Clinical characteristics and prognosis of incidentally detected lung cancers

Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population

OBJECTIVES

Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs.

DESIGN

This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs.

PARTICIPANTS

5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer).

RESULTS

Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases.

CONCLUSIONS

The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.

Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs

PURPOSE

Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer.

MATERIALS AND METHODS

Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs.

RESULTS

Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively.

CONCLUSION

This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.

Increased use of computed tomography in Denmark: stage shift toward early stage lung cancer through incidental findings

BACKGROUND

Computed tomography (CT) examinations are increasingly used worldwide and incidental findings are growing likewise. Lung cancer stage at diagnosis is pivotal to survival. The earliest stage of lung cancer, stage IA is in most cases asymptomatic. Potentially, increased use of clinical CTs could induce a stage shift toward earlier lung cancer diagnosis.

MATERIALS AND METHODS

Data on the number of CT thorax in Denmark and the stage distribution of Danish lung cancer patients 2013-2020 were acquired from, respectively, the Danish Health Data Authority and the Danish Lung Cancer Registry. Clinical auditing of stage IA lung cancer patients was performed in the period 2019-2021 in a Danish region to assess the reasons for referral. Auditing of stage IV lung cancer patients was done to see whether a CT thorax was performed in a two-year period before diagnosis.

RESULTS

All regions showed an increase in CTs per 1000 inhabitants. However, the number of CTs performed in 2013 differed by more than 50% among regions, and the increase per year also differed, from an increase of 1.9 to 3.4 more examinations per year. A significant correlation between CTs and fraction of stage IA lung cancers was seen in four out of the five regions. The audit of stage IA lung cancer cases revealed that 86.8% were incidental findings. Audit of stage IV lung cancer found that 4.3% had a nodule/infiltrate on a previous CT within a 2-year period prior to the diagnosis of lung cancer that was the probable origin of stage IV lung cancer.

CONCLUSION

The study found that the vast majority of early-stage lung cancers were incidental findings. It highlights that follow-up algorithms of incidental findings should be used in accordance with guidelines and it should be unequivocally how the CT follow-up of pulmonary infiltrates is managed.

Application of Artificial Intelligence in Lung Cancer

Simple Summary

Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising.

Abstract

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay

Although the coronavirus disease of 2019 (COVID-19) pandemic had profound pernicious effects, it revealed deficiencies in health systems, particularly among low- and middle-income countries (LMICs). With increasing uncertainty in healthcare, existing unmet needs such as poor outcomes of lung cancer (LC) patients in LMICs, mainly due to late stages at diagnosis, have been challenging-necessitating a shift in focus for judicious health resource utilization. Leveraging artificial intelligence (AI) for screening large volumes of pulmonary images performed for noncancerous reasons, such as health checks, immigration, tuberculosis screening, or other lung conditions, including but not limited to COVID-19, can facilitate easy and early identification of incidental pulmonary nodules (IPNs), which otherwise could have been missed. AI can review every chest X-ray or computed tomography scan through a trained pair of eyes, thus strengthening the infrastructure and enhancing capabilities of manpower for interpreting images in LMICs for streamlining accurate and early identification of IPNs. AI can be a catalyst for driving LC screening with enhanced efficiency, particularly in primary care settings, for timely referral and adequate management of coincidental IPN. AI can facilitate shift in the stage of LC diagnosis for improving survival, thus fostering optimal health-resource utilization and sustainable healthcare systems resilient to crisis. This article highlights the challenges for organized LC screening in LMICs and describes unique opportunities for leveraging AI. We present pilot initiatives from Asia, Latin America, and Russia illustrating AI-supported IPN identification from routine imaging to facilitate early diagnosis of LC at a potentially curable stage.

Stage I lung cancer patients with or without symptoms - are the patients different and should we treat them differently?

INTRODUCTION

A large proportion of stage I cancers are found incidentally, which appears to be a prognostic factor. We investigated stage I lung cancers according to whether, or not, there had been clinical suspicion of lung cancer prior to referral and to see, if we could detect any difference regarding patient characteristics, work-up and mortality for incidental vs non-incidental findings as well as for asymptomatic vs symptomatic patients.

