DNA image cytometry of bronchial washing as a diagnostic adjunct to radial endobronchial ultrasound-guided sampling of peripheral lung lesions: A single center prospective study

OBJECTIVE

The objective of this study is to study the adjunct role of combining DNA aneuploidy analysis with radial endobronchial ultrasound (R-EBUS)-guided sampling for diagnosis of peripheral lung lesions (PPLs).

METHOD

A single-center prospective study was conducted in patients undergoing R-EBUS-guided sampling for PPLs. DNA image cytometry (DNA-ICM) was used to analyze DNA aneuploidy in bronchial washing from the bronchial segment of the PPL. Clinical information, R-EBUS data, pathology, DNA-ICM results, and follow-up data were analyzed. Sensitivity, specificity, and predictive values for R-EBUS-guided sampling, DNA-ICM, and the two methods combined were measured. Binary logistic regression was performed to determine influencing factors on diagnostic positivity rate. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff point for DNA-ICM.

RESULTS

A total of 101 patients were enrolled. Sixty-four (63.4%) patients had confirmed malignant tumor, of whom 33 were confirmed by R-EBUS-guided sampling (biopsy and/or bronchial brush and wash cytology), and 31 by surgery or percutaneous lung biopsy. Thirty-seven patients were finally considered to have benign lesions, based on clinical information and 1-year follow-up. The sensitivity for malignant disease was 51.6% by R-EBUS, and specificity was 100%. DNA-ICM had a sensitivity of 67.2% and a specificity of 86.5%. When combining the two methods, sensitivity increased to 78.1% and specificity was 86.5%. Lesion size and whether the R-EBUS probe was located in the lesion were significantly associated with positivity rate of the combined methods. The optimal cutoff point for DNA-ICM was 5c for max DNA content, and 1 for aneuploid cell count (sensitivity 67.2%, specificity 86.5%, accuracy 63.4%).

CONCLUSION

In malignant PPLs, DNA-ICM combined with R-EBUS-guided sampling can improve diagnostic positivity compared with either method alone.

Combined Methylation of SHOX2 and RASSF1A Genes in Diagnosing Malignant Pleural Effusion

BACKGROUND

It is a significant challenge to identify pleural effusion (PE) through differential diagnosis in clinical settings. The present study endeavors to devise a strategy to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) by detecting gene methylation.

METHODS

This study recruited 214 patients with PE, among which 104 patients were identified with MPE, while the remaining 110 patients were categorized as having BPE. The methylation levels of short stature homeobox 2 (SHOX2) and RAS association domain family 1, isoform A (RASSF1A) genes were analyzed through methylation-specific polymerase chain reaction (MS-PCR).

RESULTS

The methylation status of either SHOX2 or RASSF1A genes was significantly elevated in MPE compared to BPE. The sensitivity and specificity of SHOX2 and RASSF1A methylation in diagnosing PE were 66.3% and 90.9%, respectively. The sensitivity of the combined methylation detection intended to diagnose pulmonary MPE was 73.5% and 52.8% in non-pulmonary MPE (p < 0.05), suggesting that combined detection of SHOX2 and RASSF1A methylation had high diagnostic value for lung cancer. In comparison to the results of cytology and DNA ploidy detection, methylation detection demonstrated a superior diagnostic efficiency in the diagnosis of lung cancer (p < 0.05). Additionally, the combined detection of SHOX2 and RASSF1A methylation was more potent in diagnosing BPE and MPE (p < 0.05), while compensating for the limitations of cytology and DNA ploidy detection.

CONCLUSIONS

The detection of SHOX2 and RASSF1A methylation can effectively differentiate between BPE and MPE, especially in diagnosing pulmonary MPE.

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer

Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Förster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 μL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.

