also reviewed. When feasible, bivariate meta-analyses of diagnostic accuracy were performed. Clinical outcomes were synthesised narratively. A simulation study investigated the clinical impact of using EarlyCDT Lung. Additional reviews of cost-effectiveness studies evaluated (1) other diagnostic strategies for lung cancer and (2) screening approaches for lung cancer. A conceptual model was developed.

RESULTS

A total of 47 clinical publications on EarlyCDT Lung were identified, but only five cohorts (695 patients) reported diagnostic accuracy data on patients with pulmonary nodules. All cohorts were small or at high risk of bias. EarlyCDT Lung on its own was found to have poor diagnostic accuracy, with a summary sensitivity of 20.2% (95% confidence interval 10.5% to 35.5%) and specificity of 92.2% (95% confidence interval 86.2% to 95.8%). This sensitivity was substantially lower than that estimated by the manufacturer (41.3%). No evidence on the clinical impact of EarlyCDT Lung was identified. The simulation study suggested that EarlyCDT Lung might potentially have some benefit when considering intermediate risk nodules (10-70% risk) after Herder risk analysis. Two cost-effectiveness studies on EarlyCDT Lung for pulmonary nodules were identified; none was considered suitable to inform the current decision problem. The conceptualisation process identified three core components for a future cost-effectiveness assessment of EarlyCDT Lung: (1) the features of the subpopulations and relevant heterogeneity, (2) the way EarlyCDT Lung test results affect subsequent clinical management decisions and (3) how changes in these decisions can affect outcomes. All reviewed studies linked earlier diagnosis to stage progression and stage shift to final outcomes, but evidence on these components was sparse.

LIMITATIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules was very limited, preventing meta-analyses and economic analyses.

CONCLUSIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules is insufficient to draw any firm conclusions as to its diagnostic accuracy or clinical or economic value.

FUTURE WORK

Prospective cohort studies, in which EarlyCDT Lung is used among patients with identified pulmonary nodules, are required to support a future assessment of the clinical and economic value of this test. Studies should investigate the diagnostic accuracy and clinical impact of EarlyCDT Lung in combination with Brock and Herder risk assessments. A well-designed cost-effectiveness study is also required, integrating emerging relevant evidence with the recommendations in this report.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42021242248.

FUNDING

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 49. See the NIHR Journals Library website for further project information.

Stereotactic Ablative Radiotherapy in the Treatment of Early-Stage Lung Cancer - A Done Deal?

Stereotactic ablative radiotherapy (SABR) is an important curative-intent treatment option for early-stage non-small cell lung cancer. It offers good cancer control without invasive surgery and has become the standard of care for medically inoperable patients. The literature on SABR for early-stage non-small cell lung cancer is substantial and continues to grow. However, there remain areas of controversy where data are limited - notably the use of SABR in medically operable patients. Other areas of some debate include the treatment of central/ultra-central and large (>5 cm) lesions, as well as treatment with co-existing interstitial lung disease. This review article provides an overview of the current literature together with a discussion of future directions.

The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers

BACKGROUND

Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect.

METHODS

Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets.

RESULTS

Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 &gt; 0.97 for each) with fitted parameters closely aligned with professional society guidelines.

CONCLUSIONS

The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice.

IMPACT

We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations

OBJECTIVE

To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting.

METHODS

The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs.

RESULTS

A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs.

CONCLUSION

Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.

Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making

Background

Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision‐making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision‐making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it.

Objective

To explore (1) the extent to which patient preferences are taken into consideration in non‐small‐cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM.

Design

Mixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations.

Setting and Participants

NSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics.

Results

We found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations.

Conclusion

Lung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatments. CDSSs can support SDM by presenting the harms and benefits of different treatment options rather than giving single treatment advice. External validation of CDSSs should be prioritized.

Patient or Public Contribution

This study did not involve patients or the public explicitly; however, the study design was informed by prior interviews with volunteers of a cancer patient advocacy group. The study objectives and data collection were supported by Dutch health care insurer CZ for a project titled ‘My Best Treatment’ that improves patient‐centeredness and the lung cancer patient pathway in the Netherlands.

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

BACKGROUND

To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis.

METHODS

The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0.

RESULTS

In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84-0.91), 78% (95CI%: 0.74-0.80), 3.91 (95CI%: 3.42-4.46), 0.16 (95CI%: 0.12-0.21), and 3.21 (95CI%: 2.87-3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83-0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I² = 89.07%), NLR (I² = 87.29%), and diagnostic score (I² = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539).

CONCLUSIONS

The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.