METHODS

Medical records and referral documents for 177 patients diagnosed with stage I lung cancer were reviewed and divided based on whether the initial CT scan leading to diagnosis had been made due to suspicion of lung cancer or not. Patient characteristics and mortality between groups were compared, as well as mortality between patients with and without symptoms at the time of diagnosis.

RESULTS

One-hundred-and-eight patients were diagnosed incidentally, while 69 patients were non-incidental findings. Among the incidental findings, 55% had no symptoms, whereas none in the non-incidental group were asymptomatic. Personal characteristics were comparable between the groups. Significantly more patients in the incidental group had malignant comorbidity. Non-malignant chronic co-morbidity was more prevalent in the non-incidental group, in particular lung disease. There was no difference in tumour size, histology, or survival for incidental vs non-incidental or for asymptomatic vs symptomatic patients.

CONCLUSION

A large proportion of stage I lung cancers are found incidentally, especially in patients with malignant co-morbidity. We found no difference in survival to indicate that we did or should handle these patient groups differently.

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

OBJECTIVE

Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).

METHODS

This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations.

RESULTS

With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 [95% CI, 0.55-0.67] vs. 0.72 [95% CI, 0.66-0.77], p = 0.016 for residents, and 0.76 [95% CI, 0.72-0.81] vs. 0.76 [95% CI, 0.72-0.81, p = 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 [95% CI, 0.11-0.18] vs. 0.12 [95% CI, 0.09-0.16], p = 0.13 for residents, and 0.24 [95% CI, 0.20-0.29] vs. 0.17 [95% CI, 0.13-0.20], p < 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% [95% CI, 48.2-61.2%] vs. 70.2% [95% CI, 64.2-76.2%], p < 0.001 for residents and 72.5% [95% CI, 68.0-77.1%] vs. 73.9% [95% CI, 69.4-78.3%], p = 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% [95% CI, 9.6-13.1%] vs. 9.8% [95% CI, 8.0-11.6%], p = 0.32 for residents and 16.4% [95% CI, 14.7-18.2%] vs. 11.7% [95% CI, 10.2-13.3%], p < 0.001 for radiologists).

CONCLUSIONS

AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader.

KEY POINTS

• Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer. • With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer. • With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.

Prognostic value of symptoms at lung cancer diagnosis: a three-year observational study

Background

Lung cancer is mainly diagnosed at advanced or locally advanced stages, usually when symptoms become evident. However, sometimes it may be diagnosed incidentally during routine care, while patients are still asymptomatic. Prognosis differences based on symptomatic presentation have been partially explored. Our aim was to analyze the prognostic value of the initial symptomatic state of the patients in a general lung cancer cohort.

Methods

Observational ambispective study including patients consecutively diagnosed with primary lung cancer between January 2016 and December 2018 via the lung cancer Fast Diagnostic Track (FDT). Patients were followed up until death or the end of the study in September 2019. Asymptomatic patients were compared with patients presenting symptoms. Overall survival (OS) of both groups was compared using the log-rank test. Cox regression analysis was performed to clarify the effect of the symptomatic status at diagnosis on survival. Additionally, propensity score (PS) matching analysis was performed.

Results

A total of 267 patients were analyzed; 83.5% were men, with a mean (SD) age at diagnosis of 68 (10.7) years. Incidental diagnosis was ascertained in 24.7% of cases. Asymptomatic patients presented more frequently stage I and II disease compared to symptomatic patients (51.5% vs. 14%), and exhibited a significantly better prognosis, with a 3-year OS of 63.6% (vs. 30.3%) and a median OS that was not reached during follow-up (vs. 10.3 months). With an adjusted multivariate Cox proportional hazard model, we obtained a HR (95% CI) of 2.63 (95% CI, 1.6-4.2; P<0.0001) associated with symptomatic presentation independently of age, sex, stage at diagnosis and ECOG scale. In addition, after performing the propensity score matching analysis, the Cox regression model continued to show a significantly worse prognosis for patients presenting with symptoms (P=0.041).