Promising Mass-Productive 4-Inch Commercial SERS Sensor with Particle in Micro-Nano Porous Ag/Si/Ag Structure Using in Auxiliary Diagnosis of Early Lung Cancer

The construction of commercial surface enhanced Raman scattering (SERS) sensors suitable for clinical applications is a pending problem, which is heavily limited by the low production of high-performance SERS bases, because they usually require fine or complicated micro/nano structures. To solve this issue, herein, a promising mass-productive 4-inch ultrasensitive SERS substrate available for early lung cancer diagnosis is proposed, which is designed with a special architecture of particle in micro-nano porous structure. Benefitting from the effective cascaded electric field coupling inside the particle-in-cavity structure and efficient Knudsen diffusion of molecules within the nanohole, the substrate exhibits remarkable SERS performance for gaseous malignancy biomarker, with the limit of detection is 0.1 ppb and the average relative standard deviation value at different scales (from cm² to µm² ) is ≈16.5%. In practical application, this large-sized sensor can be further divided into small ones (1 × 1 cm² ), and more than 65 chips will be obtained from just one 4-inch wafer, greatly increasing the output of commercial SERS sensor. Further, a medical breath bag composed of this small chip is designed and studied in detail here, which suggested high-specificity recognition for lung cancer biomarker in mixed mimetic exhalation tests.

Multiarray Biosensor for Diagnosing Lung Cancer Based on Gap Plasmonic Color Films

Adaptable and sensitive materials are essential for the development of advanced sensor systems such as bio and chemical sensors. Biomaterials can be used to develop multifunctional biosensor applications using genetic engineering. In particular, a plasmonic sensor system using a coupled film nanostructure with tunable gap sizes is a potential candidate in optical sensors because of its simple fabrication, stability, extensive tuning range, and sensitivity to small changes. Although this system has shown a good ability to eliminate humidity as an interferant, its performance in real-world environments is limited by low selectivity. To overcome these issues, we demonstrated the rapid response of gap plasmonic color sensors by utilizing metal nanostructures combined with genetically engineered M13 bacteriophages to detect volatile organic compounds (VOCs) and diagnose lung cancer from breath samples. The M13 bacteriophage was chosen as a recognition element because the structural protein capsid can readily be modified to target the desired analyte. Consequently, the VOCs from various functional groups were distinguished by using a multiarray biosensor based on a gap plasmonic color film observed by hierarchical cluster analysis. Furthermore, the lung cancer breath samples collected from 70 healthy participants and 50 lung cancer patients were successfully classified with a high rate of over 89% through supporting machine learning analysis.

Pilot clinical study on the prevention of complications after lung biopsy by the MIPP kit PNX device

Background

Pneumothorax (PNX), pulmonary hemorrhage, hemothorax and chest wall hematoma are the most commonly reported complications of percutaneous lung biopsy (PLB). Sealing the biopsy tract with different types of materials is an emerging way to prevent PLB complications.

Methods

To investigate the safety and efficacy of a new device, Minimally Invasive Percutaneous Procedure Kit for Pneumothorax (MIPP-Kit PNX), when used in association with a resorbable bio-compatible glue in the prevention of PLB complications. A prospective, multicenter, open-label, single-arm study was performed to evaluate the complication rate after glue administration by the new investigational device during PLBs.

Results

Fourty-three patients were enrolled after informed consent signature (40 underwent PLB, while three were screening failures). Only 3 patients (7.5%, 95% CI: 0.0-15.7%) developed complications within 48 h after glue injection during PLB: two developed minor pneumothoraces and one a pulmonary hemorrhage. No patients who showed procedural complications before glue administration were reported with any recurrent or new complications after glue administration.

Conclusions

In comparison with the data reported in the literature, this trial results support the safe and effective use of the MIPP kit PNX in the prevention of PLB complications. These promising preliminary results warrant further confirmation in larger clinical trials.

Trial Registration

ClinicalTrials.gov identifier: NCT04071509.

Handling and standardization of EBUS needle aspiration in NSCLC patients: The value of the cell block, a monoinstitutional experience

BACKGROUND

Lung cancer is the main cause of cancer-related death worldwide, and 85% of all lung tumors are non-small cell lung cancers (NSCLC). More than 60% of all lung tumors are diagnosed at an advanced stage, leading to poor prognosis. Given the growing demand for NSCLC profiling for selection of the most appropriate therapy, the acquisition of adequate tumor samples has become increasingly crucial, mostly in advanced NSCLC patients due to old age and/or comorbidities. Being a mini-invasive sampling technique, endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) represents a valuable alternative to traditional transthoracic or surgical sampling in these patients, and perfoming cell block (CB) could be crucial to maximize the potential biological information. The aim of this study is to describe a monoinstitutional interprofessional experience in handling EBUS-TBNA and CB in 464 patients.

