Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines

A Retrospective External Validation of the Cleveland Clinic Malignancy Probability Prediction Model for Indeterminate Pulmonary Nodules

Objective

To perform a retrospective, multicenter, external validation of the Cleveland Clinic malignancy probability prediction model for incidental pulmonary nodules.

Patients and Methods

From July 1, 2022, to May 31, 2023, we identified 296 patients who underwent tissue acquisition at Mayo Clinic (MC) (n=198) and Loyola University Medical Center (n=98) with histopathology indicating malignant (n=195) or benign (n=101). Data was collected at initial radiographic identification (point 1) and at the time of intervention (point 2). Point 3 represented the most recent data. The areas under the receiver operating characteristics were calculated for each model per time point. Calibration was evaluated by comparing the predicted and observed rates of malignancy.

Results

The areas under the receiver operating characteristics at time points 1, 2, and 3 for the MC model were 0.67 (95% CI, 0.61-0.74), 0.67 (95% CI, 0.58-0.77), and 0.70 (95% CI, 0.63-0.76), respectively. The Cleveland Clinic model (CCM) was 0.68 (95% CI, 0.61-0.74), 0.75 (95% CI, 0.65-0.84), and 0.72 (95% CI, 0.66-0.78), respectively. The mean ± SD estimated probability for malignant pulmonary nodules (PNs) at time points 1, 2, and 3 for the CCM was 64.2±25.9, 65.8±24.0, and 64.7±24.4, which resembled the overall proportion of malignant PNs (66%). The mean estimated probability of malignancy for the MC model at each time point was 38.3±27.4, 36.2±24.4, and 42.1±27.3, substantially lower than the observed proportion of malignancies.

Conclusion

The CCM found discrimination similar to its internal validation and good calibration. The CCM can be used to augment clinical and shared decision-making when evaluating high-risk PNs.

Early Detection and Interception of Lung Cancer

Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.

Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice

OBJECTIVE

To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice.

METHODS

This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models.

RESULTS

Volumetric analysis improved sensitivity (P = 0.009) and allowed earlier recognition (P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules (P = 0.03) and recommendation of care escalation (P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group (P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 (P = 0.004) only on the second follow-up study.

CONCLUSIONS

Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.

The incremental contribution of mobile cone-beam computed tomography to the tool-lesion relationship during shape-sensing robotic-assisted bronchoscopy

Introduction

This study aims to answer the question of whether adding mobile cone-beam computed tomography (mCBCT) imaging to shape-sensing robotic-assisted bronchoscopy (ssRAB) translates into a quantifiable improvement in the tool-lesion relationship.

Methods

Data from 102 peripheral lung lesions with ≥2 sequential mCBCT orbital spins and from 436 lesions with 0-1 spins were prospectively captured and retrospectively analysed. The primary outcome was the tool-lesion relationship status across the first and the last mCBCT spins. Secondary outcomes included 1) the change in distance between the tip of the sampling tool and the centre of the lesion between the first and the last spins and 2) the per-lesion diagnostic yield.

Results

Compared to lesions requiring 0-1 spins, lesions requiring ≥2 spins were smaller and had unfavourable bronchus sign and intra-operative sonographic view. On the first spin, 54 lesions (53%) were designated as non-tool-in-lesion (non-TIL) while 48 lesions (47%) were designated as TIL. Of the 54 initially non-TIL cases, 49 (90%) were converted to TIL status by the last spin. Overall, on the last spin, 96 out of 102 lesions (94%) were defined as TIL and six out of 102 lesions (6%) were defined as non-TIL (p<0.0001). The mean distance between the tool and the centre of the lesion decreased from 10.4 to 6.6 mm between the first and last spins (p<0.0001). The overall diagnostic yield was 77%.

Conclusion

Targeting traditionally challenging lung lesions, intra-operative volumetric imaging allowed for the conversion of 90% of non-TIL status to TIL. Guidance with mCBCT resulted in a significant decrease in the distance between the tip of the needle to lesion centre.

Diagnostic utility of adding needle aspiration (using PeriView FLEX needle) to radial endobronchial ultrasound guide sheath transbronchial lung biopsy: a single center retrospective study

Background

Radial endobronchial ultrasound (rEBUS) guide sheath (GS) transbronchial lung biopsy (TBLB) improves the diagnostic yield of peripheral lung lesions (PLL). However, its diagnostic yield is approximately 60%. We aimed to evaluate the diagnostic utility of adding rEBUS GS transbronchial needle aspiration (TBNA) using PeriView FLEX needle (Olympus, Tokyo, Japan) to rEBUS GS TBLB.

Methods

In this retrospective study, we initially screened 124 PLLs in 123 patients who underwent rEBUS GS procedures for PLLs from December 2020 to August 2021. The analysis was performed on 74 PLLs in 73 patients who underwent both rEBUS GS TBLB and TBNA.

Results

PLLs showed the following characteristics: lesion size [mean ± standard deviation (SD)], 24±12 mm; nature (solid vs. subsolid), 59 (79.7%) vs. 15 (20.3%); distance from the pleura (mean ± SD), 14±14 mm; rEBUS visualization type (probe within PLL vs. probe adjacent to PLL), 56 (75.7%) vs. 18 (24.3%). Among 74 PLLs, 47 (63.5%) were successfully diagnosed by rEBUS GS TBLB. In 27 PLLs not diagnosed by rEBUS GS TBLB, 5 (18.5%) were further diagnosed by rEBUS GS TBNA [overall diagnostic yield: 70.3% (52/74)]. EBUS visualization type of "probe adjacent to PLL" was a significant factor associated with the diagnostic yield of additional rEBUS GS TBNA.

Conclusions

In rEBUS GS procedures for PLLs, the diagnostic yield might be improved by implementing TBNA in addition to TBLB. In particular, additional TBNA is preferable if the probe is adjacent to the lesion rather than within the lesion on rEBUS.

Risk of Lung Cancer in Peripheral Pulmonary Nodules

PURPOSE

To determine the risk of lung cancer and inter-observer agreement for small pulmonary nodules either touching or near the pleura.