Conclusions

Lung cancer patients who are asymptomatic at diagnosis exhibit a significantly better prognosis, regardless of the stage of the disease, underlining the importance of an early diagnosis.

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs

IMPORTANCE

The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer.

OBJECTIVE

To assess the performance of a deep learning-based nodule detection algorithm for the detection of lung cancer on chest radiographs from participants in the National Lung Screening Trial (NLST).

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study used data from participants in the NLST ro assess the performance of a deep learning-based artificial intelligence (AI) algorithm for the detection of pulmonary nodules and lung cancer on chest radiographs using separate training (in-house) and validation (NLST) data sets. Baseline (T0) posteroanterior chest radiographs from 5485 participants (full T0 data set) were used to assess lung cancer detection performance, and a subset of 577 of these images (nodule data set) were used to assess nodule detection performance. Participants aged 55 to 74 years who currently or formerly (ie, quit within the past 15 years) smoked cigarettes for 30 pack-years or more were enrolled in the NLST at 23 US centers between August 2002 and April 2004. Information on lung cancer diagnoses was collected through December 31, 2009. Analyses were performed between August 20, 2019, and February 14, 2020.

EXPOSURES

Abnormality scores produced by the AI algorithm.

MAIN OUTCOMES AND MEASURES

The performance of an AI algorithm for the detection of lung nodules and lung cancer on radiographs, with lung cancer incidence and mortality as primary end points.

RESULTS

A total of 5485 participants (mean [SD] age, 61.7 [5.0] years; 3030 men [55.2%]) were included, with a median follow-up duration of 6.5 years (interquartile range, 6.1-6.9 years). For the nodule data set, the sensitivity and specificity of the AI algorithm for the detection of pulmonary nodules were 86.2% (95% CI, 77.8%-94.6%) and 85.0% (95% CI, 81.9%-88.1%), respectively. For the detection of all cancers, the sensitivity was 75.0% (95% CI, 62.8%-87.2%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.8% (95% CI, 2.6%-5.0%), and the negative predictive value was 99.8% (95% CI, 99.6%-99.9%). For the detection of malignant pulmonary nodules in all images of the full T0 data set, the sensitivity was 94.1% (95% CI, 86.2%-100.0%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.4% (95% CI, 2.2%-4.5%), and the negative predictive value was 100.0% (95% CI, 99.9%-100.0%). In digital radiographs of the nodule data set, the AI algorithm had higher sensitivity (96.0% [95% CI, 88.3%-100.0%] vs 88.0% [95% CI, 75.3%-100.0%]; P = .32) and higher specificity (93.2% [95% CI, 89.9%-96.5%] vs 82.8% [95% CI, 77.8%-87.8%]; P = .001) for nodule detection compared with the NLST radiologists. For malignant pulmonary nodule detection on digital radiographs of the full T0 data set, the sensitivity of the AI algorithm was higher (100.0% [95% CI, 100.0%-100.0%] vs 94.1% [95% CI, 82.9%-100.0%]; P = .32) compared with the NLST radiologists, and the specificity (90.9% [95% CI, 89.6%-92.1%] vs 91.0% [95% CI, 89.7%-92.2%]; P = .91), positive predictive value (8.2% [95% CI, 4.4%-11.9%] vs 7.8% [95% CI, 4.1%-11.5%]; P = .65), and negative predictive value (100.0% [95% CI, 100.0%-100.0%] vs 99.9% [95% CI, 99.8%-100.0%]; P = .32) were similar to those of NLST radiologists.

CONCLUSIONS AND RELEVANCE

In this study, the AI algorithm performed better than NLST radiologists for the detection of pulmonary nodules on digital radiographs. When used as a second reader, the AI algorithm may help to detect lung cancer.

Recognition of breathprints of lung cancer and chronic obstructive pulmonary disease using the Aeonose® electronic nose

OBJECTIVES

There is a high unmet need in a non-invasive screening of lung cancer (LC). We conducted this single-center trial to evaluate the effectiveness of the electronic nose Aeonose ^® in LC recognition.