METHODS

We retrospectively collected all the consecutive CBs obtained from EBUS TBNA performed between 2014 and 2021 on the lung lesions or mediastinal lymph nodes. All the CBs were handled in a standardized method.

RESULTS

A total of 95.5% (448/464 samples) of adequacy for site and 92.6% (430/464) of adequacy for diagnosis were observed. Moreover, in the adenocarcinoma histotype, ALK, ROS1 and tumor proportion score (TPS) PD-L1 assessment by IHC was possible in 96% (140/146) of cases, and molecular profile was obtained in 93.8% (137/146) of cases. In the squamous cell carcinoma histotype, TPS PD-L1 assessment was possible in 81% (13/16) of cases. All four CB results obtained from carcinoma NOS were adequate for ALK, ROS1 and PD-L1 assessment and molecular profiling. All 39 metastatic samples from extra-pulmonary primary were adequate for immunohistochemical characterization and molecular profiling. Finally, reporting of the tumor sample adequacy to the clinicians took a median time of about 30 h (range: 24-80 h).

CONCLUSION

Careful cytological smear management together with the handling and standardization of CB obtained from EBUS-TBNA could represent an effective method to increase the adequacy of the tumor specimen for both diagnosis and molecular profile.

Surgery without preoperative histological confirmation of lung cancer: what is the current clinical practice?

Background

There are discordances in the guidelines regarding the need to acquire histological diagnosis before surgical treatment of (presumed) lung cancer. Preoperative histological confirmation is always encouraged in this setting to prevent unnecessary surgery or when sublobar resection for small-sized tumors is considered. The aim of this retrospective cohort study was to assess the proportion of patients undergoing lung cancer resection in the Netherlands without preoperative pathological confirmation, based on the intraoperative pathological diagnosis (IOD) rate, and to determine characteristics that may influence IOD frequency.

Methods

Data on 10,226 patients, who underwent surgical treatment for lung cancer from 2010 to 2015, were retrieved from the Netherlands National Cancer Registry. We registered an IOD when the date of diagnosis equaled the date of the first surgical intervention. Tabulations and multivariable logistic regression were used to identify predictive parameters for IOD.

Results

36% of surgical procedures were classified as IOD, and decreased with increasing tumor size and extent of surgery (57% for segmentectomy, 39% for lobectomy and 11% for pneumonectomy). IOD was more frequently observed in adenocarcinoma (41%), varied between hospitals from 13% to 66% and was less common when patients were referred from a hospital where thoracic surgery was not performed. Previous history of cancer did not affect IOD.

Conclusions

More than one-third of patients with suspected lung cancer in the Netherlands was operated without preoperative histological confirmation. There was significant variation in IOD rates between different hospitals, which deserves further detailed analysis when striving for uniform surgical quality of care for patients with lung cancer.

MGMT gene promoter methylation in humoral tissue as biomarker for lung cancer diagnosis: An update meta-analysis

Objective

To investigate O‐6‐methylguanine‐DNA methyltransferase (MGMT) gene promoter methylation in humoral tissue as biomarker for lung cancer diagnosis by pooling relevant open published data.

Methods

Clinical studies relevant to MGMT gene promoter methylation and lung cancer were systematic electronic searched in the databases of Medline, EMBASE, Ovid, Web of Science, and CNKI. Data of true positive (tp), false positive (fp), false negative (fn), and true negative (tn) were extracted from the included studies and made combination. The diagnostic sensitivity, specificity, diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) of MGMT gene methylation for lung cancer diagnosis were pooled.

Results

Twelve studies were included in the meta‐analysis. The diagnostic sensitivity, specificity, DOR were 0.39 (95% CI = 0.31–0.49) 0.92 (95% CI = 0.77–0.97), and 4.20 (95% CI = 2.09–8.44), respectively under random effect model. The SROC of MGMT gene methylation for lung cancer diagnosis was 0.58 (95% CI = 0.53–0.62).

Conclusion

MGMT methylation rate was higher in plasma and bronchoalveolar lavage fluid (BLAF) of lung cancer cases compared to controls. High diagnostic specificity indicated that MGMT methylation in plasma and BLAF can be applied as lung cancer confirmation test.