METHODS

Nodules were derived from two cohorts: patients from the National Lung Screening Trial with a solid nodule measuring 6-9.5 mm; and patients with incidental pulmonary nodules in our healthcare system with a solid nodule measuring 1-8 mm. Only the dominant nodule was evaluated for each patient. All malignant nodules as well as a random sample of 200 benign nodules from each cohort were included. Two fellowship-trained thoracic radiologists independently reviewed each case to record nodule morphology (compatible with lymph node or not) and nodule location (pleural-based, septal connection to the pleura, or neither). One radiologist measured the distance to the pleura.

RESULTS

After exclusion criteria were applied, a total of 434 nodules were included, of which 45 were lung cancers. Considering all pleural-based nodules with lymph node morphology as benign, 0-7% of cancers were misclassified as benign, specificity 33%, and κ = 0.69. Considering subpleural nodules and those with septal connection to the pleura, 7-11% of cancers were misclassified (p = 0.16-0.25 versus pleural-based), specificity 40-52% (p < .0001), and κ = 0.60. Considering nodules with lymph node morphology ≤ 2 mm from the pleura, 2-7% of cancers were misclassified (p = 1 versus pleural-based), specificity 41-36% (p < .0001), and κ = 0.78.

CONCLUSION

Considering nodules with lymph node morphology with septal connection, or those ≤ 2 mm from the pleura, as benign does not lead to significant misclassification of lung cancers as benign.

Efficiency of pulmonary nodule risk scoring systems in Turkish population

Malignancy risk calculation models were developed using the clinical and radiological features. It was aimed to compare pulmonary nodule risk calculation models and evaluate their effectiveness and applicability for the Turkish population. Between 2014 and 2019, 351 patients who were operated on for pulmonary nodules were evaluated with the following data: age, gender, smoking history, family history of lung cancer, extrapulmonary malignancy and granulomatous disease, nodule diameter, attenuation character, side, localization, spiculation, nodule count, presence of pulmonary emphysema, FDG uptake in PET/CT of the nodule, and definitive pathology data. Malignancy risk scores were calculated using the equations of the Brock, Mayo, and Herder models. The results were evaluated statistically. The mean age of the 351 patients (236 men, 115 women) was 57.84 ± 10.87 (range 14-79) years, and 226 malignant and 125 benign nodules were observed. Significant correlations were found between malignancy and age (p < 0.001), nodule diameter (p < 0.001), gender (p < 0.009), speculation (p < 0.001), emphysema (p < 0.05), FDG uptake (p < 0.001). All three models were found effective in the differentiation (p < 0.001). The ideal threshold value was determined for the Brock (19.5%), Mayo (23.1%), and Herder (56%) models. All models were effective for nodules of > 10 mm, but none of them were for 0-10 mm. Brock was effective in ground-glass nodules (p = 0.02) and all models were effective for semi-solid and solid nodules. None of the groups could provide AUC values as high as those achieved in the original studies. This suggests the need to optimize models and malignancy risk thresholds for Turkish population.

Ultrathin Bronchoscopy Without Virtual Navigation for Diagnosis of Peripheral Lung Lesions

BACKGROUND

The increasing incidence of encountering lung nodules necessitates an ongoing search for improved diagnostic procedures. Various bronchoscopic technologies have been introduced or are in development, but further studies are needed to define a method that fits best in clinical practice and health care systems.

RESEARCH QUESTION

How do basic bronchoscopic tools including a combination of thin (outer diameter 4.2 mm) and ultrathin bronchoscopes (outer diameter 3.0 mm), radial endobronchial ultrasound (rEBUS) and fluoroscopy perform in peripheral pulmonary lesion diagnosis?

STUDY DESIGN AND METHODS

This is a retrospective review of the performance of peripheral bronchoscopy using thin and ultrathin bronchoscopy with rEBUS and 2D fluoroscopy without a navigational system for evaluating peripheral lung lesions in a single academic medical center from 11/2015 to 1/2021. We used a strict definition for diagnostic yield and assessed the impact of different variables on diagnostic yield, specifically after employment of the ultrathin bronchoscope. Logistic regression models were employed to assess the independent associations of the most impactful variables.

RESULTS

A total of 322 patients were included in this study. The median of the long axis diameter was 2.2 cm and the median distance of the center of the lesion from the visceral pleural surface was 1.9 cm. Overall diagnostic yield was 81.3% after employment of the ultrathin bronchoscope, with more detection of concentric rEBUS views (93% vs. 78%, p < 0.001). Sensitivity for detecting malignancy also increased from 60.5% to 74.7% (p = 0.033) after incorporating the ultrathin scope into practice, while bronchus sign and peripheral location of the lesion were not found to affect diagnostic yield. Concentric rEBUS view, solid appearance, upper/middle lobe location and larger size of the nodules were found to be independent predictors of successful achievement of diagnosis at bronchoscopy.

INTERPRETATION

This study demonstrates a high diagnostic yield of biopsy of lung lesions achieved by utilization of thin and ultrathin bronchoscopes. Direct visualization of small peripheral airways with simultaneous rEBUS confirmation increased localization rate of small lesions in a conventional bronchoscopy setting without virtual navigational planning.

Cone Beam CT Derived Laser-Guided Percutaneous Lung Ablation: Minimizing Needle-Related Complications Under General Anesthesia with Lung Separation

RATIONALE AND OBJECTIVES

Percutaneous lung tumor ablations are mostly performed in computed tomography (CT) rooms under local anesthesia with conscious sedation. However, maintaining the breath-hold phase during this can be challenging, affecting image quality and increasing complications. With the advent of hybrid operating rooms (HORs), this procedure can be performed with endotracheal tube (ETGA) intubation under general anesthesia with lung separation, ensuring precise imaging in a single-stage setting. Lung separation provides surgical exposure of one lung while ensuring ample gas exchange with the other. This study evaluated tumor ablations performed in an HOR equipped with cone beam CT and laser guidance.

MATERIALS AND METHODS

This retrospective study included patients who underwent lung tumor ablation under general anesthesia with an ETGA in an HOR between July 2020 and May 2023. Anesthesia considerations, perioperative management, and postoperative follow-ups were evaluated.