MATERIALS AND METHODS

Exhaled volatile organic compound (VOC) signatures were collected by Aeonose ^® in 42 incident and 78 prevalent LC patients, of them 29 LC patients in complete remission (LC CR), 33 healthy controls (HC) and 23 COPD patients. By dichotomous comparison of VOC's between incident LC and HC, a discriminating algorithm was established and also applied to LC CR and COPD subjects. Area under Curve (AUC), sensitivity, specificity and Matthews's correlation coefficient (MC) were used to interpret the data.

RESULTS

The established algorithm of Aeonose ^® signature allowed safe separation of LC and HC, showing an AUC of 0.92, sensitivity of 0.84 and a specificity of 0.97. When tested in a blinded fashion, the device recognized 19 out of 29 LC CR patients (=65.5%) as LC-positive, of which only five developed recurrent LC later on (after 18.6 months [Formula: see text]; mean value [Formula: see text]). Unfortunately, the algorithm also recognized 11 of 24 COPD patients as being LC positive (with only one of the 24 COPD patients developing LC 56 months after the measurement).

CONCLUSION

The Aeonose ^® revealed some potential in distinguishing LC from HC, however, with low specificity when applying the algorithm in a blinded fashion to other disease cohorts. We conclude that relevant VOC signals originating from comorbidities in LC such as COPD may have erroneously led to the separation between LC and controls.

CLINICAL TRIAL REGISTRATION

(NCT02951416).

Differences in lung cancer characteristics and mortality rate between screened and non-screened cohorts

Screening programs for lung cancer aim to allow diagnosis at the early stage, and therefore the decline in mortality rates. Thus, the aim of this retrospective cohort study was to the comparison of screened and non-screened lung cancer in terms of lung cancer characteristics, overdiagnosis and survival rate. A retrospective study in which 2883 patients with 2883 lung cancer diagnosed according to the hospital-based lung cancer register database between 2007 and 2017. A comparison was performed in term of clinical characteristics and outcomes of lung cancer between the screened and non-screening patient groups. 2883 subjects were identified (93 screened and 2790 non-screened). Screened group patients were younger (59.91 ± 8.14 versus 67.58 ± 12.95; p < 0.0001), and were more likely to be female than non-screened group (61.3% versus 36.8%; p < 0.0001). The screened group showed significantly better outcomes in overall mortality than the non-screened group (10.75% versus 79.06%; <0.0001). In a Cox proportional hazard model, lung cancer in the screened group proved to be an independent prognostic factor in lung cancer subjects. Our findings point to the improved survival outcome in the screened group and might underline the benefit of low-dose computed tomography (LDCT) screening program in Asian populations with the high prevalence of non-smoking-related lung cancer. Further study aimed at the LDCT mass screening program targeting at light smokers and non-smoker outside of existing screening criteria is warranted.

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

Background

The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures.

Objectives

The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images.

Methods

A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules. SINRSB CT scans were performed using the same noncontrast enhanced CT protocol and equipment; the malignant nodule data were acquired from several databases. The kurtosis (KUR) and skewness (SKW) values of these tests were determined for the whole volume of each nodule, and the histograms were classified into two basic patterns: peaks or plateaus.

Results

The mean (MEN) KUR values of the SINRSBs and malignant nodules were 3.37 ± 3.88 and 5.88 ± 5.11, respectively. The receiver operating characteristic (ROC) curve showed that the sensitivity and specificity for distinguishing SINRSBs from malignant nodules were 65% and 66% for KUR values >6, respectively, with an area under the curve (AUC) of 0.709 (p < 0.0001). The MEN SKW values of the SINRSBs and malignant nodules were 1.73 ± 0.94 and 2.07 ± 1.01, respectively. The ROC curve showed that the sensitivity and specificity for distinguishing malignant nodules from SINRSBs were 65% and 66% for SKW values >3.1, respectively, with an AUC of 0.709 (p < 0.0001). An analysis of the peak and plateau histograms revealed sensitivity, specificity, and accuracy values of 84%, 74%, and 79%, respectively.