Robotic Bronchoscopy for Peripheral Pulmonary Lesion Biopsy: Evidence-Based Review of the Two Platforms

Despite many advancements in recent years for the sampling of peripheral pulmonary lesions, the diagnostic yield remains low. Initial excitement about the current electromagnetic navigation platforms has subsided as the real-world data shows a significantly lower diagnostic sensitivity of ~70%. “CT-to-body divergence” has been identified as a major limitation of this modality. In-tandem use of the ultrathin bronchoscope and radial endobronchial ultrasound probe has yielded only comparable results, attributable to the limited peripheral reach, device maneuverability, stability, and distractors like atelectasis. As such, experts have identified three key steps in peripheral nodule sampling—navigation (to the lesion), confirmation (of the correct location), and acquisition (tissue sampling by tools). Robotic bronchoscopy (RB) is a novel innovation that aspires to improve upon these aspects and consequently, achieve a better diagnostic yield. Through this publication, we aim to review the technical aspects, safety, feasibility, and early efficacy data for this new diagnostic modality.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

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

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

Lung cancer - dia nosis and early detection

Decisions about the treatment of a patient with lung cancer depend on the clinical stage of the disease, morphological diagnosis, examination of predictive markers and overall clinical condition; the wishes of a well-informed patient must also be taken into account. Accurate diagnosis is essential for the future of a patient with lung cancer. The epidemiology of lung cancer is related to cigarette consumption. The risk of the disease increases with the number of cigarettes smoked. The relative risk for smokers is 22.4, for very heavy smokers with a load of more than 25 packets, it can reach up to 50. Most cases of lung cancer are caught in the advanced stages of the disease, when surgery and sometimes other active treatments are no longer possible. Searching for lung cancer in at-risk groups is essential for reducing lung cancer mortality, leading to the detection of the disease at a low stage when the tumor is operable.

Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment

Simple Summary

In this study we explored the predictive ability of radiomics in non-small cell lung cancer patients, and reported the complementary role of radiomics in predicting the treatment response of the lymph nodes. Radiomics analysis is a cutting-edge technology for the noninvasive assessment of tumor biology, which converts medical images into mineable high-dimensional data. Our method is cost-effective with no need for additional studies, and moreover, we used an easily reproducible study method that can be applicable in further studies using radiomics in oncology.

Abstract

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the pathologic complete response of LNs showing residual uptake after neoadjuvant concurrent chemoradiotherapy (NeoCCRT) in patients with non-small cell lung cancer (NSCLC). Two hundred and thirty-seven patients who underwent NeoCCRT for stage IIIa NSCLC were included. Two hundred fifty-two CT radiomics features were extracted from LNs showing remaining positive FDG uptake upon restaging PET-CT. A multivariable logistic regression analysis of radiomics features and clinicopathologic characteristics was used to develop a prediction model. Of the 237 patients, 135 patients (185 nodes) met our inclusion criteria. Eighty-seven LNs were proven to be malignant (47.0%, 87/185). Upon multivariable analysis, metastatic LNs were significantly prevalent in females and patients with adenocarcinoma (odds ratio (OR) = 2.02, 95% confidence interval (CI) = 0.88–4.62 and OR = 0.39, 95% CI = 0.19–0.77 each). Metastatic LNs also had a larger maximal 3D diameter and higher cluster tendency (OR = 9.92, 95% CI = 3.15–31.17 and OR = 2.36, 95% CI = 1.22–4.55 each). The predictive model for metastasis showed a discrimination performance with an area under the receiver operating characteristic curve of 0.728 (95% CI = 0.654–0.801, p value < 0.001). The radiomics approach allows for the noninvasive detection of metastases in LNs with residual FDG uptake after the treatment of NSCLC patients.

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods have demonstrated performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by end users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level semantic features; and 2) a high-level prediction of nodule malignancy. The low-level outputs reflect diagnostic features often reported by radiologists and serve to explain how the model interprets the images in an expert-interpretable manner. The information from these low-level outputs, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level output. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to using a 3D CNN alone.

A Community-based Pulmonary Nodule Clinic: Improving Lung Cancer Stage at Diagnosis

Objective

Pulmonary nodules (PNs) are a common incidental finding and are often how lung cancer is discovered. Our goal was to determine if establishing a pulmonary nodule clinic (PNC) in a community healthcare setting would lead to an earlier stage at diagnosis.