The use of carcinoembryonic antigen levels to predict lung nodule malignancy: a meta-analysis

OBJECTIVES

To assess the diagnostic value of serum carcinoembryonic antigen (CEA) as a diagnostic biomarker that can be used to differentiate between benign and malignant lung nodules (LNs).

METHODS

PubMed, Cochrane Library, and Embase were reviewed from January 2000 to April 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis.

RESULTS

Our initial literature search identified 511 potentially relevant studies, of which 11 were ultimately included in the present meta-analysis. Ten studies were retrospective and only 1 study was prospective. Overall these studies incorporated 2760 patients and 2760 total LNs (1733 malignant, 1027 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) values for these studies were 0.33 (95% CI: 0.20-0.49), 0.92 (95% CI: 0.85-0.96), 3.96 (95% CI: 2.84-5.54), 0.73 (95% CI: 0.62-0.87), and 5.42 (95% CI: 3.77-7.78), respectively. The area under curve (AUC) value was 0.77, consistent with moderate diagnostic accuracy. We detected significant heterogeneity when calculating pooled sensitivity (I² = 95.9%, P = 0.00), specificity (I² = 92.0%, P = 0.00), PLR (I² = 61.7%, P = 0.00), NLR (I² = 92.8%, P = 0.00), and DOR (I² = 93.8%, P = 0.00). No significant evidence of publication bias was detected via Deeks' funnel plot asymmetry test (P = 0.371). Meta-regression analysis revealed different reference standards to be closely associated with both sensitivity and specificity.

CONCLUSIONS

Serum CEA can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.

Management of Lung Cancer Screening Results Based on Individual Prediction of Current and Future Lung Cancer Risks

OBJECTIVES

We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals.

METHODS

We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal).

RESULTS

Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol.

CONCLUSIONS

Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.

Comprehensive analysis of clinical logistic and machine learning based models for the evaluation of pulmonary nodules

Introduction

Over the years, multiple models have been developed for the evaluation of pulmonary nodules (PNs). This study aimed to comprehensively investigate clinical models for estimating the malignancy probability in patients with PNs.

Methods

PubMed, EMBASE, Cochrane Library, and Web of Science were searched for studies reporting mathematical models for PN evaluation until March 2020. Eligible models were summarized, and network meta-analysis was performed on externally validated models (PROSPERO database CRD42020154731). The cut-off value of 40% was used to separate patients into high prevalence (HP) and low prevalence (LP), and a subgroup analysis was performed.

Results

A total of 23 original models were proposed in 42 included articles. Age and nodule size were most often used in the models, whereas results of positron emission tomography-computed tomography were used when collected. The Mayo model was validated in 28 studies. The area under the curve values of four most often used models (PKU, Brock, Mayo, VA) were 0.830, 0.785, 0.743, and 0.750, respectively. High-prevalence group (HP) models had better results in HP patients with a pooled sensitivity and specificity of 0.83 (95% confidence interval [CI]: 0.78–0.88) and 0.71 (95% CI: 0.71–0.79), whereas LP models only achieved pooled sensitivity and specificity of 0.70 (95% CI: 0.60–0.79) and 0.70 (95% CI: 0.62–0.77). For LP patients, the pooled sensitivity and specificity decreased from 0.68 (95% CI: 0.57–0.78) and 0.93 (95% CI: 0.87–0.97) to 0.57 (95% CI: 0.21–0.88) and 0.82 (95% CI: 0.65–0.92) when the model changed from LP to HP models. Compared with the clinical models, artificial intelligence-based models have promising preliminary results.

Conclusions

Mathematical models can facilitate the evaluation of lung nodules. Nevertheless, suitable model should be used on appropriate cohorts to achieve an accurate result.

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.

Absence of clinical benefit of FDG PET-CT in staging T1 part-solid lung adenocarcinoma

AIM

To assess the rates of nodal and metastatic disease and change in management when staging part-solid T1 lung adenocarcinomas using integrated 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-computed tomography (CT) in a UK population.

MATERIALS AND METHODS

This was a retrospective review of PET-CT examinations performed to stage radiologically suspected T1 part-solid lung adenocarcinoma (n=58) from two different centres. Rates of detection of nodal and metastatic disease, change in management, and final patient outcome were recorded.

RESULTS

PET-CT changed the stage in one patient from N0 to N1. It did not change final management in any patient.

CONCLUSIONS

In this UK population, PET-CT had minimal additional diagnostic benefit in staging patients with T1 part-solid lung adenocarcinoma. Especially given its cost, the inclusion of PET-CT for this indication in guidelines should be reviewed.

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

BACKGROUND

Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.

METHODS

We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.

RESULTS

A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.

CONCLUSIONS

The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020161559.