Conclusions

KUR, SKW, and histogram shape can help to noninvasively diagnose SINRSBs but should not be used alone or without considering clinical data.

Prevalence of and risk factors for presenting initial respiratory symptoms in patients undergoing surgery for lung cancer

Background: Patients with early stage lung cancer seldom present initial respiratory symptoms, causing a delayed diagnosis and missed opportunity to receive operation. This study aimed to investigate the prevalence of initial respiratory symptoms and identity what factors would predispose lung cancer patients to present initial respiratory symptoms in patients undergoing lung cancer surgery. Methods: A retrospective chart review was conducted on 3,203 patients undergoing surgery for primary lung cancer. The prevalence of initial respiratory symptoms was investigated and the comparisons of clinicopathological parameters were performed between patients with and without initial respiratory symptoms or between patients with single and multiple initial respiratory symptoms. Independent risk factors for presenting initial respiratory symptoms or multiple initial respiratory symptoms were identified using a logistic regression. Results: A total of 1,474 (46.0%) patients with lung cancer were admitted to hospital due to present initial respiratory symptoms. Symptom clusters of cough or sputum (33.1%) and bloody sputum or hemoptysis (16.7%) presented as the two major chief complaints for medical consultation while chest pain (6.9%) and chest distress or dyspnea (5.6%) remained relatively unusual. Multiple analyses found that coexisting chronic obstructive pulmonary disease (OR=1.70, 95% CI=1.41-2.05), tumor size >3 cm (OR=2.27, 95% CI=1.93-2.67), squamous cell carcinoma (OR=2.22, 95% CI=1.86-2.65), tumor located in left lower lung (OR=1.39, 95% CI=1.10-1.74) and advanced tumor stage (OR=1.27, 95% CI=1.06-1.52) were independent risk factors for presenting initial respiratory symptoms. Furthermore, current smoking (OR=1.36, 95% CI=1.07-1.73), tumor size >3 cm (OR=1.53, 95% CI=1.21-1.93) and squamous cell carcinoma (OR=1.68, 95% CI=1.32-2.15) were demonstrated to be independent risk factors for presenting multiple initial respiratory symptoms. Conclusions: Presenting initial respiratory symptoms was the common cause for medical consultation in patients undergoing lung cancer surgery. Patients with lung cancer in larger tumor size or squamous cell carcinoma more likely presented initial and even multiple initial respiratory symptoms.

Incidental detection by computed tomography is an independent prognostic factor for survival in patients operated for nonsmall cell lung carcinoma

We studied the rate of incidental detection of lung carcinomas and its effect on long-term survival in a nationwide cohort of patients operated for nonsmall cell lung cancer (NSCLC).

All patients operated for NSCLC in Iceland during 1991–2010 were included. Demographic and clinicopathological features were compared in patients diagnosed incidentally using chest radiography or computed tomography (CT), and in those with symptomatic presentation. Multivariate analysis was used to evaluate prognostic factors.

Out of 508 patients, 174 (34%) were diagnosed incidentally; in 26% of cases by chest radiography and in 8% by CT. The CT-detected tumours were significantly smaller than symptomatic tumours, diagnosed at earlier TNM (tumour, node and metastasis) stages and more often of adenocarcinoma histology. 5-year cancer-specific survival for symptomatic versus incidentally diagnosed patients detected by chest radiography and CT was 41%, 57% and 68%, respectively (p=0.003). After adjusting for stage, the hazard ratio (HR) for NSCLC mortality was significantly lower for incidental diagnosis by CT (HR 0.55, 95% CI 0.31‒0.98; p=0.04) compared to incidental diagnosis by chest radiography (HR 0.95, 95% CI 0.70‒1.27; p=0.71) or symptomatic diagnosis (HR 1.0).

One-third of surgically treated NSCLCs were detected incidentally, with an increasing rate of incidental CT diagnosis. NSCLC patients diagnosed incidentally by CT appear to have better survival than those diagnosed incidentally by chest radiography, and particularly those who present with symptoms.

A third of surgically treated NSCLC cases were detected incidentally; those detected by CT may have better survivalhttp://ow.ly/S24I309M1VK