RESULTS

65 patients (78 tumors) underwent ablation using two types of lung ventilation methods including a single-lumen tube with a blocker (SLT/BL) (n = 15) and double-lumen tube (DLT) (n = 50). Most patients experienced desaturation during the apnea phase of dynamic CT and needling. The average SpO2 value was significantly lower in the DLT group than in the SLT/BL group during the procedure (81.1% versus 88.7%, P = 0.033). Five, three, and two patients developed pneumothorax, subcutaneous emphysema, and pleural effusion, respectively.

CONCLUSION

Percutaneous ablation under general anesthesia with endotracheal intubation and lung separation performed in HORs was feasible and safe. The setup minimized complication risks and maintained a balance between patient safety and successful procedures.

Overcoming barriers to lung cancer screening using a systemwide approach with additional focus on the non-screened

BACKGROUND

The lung cancer screening program at St Elizabeth Healthcare (Kentucky, USA) began in 2013. Over 33,000 low-dose computed tomography lung cancer screens have been performed. From 2015 through 2021, 2595 lung cancers were diagnosed systemwide. A Screening Program with Impactful Results from Early Detection, reviews that experience; 342 (13.2%) were diagnosed by screening and 2253 (86.8%) were non-screened. As a secondary objective, the non-screened cohort was queried to determine how many additional individuals could have been screened, identifying barriers and failures to meet eligibility.

METHODS

Our QlikSense database extracted the lung cancer patients from the Cancer Patient Data and Management System, and identified and categorized them separately as screened or non-screened populations. Stage distribution was compared in screened and non-screened groups. Those meeting age criteria, with any smoking history, were further queried for screening eligibility, accessing the electronic medical record smoking history and audit trail, and determining if enough information was available to substantiate screening eligibility. The same methodology was applied to CMS 2015 and USPSTF 2021 criteria.

RESULTS

The screened and non-screened patients were accounted for in a stage migration chart demonstrating clear shift to early stage among screened lung cancer patients. Additionally, analysis of non-screened individuals is presented.

CONCLUSION

Of the St Elizabeth Healthcare eligible patients attributed to primary care providers, 49.6% were screened in 2021. Despite this level of success, this study highlighted a sizeable pool of additional individuals that could have been screened. We are shifting focus to the non-screened pool of patients that meet eligibility, further enhancing the impact on our community.

Subsolid Nodules: Significance and Current Understanding

Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.

Computed-Tomography-Guided Lung Biopsy: A Practice-Oriented Document on Techniques and Principles and a Review of the Literature

Computed tomography (CT)-guided lung biopsy is one of the oldest and most widely known minimally invasive percutaneous procedures. Despite being conceptually simple, this procedure needs to be performed rapidly and can be subject to meaningful complications that need to be managed properly. Therefore, knowledge of principles and techniques is required by every general or interventional radiologist who performs the procedure. This review aims to contain all the information that the operator needs to know before performing the procedure. The paper starts with the description of indications, devices, and types of percutaneous CT-guided lung biopsies, along with their reported results in the literature. Then, pre-procedural evaluation and the practical aspects to be considered during procedure (i.e., patient positioning and breathing) are discussed. The subsequent section is dedicated to complications, with their incidence, risk factors, and the evidence-based measures necessary to both prevent or manage them; special attention is given to pneumothorax and hemorrhage. After conventional CT, this review describes other available CT modalities, including CT fluoroscopy and cone-beam CT. At the end, more advanced techniques, which are already used in clinical practice, like fusion imaging, are included.

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.

Multidimensional biological characteristics of ground glass nodules

The detection rate of ground glass nodules (GGNs) has increased in recent years because of their malignant potential but relatively indolent biological behavior; thus, correct GGN recognition and management has become a research focus. Many scholars have explored the underlying mechanism of the indolent progression of GGNs from several perspectives, such as pathological type, genomic mutational characteristics, and immune microenvironment. GGNs have different major mutated genes at different stages of development; EGFR mutation is the most common mutation in GGNs, and p53 mutation is the most abundant mutation in the invasive stage of GGNs. Pure GGNs have fewer genomic alterations and a simpler genomic profile and exhibit a gradually evolving genomic mutation profile as the pathology progresses. Compared to advanced lung adenocarcinoma, GGN lung adenocarcinoma has a higher immune cell percentage, is under immune surveillance, and has less immune escape. However, as the pathological progression and solid component increase, negative immune regulation and immune escape increase gradually, and a suppressive immune environment is established gradually. Currently, regular computer tomography monitoring and surgery are the main treatment strategies for persistent GGNs. Stereotactic body radiotherapy and radiofrequency ablation are two local therapeutic alternatives, and systemic therapy has been progressively studied for lung cancer with GGNs. In the present review, we discuss the characterization of the multidimensional molecular evolution of GGNs that could facilitate more precise differentiation of such highly heterogeneous lesions, laying a foundation for the development of more effective individualized treatment plans.

Cone-beam computed tomography image-guided percutaneous microwave ablation for lung nodules in a hybrid operating room: an initial experience

OBJECTIVES

The experience of thermal ablation of lung lesions is limited, especially performing the procedure under localisation by cone-beam CT in the hybrid operation room (HOR). Here, we present the experience of microwave ablation (MWA) of lung nodules in the HOR.

METHODS

We reviewed patients who underwent image-guide percutaneous MWA for lung nodules in the HOR under general anaesthesia between July 2020 and July 2022. The workflow in the HOR including the pre-procedure preparation, anaesthesia consideration, operation methods, and postoperative care was clearly described.

RESULTS

Forty lesions in 33 patients who underwent MWA under general anaesthesia (GA) in the HOR were analysed. Twenty-seven patients had a single pulmonary nodule, and the remaining six patients had multiple nodules. The median procedure time was 41.0 min, and the median ablation time per lesion was 6.75 min. The median global operation room time was 115.0 min. The median total dose area product was 14881 μGym2. The median ablation volume was 111.6 cm3. All patients were discharged from the hospital with a median postoperative stay of 1 day. Four patients had pneumothorax, two patients had pleural effusion during the first month of outpatient follow-up, and one patient reported intercostal neuralgia during the 3-month follow-up.

CONCLUSIONS

Thermal ablation of pulmonary nodules under GA in the HOR can be performed safely and efficiently if we follow the workflow provided. The procedure provides an alternative to managing pulmonary nodules in patients.