Methods

A single healthcare system retrospective review was conducted of all PNC patients from 2010-2015 diagnosed with lung cancer. The stage at diagnosis was analyzed and compared to lung cancer patients in our healthcare system outside the PNC and to national data. Five-year survival rates for PNC patients from 2010-2012 were also analyzed.

Results

 A total of 119 patients and 127 lung cancers were diagnosed through the PNC from 2010-2015. There were 990 lung cancers, with a known stage, diagnosed outside the PNC in our healthcare system from 2010 to 2015. Two hundred and eighty one (28.4%) cancers were Stage I, compared to 69 (54.3%) (p <0.0001) through the PNC; 110 (11.1%) cancers were diagnosed at Stage II compared to 17 (13.4%) through the PNC (0.4471); 277 (25.7%) cancers were diagnosed at Stage III, compared to 21 (16.5%) through the PNC (p 0.0060); 598 (60.4%) cancers were diagnosed at Stage IV, compared to 20 (15.7%) through the PNC (p <0.0001). Five-year survival rates for patients diagnosed in 2010 were 80% (four of five patients), 79.2% (19/24) in 2011, and 62.2% (23/37) in 2012.

Conclusions

 Lung cancer survival is directly related to the stage at diagnosis. Establishment of our PNC has led to an earlier stage at diagnosis compared to the general lung cancer population in our community.

Utility of Various Bronchoscopic Modalities in Lung Cancer Diagnosis

Background: Bronchoscopy and different techniques to obtain tissue sample form the cornerstone of lung cancer diagnostics. The utility, specificity and sensitivity of various techniques are compared against each other and also in relation to the tumour type, location and morphology. Aim: To assess and compare the utility of various bronchoscopic procedures in lung malignancies. Study also compares the utility of these techniques on tumour site, morphology and cell type. Methods: One hundred and fifty six patients with suspected malignancies in whom bronchoscopy was deemed as the primary diagnostic procedure were selected. These patients underwent bronchoscopic lavage, brushings and biopsy. Samples were assessed by microbiological, cytology and histopathological analysis. Results: Bronchoscopic procedures have a high diagnostic accuracy of 81.25% in confirming lung malignancies in central tumours and also in non-accessible peripheral tumours. Bronchial brushings had the highest yield in central tumours (55.9%) and a reasonably good yield even in perpipheral tumours (40.8%). When all the modalities were compared against each other, brushings was the single most decisive technique as it alone yielded a diagnosis in a significant 33% of cases, whereas the comparative diagnosis by biopsy alone was in a minority 7.6% of cases.

Evaluation of an interactive science publishing tool: toward enabling three-dimensional analysis of medical images

RATIONALE AND OBJECTIVES

Higher resolution medical imaging platforms are rapidly emerging, but there is a challenge in applying these tools in a clinically meaningful way. The purpose of the current study was to evaluate a novel three-dimensional (3D) software imaging environment, known as interactive science publishing (ISP), in appraising 3D computed tomography images and to compare this approach with traditional planar (2D) imaging in a series of lung cancer cases.

MATERIALS AND METHODS

Twenty-four physician volunteers at different levels of training across multiple specialties were recruited to evaluate eight lung cancer-related clinical vignettes. The volunteers were asked to compare the performance of traditional 2D versus the ISP 3D imaging in assessing different visualization environments for diagnostic and measurement processes and to further evaluate the ISP tool in terms of general satisfaction, usability, and probable applicability.

RESULTS

Volunteers were satisfied with both imaging methods; however, the 3D environment had significantly higher ratings. Measurement performance was comparable using both traditional 2D and 3D image evaluation. Physicians not trained in 2D measurement approaches versus those with such training demonstrated better performance with ISP and preferred working in the ISP environment.

CONCLUSIONS

Recent postgraduates with only modest self-administered training performed equally well on 3D and 2D cases. This suggests that the 3D environment has no reduction in accuracy over the conventional 2D approach, while providing the advantage of a digital environment for cross-disciplinary interaction for shared problem solving. Exploration of more effective, efficient, self-directed training could potentially result in further improvement in image evaluation proficiency and potentially decrease training costs.