CLINICAL RELEVANCE STATEMENT

Thermal ablation of pulmonary nodules under GA in the HOR can be performed safely and efficiently if the provided workflow is followed.

KEY POINTS

• We tested the feasibility of microwave ablation of lung lesions performed in a hybrid operating room. • To this end, we provide a description of microwave ablation of the lung under cone-beam CT localisation. • We describe a workflow by which ablation of the pulmonary nodule can be performed safely under general anaesthesia.

Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules

OBJECTIVES

The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN).

METHODS

CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach).

RESULTS

In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group.

CONCLUSION

CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management.

CLINICAL RELEVANCE STATEMENT

Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts.

KEY POINTS

• LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.

Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI

BACKGROUND

The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities.

PURPOSE

To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules.

STUDY TYPE

Prospective.

POPULATION

54 patients, including 21 men and 33 women.

FIELD STRENGTH/SEQUENCE

Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo.

ASSESSMENT

Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded.

STATISTICAL TESTS

The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant.

RESULTS

A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements.

DATA CONCLUSION

The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Synchronous Computed Tomography-Guided Percutaneous Transthoracic Needle Biopsy and Microwave Ablation for Highly Suspicious Malignant Pulmonary Ground-Glass Nodules

INTRODUCTION

There is no consensus regarding the most appropriate management of suspected malignant pulmonary ground-glass nodules (GGNs).

OBJECTIVE

We aimed to explore the feasibility and safety of synchronous computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) and microwave ablation (MWA) for patients highly suspicious of having malignant GGNs.

METHODS

We retrospectively reviewed medical records between July 2020 and April 2023 from our medical center. Eligible patients synchronously underwent PTNB and MWA (either MWA immediately after PTNB [PTNB-first group] or PTNB immediately after MWA [MWA-first group]) at the the physician's discretion. We analyzed the rate of definitive diagnosis and technical success, the length of hospital stay, the postoperative efficacy, and periprocedural complications.

RESULTS

Of 65 patients who were enrolled, the rate of definitive diagnosis was 86.2%, which did not differ when stratified by the tumor size, the consolidation-to-tumor ratio, or the sequence of the two procedures (all p > 0.05). The diagnostic rate of malignancy was 83.1%. After the median follow-up duration of 18.5 months, the local control rate was 98.2% and the rate of completed ablation was 48.2%. The rate of perioperative minor and major complications was 44.6% and 6.2%, respectively. The most common adverse events included pain, cough, and mild hemorrhage. Mild hemorrhage took place significantly less frequently in the MWA-first group than in the PTNB-first group (16.7% vs. 45.5%, p < 0.05).

CONCLUSION

Synchronous PTNB and MWA are feasible and well tolerated for patients highly suspicious of having malignant GGNs, providing an alternative option for patients who are ineligible for surgical resection.

Percutaneous Lung Biopsies With Robotic Systems: A Systematic Review of Available Clinical Solutions

Objectives: This systematic review aims to assess existing research concerning the use of robotic systems to execute percutaneous lung biopsy. Methods: A systematic review was performed and identified 4 studies involving robotic systems used for lung biopsy. Outcomes assessed were operation time, radiation dose to patients and operators, technical success rate, diagnostic yield, and complication rate. Results: One hundred and thirteen robot-guided percutaneous lung biopsies were included. Technical success and diagnostic yield were close to 100%, comparable to manual procedures. Technical accuracy, illustrated by needle positioning, showed less frequent needle adjustments in robotic guidance than in manual guidance (P < .001): 2.7 ± 2.6 (range 1-4) versus 6 ± 4 (range 2-12). Procedure time ranged from comparable to reduced by 35% on average (20.1 ± 11.3 minutes vs 31.4 ± 10.2 minutes, P = .001) compared to manual procedures. Patient irradiation ranged from comparable to reduced by an average of 40% (324 ± 114.5 mGy vs 541.2 ± 446.8 mGy, P = .001). There was no significant difference in reported complications between manual biopsy and biopsies that utilized robotic guidance. Conclusion: Robotic systems demonstrate promising results for percutaneous lung biopsy. These devices provide adequate accuracy in probe placement and could both reduce procedural duration and mitigate radiation exposure to patients and practitioners. However, this review underscores the need for larger, controlled trials to validate and extend these findings.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer

Background and objective

Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models.

Patients and methods

A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots.

Results

A total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models.

Conclusion

The GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

Clinical Utility of a CT-based AI Prognostic Model for Segmentectomy in Non-Small Cell Lung Cancer

Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer-specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection. Results The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56-70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58-71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity. Conclusion The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria. © RSNA, 2024 Supplemental material is available for this article.

Introduction of the Danish Lung Nodule Registry: A part of the Danish Lung Cancer Registry

BACKGROUND

The majority of lung cancer cases are diagnosed late, resulting in poor prognosis and high mortality rates. Early detection and management of lung cancer can improve patient outcomes and reduce mortality rates. Pulmonary nodules are key factors in the early detection of lung cancer, they are common in high-risk populations and require correct classification to determine whether they are benign or malignant. Over the last decade a steep increase in the number of thoracic CT scans has been seen in Denmark, resulting in substantial resources allocated to CT follow-up of incidentally detected pulmonary nodules. The implementation of a nationwide Danish prospective pulmonary nodule registry is to methodically record pulmonary nodules and thereby evaluate the scope of pulmonary nodule follow-up, the nature of the nodules, and the clinical progression of patients with pulmonary nodules.

METHODS

A prospective pulmonary nodule registry (Danish Lung Nodule Registry) will be a natural appendix to the Danish Lung Cancer Registry. Three new ICD-10 classification codes will be introduced, defining the type of nodule: /DR91.1/ Solid nodule /DR91.2/ Part-solid nodule; /DR91.3/ Non-solid nodule. Furthermore, an additional letter will describe whether the imaging exam is performed on suspicion of lung cancer (A), or the finding is incidental (B). Registration of the nodules will be performed by the departments of respiratory medicine who manage follow-up of pulmonary nodules. It is estimated that around 7000 nodules will be registered annually.

DISCUSSION

The registration of patients in the lung nodule registry complies with current Danish legislation. The registry will be seamlessly integrated with other nationwide Danish registries, including the Danish Lung Cancer Registry, to collect additional patient data and improve the quality and scope of the data acquired. The results from these comprehensive epidemiological studies will be of significant interest and offer valuable research opportunities.

A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules

BACKGROUND

Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked.

RESEARCH QUESTION

Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing?

STUDY DESIGN AND METHODS

Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome.

RESULTS

In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not.

INTERPRETATION

The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Diagnostic Performance of MRI for the Detection of Pulmonary Nodules: A Systematic Review and Meta-Analysis

Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Keywords: Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT Supplemental material is available for this article. © RSNA, 2024.

Significance of lung nodules detected on chest CT among adult Aboriginal Australians - a retrospective descriptive study

INTRODUCTION

There are limited data on chest computed tomography (CT) findings in the assessment of lung nodules among adult Aboriginal Australians. In this retrospective study, we assessed lung nodules among a group of adult Aboriginal Australians in the Northern Territory of Australia.

METHODS

Patients who underwent at least two chest CT scans between 2012 and 2020 among those referred to undergo lung function testing (spirometry) were included. Chest CT scans were assessed for the number, location, size and morphological characteristics of lung nodules.

RESULTS

Of the 402 chest CTs assessed, 75 patients (18.7%) had lung nodules, and 57 patients were included in the final analysis with at least two CT scans available for assessment over a median follow-up of 87 weeks. Most patients (68%) were women, with a median age of 58 years and smoking history in 83%. The majority recorded only a single nodule 43 (74%). Six patients (10%) were diagnosed with malignancy, five with primary lung cancer and one with metastatic thyroid cancer. Of the 51 (90%) patients assessed to be benign, 64 nodules were identified, of which 25 (39%) resolved, 38 (59%) remained stable and one (1.8%) enlarged on follow-up. Nodules among patients with malignancy were typically initially larger and enlarged over time, had spiculated margins and were solid, showing no specific lobar predilection.

CONCLUSIONS

Most lung nodules in Aboriginal Australians are likely to be benign. However, a proportion could be malignant. Further prospective studies are required for prognostication and monitoring of lung nodules in this population.

The association of regional block with intraoperative opioid consumption in patients undergoing video-assisted thoracoscopic surgery: a single-center, retrospective study

BACKGROUND

Regional block, such as thoracic epidural analgesia (TEA), thoracic paravertebral block (TPVB), or serratus anterior plane block (SAPB) has been recommended to reduce postoperative opioid use in recent guidelines, but the optimal options for intraoperative opioid minimization remain unclear. The aim of this study was to evaluate the intraoperative opioids-sparing effects of three regional blocks (TEA, TPVB, and SAPB) in patients undergoing video-assisted thoracoscopic surgery (VATs).

METHODS

This was a retrospective study of the adults undergoing VATs at a tertiary medical center between January 2020 and February 2022. According to the type of regional block used, patients were classified into 4 groups: GA group (general anesthesia without any regional block), TEA group (general anesthesia combined with TEA), TPVB group (general anesthesia combined with TPVB), and SAPB group (general anesthesia combined with SAPB). Cases were matched with a 1:1:1:1 ratio for analysis by age, sex, ASA physical status, and operation duration. The primary outcome was the total intraoperative opioid consumption standardized to Oral Morphine Equivalents (OME). Multivariable linear regression was used to estimate the association of the three regional blocks with the OME.

RESULTS

A total of 2159 cases met the eligibility criteria. After matching, 168 cases (42 in each group) were included in analysis. Compared with GA without any reginal block, the use of TEA, TPVB, and SAPB reduced the median of intraoperative OME by 78.45 mg (95% confidence interval [CI], -141.34 to -15.56; P = 0.014), 94.92 mg (95% CI, -154.48 to -35.36; P = 0.020), and 11.47 mg (95% CI, -72.07 to 49.14; P = 0.711), respectively.

CONCLUSIONS

The use of TEA or TPVB was associated with an intraoperative opioid-sparing effect in adults undergoing VATs, whereas the intraoperative opioid-sparing effect of SAPB was not yet clear.

Tackling Guideline Non-concordance: Primary Care Barriers to Incorporating Life Expectancy into Lung Cancer Screening Decision-Making-A Qualitative Study

BACKGROUND

Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS.

OBJECTIVE

We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection.

DESIGN

Qualitative study based on semi-structured telephone interviews.

PARTICIPANTS

Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities.

APPROACH

We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations.

KEY RESULTS

PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows.

CONCLUSIONS

Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS.

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models

OBJECTIVE

To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features.

METHODS

We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance.

RESULTS

On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840.

CONCLUSION

Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.

New developments in the imaging of lung cancer

Radiological and nuclear medicine methods play a fundamental role in the diagnosis and staging of patients with lung cancer. Imaging is essential in the detection, characterisation, staging and follow-up of lung cancer. Due to the increasing evidence, low-dose chest computed tomography (CT) screening for the early detection of lung cancer is being introduced to the clinical routine in several countries. Radiomics and radiogenomics are emerging fields reliant on artificial intelligence to improve diagnosis and personalised risk stratification. Ultrasound- and CT-guided interventions are minimally invasive methods for the diagnosis and treatment of pulmonary malignancies. In this review, we put more emphasis on the new developments in the imaging of lung cancer.

Management of screening-detected ground glass nodules: a narrative review

Wide-scale application of low-dose computed tomography (LDCT) in lung cancer screening has led to an increased detection of ground glass nodule (GGN) lesions. However, there is still no clear management plan for these lesions after detection. Clinicians are usually faced with a dilemma in choosing the best initial management approach that not only limits overtreatment but also avoids the possibility of lesions growing into invasive carcinoma. Most current and past guidelines favor surveillance with computed tomography (CT) as the initial management approach based on the notion that the majority of GGN lesions are indolent tumors. Immediate surgery is generally considered overtreatment and is usually only recommended when the lesion grows in size, persists, or increases its solid component during follow-up CT surveillance. However, due to evolution of surgery to minimal invasive procedures, such as uniportal video-assisted thoracic surgery, and the development of enhanced recovery after thoracic surgery protocols, modern surgery is now safer and associated with less postoperative mortality. Additionally, intraoperative frozen sections can be used to guide resection, making initial management via surgery more attractive than before. Based on these developments, this review recommends that immediate surgery should be considered at the same level as follow-up CT surveillance when making multidisciplinary team decisions for screening-detected GGNs, as it provides both a diagnostic and treatment role.

Imaging modalities during navigational bronchoscopy

INTRODUCTION

Lung nodules are commonly encountered in clinical practice. Technological advances in navigational bronchoscopy and imaging modalities have led to paradigm shift from nodule screening or follow-up to early lung cancer detection. This is due to improved nodule localization and biopsy confirmation with combined modalities of navigational platforms and imaging tools. To conduct this article, relevant literature was reviewed via PubMed from January 2014 until January 2024.

AREAS COVERED

This article highlights the literature on different imaging modalities combined with commonly used navigational platforms for diagnosis of peripheral lung nodules. Current limitations and future perspectives of imaging modalities will be discussed.

EXPERT OPINION

The development of navigational platforms improved localization of targets. However, published diagnostic yield remains lower compared to percutaneous-guided biopsy. The discordance between the actual location of lung nodule during the procedure and preprocedural CT chest is the main factor impacting accurate biopsies. The utilization of advanced imaging tools with navigation-based bronchoscopy has been shown to assist with localizing targets in real-time and improving biopsy success. However, it is important for interventional bronchoscopists to understand the strengths and limitations of these advanced imaging technologies.

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.

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).

Robotic-assisted bronchoscopy for the diagnosis of peripheral pulmonary lesions: A systematic review and meta-analysis

Robotic-assisted bronchoscopy (RAB) is a newly developed bronchoscopic technique for the diagnosis of peripheral pulmonary lesions (PPLs). The objective of this meta-analysis was to analyze the diagnostic yield and safety of RAB in patients with PPLs. Five databases (PubMed, Embase, Web of Science, CENTRAL, and ClinicalTrials.gov) were searched from inception to April 2023. Two independent investigators screened retrieved articles, extracted data, and assessed the study quality. The pooled diagnostic yield and complication rate were estimated. Subgroup analysis was used to explore potential sources of heterogeneity. Publication bias was assessed using funnel plots and the Egger test. Sensitivity analysis was also conducted to assess the robustness of the synthesized results. A total of 725 lesions from 10 studies were included in this meta-analysis. No publication bias was found. Overall, RAB had a pooled diagnostic yield of 80.4% (95% CI: 75.7%-85.1%). Lesion size of >30 mm, presence of a bronchus sign, and a concentric radial endobronchial ultrasound view were associated with a statistically significantly higher diagnostic yield. Heterogeneity exploration showed that studies using cryoprobes reported better yields than those without cryoprobes (90.0%, 95% CI: 83.2%-94.7% vs. 79.0%, 95% CI: 75.8%-82.2%, p < 0.01). The pooled complication rate was 3.0% (95% CI: 1.6%-4.4%). In conclusion, RAB is an effective and safe technique for PPLs diagnosis. Further high-quality prospective studies still need to be conducted.

Sub-lobar resections for peripheral non-small cell lung cancer measuring ≤ 2 cm: Insights from recent clinical trials

The findings of two well conducted trials that randomised 1803 patients with a peripheral non-small cell lung cancer measuring ≤ 2 cm to a lobar to sub-lobar resection have established the latter as a new standard of care. It is important for non-surgical oncologists to appreciate the details of study design and outcomes of both studies, given the possible impact they have for considerations of stereotactic ablative radiotherapy (SABR) for operable patients with early-stage NSCLC. Differences in overall survival between the study populations highlight the impact of confounding factors like smoking history and comorbidities on reported outcomes. For example, despite low post-operative mortality rates in both trials, the 5-year disease-free survival rate in the CALGB 140503 trial was only approximately 60 % with either surgical procedure. Both phase III trials required guideline recommended nodal staging, which does not reflect real world surgical practice, and which may limit the generalisability of the reported findings to local institutional outcomes. Furthermore, the emergence of other malignancies was recorded in 15-18 % of study patients during follow-up, and patients who underwent sub-lobar resections had a better long-term survival associated with a higher likelihood of undergoing additional curative treatments. These findings from the JCOG0802 and the CALGB 140503 will encourage more interest in enrolling patients into ongoing trials comparing surgical resection with SABR.

Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

Multimodal fusion of liquid biopsy and CT enhances differential diagnosis of early-stage lung adenocarcinoma

This research explores the potential of multimodal fusion for the differential diagnosis of early-stage lung adenocarcinoma (LUAD) (tumor sizes < 2 cm). It combines liquid biopsy biomarkers, specifically extracellular vesicle long RNA (evlRNA) and the computed tomography (CT) attributes. The fusion model achieves an impressive area under receiver operating characteristic curve (AUC) of 91.9% for the four-classification of adenocarcinoma, along with a benign-malignant AUC of 94.8% (sensitivity: 89.1%, specificity: 94.3%). These outcomes outperform the diagnostic capabilities of the single-modal models and human experts. A comprehensive SHapley Additive exPlanations (SHAP) is provided to offer deep insights into model predictions. Our findings reveal the complementary interplay between evlRNA and image-based characteristics, underscoring the significance of integrating diverse modalities in diagnosing early-stage LUAD.

An International Survey of Practices in the Investigation and Endoscopic Treatment of Peripheral Pulmonary Lesions amongst Interventional Bronchoscopists

INTRODUCTION

The investigation of peripheral pulmonary lesions (PPLs) can be challenging. Several bronchoscopic modalities have been developed to reach and biopsy PPL but the level of adoption of these techniques by interventional pulmonologists (IPs) is unknown. This international survey was conducted to describe current practices in PPL investigation among IP.

METHODS

This survey was sent to all members of the World Association for Bronchology and Interventional Pulmonology, Canadian Thoracic Society Procedures Assembly, AABIP, and the Groupe d'Endoscopie Thoracique et Interventionnel Francophone. The survey was composed of 48 questions and three clinical cases to establish a portrait of modalities used to investigate and treat PPL by IP around the world.

RESULTS

Three hundred and twelve IP responded to the survey. Most of them practice in Europe (n = 122), North America (n = 97), and Asia (n = 49). Half of responders perform more than 100 endoscopic procedures for PPL annually. General anesthesia and conscious sedation are used in similar proportions (53% and 47%, respectively). Rapid on site evaluation (ROSE) is used when sampling PPL by 42%. Radial EBUS (69%), fluoroscopy (55%), and electromagnetic navigation (27%) are the most widely used techniques. Most IP combine techniques (89%). Robotic bronchoscopy (15%) and cone-beam CT (8%) are almost exclusively used in the USA where, respectively, 60% and 37% of respondents reported using these modalities. Ten percent of IP currently had access to endoscopic treatment modalities for PPL. However, half of the remaining IP plan to acquire an endoscopic treatment modality in the next 2 years.

CONCLUSION

Available techniques and practices worldwide vary significantly regarding PPL investigation and treatment.

Incidental pulmonary nodules may lead to a high proportion of early-stage lung cancer: but it requires more than a high CT volume to achieve this

Background

The management of pulmonary nodules plays a critical role in early detection of lung cancer. Computed tomography (CT) has led to a stage-shift towards early-stage lung cancer, but regional differences in survival rates have been reported in Denmark. This study aimed to evaluate whether variations in nodule management among Danish health regions contributed to these differences.

Material and Methods

The Danish Health Data Authority and Danish Lung Cancer Registry provided data on CT usage and lung cancer stage distribution, respectively. Auditing of lung cancer stage IA patient referrals and nodule management of stage IV lung cancer patients was conducted in seven Danish lung cancer investigation centers, covering four of the five Danish health regions. CT scans were performed up to 2 years before the patients' diagnosis from 2019 to 2021.

Results

CT usage has increased steadily in Denmark over the past decade, with a simultaneous increase in the proportion of early-stage lung cancers, particularly stage IA. However, one Danish health region, Region Zealand, exhibited lower rates of early-stage lung cancer and overall survival despite a CT usage roughly similar to that of the other health regions. The audit did not find significant differences in pulmonary nodule management or a higher number of missed nodules by radiologists in this region compared to others.

Conclusion

This study suggests that a high CT scan volume alone is not sufficient for the early detection of lung cancer. Factors beyond hospital management practices, such as patient-related delays in socioeconomically disadvantaged areas, may contribute to regional differences in survival rates. This has implications for future strategies for reducing these differences.

Synchrotron Radiation Refraction-Contrast Computed Tomography Based on X-ray Dark-Field Imaging Optics of Pulmonary Malignancy: Comparison with Pathologic Examination

Refraction-contrast computed tomography based on X-ray dark-field imaging (XDFI) using synchrotron radiation (SR) has shown superior resolution compared to conventional absorption-based methods and is often comparable to pathologic examination under light microscopy. This study aimed to investigate the potential of the XDFI technique for clinical application in lung cancer diagnosis. Two types of lung specimens, primary and secondary malignancies, were investigated using an XDFI optic system at beamline BL14B of the High-Energy Accelerator Research Organization Photon Factory, Tsukuba, Japan. Three-dimensional reconstruction and segmentation were performed on each specimen. Refraction-contrast computed tomographic images were compared with those obtained from pathological examinations. Pulmonary microstructures including arterioles, venules, bronchioles, alveolar sacs, and interalveolar septa were identified in SR images. Malignant lesions could be distinguished from the borders of normal structures. The lepidic pattern was defined as the invasive component of the same primary lung adenocarcinoma. The SR images of secondary lung adenocarcinomas of colorectal origin were distinct from those of primary lung adenocarcinomas. Refraction-contrast images based on XDFI optics of lung tissues correlated well with those of pathological examinations under light microscopy. This imaging method may have the potential for use in lung cancer diagnosis without tissue damage. Considerable equipment modifications are crucial before implementing them from the lab to the hospital in the near future.

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.

Radiologic patterns of distant organ metastasis in advanced breast cancer patients: Prospective review of computed tomography images

BACKGROUND

Breast cancer (BC) metastases to the abdomen and pelvis affect the liver, mesentery, retroperitoneum, peritoneum, bladder, kidney, ovary, and uterus. The study documented the radiological pattern and features of the chest, bone, abdominal and pelvic (AP) metastases among advanced BC patients.

AIM

The aim is to document the radiological pattern and features of breast cancer metastasis in the chest, abdomen, pelvis and bones.

MATERIALS AND RESULTS

Chest, abdominal, and pelvic computed tomography scan images of 36 patients with advanced BC were collated from Cape Coast Teaching Hospital and RAAJ Diagnostics. The images were prospectively assessed for metastasis to the organs of the chest, AP soft tissues, and bones. Radiologic features of metastasis of the lungs, liver, lymph nodes (LNs), and bones were documented. Patients' demographics, clinical data, and histopathology reports were also collected. The data were captured using UVOSYO and exported to Microsoft Excel templates. The data obtained were descriptively analyzed. Only 2.8% of BCs exhibited metaplastic BC, whereas 97.2% had invasive ductal BC. Triple-negative cases were 55.6%. Of 36 patients, 31 (86.1%), 21 (58.3%), and 14(38.8%) were diagnosed of chest, AP, and bone tissues metastasis, respectively. LN involvement was reported in 26 (72.2%) patients. Majority, 21 (58.3%) were diagnosed of multiple sites metastasis with 15 (41.7%) showing single site. Lungs (77.4%, 24/31) and liver (47.6%, 10/21) were the most affected distant organs. Most bone metastases were lytic lesions (92.9%, 13/14) with the vertebrae (85.7%, 12/14) been the most affected.

CONCLUSION

According to the study, advanced BC patients have a higher-than-average radiologic incidence of lung, liver, bone, and LN metastases.

The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes

Background

Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes.

Methods

From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs.

Results

In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001).

Conclusions

CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.

Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules

BACKGROUND

During lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning (ML) models. We incorporated dedicated techniques to enhance prediction explainability.

METHODS

In total, 724 IPNs (size 50-500 mm3, 575 participants) from the Dutch-Belgian Randomized Lung Cancer Screening Trial were used. We implemented six ML models and 14 factors to predict nodule disappearance. Random search was applied to determine the optimal hyperparameters on the training set (579 nodules). ML models were trained using 5-fold cross-validation and tested on the test set (145 nodules). Model predictions were evaluated by utilizing the recall, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). The best-performing model was used for three feature importance techniques: mean decrease in impurity (MDI), permutation feature importance (PFI), and SHAPley Additive exPlanations (SHAP).

RESULTS

The random forest model outperformed the other ML models with an AUC of 0.865. This model achieved a recall of 0.646, a precision of 0.816, and an F1 score of 0.721. The evaluation of feature importance achieved consistent ranking across all three methods for the most crucial factors. The MDI, PFI, and SHAP methods highlighted volume, maximum diameter, and minimum diameter as the top three factors. However, the remaining factors revealed discrepant ranking across methods.

CONCLUSION

ML models effectively predict IPN disappearance using participant demographics and nodule characteristics. Explainable techniques can assist clinicians in developing understandable preliminary assessments.

Traumatic tumor hemorrhage of inflammatory myofibroblastic tumor of the lung

A 23-year-old female with a history of idiopathic epilepsy was found to have a right chest cavity shadow in a school health checkup 5 years before. CT revealed a thin-walled cavity lesion in the right middle lobe containing a ball-like mass, showing air crescent sign. After falling due to a seizure, she was transported by ambulance and admitted. CT revealed diffuse ground-glass opacities throughout the right lung field. Bronchoscopy revealed bloody bronchial alveolar lavage fluid. Due to the tumor hemorrhage, an elective simple right middle lobe resection was performed without complications. The initial immunohistochemical staining was negative for ALK using ALK1 clone; however, subsequent staining of ALK by D5F3 and 5A4 clone was positive. Immunostaining findings led to a diagnosis of inflammatory myofibroblastic tumor. The patient remains under regular observation and has experienced no recurrence over the 6-year postoperative period. This case contains two different points: the first is that a cavity lesion of inflammatory myofibroblastic tumor may cause traumatic bleeding and should be treated with caution; the second is that attention should be paid to differences in stainability among clones when diagnosing inflammatory myofibroblastic tumor.

A user-friendly deep learning application for accurate lung cancer diagnosis

BACKGROUND

Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis.

METHODS

This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit.

RESULTS

The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful.

CONCLUSIONS

The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.

A Prospective Observational Study of Diagnostic Reliability of Semiquantitative and Quantitative High b-Value Diffusion-Weighted MRI in Distinguishing between Benign and Malignant Lung Lesions at 3 Tesla

Aim  The aim of this study was to evaluate the usefulness of high b-value diffusion-weighted imaging (DWI) to differentiate benign and malignant lung lesions in 3 Tesla magnetic resonance imaging (MRI). Materials and Methods  Thirty-one patients with lung lesions underwent a high b-value (b= 1000 s/mm 2 ) DW MRI in 3 Tesla. Thirty lesions were biopsied, followed by histopathological analysis, and one was serially followed up for 2 years. Statistical analysis was done to calculate the sensitivity, specificity, and accuracy of different DWI parameters in distinguishing benign and malignant lesions. Receiver operating characteristic (ROC) curves were used to determine the cutoff values of different parameters. Results  The qualitative assessment of signal intensity on DWI based on a 5-point rank scale had a mean score of 2.71 ± 0.75 for benign and 3. 75 ± 0.60 for malignant lesions. With a cutoff of 3.5, the sensitivity, specificity, and accuracy were 75, 86, and 77.6%, respectively. The mean ADC min (minimum apparent diffusion coefficient) value of benign and malignant lesions was 1. 49 ± 0.38 × 10-3 mm 2 /s and 1.11 ± 0.20 ×10-3 mm 2 /s, respectively. ROC curve analysis showed a cutoff value of 1.03 × 10-3 mm 2 /s; the sensitivity, specificity, and accuracy were 87.5, 71.4, and 83.3%, respectively. For lesion to spinal cord ratio and lesion to spinal cord ADC ratio with a cutoff value of 1.08 and 1.38, the sensitivity, specificity, and accuracy were 83.3 and 87.5%, 71.4 and 71.4%, and 80.6 and 83.8%, respectively. The exponential ADC showed a low accuracy rate. Conclusion  The semiquantitative and quantitative parameters of high b-value DW 3 Tesla MRI can differentiate benign from malignant lesions with high accuracy and make it a reliable nonionizing modality for characterizing lung lesions.

Cardiorespiratory fitness and morbidity and mortality in patients with non-small cell lung cancer: a prospective study with propensity score weighting

INTRODUCTION

This study aimed to investigate the association between cardiorespiratory fitness (CRF) and perioperative morbidity and long-term mortality in operable patients with early-stage non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

This prospective study included consecutive patients with early-stage NSCLC who underwent presurgical cardiopulmonary exercise testing between November 2014 and December 2019 (registration number: ChiCTR2100048120). Logistic and Cox proportional hazards regression were applied to evaluate the correlation between CRF and perioperative complications and long-term mortality, respectively. Propensity score overlap weighting was used to adjust for the covariates. We performed sensitivity analyses to determine the stability of our results.

RESULTS

A total of 895 patients were followed for a median of 40 months [interquartile range 25]. The median age of the patients was 59 years [range 26-83], and 62.5% were male. During the study period, 156 perioperative complications and 146 deaths were observed. Low CRF was associated with a higher risk of death (62.9 versus 33.6 per 1000 person-years; weighted incidence rate difference, 29.34 [95% CI, 0.32 to 58.36] per 1000 person-years) and perioperative morbidity (241.6 versus 141.9 per 1000 surgeries; weighted incidence rate difference, 99.72 [95% CI, 34.75 to 164.70] per 1000 surgeries). A CRF of ≤ 20 ml/kg/min was significantly associated with a high risk of long-term mortality (weighted hazard ratio, 1.98 [95% CI, 1.31 to 2.98], p < 0.001) and perioperative morbidity (weighted odds ratio, 1.93 [1.28 to 2.90], p = 0.002) compared to higher CRF.

CONCLUSION

The study found that low CRF is significantly associated with increased perioperative morbidity and long-term mortality in operable patients with early-stage NSCLC.